WO2025231709A1

WO2025231709A1 - Beam information signaling associated with beam prediction

Info

Publication number: WO2025231709A1
Application number: PCT/CN2024/091928
Authority: WO
Inventors: Qiaoyu Li; Hamed Pezeshki; Mahmoud Taherzadeh Boroujeni
Original assignee: Qualcomm Inc
Current assignee: Qualcomm Inc
Priority date: 2024-05-09
Filing date: 2024-05-09
Publication date: 2025-11-13
Anticipated expiration: 2026-11-09

Abstract

Certain aspects of the present disclosure provide techniques for wireless communications by an apparatus. A method includes receiving an identifier associated with a beam prediction technique; receiving an indication of one or more of: a first total number of a first set of beams to be predicted using the beam prediction technique; or a second total number of a second set of beams used for predicting the first set of beams; and sending one or more measurements associated with the first set of beams, the second set of beams, or both.

Description

BEAM INFORMATION SIGNALING ASSOCIATED WITH BEAM PREDICTION

INTRODUCTION

Field of the Disclosure

Aspects of the present disclosure relate to wireless communications, and more particularly, to techniques for beam prediction.

Description of Related Art

Wireless communications systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, broadcasts, or other similar types of services. These wireless communications systems may employ multiple-access technologies capable of supporting communications with multiple users by sharing available wireless communications system resources with those users.

Although wireless communications systems have made great technological advancements over many years, challenges still exist. For example, complex and dynamic environments can still attenuate or block signals between wireless transmitters and wireless receivers. Accordingly, there is a continuous desire to improve the technical performance of wireless communications systems, including, for example: improving speed and data carrying capacity of communications, improving efficiency of the use of shared communications mediums, reducing power used by transmitters and receivers while performing communications, improving reliability of wireless communications, avoiding redundant transmissions and/or receptions and related processing, improving the coverage area of wireless communications, increasing the number and types of devices that can access wireless communications systems, increasing the ability for different types of devices to intercommunicate, increasing the number and type of wireless communications mediums available for use, and the like. Consequently, there exists a need for further improvements in wireless communications systems to overcome the aforementioned technical challenges and others.

SUMMARY

In certain aspects, an entity (e.g., a prediction entity, such as a machine learning (ML) entity) may be configured to perform beam prediction, such as by using an ML model running at the entity or using some other beam prediction technique (e.g., statistical, algorithmic, etc. ) . The entity may perform the beam prediction according to a particular beam prediction technique, which may refer to using a particular dataset, configuration, scenario, codebook, functionality, ML model, or the like. The beam prediction technique may be associated with an identifier (e.g., associated identifier, model identifier, etc. ) . The entity may be configured to predict one or more channel characteristics (e.g., one or more reference signal received power (RSRP) , channel quality indicator (CQI) , reference signal received quality (RSRQ) , signal to noise ratio (SNR) , etc. ) associated with a first set of beams (e.g., Set-Abeams) based on one or more channel characteristics associated with a second set of beams (e.g., Set-B beams) . In certain aspects, to predict a beam, as referred to herein, refers to predicting one or more channel characteristics associated with the beam (e.g., one or more channel characteristics of one or more prediction targets associated with the beam) . A prediction target may refer to an actually transmitted reference signal (e.g., transmitted in a communication resource, such as a time-frequency resource) the measurement of which is predicted, a “virtual resource” (e.g., a communication resource in which a reference signal is not actually transmitted but the measurement of such a reference signal is predicted as though it was transmitted in the communication resource) , a target beam, or the like. Further, to measure a beam, as referred to herein, refers to performing one or more measurements of one or more channel characteristics associated with the beam (e.g., one or more measurements of one or more reference signals communicated via the beam) .

For example, one or more measurements of one or more channel characteristics associated with the second set of beams may be obtained, such as by a user equipment (UE) measuring one or more reference signals (RSs) (e.g., channel state information (CSI) reference signals (RSs) (CSI-RSs) , synchronization signal blocks (SSBs) , etc. ) communicated using the second set of beams to determine the one or more channel characteristics of the second set of beams. For example, the second set of beams may refer to transmit beam (s) of a network entity, such as a base station (or alternatively they may refer to receive beam (s) of the UE) . The network entity may transmit, and the UE may receive and measure, respective one or more RSs via each beam of the second set of beams to determine respective one or more channel characteristics for each beam.

The one or more channel characteristics associated with the second set of beams may be used by the entity for predicting the one or more channel characteristics associated with the first set of beams, such as using the ML model (e.g., during inference) , such that the one or more channel characteristics of the first set of beams are predicted, such as using the ML model. For example, during inference at the ML model, the one or more measurements of the one or more channel characteristics associated with the second set of beams may be input into the ML model (e.g., along with other information, such as beam identifier information) , and the ML model may output one or more predicted channel characteristics (e.g., one or more predicted measurements of the one or more channel characteristics) of the first set of beams. The first set of beams may refer to transmit beams of the network entity (or alternatively they may refer to receive beams of the UE) . Accordingly, the entity can predict the first set of beams, without actually measuring the first set of beams, based on measurements of the second set of beams.

The entity performing the prediction may be the UE, the network entity, or a separate entity (e.g., within or outside a core network) .

In certain aspects, in order to train the entity, such as during training of the ML model, training data may be collected, such as by one or more UEs. The training data may include measurements of the first set of beams (e.g., as a ground truth) and measurements of the second set of beams (e.g., as input to the ML model) . For example, the ML model may be trained to minimize a loss function between predicted measurements of the first set of beams output by the ML model based on the measurements of the second set of beams as input and the ground truth measurements of the first set of beams.

In some cases, the entity may not have information as to when there is sufficient training data. For example, the ML model may have an architecture (e.g., number of features, such as number of input features and number of output features) based on a (e.g., total) number of the first set of beams (e.g., corresponding to the number of output features) to be predicted and a (e.g., total) number of the second set of beams (e.g., corresponding to the number of input features) to be used to predict the first set of beams. For example, the total number of the first set of beams may refer to the total number of different beams that can be predicted by the entity, such as by a prediction technique, such as the ML model. Further, the total number of the second set of beams may refer to the total number of different beams for which measurements can be used for prediction by the entity, such as by a prediction technique, such as the ML model.

For training, the entity may use training data that corresponds to each of the first set of beams and/or the second set of beams. However, in some cases, the entity may obtain the training data, such as from one or more UEs, over time, and it may be that training data associated with certain beams may not be available immediately. For example, assuming the total number of beams (of the first set of beams or second set of beams) is 6. The entity may receive training data for beams associated with identifiers 1-4 at a particular time, but may not receive training data for beams associated with identifiers 5-6. At the time the entity may receive training data for beams associated with identifiers 1-4, the entity may not have information that there are additional beams associated with identifiers 5-6. Accordingly, the entity may not have information that the training data is sufficient, as additional training data associated with the additional beams associated with identifiers 5-6 may be needed. Therefore, the entity may prematurely stop training, such as of the ML model, which may degrade prediction performance.

Certain aspects herein provide for communication of the total number of the first set of beams and/or the total number of the second set of beams, such as from a network entity to a UE, and from the UE to the entity. In certain aspects, the UE may use information about the total number of the first set of beams and/or the total number of the second set of beams to determine input/output feature dimensions of an ML model used by the entity for beam prediction. The entity may utilize such information, to determine when there is sufficient training data, which may allow the entity to continue training until there is sufficient training data, leading to improved prediction performance. Such improved prediction performance may improve communications, such as between the UE and the network entity, as then an appropriate beam can be selected for communication between the UE and the network entity, which may improve throughout, error rate, etc.

In certain aspects, since the entity has information about the total number of the first set of beams and/or the total number of the second set of beams, the number of the first set of beams and/or second set of beams for which reference signals are transmitted, and/or the number of the first set of beams predicted, can be flexibly scheduled by the network entity, such as for training data collection and/or ML model inference. The flexible scheduling may be via various resource or prediction target set configurations, or even via various signaling frameworks (e.g., hybrid of L1 based and L3 based data collection) .

The identifier associated with the beam prediction technique may also be communicated, such as from a network entity to a UE, and from the UE to the entity, to help the entity determine what beam prediction technique to use, such as if the entity supports multiple types of prediction.

One aspect provides a method for wireless communications by an apparatus. The method includes receiving an identifier associated with a beam prediction technique; receiving an indication of one or more of: a first total number of a first set of beams to be predicted using the beam prediction technique; or a second total number of a second set of beams used for predicting the first set of beams; and sending one or more measurements associated with the first set of beams, the second set of beams, or both.

Another aspect provides a method for wireless communications by an apparatus. The method includes sending an identifier associated with a beam prediction technique; sending an indication of one or more of: a first total number of a first set of beams to be predicted using the beam prediction technique; or a second total number of a second set of beams used for predicting the first set of beams; and receiving one or more measurements associated with the first set of beams, the second set of beams, or both.

Other aspects provide: one or more apparatuses operable, configured, or otherwise adapted to perform any portion of any method described herein (e.g., such that performance may be by only one apparatus or in a distributed fashion across multiple apparatuses) ; one or more non-transitory, computer-readable media comprising instructions that, when executed by one or more processors of one or more apparatuses, cause the one or more apparatuses to perform any portion of any method described herein (e.g., such that instructions may be included in only one computer-readable medium or in a distributed fashion across multiple computer-readable media, such that instructions may be executed by only one processor or by multiple processors in a distributed fashion, such that each apparatus of the one or more apparatuses may include one processor or multiple processors, and/or such that performance may be by only one apparatus or in a distributed fashion across multiple apparatuses) ; one or more computer program products embodied on one or more computer-readable storage media comprising code for performing any portion of any method described herein (e.g., such that code may be stored in only one computer-readable medium or across computer-readable media in a distributed fashion) ; and/or one or more apparatuses comprising one or more means for performing any portion of any method described herein (e.g., such that performance would be by only one apparatus or by multiple apparatuses in a distributed fashion) . By way of example, an apparatus may comprise a processing system, a device with a processing system, or processing systems cooperating over one or more networks. An apparatus may comprise one or more memories; and one or more processors configured to cause the apparatus to perform any portion of any method described herein. In some examples, one or more of the processors may be preconfigured to perform various functions or operations described herein without requiring configuration by software.

The following description and the appended figures set forth certain features for purposes of illustration.

BRIEF DESCRIPTION OF DRAWINGS

The appended figures depict certain features of the various aspects described herein and are not to be considered limiting of the scope of this disclosure.

FIG. 1 depicts an example wireless communications network.

FIG. 2 depicts an example disaggregated base station architecture.

FIG. 3 depicts aspects of an example base station and an example user equipment (UE) .

FIGS. 4A, 4B, 4C, and 4D depict various example aspects of data structures for a wireless communications network.

FIG. 5 illustrates example operations for radio resource control (RRC) connection establishment and beam management.

FIG. 6 is a diagram illustrating examples of beam management procedures.

FIG. 7A illustrates an example artificial neural network.

FIG. 7B is a diagram illustrating example beam prediction.

FIG. 8 illustrates an example Channel State Information (CSI) report configuration.

FIG. 9 depicts a process flow for communications in a network between a network entity, a user equipment (UE) , and a prediction entity.

FIG. 10 depicts a method for wireless communications.

FIG. 11 depicts another method for wireless communications.

FIG. 12 depicts aspects of an example communications device.

FIG. 13 depicts aspects of an example communications device.

DETAILED DESCRIPTION

Aspects of the present disclosure provide apparatuses, methods, processing systems, and computer-readable mediums for signaling of beam information for beam prediction.

As discussed, an entity may be configured to predict one or more channel characteristics associated with a first set of beams (e.g., Set-Abeams) based on one or more channel characteristics associated with a second set of beams (e.g., Set-B beams) . Certain aspects herein provide for communication of the total number of the first set of beams and/or the total number of the second set of beams, such as from a network entity to a UE, and from the UE to the entity. In certain aspects, the UE may use information about the total number of the first set of beams and/or the total number of the second set of beams to determine input/output feature dimensions of an ML model or beam prediction technique used by the entity for beam prediction. The entity may utilize such information, to determine when there is sufficient training data, the determination of which is a technical problem, which may allow the entity to continue training until there is sufficient training data, leading to improved prediction performance. Such improved prediction performance may provide a technical benefit of improved communications, such as between the UE and the network entity, as then an appropriate beam can be selected for communication between the UE and the network entity, which may improve throughout, error rate, etc.

In certain aspects, since the entity has information about the total number of the first set of beams and/or the total number of the second set of beams, the number of the first set of beams and/or second set of beams for which reference signals are transmitted, and/or the number of the first set of beams predicted, can be flexibly scheduled by the network entity, such as for training data collection and/or ML model inference, providing a technical benefit of flexible communications, such as to adapt to channel conditions.

Introduction to Wireless Communications Networks

The techniques and methods described herein may be used for various wireless communications networks. While aspects may be described herein using terminology commonly associated with 3G, 4G, 5G, 6G, and/or other generations of wireless technologies, aspects of the present disclosure may likewise be applicable to other communications systems and standards not explicitly mentioned herein.

FIG. 1 depicts an example of a wireless communications network 100, in which aspects described herein may be implemented.

Generally, wireless communications network 100 includes various network entities (alternatively, network elements or network nodes) . A network entity is generally a communications device and/or a communications function performed by a communications device (e.g., a user equipment (UE) , a base station (BS) , a component of a BS, a server, etc. ) . As such communications devices are part of wireless communications network 100, and facilitate wireless communications, such communications devices may be referred to as wireless communications devices. For example, various functions of a network as well as various devices associated with and interacting with a network may be considered network entities. Further, wireless communications network 100 includes terrestrial aspects, such as ground-based network entities (e.g., BSs 102) , and non-terrestrial aspects (also referred to herein as non-terrestrial network entities) , such as satellite 140 and/or aerial or spaceborne platform (s) , which may include network entities on-board (e.g., one or more BSs) capable of communicating with other network elements (e.g., terrestrial BSs) and UEs.

In the depicted example, wireless communications network 100 includes BSs 102, UEs 104, and one or more core networks, such as an Evolved Packet Core (EPC) 160 and 5G Core (5GC) network 190, which interoperate to provide communications services over various communications links, including wired and wireless links.

FIG. 1 depicts various example UEs 104, which may more generally include: a cellular phone, smart phone, session initiation protocol (SIP) phone, laptop, personal digital assistant (PDA) , satellite radio, global positioning system, multimedia device, video device, digital audio player, camera, game console, tablet, smart device, wearable device, vehicle, electric meter, gas pump, large or small kitchen appliance, healthcare device, implant, sensor/actuator, display, internet of things (IoT) devices, always on (AON) devices, edge processing devices, data centers, or other similar devices. UEs 104 may also be referred to more generally as a mobile device, a wireless device, a station, a mobile station, a subscriber station, a mobile subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a remote device, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, and others.

BSs 102 wirelessly communicate with (e.g., transmit signals to or receive signals from) UEs 104 via communications links 120. The communications links 120 between BSs 102 and UEs 104 may include uplink (UL) (also referred to as reverse link) transmissions from a UE 104 to a BS 102 and/or downlink (DL) (also referred to as forward link) transmissions from a BS 102 to a UE 104. The communications links 120 may use multiple-input and multiple-output (MIMO) antenna technology, including spatial multiplexing, beamforming, and/or transmit diversity in various aspects.

BSs 102 may generally include: a NodeB, enhanced NodeB (eNB) , next generation enhanced NodeB (ng-eNB) , next generation NodeB (gNB or gNodeB) , access point, base transceiver station, radio base station, radio transceiver, transceiver function, transmission reception point, and/or others. Each of BSs 102 may provide communications coverage for a respective coverage area 110, which may sometimes be referred to as a cell, and which may overlap in some cases (e.g., small cell 102’ may have a coverage area 110’ that overlaps the coverage area 110 of a macro cell) . A BS may, for example, provide communications coverage for a macro cell (covering relatively large geographic area) , a pico cell (covering relatively smaller geographic area, such as a sports stadium) , a femto cell (relatively smaller geographic area (e.g., a home) ) , and/or other types of cells.

Generally, a cell may refer to a portion, partition, or segment of wireless communication coverage served by a network entity within a wireless communication network. A cell may have geographic characteristics, such as a geographic coverage area, as well as radio frequency characteristics, such as time and/or frequency resources dedicated to the cell. For example, a specific geographic coverage area may be covered by multiple cells employing different frequency resources (e.g., bandwidth parts) and/or different time resources. As another example, a specific geographic coverage area may be covered by a single cell. In some contexts (e.g., a carrier aggregation scenario and/or multi-connectivity scenario) , the terms “cell” or “serving cell” may refer to or correspond to a specific carrier frequency (e.g., a component carrier) used for wireless communications, and a “cell group” may refer to or correspond to multiple carriers used for wireless communications. As examples, in a carrier aggregation scenario, a UE may communicate on multiple component carriers corresponding to multiple (serving) cells in the same cell group, and in a multi-connectivity (e.g., dual connectivity) scenario, a UE may communicate on multiple component carriers corresponding to multiple cell groups.

While BSs 102 are depicted in various aspects as unitary communications devices, BSs 102 may be implemented in various configurations. For example, one or more components of a base station may be disaggregated, including a central unit (CU) , one or more distributed units (DUs) , one or more radio units (RUs) , a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC) , or a Non-Real Time (Non-RT) RIC, to name a few examples. In another example, various aspects of a base station may be virtualized. More generally, a base station (e.g., BS 102) may include components that are located at a single physical location or components located at various physical locations. In examples in which a base station includes components that are located at various physical locations, the various components may each perform functions such that, collectively, the various components achieve functionality that is similar to a base station that is located at a single physical location. In some aspects, a base station including components that are located at various physical locations may be referred to as a disaggregated radio access network architecture, such as an Open RAN (O-RAN) or Virtualized RAN (VRAN) architecture. FIG. 2 depicts and describes an example disaggregated base station architecture.

Different BSs 102 within wireless communications network 100 may also be configured to support different radio access technologies, such as 3G, 4G, and/or 5G. For example, BSs 102 configured for 4G LTE (collectively referred to as Evolved Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access Network (E-UTRAN) ) may interface with the EPC 160 through first backhaul links 132 (e.g., an S1 interface) . BSs 102 configured for 5G (e.g., 5G NR or Next Generation RAN (NG-RAN) ) may interface with 5GC 190 through second backhaul links 184. BSs 102 may communicate directly or indirectly (e.g., through the EPC 160 or 5GC 190) with each other over third backhaul links 134 (e.g., X2 interface) , which may be wired or wireless.

Wireless communications network 100 may subdivide the electromagnetic spectrum into various classes, bands, channels, or other features. In some aspects, the subdivision is provided based on wavelength and frequency, where frequency may also be referred to as a carrier, a subcarrier, a frequency channel, a tone, or a subband. For example, 3GPP currently defines Frequency Range 1 (FR1) as including 410 MHz –7125 MHz, which is often referred to (interchangeably) as “Sub-6 GHz” . Similarly, 3GPP currently defines Frequency Range 2 (FR2) as including 24,250 MHz – 71,000 MHz, which is sometimes referred to (interchangeably) as a “millimeter wave” ( “mmW” or “mmWave” ) . In some cases, FR2 may be further defined in terms of sub-ranges, such as a first sub-range FR2-1 including 24,250 MHz –52,600 MHz and a second sub-range FR2-2 including 52,600 MHz –71,000 MHz. A base station configured to communicate using mmWave/near mmWave radio frequency bands (e.g., a mmWave base station such as BS 180) may utilize beamforming (e.g., 182) with a UE (e.g., 104) to improve path loss and range.

The communications links 120 between BSs 102 and, for example, UEs 104, may be through one or more carriers, which may have different bandwidths (e.g., 5, 10, 15, 20, 100, 400, and/or other MHz) , and which may be aggregated in various aspects. Carriers may or may not be adjacent to each other. Allocation of carriers may be asymmetric with respect to DL and UL (e.g., more or fewer carriers may be allocated for DL than for UL) .

Communications using higher frequency bands may have higher path loss and a shorter range compared to lower frequency communications. Accordingly, certain base stations (e.g., 180 in FIG. 1) may utilize beamforming 182 with a UE 104 to improve path loss and range. For example, BS 180 and the UE 104 may each include a plurality of antennas, such as antenna elements, antenna panels, and/or antenna arrays to facilitate the beamforming. In some cases, BS 180 may transmit a beamformed signal to UE 104 in one or more transmit directions 182’ . UE 104 may receive the beamformed signal from the BS 180 in one or more receive directions 182” . UE 104 may also transmit a beamformed signal to the BS 180 in one or more transmit directions 182” . BS 180 may also receive the beamformed signal from UE 104 in one or more receive directions 182’ . BS 180 and UE 104 may then perform beam training to determine the best receive and transmit directions for each of BS 180 and UE 104. Notably, the transmit and receive directions for BS 180 may or may not be the same. Similarly, the transmit and receive directions for UE 104 may or may not be the same.

Wireless communications network 100 further includes a Wi-Fi AP 150 in communication with Wi-Fi stations (STAs) 152 via communications links 154 in, for example, a 2.4 GHz and/or 5 GHz unlicensed frequency spectrum.

Certain UEs 104 may communicate with each other using device-to-device (D2D) communications link 158. D2D communications link 158 may use one or more sidelink channels, such as a physical sidelink broadcast channel (PSBCH) , a physical sidelink discovery channel (PSDCH) , a physical sidelink shared channel (PSSCH) , a physical sidelink control channel (PSCCH) , and/or a physical sidelink feedback channel (PSFCH) .

EPC 160 may include various functional components, including: a Mobility Management Entity (MME) 162, other MMEs 164, a Serving Gateway 166, a Multimedia Broadcast Multicast Service (MBMS) Gateway 168, a Broadcast Multicast Service Center (BM-SC) 170, and/or a Packet Data Network (PDN) Gateway 172, such as in the depicted example. MME 162 may be in communication with a Home Subscriber Server (HSS) 174. MME 162 is the control node that processes the signaling between the UEs 104 and the EPC 160. Generally, MME 162 provides bearer and connection management.

Generally, user Internet protocol (IP) packets are transferred through Serving Gateway 166, which itself is connected to PDN Gateway 172. PDN Gateway 172 provides UE IP address allocation as well as other functions. PDN Gateway 172 and the BM-SC 170 are connected to IP Services 176, which may include, for example, the Internet, an intranet, an IP Multimedia Subsystem (IMS) , a Packet Switched (PS) streaming service, and/or other IP services.

BM-SC 170 may provide functions for MBMS user service provisioning and delivery. BM-SC 170 may serve as an entry point for content provider MBMS transmission, may be used to authorize and initiate MBMS Bearer Services within a public land mobile network (PLMN) , and/or may be used to schedule MBMS transmissions. MBMS Gateway 168 may be used to distribute MBMS traffic to the BSs 102 belonging to a Multicast Broadcast Single Frequency Network (MBSFN) area broadcasting a particular service, and/or may be responsible for session management (start/stop) and for collecting eMBMS related charging information.

5GC 190 may include various functional components, including: an Access and Mobility Management Function (AMF) 192, other AMFs 193, a Session Management Function (SMF) 194, and a User Plane Function (UPF) 195. AMF 192 may be in communication with Unified Data Management (UDM) 196.

AMF 192 is a control node that processes signaling between UEs 104 and 5GC 190. AMF 192 provides, for example, quality of service (QoS) flow and session management.

Internet protocol (IP) packets are transferred through UPF 195, which is connected to the IP Services 197, and which provides UE IP address allocation as well as other functions for 5GC 190. IP Services 197 may include, for example, the Internet, an intranet, an IMS, a PS streaming service, and/or other IP services.

In various aspects, a network entity or network node can be implemented as an aggregated base station, as a disaggregated base station, a component of a base station, an integrated access and backhaul (IAB) node, a relay node, a sidelink node, to name a few examples.

FIG. 2 depicts an example disaggregated base station 200 architecture. The disaggregated base station 200 architecture may include one or more central units (CUs) 210 that can communicate directly with a core network 220 via a backhaul link, or indirectly with the core network 220 through one or more disaggregated base station units (such as a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC) 225 via an E2 link, or a Non-Real Time (Non-RT) RIC 215 associated with a Service Management and Orchestration (SMO) Framework 205, or both) . A CU 210 may communicate with one or more distributed units (DUs) 230 via respective midhaul links, such as an F1 interface. The DUs 230 may communicate with one or more radio units (RUs) 240 via respective fronthaul links. The RUs 240 may communicate with respective UEs 104 via one or more radio frequency (RF) access links. In some implementations, the UE 104 may be simultaneously served by multiple RUs 240.

Each of the units, e.g., the CUs 210, the DUs 230, the RUs 240, as well as the Near-RT RICs 225, the Non-RT RICs 215 and the SMO Framework 205, may include one or more interfaces or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, signals) via a wired or wireless transmission medium. Each of the units, or an associated processor or controller providing instructions to the communications interfaces of the units, can be configured to communicate with one or more of the other units via the transmission medium. For example, the units can include a wired interface configured to receive or transmit signals over a wired transmission medium to one or more of the other units. Additionally or alternatively, the units can include a wireless interface, which may include a receiver, a transmitter or transceiver (such as a radio frequency (RF) transceiver) , configured to receive or transmit signals, or both, over a wireless transmission medium to one or more of the other units.

In some aspects, the CU 210 may host one or more higher layer control functions. Such control functions can include radio resource control (RRC) , packet data convergence protocol (PDCP) , service data adaptation protocol (SDAP) , or the like. Each control function can be implemented with an interface configured to communicate signals with other control functions hosted by the CU 210. The CU 210 may be configured to handle user plane functionality (e.g., Central Unit –User Plane (CU-UP) ) , control plane functionality (e.g., Central Unit –Control Plane (CU-CP) ) , or a combination thereof. In some implementations, the CU 210 can be logically split into one or more CU-UP units and one or more CU-CP units. The CU-UP unit can communicate bidirectionally with the CU-CP unit via an interface, such as the E1 interface when implemented in an O-RAN configuration. The CU 210 can be implemented to communicate with the DU 230, as necessary, for network control and signaling.

The DU 230 may correspond to a logical unit that includes one or more base station functions to control the operation of one or more RUs 240. In some aspects, the DU 230 may host one or more of a radio link control (RLC) layer, a medium access control (MAC) layer, and one or more high physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, or the like) depending, at least in part, on a functional split, such as those defined by the 3^rd Generation Partnership Project (3GPP) . In some aspects, the DU 230 may further host one or more low PHY layers. Each layer (or module) can be implemented with an interface configured to communicate signals with other layers (and modules) hosted by the DU 230, or with the control functions hosted by the CU 210.

Lower-layer functionality can be implemented by one or more RUs 240. In some deployments, an RU 240, controlled by a DU 230, may correspond to a logical node that hosts RF processing functions, or low-PHY layer functions (such as performing fast Fourier transform (FFT) , inverse FFT (iFFT) , digital beamforming, physical random access channel (PRACH) extraction and filtering, or the like) , or both, based at least in part on the functional split, such as a lower layer functional split. In such an architecture, the RU (s) 240 can be implemented to handle over the air (OTA) communications with one or more UEs 104. In some implementations, real-time and non-real-time aspects of control and user plane communications with the RU (s) 240 can be controlled by the corresponding DU 230. In some scenarios, this configuration can enable the DU (s) 230 and the CU 210 to be implemented in a cloud-based RAN architecture, such as a vRAN architecture.

The SMO Framework 205 may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO Framework 205 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements which may be managed via an operations and maintenance interface (such as an O1 interface) . For virtualized network elements, the SMO Framework 205 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) 290) to perform network element life cycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface (such as an O2 interface) . Such virtualized network elements can include, but are not limited to, CUs 210, DUs 230, RUs 240 and Near-RT RICs 225. In some implementations, the SMO Framework 205 can communicate with a hardware aspect of a 4G RAN, such as an open eNB (O-eNB) 211, via an O1 interface. Additionally, in some implementations, the SMO Framework 205 can communicate directly with one or more DUs 230 and/or one or more RUs 240 via an O1 interface. The SMO Framework 205 also may include a Non-RT RIC 215 configured to support functionality of the SMO Framework 205.

The Non-RT RIC 215 may be configured to include a logical function that enables non-real-time control and optimization of RAN elements and resources, Artificial Intelligence/Machine Learning (AI/ML) workflows including model training and updates, or policy-based guidance of applications/features in the Near-RT RIC 225. The Non-RT RIC 215 may be coupled to or communicate with (such as via an A1 interface) the Near-RT RIC 225. The Near-RT RIC 225 may be configured to include a logical function that enables near-real-time control and optimization of RAN elements and resources via data collection and actions over an interface (such as via an E2 interface) connecting one or more CUs 210, one or more DUs 230, or both, as well as an O-eNB, with the Near-RT RIC 225.

In some implementations, to generate AI/ML models to be deployed in the Near-RT RIC 225, the Non-RT RIC 215 may receive parameters or external enrichment information from external servers. Such information may be utilized by the Near-RT RIC 225 and may be received at the SMO Framework 205 or the Non-RT RIC 215 from non-network data sources or from network functions. In some examples, the Non-RT RIC 215 or the Near-RT RIC 225 may be configured to tune RAN behavior or performance. For example, the Non-RT RIC 215 may monitor long-term trends and patterns for performance and employ AI/ML models to perform corrective actions through the SMO Framework 205 (such as reconfiguration via O1) or via creation of RAN management policies (such as A1 policies) .

FIG. 3 depicts aspects of an example BS 102 and a UE 104.

Generally, BS 102 includes various processors (e.g., 318, 320, 330, 338, and 340) , antennas 334a-t (collectively 334) , transceivers 332a-t (collectively 332) , which include modulators and demodulators, and other aspects, which enable wireless transmission of data (e.g., data source 312) and wireless reception of data (e.g., data sink 314) . For example, BS 102 may send and receive data between BS 102 and UE 104. BS 102 includes controller/processor 340, which may be configured to implement various functions described herein related to wireless communications. Note that the BS 102 may have a disaggregated architecture as described herein with respect to FIG. 2.

Generally, UE 104 includes various processors (e.g., 358, 364, 366, 370, and 380) , antennas 352a-r (collectively 352) , transceivers 354a-r (collectively 354) , which include modulators and demodulators, and other aspects, which enable wireless transmission of data (e.g., retrieved from data source 362) and wireless reception of data (e.g., provided to data sink 360) . UE 104 includes controller/processor 380, which may be configured to implement various functions described herein related to wireless communications.

In regards to an example downlink transmission, BS 102 includes a transmit processor 320 that may receive data from a data source 312 and control information from a controller/processor 340. The control information may be for the physical broadcast channel (PBCH) , physical control format indicator channel (PCFICH) , physical hybrid automatic repeat request (HARQ) indicator channel (PHICH) , physical downlink control channel (PDCCH) , group common PDCCH (GC PDCCH) , and/or others. The data may be for the physical downlink shared channel (PDSCH) , in some examples.

Transmit processor 320 may process (e.g., encode and symbol map) the data and control information to obtain data symbols and control symbols, respectively. Transmit processor 320 may also generate reference symbols, such as for the primary synchronization signal (PSS) , secondary synchronization signal (SSS) , PBCH demodulation reference signal (DMRS) , and channel state information reference signal (CSI-RS) .

Transmit (TX) multiple-input multiple-output (MIMO) processor 330 may perform spatial processing (e.g., precoding) on the data symbols, the control symbols, and/or the reference symbols, if applicable, and may provide output symbol streams to the modulators (MODs) in transceivers 332a-332t. Each modulator in transceivers 332a-332t may process a respective output symbol stream to obtain an output sample stream. Each modulator may further process (e.g., convert to analog, amplify, filter, and upconvert) the output sample stream to obtain a downlink signal. Downlink signals from the modulators in transceivers 332a-332t may be transmitted via the antennas 334a-334t, respectively.

In order to receive the downlink transmission, UE 104 includes antennas 352a-352r that may receive the downlink signals from the BS 102 and may provide received signals to the demodulators (DEMODs) in transceivers 354a-354r, respectively. Each demodulator in transceivers 354a-354r may condition (e.g., filter, amplify, downconvert, and digitize) a respective received signal to obtain input samples. Each demodulator may further process the input samples to obtain received symbols.

RX MIMO detector 356 may obtain received symbols from all the demodulators in transceivers 354a-354r, perform MIMO detection on the received symbols if applicable, and provide detected symbols. Receive processor 358 may process (e.g., demodulate, deinterleave, and decode) the detected symbols, provide decoded data for the UE 104 to a data sink 360, and provide decoded control information to a controller/processor 380.

In regards to an example uplink transmission, UE 104 further includes a transmit processor 364 that may receive and process data (e.g., for the PUSCH) from a data source 362 and control information (e.g., for the physical uplink control channel (PUCCH) ) from the controller/processor 380. Transmit processor 364 may also generate reference symbols for a reference signal (e.g., for the sounding reference signal (SRS) ) . The symbols from the transmit processor 364 may be precoded by a TX MIMO processor 366 if applicable, further processed by the modulators in transceivers 354a-354r (e.g., for SC-FDM) , and transmitted to BS 102.

At BS 102, the uplink signals from UE 104 may be received by antennas 334a-t, processed by the demodulators in transceivers 332a-332t, detected by a RX MIMO detector 336 if applicable, and further processed by a receive processor 338 to obtain decoded data and control information sent by UE 104. Receive processor 338 may provide the decoded data to a data sink 314 and the decoded control information to the controller/processor 340.

Memories 342 and 382 may store data and program codes for BS 102 and UE 104, respectively.

Scheduler 344 may schedule UEs for data transmission on the downlink and/or uplink.

In various aspects, BS 102 may be described as transmitting and receiving various types of data associated with the methods described herein. In these contexts, “transmitting” may refer to various mechanisms of outputting data, such as outputting data from data source 312, scheduler 344, memory 342, transmit processor 320, controller/processor 340, TX MIMO processor 330, transceivers 332a-t, antenna 334a-t, and/or other aspects described herein. Similarly, “receiving” may refer to various mechanisms of obtaining data, such as obtaining data from antennas 334a-t, transceivers 332a-t, RX MIMO detector 336, controller/processor 340, receive processor 338, scheduler 344, memory 342, and/or other aspects described herein.

In various aspects, UE 104 may likewise be described as transmitting and receiving various types of data associated with the methods described herein. In these contexts, “transmitting” may refer to various mechanisms of outputting data, such as outputting data from data source 362, memory 382, transmit processor 364, controller/processor 380, TX MIMO processor 366, transceivers 354a-t, antenna 352a-t, and/or other aspects described herein. Similarly, “receiving” may refer to various mechanisms of obtaining data, such as obtaining data from antennas 352a-t, transceivers 354a-t, RX MIMO detector 356, controller/processor 380, receive processor 358, memory 382, and/or other aspects described herein.

In some aspects, a processor may be configured to perform various operations, such as those associated with the methods described herein, and transmit (output) to or receive (obtain) data from another interface that is configured to transmit or receive, respectively, the data.

In various aspects, artificial intelligence (AI) processors 318 and 370 may perform AI processing for BS 102 and/or UE 104, respectively. The AI processor 318 may include AI accelerator hardware or circuitry such as one or more neural processing units (NPUs) , one or more neural network processors, one or more tensor processors, one or more deep learning processors, etc. The AI processor 370 may likewise include AI accelerator hardware or circuitry. As an example, the AI processor 370 may perform AI-based beam management, AI-based channel state feedback (CSF) , AI-based antenna tuning, and/or AI-based positioning (e.g., non-line of sight positioning prediction) . In some cases, the AI processor 318 may process feedback from the UE 104 (e.g., CSF) using hardware accelerated AI inferences and/or AI training. The AI processor 318 may decode compressed CSF from the UE 104, for example, using a hardware accelerated AI inference associated with the CSF. In certain cases, the AI processor 318 may perform certain RAN-based functions including, for example, network planning, network performance management, energy-efficient network operations, etc.

FIGS. 4A, 4B, 4C, and 4D depict aspects of data structures for a wireless communications network, such as wireless communications network 100 of FIG. 1.

In particular, FIG. 4A is a diagram 400 illustrating an example of a first subframe within a 5G (e.g., 5G NR) frame structure, FIG. 4B is a diagram 430 illustrating an example of DL channels within a 5G subframe, FIG. 4C is a diagram 450 illustrating an example of a second subframe within a 5G frame structure, and FIG. 4D is a diagram 480 illustrating an example of UL channels within a 5G subframe.

Wireless communications systems may utilize orthogonal frequency division multiplexing (OFDM) with a cyclic prefix (CP) on the uplink and downlink. Such systems may also support half-duplex operation using time division duplexing (TDD) . OFDM and single-carrier frequency division multiplexing (SC-FDM) partition the system bandwidth (e.g., as depicted in FIGS. 4B and 4D) into multiple orthogonal subcarriers. Each subcarrier may be modulated with data. Modulation symbols may be sent in the frequency domain with OFDM and/or in the time domain with SC-FDM.

A wireless communications frame structure may be frequency division duplex (FDD) , in which, for a particular set of subcarriers, subframes within the set of subcarriers are dedicated for either DL or UL. Wireless communications frame structures may also be time division duplex (TDD) , in which, for a particular set of subcarriers, subframes within the set of subcarriers are dedicated for both DL and UL.

In FIG. 4A and 4C, the wireless communications frame structure is TDD where D is DL, U is UL, and X is flexible for use between DL/UL. UEs may be configured with a slot format through a received slot format indicator (SFI) (dynamically through DL control information (DCI) , or semi-statically/statically through radio resource control (RRC) signaling) . In the depicted examples, a 10 ms frame is divided into 10 equally sized 1 ms subframes. Each subframe may include one or more time slots. In some examples, each slot may include 12 or 14 symbols, depending on the cyclic prefix (CP) type (e.g., 12 symbols per slot for an extended CP or 14 symbols per slot for a normal CP) . Subframes may also include mini-slots, which generally have fewer symbols than an entire slot. Other wireless communications technologies may have a different frame structure and/or different channels.

In certain aspects, the number of slots within a subframe (e.g., a slot duration in a subframe) is based on a numerology, which may define a frequency domain subcarrier spacing and symbol duration as further described herein. In certain aspects, given a numerology μ, there are 2^μ slots per subframe. Thus, numerologies (μ) 0 to 6 may allow for 1, 2, 4, 8, 16, 32, and 64 slots, respectively, per subframe. In some cases, the extended CP (e.g., 12 symbols per slot) may be used with a specific numerology, e.g., numerology 2 allowing for 4 slots per subframe. The subcarrier spacing and symbol length/duration are a function of the numerology. The subcarrier spacing may be equal to 2^μ×15 kHz, where μ is the numerology 0 to 6. As an example, the numerology μ=0 corresponds to a subcarrier spacing of 15 kHz, and the numerology μ=6 corresponds to a subcarrier spacing of 960 kHz. The symbol length/duration is inversely related to the subcarrier spacing. FIGS. 4A, 4B, 4C, and 4D provide an example of a slot format having 14 symbols per slot (e.g., a normal CP) and a numerology μ=2 with 4 slots per subframe. In such a case, the slot duration is 0.25 ms, the subcarrier spacing is 60 kHz, and the symbol duration is approximately 16.67 μs.

As depicted in FIGS. 4A, 4B, 4C, and 4D, a resource grid may be used to represent the frame structure. Each time slot includes a resource block (RB) (also referred to as physical RBs (PRBs) ) that extends, for example, 12 consecutive subcarriers. The resource grid is divided into multiple resource elements (REs) . The number of bits carried by each RE depends on the modulation scheme including, for example, quadrature phase shift keying (QPSK) or quadrature amplitude modulation (QAM) .

As illustrated in FIG. 4A, some of the REs carry reference (pilot) signals (RS) for a UE (e.g., UE 104 of FIGS. 1 and 3) . The RS may include demodulation RS (DMRS) and/or channel state information reference signals (CSI-RS) for channel estimation at the UE.The RS may also include beam measurement RS (BRS) , beam refinement RS (BRRS) , and/or phase tracking RS (PT-RS) .

FIG. 4B illustrates an example of various DL channels within a subframe of a frame. The physical downlink control channel (PDCCH) carries DCI within one or more control channel elements (CCEs) , each CCE including, for example, nine RE groups (REGs) , each REG including, for example, four consecutive REs in an OFDM symbol.

A primary synchronization signal (PSS) may be within symbol 2 of particular subframes of a frame. The PSS is used by a UE (e.g., 104 of FIGS. 1 and 3) to determine subframe/symbol timing and a physical layer identity.

A secondary synchronization signal (SSS) may be within symbol 4 of particular subframes of a frame. The SSS is used by a UE to determine a physical layer cell identity group number and radio frame timing.

Based on the physical layer identity and the physical layer cell identity group number, the UE can determine a physical cell identifier (PCI) . Based on the PCI, the UE can determine the locations of the aforementioned DMRS. The physical broadcast channel (PBCH) , which carries a master information block (MIB) , may be logically grouped with the PSS and SSS to form a synchronization signal (SS) /PBCH block (SSB) , and in some cases, referred to as a synchronization signal block (SSB) . The MIB provides a number of RBs in the system bandwidth and a system frame number (SFN) . The physical downlink shared channel (PDSCH) carries user data, broadcast system information not transmitted through the PBCH such as system information blocks (SIBs) , and/or paging messages.

As illustrated in FIG. 4C, some of the REs carry DMRS (indicated as R for one particular configuration, but other DMRS configurations are possible) for channel estimation at the base station. The UE may transmit DMRS for the PUCCH and DMRS for the PUSCH. The PUSCH DMRS may be transmitted, for example, in the first one or two symbols of the PUSCH. The PUCCH DMRS may be transmitted in different configurations depending on whether short or long PUCCHs are transmitted and depending on the particular PUCCH format used. UE 104 may transmit sounding reference signals (SRS) . The SRS may be transmitted, for example, in the last symbol of a subframe. The SRS may have a comb structure, and a UE may transmit SRS on one of the combs. The SRS may be used by a base station for channel quality estimation to enable frequency-dependent scheduling on the UL.

FIG. 4D illustrates an example of various UL channels within a subframe of a frame. The PUCCH may be located as indicated in one configuration. The PUCCH carries uplink control information (UCI) , such as scheduling requests, a channel quality indicator (CQI) , a precoding matrix indicator (PMI) , a rank indicator (RI) , and HARQ ACK/NACK feedback. The PUSCH carries data, and may additionally be used to carry a buffer status report (BSR) , a power headroom report (PHR) , and/or UCI.

Aspects Related to Beam Management

FIG. 5 illustrates example operations 500 for radio resource control (RRC) connection establishment and beam management. As shown, at block 502, a UE may initially be in an RRC idle state (or an RRC inactivate state) . An RRC idle state refers to a state of a UE where the UE is switched on but does not have any established RRC connection (e.g., an assigned communication link) to the RAN. The RRC idle state allows the UE to reduce battery power consumption, for example, relative to an RRC connected state. For example, in the RRC idle state, the UE may periodically monitor for paging from the RAN. The UE may be in an RRC idle state when the UE does not have data to be transmitted or received. In an RRC connected state, the UE is connected to the RAN and radio resources are allocated to the UE. In some cases, the UE is actively communicating with the RAN when in the RRC connected state.

In order to perform data transfer and/or make/receive calls, the UE establishes a connection with the RAN using an initial access procedure, at block 504. For example, the UE establishes a connection to a particular serving cell of the RAN. The initial access procedure is a sequence of processes performed between the UE and the RAN to establish the RRC connection. For example, the UE may initiate a random access procedure that includes an RRC setup request or an RRC connection request. The UE may be in an RRC connected state subsequent to establishing the connection.

In some cases, the UE may perform beam management operations at block 506 in response to entering the RRC connected state. Beam management operations include a set of operations used to determine certain receive beam (s) (e.g., of the UE and/or network entity) and/or transmit beams (e.g., of the UE and/or network entity) that can be used for wireless communications (e.g., transmission and/or reception at the UE) . For example, the beam management may include certain P1, P2, and/or P3 beam management procedures.

Beam management procedures may further include beam failure detection operations at block 508 and beam failure recovery operations at block 510. For example, a UE may detect a beam failure when a layer 1 (L1) reference signal received power (RSRP) for a connected beam falls below a certain limit (e.g., a limit corresponding to a block error rate (BER) ) . In response to detecting beam failure at block 508, the UE identifies a candidate beam suitable for communication and performs beam failure recovery (BFR) . For example, the UE may send, to the RAN, a request to switch to the candidate beam for communications. In some cases, the UE may send the beam switch request via a random access procedure using the candidate beam. The RAN may activate the candidate beam or a different beam at the UE. If the BFR is not successful, the UE may declare a radio link failure (RLF) for the serving cell, at block 512. In response to RLF, the UE may perform a cell reselection process to establish a communication link on a different serving cell.

FIG. 6 is a diagram illustrating examples 600, 610, and 620 of beam management procedures. As shown in FIG. 6, examples 600, 610, and 620 include a UE 104 in communication with a BS 102 in a wireless network (e.g., wireless communications network 100 in FIG. 1) . However, the devices shown in FIG. 6 are provided as examples, and the wireless network may support communication and beam management between other devices (e.g., between a UE 104 and a network entity, a UE 104 and a transmission reception point (TRP) , between a mobile termination node and a control node, between an integrated access and backhaul (IAB) child node and an IAB parent node, between a scheduled node and a scheduling node, and/or the like) . In some aspects, the UE 104 and the BS 102 are in a connected state (e.g., RRC connected state and/or the like) .

BS 102 and UE 104 may communicate to perform beam management using reference signals (RSs) (e.g., synchronization (SSBs) , demodulation reference signals (DM-RSs) , channel state information reference signals (CSI-RSs) , etc. ) .

Example 600 depicts a first beam management procedure (e.g., such as a P1 CSI-RS beam management procedure) . The first beam management procedure may be referred to as a beam selection procedure, an initial beam acquisition procedure, a beam sweeping procedure, a cell search procedure, a beam search procedure, and/or the like. In example 600, reference signals are configured to be transmitted from the BS 102 to UE 104. The reference signals may be configured to be periodic (e.g., using RRC signaling) , semi-persistent (e.g., using media access control (MAC) control element (MAC-CE) signaling) , and/or aperiodic (e.g., using downlink control information (DCI) ) .

As illustrated, the first beam management procedure may include BS 102 performing beam sweeping over multiple transmit (TX) beams 602 of BS 102. A transmit beam is a beam that is used by a wireless communication device (e.g., a BS 102 and/or UE 104) for transmitting signals. For example, BS 102 may transmit a reference signal using each of the transmit beams 602 associated with BS 102 for beam management. To enable UE 104 to perform receive (RX) beam sweeping, BS 102 uses a transmit beam to transmit (e.g., with repetitions) each reference signal at multiple times within a same resource set to enable UE 104 to sweep through receive beams 604 of UE 104 in multiple transmission instances. A receive beam is a beam that is used by a wireless communication device for receiving signals. For example, if BS 102 has a set of N transmit beams 602 and UE 104 has a set of M receive beams 604, then the reference signal may be transmitted on each of the N transmit beams 602 M times such that UE 104 receives M instances of the reference signals per transmit beam. As a result, the first beam management procedure helps to enable UE 104 to measure a reference signal on different transmit beams, using different receive beams, to support the selection of a receive beam for a transmit beam. UE 104 may report the measurements to BS 102 to enable BS 102 to select one or more beam pair (s) for communication between BS 102 and UE 104, as further described herein with respect to channel state feedback corresponding to receive beam hypotheses.

Example 610, illustrated in FIG. 6, depicts a second beam management procedure (e.g., such as a P2 CSI-RS beam management procedure) . The second beam management procedure may be referred to as a beam refinement procedure, a BS beam refinement procedure, a TRP beam refinement procedure, a transmit beam refinement procedure, and/or the like.

As illustrated, the second beam management procedure includes BS 102 performing beam sweeping over one or more transmit beams 612. The transmit beam (s) 612 may be a subset of all transmit beams associated with BS 102 (e.g., determined based, at least in part, on measurements reported by UE 104 in connection with the first beam management procedure) . BS 102 transmits a reference signal using each of the transmit beam (s) 612. UE 104 measures each instance of the reference signal using a single (e.g., a same) receive beam 614 (e.g., determined based, at least in part, on measurements performed in connection with the first beam management procedure) . As such, the second beam management procedure may enable BS 102 to select a suitable (e.g., best, that meets a threshold measurement, etc. ) transmit beam based on measurements of the reference signals (e.g., measured by UE 104 using the single receive beam 614) reported by UE 104.

Example 620, illustrated in FIG. 6, depicts a third beam management procedure (e.g., such as a P3 CSI-RS beam management procedure) . The third beam management procedure may be referred to as a beam refinement procedure, a UE beam refinement procedure, a receive beam refinement procedure, and/or the like.

As illustrated, the third beam management procedure includes BS 102 transmitting one or more reference signals using a single transmit beam 622 (e.g., determined based, at least in part, on measurements reported by UE 104 in connection with the first beam management procedure and/or the second beam management procedure) . To enable UE 104 to perform receive beam sweeping, BS 102 may use a transmit beam to transmit (e.g., with repetitions) reference signals at multiple times within a same resource set such that UE 104 can sweep through one or more receive beams 624 in multiple transmission instances. The receive beam (s) 624 may be a subset of all receive beams associated with UE 104 (e.g., determined based on measurements performed in connection with the first beam management procedure and/or the second beam management procedure) . The third beam management procedure helps to enable BS 102 and/or UE 104 to select a suitable (e.g., best, that meets a threshold measurement, etc. ) receive beam based on reported measurements received from UE 104 (e.g., of the reference signal of the transmit beam using the one or more receive beams) .

FIG. 6 is provided as an example of beam management procedures for determining transmit beam (s) and/or receive beam (s) for wireless communications between a UE and a network entity. Other examples of beam management procedures that differ from what is described with respect to FIG. 6, however, may be considered when determining transmit beam (s) and/or receive beam (s) for wireless communications. Aspects Related to Machine Learning Based Beam Prediction

Certain aspects described herein may be implemented, at least in part, using some form of artificial intelligence (AI) , e.g., the process of using a machine learning (ML) model to infer or predict output data based on input data. Though certain aspects are discussed using an ML model to perform beam prediction, other beam prediction techniques may be used, as previously mentioned, in lieu of an ML model. An example ML model may include a mathematical representation of one or more relationships among various objects to provide an output representing one or more predictions or inferences. Once an ML model has been trained, the ML model may be deployed to process data that may be similar to, or associated with, all or part of the training data and provide an output representing one or more predictions or inferences based on the input data.

ML is often characterized in terms of types of learning that generate specific types of learned models that perform specific types of tasks. For example, different types of machine learning include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Supervised learning algorithms generally model relationships and dependencies between input features (e.g., a feature vector) and one or more target outputs. Supervised learning uses labeled training data, which are data including one or more inputs and a desired output. Supervised learning may be used to train models to perform tasks like classification, where the goal is to predict discrete values, or regression, where the goal is to predict continuous values. Some example supervised learning algorithms include nearest neighbor, naive Bayes, decision trees, linear regression, support vector machines (SVMs) , and artificial neural networks (ANNs) .

Unsupervised learning algorithms work on unlabeled input data and train models that take an input and transform it into an output to solve a practical problem. Examples of unsupervised learning tasks are clustering, where the output of the model may be a cluster identification, dimensionality reduction, where the output of the model is an output feature vector that has fewer features than the input feature vector, and outlier detection, where the output of the model is a value indicating how the input is different from a typical example in the dataset. An example unsupervised learning algorithm is k-Means.

Semi-supervised learning algorithms work on datasets containing both labeled and unlabeled examples, where often the quantity of unlabeled examples is much higher than the number of labeled examples. However, the goal of a semi-supervised learning is that of supervised learning. Often, a semi-supervised model includes a model trained to produce pseudo-labels for unlabeled data that is then combined with the labeled data to train a second classifier that leverages the higher quantity of overall training data to improve task performance.

Reinforcement Learning algorithms use observations gathered by an agent from an interaction with an environment to take actions that may maximize a reward or minimize a risk. Reinforcement learning is a continuous and iterative process in which the agent learns from its experiences with the environment until it explores, for example, a full range of possible states. An example type of reinforcement learning algorithm is an adversarial network. Reinforcement learning may be particularly beneficial when used to improve or attempt to optimize a behavior of a model deployed in a dynamically changing environment, such as a wireless communication network.

ML models may be deployed in one or more devices (e.g., network entities such as base station (s) and/or user equipment (s) ) to support various wired and/or wireless communication aspects of a communication system. For example, an ML model may be trained to identify patterns and relationships in data corresponding to a network, a device, an air interface, or the like. An ML model may improve operations relating to one or more aspects, such as transceiver circuitry controls, frequency synchronization, timing synchronization, channel state estimation, channel equalization, channel state feedback, modulation, demodulation, device positioning, transceiver tuning, beamforming, signal coding/decoding, network routing, load balancing, and energy conservation (to name just a few) associated with communications devices, services, and/or networks. AI-enhanced transceiver circuitry controls may include, for example, filter tuning, transmit power controls, gain controls (including automatic gain controls) , phase controls, power management, and the like.

Aspects of the present disclosure may describe the performance of certain tasks and the technical solution of various technical problems by application of a specific type of ML model, such as an artificial neural network (ANN) . It should be understood, however, that other type (s) of models or techniques may be used in addition to or instead of an ANN. An ML model may be an example of an AI model, and any suitable AI model may be used in addition to or instead of any of the ML models described herein. Hence, unless expressly recited, subject matter regarding an ML model is not necessarily intended to be limited to just an ANN solution or machine learning. Further, it should be understood that, unless otherwise specifically stated, terms such “AI model, ” “ML model, ” “AI/ML model, ” and the like are intended to be interchangeable.

FIG. 7A is an illustrative block diagram of an example artificial neural network (ANN) 700.

ANN 700 may receive input data 706 which may include one or more bits of data 702, pre-processed data output from pre-processor 704 (optional) , or some combination thereof. Here, data 702 may include training data, verification data, application-related data, or the like, e.g., depending on the stage of development and/or deployment of ANN 700. Pre-processor 704 may be included within ANN 700 in some other implementations. Pre-processor 704 may, for example, process all or a portion of data 702 which may result in some of data 702 being changed, replaced, deleted, etc. In some implementations, pre-processor 704 may add additional data to data 702.

ANN 700 includes at least one first layer 708 of artificial neurons 710 (e.g., perceptrons) to process input data 706 and provide resulting first layer output data via edges 712 to at least a portion of at least one second layer 714. Second layer 714 processes data received via edges 712 and provides second layer output data via edges 716 to at least a portion of at least one third layer 718. Third layer 718 processes data received via edges 716 and provides third layer output data via edges 720 to at least a portion of a final layer 722 including one or more neurons to provide output data 724. All or part of output data 724 may be further processed in some manner by (optional) post-processor 726. Thus, in certain examples, ANN 700 may provide output data 728 that is based on output data 724, post-processed data output from post-processor 726, or some combination thereof. Post-processor 726 may be included within ANN 700 in some other implementations. Post-processor 726 may, for example, process all or a portion of output data 724 which may result in output data 728 being different, at least in part, to output data 724, e.g., as result of data being changed, replaced, deleted, etc. In some implementations, post-processor 726 may be configured to add additional data to output data 724. In this example, second layer 714 and third layer 718 represent intermediate or hidden layers that may be arranged in a hierarchical or other like structure. Although not explicitly shown, there may be one or more further intermediate layers between the second layer 714 and the third layer 718.

The structure and training of artificial neurons 710 in the various layers may be tailored to specific requirements of an application. Within a given layer of an ANN, some or all of the neurons may be configured to process information provided to the layer and output corresponding transformed information from the layer. For example, transformed information from a layer may represent a weighted sum of the input information associated with or otherwise based on a non-linear activation function or other activation function used to “activate” artificial neurons of a next layer. Artificial neurons in such a layer may be activated by or be responsive to weights and biases that may be adjusted during a training process. Weights of the various artificial neurons may act as parameters to control a strength of connections between layers or artificial neurons, while biases may act as parameters to control a direction of connections between the layers or artificial neurons. An activation function may select or determine whether an artificial neuron transmits its output to the next layer or not in response to its received data. Different activation functions may be used to model different types of non-linear relationships. By introducing non-linearity into an ML model, an activation function allows the ML model to “learn” complex patterns and relationships in the input data (e.g., 742 in FIG. 7B) . Some non-exhaustive example activation functions include a linear function, binary step function, sigmoid, hyperbolic tangent (tanh) , a rectified linear unit (ReLU) and variants, exponential linear unit (ELU) , Swish, Softmax, and others.

Design tools (such as computer applications, programs, etc. ) may be used to select appropriate structures for ANN 700 and a number of layers and a number of artificial neurons in each layer, as well as selecting activation functions, a loss function, training processes, etc. Once an initial model has been designed, training of the model may be conducted using training data. Training data may include one or more datasets within which ANN 700 may detect, determine, identify or ascertain patterns. Training data may represent various types of information, including written, visual, audio, environmental context, operational properties, etc. During training, parameters of artificial neurons 710 may be changed, such as to minimize or otherwise reduce a loss function or a cost function. A training process may be repeated multiple times to fine-tune ANN 700 with each iteration.

Various ANN model structures are available for consideration. For example, in a feedforward ANN structure each artificial neuron 710 in a layer receives information from the previous layer and likewise produces information for the next layer. In a convolutional ANN structure, some layers may be organized into filters that extract features from data (e.g., training data and/or input data) . In a recurrent ANN structure, some layers may have connections that allow for processing of data across time, such as for processing information having a temporal structure, such as time series data forecasting.

In an autoencoder ANN structure, compact representations of data may be processed and the model trained to predict or potentially reconstruct original data from a reduced set of features. An autoencoder ANN structure may be useful for tasks related to dimensionality reduction and data compression.

A generative adversarial ANN structure may include a generator ANN and a discriminator ANN that are trained to compete with each other. Generative-adversarial networks (GANs) are ANN structures that may be useful for tasks relating to generating synthetic data or improving the performance of other models.

A transformer ANN structure makes use of attention mechanisms that may enable the model to process input sequences in a parallel and efficient manner. An attention mechanism allows the model to focus on different parts of the input sequence at different times. Attention mechanisms may be implemented using a series of layers known as attention layers to compute, calculate, determine or select weighted sums of input features based on a similarity between different elements of the input sequence. A transformer ANN structure may include a series of feedforward ANN layers that may learn non-linear relationships between the input and output sequences. The output of a transformer ANN structure may be obtained by applying a linear transformation to the output of a final attention layer. A transformer ANN structure may be of particular use for tasks that involve sequence modeling, or other like processing.

Another example type of ANN structure, is a model with one or more invertible layers. Models of this type may be inverted or “unwrapped” to reveal the input data that was used to generate the output of a layer.

Other example types of ANN model structures include fully connected neural networks (FCNNs) and long short-term memory (LSTM) networks.

ANN 700 or other ML models may be implemented in various types of processing circuits along with memory and applicable instructions therein, for example, as described herein with respect to FIGS. 3 and 7B. For example, general-purpose hardware circuits, such as, such as one or more central processing units (CPUs) and one or more graphics processing units (GPUs) may be employed to implement a model. One or more ML accelerators, such as tensor processing units (TPUs) , embedded neural processing units (eNPUs) , or other special-purpose processors, and/or field-programmable gate arrays (FPGAs) , application-specific integrated circuits (ASICs) , or the like also may be employed. Various programming tools are available for developing ANN models.

There are a variety of model training techniques and processes that may be used prior to, or at some point following, deployment of an ML model, such as ANN 700 of FIG. 7A.

As part of a model development process, information in the form of applicable training data may be gathered or otherwise created for use in training an ML model accordingly. For example, training data may be gathered or otherwise created regarding information associated with received/transmitted signal strengths, interference, and resource usage data, as well as any other relevant data that might be useful for training a model to address one or more problems or issues in a communication system. In certain instances, all or part of the training data may originate in one or more user equipments (UEs) , one or more network entities, or one or more other devices in a wireless communication system. In some cases, all or part of the training data may be aggregated from multiple sources (e.g., one or more UEs, one or more network entities, the Internet, etc. ) . For example, wireless network architectures, such as self-organizing networks (SONs) or mobile drive test (MDT) networks, may be adapted to support collection of data for ML model applications. In another example, training data may be generated or collected online, offline, or both online and offline by a UE, network entity, or other device (s) , and all or part of such training data may be transferred or shared (in real or near-real time) , such as through store and forward functions or the like. Offline training may refer to creating and using a static training dataset, e.g., in a batched manner, whereas online training may refer to a real-time or near-real-time collection and use of training data. For example, an ML model at a network device (e.g., a UE) may be trained and/or fine-tuned using online or offline training. For offline training, data collection and training can occur in an offline manner at the network side (e.g., at a base station or other network entity) or at the UE side. For online training, the training of a UE-side ML model may be performed locally at the UE or by a server device (e.g., a server hosted by a UE vendor) in a real-time or near-real-time manner based on data provided to the server device from the UE.

In certain instances, all or part of the training data may be shared within a wireless communication system, or even shared (or obtained from) outside of the wireless communication system.

Once an ML model has been trained with training data, its performance may be evaluated. In some scenarios, evaluation/verification tests may use a validation dataset, which may include data not in the training data, to compare the model’s performance to baseline or other benchmark information. If model performance is deemed unsatisfactory, it may be beneficial to fine-tune the model, e.g., by changing its architecture, re-training it on the data, or using different optimization techniques, etc. Once a model’s performance is deemed satisfactory, the model may be deployed accordingly. In certain instances, a model may be updated in some manner, e.g., all or part of the model may be changed or replaced, or undergo further training, just to name a few examples.

As part of a training process for an ANN, such as ANN 700 of FIG. 7A, parameters affecting the functioning of the artificial neurons and layers may be adjusted. For example, backpropagation techniques may be used to train the ANN by iteratively adjusting weights and/or biases of certain artificial neurons associated with errors between a predicted output of the model and a desired output that may be known or otherwise deemed acceptable. Backpropagation may include a forward pass, a loss function, a backward pass, and a parameter update that may be performed in training iteration. The process may be repeated for a certain number of iterations for each set of training data until the weights of the artificial neurons/layers are adequately tuned.

Backpropagation techniques associated with a loss function may measure how well a model is able to predict a desired output for a given input. An optimization algorithm may be used during a training process to adjust weights and/or biases to reduce or minimize the loss function which should improve the performance of the model. There are a variety of optimization algorithms that may be used along with backpropagation techniques or other training techniques. Some initial examples include a gradient descent based optimization algorithm and a stochastic gradient descent based optimization algorithm. A stochastic gradient descent (or ascent) technique may be used to adjust weights/biases in order to minimize or otherwise reduce a loss function. A mini-batch gradient descent technique, which is a variant of gradient descent, may involve updating weights/biases using a small batch of training data rather than the entire dataset. A momentum technique may accelerate an optimization process by adding a momentum term to update or otherwise affect certain weights/biases.

An adaptive learning rate technique may adjust a learning rate of an optimization algorithm associated with one or more characteristics of the training data. A batch normalization technique may be used to normalize inputs to a model in order to stabilize a training process and potentially improve the performance of the model.

A “dropout” technique may be used to randomly drop out some of the artificial neurons from a model during a training process, e.g., in order to reduce overfitting and potentially improve the generalization of the model.

An “early stopping” technique may be used to stop an on-going training process early, such as when a performance of the model using a validation dataset starts to degrade.

Another example technique includes data augmentation to generate additional training data by applying transformations to all or part of the training information.

A transfer learning technique may be used which involves using a pre-trained model as a starting point for training a new model, which may be useful when training data is limited or when there are multiple tasks that are related to each other.

A multi-task learning technique may be used which involves training a model to perform multiple tasks simultaneously to potentially improve the performance of the model on one or more of the tasks. Hyperparameters or the like may be input and applied during a training process in certain instances.

Another example technique that may be useful with regard to an ML model is some form of a “pruning” technique. A pruning technique, which may be performed during a training process or after a model has been trained, involves the removal of unnecessary (e.g., because they have no impact on the output) or less necessary (e.g., because they have negligible impact on the output) , or possibly redundant features from a model. In certain instances, a pruning technique may reduce the complexity of a model or improve efficiency of a model without undermining the intended performance of the model.

Pruning techniques may be particularly useful in the context of wireless communication, where the available resources (such as power and bandwidth) may be limited. Some example pruning techniques include a weight pruning technique, a neuron pruning technique, a layer pruning technique, a structural pruning technique, and a dynamic pruning technique. Pruning techniques may, for example, reduce the amount of data corresponding to a model that may need to be transmitted or stored.

Weight pruning techniques may involve removing some of the weights from a model. Neuron pruning techniques may involve removing some neurons from a model. Layer pruning techniques may involve removing some layers from a model. Structural pruning techniques may involve removing some connections between neurons in a model. Dynamic pruning techniques may involve adapting a pruning strategy of a model associated with one or more characteristics of the data or the environment. For example, in certain wireless communication devices, a dynamic pruning technique may more aggressively prune a model for use in a low-power or low-bandwidth environment, and less aggressively prune the model for use in a high-power or high-bandwidth environment. In certain aspects, pruning techniques also may be applied to training data, e.g., to remove outliers, etc. In some implementations, pre-processing techniques directed to all or part of a training dataset may improve model performance or promote faster convergence of a model. For example, training data may be pre-processed to change or remove unnecessary data, extraneous data, incorrect data, or otherwise identifiable data. Such pre-processed training data may, for example, lead to a reduction in potential overfitting, or otherwise improve the performance of the trained model.

One or more of the example training techniques presented above may be employed as part of a training process. As above, some example training processes that may be used to train an ML model include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning technique.

Decentralized, distributed, or shared learning, such as federated learning, may enable training on data distributed across multiple devices or organizations, without the need to centralize data or the training. Federated learning may be particularly useful in scenarios where data is sensitive or subject to privacy constraints, or where it is impractical, inefficient, or expensive to centralize data. In the context of wireless communication, for example, federated learning may be used to improve performance by allowing an ML model to be trained on data collected from a wide range of devices and environments. For example, an ML model may be trained on data collected from a large number of wireless devices in a network, such as distributed wireless communication nodes, smartphones, or internet-of-things (IoT) devices, to improve the network's performance and efficiency. With federated learning, a user equipment (UE) or other device may receive a copy of all or part of a model and perform local training on such copy of all or part of the model using locally available training data. Such a device may provide update information (e.g., trainable parameter gradients) regarding the locally trained model to one or more other devices (such as a network entity or a server) where the updates from other-like devices (such as other UEs) may be aggregated and used to provide an update to a shared model or the like. A federated learning process may be repeated iteratively until all or part of a model obtains a satisfactory level of performance. Federated learning may enable devices to protect the privacy and security of local data, while supporting collaboration regarding training and updating of all or part of a shared model.

In some implementations, one or more devices or services may support processes relating to a ML model’s usage, maintenance, activation, reporting, or the like. In certain instances, all or part of a dataset or model may be shared across multiple devices, e.g., to provide or otherwise augment or improve processing. In some examples, signaling mechanisms may be utilized at various nodes of wireless network to signal the capabilities for performing specific functions related to ML model, support for specific ML models, capabilities for gathering, creating, transmitting training data, or other ML related capabilities. ML models in wireless communication systems may, for example, be employed to support decisions relating to wireless resource allocation or selection, wireless channel condition estimation, interference mitigation, beam management, positioning accuracy, energy savings, or modulation or coding schemes, etc. In some implementations, model deployment may occur jointly or separately at various network levels, such as, a central unit (CU) , a distributed unit (DU) , a radio unit (RU) , or the like.

AI/ML techniques for beam prediction have been introduced to help reduce the complexity involved in beam selection and the overhead associated with beam management without sacrificing system performance. For example, with the help of ML techniques for beam prediction, beam selection may be performed in a fraction of the time taken by conventional exhaustive search methods and with performance comparable to that of such methods.

In certain aspects, an ML model is deployed at an entity (e.g., UE 104 of FIG. 1, BS 102 of FIG. 1, a disaggregated base station depicted and described with respect to FIG. 2, another network entity, or another entity (e.g., on the network side, such as a training management entity) ) . In certain aspects, the ML model may be trained at a first entity (e.g., training management entity, network entity, etc. ) , and deployed for inference at a second entity (e.g., UE) .

In certain aspects, the ML model is configured to perform beam prediction, such as spatial domain (SD) , temporal domain (TD) , and/or frequency domain (FD) beam prediction. The TD refers to the analytic space in which signals are conveyed in terms of time. For example, TD beam prediction may refer to using a past measurement at a first time for a beam to predict a future measurement at a second time for the beam, wherein the assumption may be that communications on the beam at the first time and the second time are in the same frequency. The FD refers to the analytic space in which signals are conveyed in terms of frequency. For example, FD beam prediction may refer to using a measurement at a first frequency for a beam to predict a measurement at a second frequency for the beam, wherein the assumption is that communications on the beam at the first frequency and the second frequency may be at the same time. The SD refers to the analytic space in which signals are conveyed spatially, such as using different beams. For example, a measurement for a first spatial beam (e.g., transmit beam of a network entity) may be used to predict a measurement for a second spatial beam (e.g., transmit beam of a network entity) . For example, the ML model may be configured to predict one or more channel characteristics associated with a first set of beams (e.g., Set-Abeams) based on one or more channel characteristics associated with a second set of beams (e.g., Set-B beams) .

A scenario where the ML model is used to perform SD beam prediction for downlink transmit beams of a network entity for Set-Abeams based on measurement results of a Set-B beams may be referred to as a beam management case 1, or simply “BM-Case1. ” Additionally, a scenario where the ML model is used to perform TD beam prediction for downlink transmit beams of a network entity for Set-Abeams based on the historic measurement results of a Set-B beams may be referred to as a beam management case 2, or simply “BM-Case2. ”

In certain aspects, the ML model may be used to predict characteristics associated with the first set of beams (e.g., Set-Abeams) ; and DL beam measurements associated with the second set of beams (e.g., Set-B beams) as discussed may be used as input data for the ML model. For BM-Case1 and BM-Case2, the beams in the first set of beams and the second set of beams may be in the same Frequency Range (e.g., FR1 and/or FR2) . In some cases, the second set of beams may be a subset of the first set of beams. There may be any number of beams in each of the first set of beams and the second set of beams. There may be quasi-colocation (QCL) relationships between the first set of beams and the second set of beams.

FIG. 7B is a diagram illustrating example beam prediction 730. For example, during inference, a network entity (e.g., a base station or any disaggregated entity thereof) may transmit one or more signals (e.g., SSB (s) , DM-RS (s) , CSI-RS (s) ) , via a second set of transmit beams 734, in a set of communication resources (e.g., an SSB resource, a DM-RS resource, and/or a CSI-RS resource, such as any time-frequency resource (s) ) . A UE may perform measurements (e.g., L1-RSRP measurements and/or other measurements) of the one or more signals transmitted in the set of communication resources, or a subset thereof, to obtain input data, which may include a set of measurements 742 (sometimes referred to as parameters, channel characteristics, or channel properties) (e.g., input data 702) . For example, each transmit beam 734 (or a subset thereof) , from the second set of beams carrying the one or more signals, may be associated with one or more measurements 742 performed by the UE.

The set of measurements 742 (e.g., L1 RSRP measurement values) may be input into the ML model 740 (e.g., ANN 700) , which may run at the UE, the network entity, or another entity, as discussed. Further input into the ML model 740 may include information associated with the second set of beams and/or set of communication resources (or a subset thereof) . The information associated with the second set of beams may include a beam direction (e.g., a spatial direction) , beam width, beam shape, and/or other characteristics of the respective beam. For example, the information may include for each respective beam, a respective beam identifier (e.g., associated with a beam direction (e.g., a spatial direction) , beam width, beam shape, and/or other characteristics of the respective beam) . Such beam identifiers may help the ML model 740 learn during training associations between beams, such as which beams may have similar measurements.

The ML model 740 may provide output data 744 (e.g., output data 728) , for example, including one or more predictions. More specifically, ML model 740 may provide one or more predicted measurement values 744 for a set of prediction targets associated with a first set of transmit beams 736. The one or more measurement values 744 may include predicted channel characteristics (e.g., predicted L1-RSRP measurement values) associated with the set of prediction targets, where the set of prediction targets are associated with the first set of transmit beams 736.

In some examples, the second set of beams 734 (e.g., that are measured) may be referred to as “Set B beams” and the first set of beams 736 (e.g., that are associated with predicted measurements for the prediction targets) may be referred to as “Set A beams. ” Put another way, the “Set B beams” are a set of beams for which measurements are taken and used to determine input data based on such measurements for the ML model 740, whereas the “Set A beams” are a set of beams for which ML model 740 performs predictions.

In some examples, second set of beams 734 are a subset of the first set of beams 736. In some other examples, second set of beams 734 and first set of beams 736 are different beams and/or may be mutually exclusive sets. For example, second set of beams 734 may include wide beams (e.g., unrefined beams or beams having a beam width that satisfies a first threshold) , and first set of beams 736 may include narrow beams (e.g., refined beams or beams having a beam width that satisfies a second threshold) .

Use of the ML model 740 for beam prediction may reduce a quantity of beam measurements that are performed by the UE (e.g., compared to exhaustive search methods described above with respect to FIG. 6) , thereby conserving power at the UE and/or network resources that would have otherwise been used to measure all beams included in at least the first set of beams.

In some aspects, this beam prediction technique may be referred to as a codebook-based SD selection or prediction. The codebook-based SD prediction/selection may be associated with an initial access, a secondary cell group (SCG) setup, a serving beam refinement, and/or a link quality (e.g., channel quality indicator (CQI) or precoding matrix indicator (PMI) ) and interference adaptation.

As another example, an output of the ML model 740 may include a point-direction, an angle of departure (AoD) , and/or an angle of arrival (AoA) of a beam included in the first set of beams (e.g., the “Set A beams” ) . This beam prediction technique may be referred to as a non-codebook-based SD selection or prediction. The non-codebook-based prediction/selection may be associated with a serving beam refinement, and/or a link quality (e.g., CQI or PMI) and interference adaptation. As another example, multiple measurement reports and/or values, collected at different points in time, may be input to ML model 740. This may enable ML model 740 to output codebook-based and/or non-codebook-based predictions for a measurement value, an AoD, and/or an AoA, among other examples, of a beam at a future time. The output (s) of ML model 740, may facilitate initial access procedures, carrier aggregation (e.g., secondary cell setup) , dual connectivity (e.g., secondary cell group (SCG) setup) , beam refinement procedures (e.g., a P2 beam management procedure and/or a P3 beam management procedure as described above with respect to FIG. 5) , link quality or interference adaptation procedures, beam failure and/or beam blockage predictions, and/or radio link failure predictions, among other examples.

In certain aspects, an output of ML model 740 may include a temporal beam prediction. The TD beam prediction may be associated with a serving beam refinement, a link quality (e.g., CQI or PMI) and interference adaptation, a beam failure/blockage prediction, and/or a radio link failure (RLF) prediction.

In certain aspects, ML model 740 performs SD downlink beam predictions for beams included in the “Set A beams” based on measurement results of beams included in the “Set B beams. ” In some aspects, ML model 740 performs TD downlink beam prediction for beams included in the “Set A beams” based on historic measurement results of beams included in the “Set B beams. ”

In certain aspects, the ML model 740 is configured with or architected to have a number of input features 738 (also referred to as input feature dimensions) and a number of output features 739 (also referred to as output feature dimensions) . Where the ML model 740 corresponds to an ANN, such as ANN 700, the number of input features 738 may correspond to the number of neurons 710 of the first input layer 708 and the number of output features 739 may correspond to the number of neurons of the final layer 722. For example, ML model 740 is shown as having three input features 738 and nine output features 739, though any number of each of the input features and output features are possible. Each input feature 738 may be associated with a particular beam of the second set of beams 734 (illustrated as three beams corresponding to the three input features 738) . Each output feature 739 may be associated with a particular beam of the first set of beams 736 (illustrated as nine beams corresponding to the nine output features 739) . For example, the ML model 740 may be configured to receive measurement (s) for a particular beam at a particular input feature, and output prediction (s) for a particular beam at a particular output feature. This may help ensure consistent mapping between the second set of beams 734 and first set of beams 736 for prediction.

Accordingly, to ensure consistent mapping between the second set of beams 734 and first set of beams 736 for prediction, beam identifier labeling consistency or correspondence for life cycle management of ML model 740 may be important. For example, measurement data 742 may be collected from a number of UEs, or may be collected at different times, for different beams. Such beams may need to be labeled with consistent beam identifiers. For example, any measurement data associated with a same beam should be associated with a same beam identifier. Further, any prediction data associated with a same beam should be associated with a same beam identifier. Accordingly, techniques are discussed herein for determining a beam identifier for a beam, such as to ensure consistent beam identifier labeling, which may improve prediction accuracy.

In certain aspects, ML model 740 may be trained, such as using data collected from one or more UEs. For example, during training, the network entity may transmit one or more signals, via the first set of transmit beams 736, in a second set of communication resources (e.g., an SSB resource, a DM-RS resource, and/or a CSI-RS resource, such as any time-frequency resource (s) ) . The UE may perform measurements (e.g., L1-RSRP measurements and/or other measurements) of the one or more signals transmitted in the second set of communication resources, or a subset thereof, to obtain input data, which may include a second set of measurements (sometimes referred to as parameters, channel characteristics, or channel properties) . For example, each transmit beam 736 (or a subset thereof) , from the first set of beams carrying the one or more signals, may be associated with one or more second measurements performed by the UE. The network entity and UE may similarly communicate and measure signals to determine a set of measurements (e.g., similar to measurement data 742) for the second set of transmit beams 734.

In certain aspects, in order to train the ML model 740, the set of measurements (e.g., along with associated beam identifiers) associated with the second set of transmit beams 734 may be input to the ML model 740, which outputs a predicted set of measurements for the first set of transmit beams 736 as discussed. The predicted set of measurements are compared to the one or more second measurements actually measured for the first set of transmit beams 736, and the ML model 740 adjusted (e.g., weights adjusted, such as using backpropagation techniques as discussed) so as to better align the predicted set of measurements to the one or more second measurements actually measured for the first set of transmit beams 736. Such training may be performed iteratively, until the ML model 740 can predict the set of measurements with a threshold accuracy. For example, the ML model may be trained to minimize a loss function between the predicted set of measurements of the first set of beams output by the ML model based on the measurements of the second set of beams as input and the ground truth second set of measurements of the first set of beams.

Aspects Related to Channel State Information (CSI) Configuration

In certain aspects, a UE (e.g., the UE 104 of FIG. 1) may be configured with a CSI report configuration (also referred to as a CSI report setting) , such as by receiving the CSI report configuration from a network entity (e.g., the BS 102 depicted and described with respect to FIGS. 1 and 3 or a disaggregated base station depicted and described with respect to FIG. 2. ) , such as via RRC signaling. FIG. 8 illustrates an example CSI report configuration 802. CSI report configuration 802 identifies a CSI resource setting 804 (also referred to as a CSI resource configuration) among other information (not shown) , such as a report quantity (e.g., the reportQuantity field) . The report quantity may specify the content of a CSI report to be provided by the UE, such as CSI including CQI, RI, PMI, and/or RSRP. The CSI report configuration 802 may configure periodic, semi-persistent, or aperiodic reporting of CSI. The CSI resource setting 804 identifies one or more measurement resource sets 806 (hereinafter “the measurement resource set 806” ) , also referred to as measurement resource set configuration. A measurement resource set may be, for example, a CSI-RS resource set or CSI-RS resource set configuration, or a SSB resource set or SSB resource set configuration. The measurement resource set 806 defines a group (or set) of one or more measurement resources 808a-n, which may be periodic, semi-persistent, and/or aperiodic. Note that the CSI resource setting 804 may also be referred to as CSI resource configuration. For example, each measurement resource 808a-n may be associated with an entry in measurement resource set 806 that is assigned a resource entry identifier.

The measurement resource set 806 identified in the CSI resource setting 804 may include a measurement resource set of SSBs, a measurement resource set of non-zero-power (NZP) CSI-RS resources, and/or a measurement resource set of interference measurement resources. In certain aspects, the measurement resource set 806 may include measurement resources 808a-n that correspond to the Set B beams (e.g., the transmit beams 734) as discussed herein with respect to FIG. 7B.

A measurement resource (e.g., the first measurement resource 808a) may include a channel measurement resource (e.g., an SSB resource and/or a NZP CSI-RS resource) and/or an interference measurement resource. A measurement resource may be or include one or more time-frequency resources. In certain aspects, the CSI resource setting 804 may be associated with a particular bandwidth part (BWP) of a serving cell, and certain measurement resources (e.g., CSI-RS resources) of the CSI resource setting 804 may be located in the respective BWP associated with the CSI resource setting.

In certain aspects, the CSI resource setting 804, another CSI resource setting of CSI report configuration 802, or somewhere else in CSI report configuration 802 may include a prediction target set, also referred to as a prediction target set configuration. The prediction target set defines a group (or set) of one or more prediction targets, which may be periodic, semi-persistent, and/or aperiodic. In certain aspects, the prediction target set may include prediction targets that correspond to the Set A beams (e.g., the transmit beams 736) as discussed herein with respect to FIG. 7B. A prediction target may refer to an actually transmitted reference signal (e.g., transmitted in a communication resource using the transmit beam 736) the measurement of which is predicted, a “virtual resource” (e.g., a communication resource in which a reference signal is not actually transmitted but the measurement of such a reference signal is predicted as though it was transmitted in the communication resource using the transmit beam 736) , a target beam (e.g., transmit beam 736) , or the like. For example, each prediction target may be associated with an entry in the prediction target set that is assigned a target entry identifier.

Example Signaling of Beam Information for Beam Prediction

FIG. 9 depicts a process flow 900 for communications in a network between a network entity 902, a user equipment (UE) 904, and a prediction entity 906. In some aspects, the network entity 902 may be an example of the BS 102 depicted and described with respect to FIGS. 1 and 3 or a disaggregated base station depicted and described with respect to FIG. 2. Similarly, the UE 904 may be an example of UE 104 depicted and described with respect to FIGS. 1 and 3. However, in other aspects, UE 904 may be another type of wireless communications device and network entity 902 may be another type of network entity or network node, such as those described herein. Further, the prediction entity 906 may be the network entity 902, such that they are the same entity, or may be another entity (e.g., server, computing device, virtual function, application layer function, etc. ) separate from network entity 902, such as another entity in core network 190 of FIG. 1 or core network 220 of FIG. 2.

At 908, UE 904 obtains (e.g., receives) an indication of one or more of: (1) a first total number of a first set of beams to be predicted using the beam prediction technique; or (2) a second total number of a second set of beams used for predicting the first set of beams. For example, the total number of the first set of beams may refer to the total number of different beams that can be predicted by the prediction entity 906, such as by a beam prediction technique, such as an ML model (e.g., ML model 740 of FIG. 7B) . Further, the total number of the second set of beams may refer to the total number of different beams for which measurements can be used for prediction by the prediction entity 906, such as by the beam prediction technique, such as the ML model.

For example, the first set of beams may be Set-Abeams, as discussed, and the second set of beams may be Set-B beams as discussed. In some cases, both the first total number and the second total number are obtained. As discussed, the beam prediction technique may be an ML model, or other beam prediction technique, and may be associated with an identifier (e.g., associated identifier, model identifier, etc. ) .

In some aspects, the UE 904 further obtains the identifier associated with the beam prediction technique, such as to associate the first total number and/or the second total number with the beam prediction technique.

In certain aspects, measurements of the first set of beams and the second set of beams are used for training a beam prediction technique, such as an ML model, such as at prediction entity 906. Accordingly, in certain aspects, the UE 904 may also obtain an indication that the first total number and the second total number are to be used by the UE 904 for training data collection based on measurement (s) of the first set of beams and the second set of beams.

In certain aspects, measurements of the second set of beams are used to predict the first set of beams, such as part of inference using the beam prediction technique. Accordingly, in certain aspects, the UE 904 may also obtain an indication that the first total number and the second total number are to be used by the UE 904 for collecting measurements of the second set of beams for predicting the first set of beams. In certain aspects, for inference, the UE 904 does not obtain an indication of the first total number or the second total number.

As shown, the UE 904, in certain aspects may obtain the indication of the first total number and/or the second total number from the network entity 902 or from the prediction entity 906 (or both) . In certain aspects, network entity 902 and prediction entity 906 may share the first total number and/or the second total number between one another and or assign the first total number and/or the second total number, such as different totals for various different identifiers of various different beam prediction techniques.

In certain aspects, such as where the UE 904 obtains the indication of the first total number and/or the second total number from network entity 902, the network entity 902 sends, and the UE 904 obtains, the indication (e.g., via RRC signaling) in one or more of: (1) a channel state information (CSI) report configuration indicating the identifier; (2) a CSI resource configuration indicating the identifier; (3) a CSI reference signal (CSI-RS) resource set configuration indicating the identifier; (4) a synchronization signal block (SSB) resource set configuration indicating the identifier; or (5) signaling indicating at least one of the CSI report configuration, the CSI resource configuration, the CSI-RS resource set, the SSB resource set, or the identifier.

For example, the identifier of the beam prediction technique may be signaled to UE 904, by network entity 902 (e.g., in RRC signaling) , such as in a CSI report configuration (e.g., CSI report configuration 802 of FIG. 8) , a CSI resource configuration (e.g., CSI resource setting 804 of FIG. 8) , a CSI-RS resource set configuration (e.g., measurement resource set 806 of FIG. 8) , or a SSB resource set configuration (e.g., measurement resource set 806 of FIG. 8) , for example, that schedules/associates reference signal (s) and/or prediction target (s) for the first set of beams and/or the second set of beams, such as for collecting training data for training the beam prediction technique or for collecting measurement data to perform inference by the beam prediction technique to predict the first set of beams.

In certain aspects, such CSI report configuration, CSI resource configuration, CSI-RS resource set configuration, or SSB resource set configuration may further include the indication of the first total number and/or the second total number.

In certain aspects, the indication of the first total number and/or the second total number are signaled by network entity 902 to UE 904 separately from such CSI report configuration, CSI resource configuration, CSI-RS resource set configuration, or SSB resource set configuration, but are signaled along with an indication of such CSI report configuration, CSI resource configuration, CSI-RS resource set configuration, or SSB resource set configuration, such as to associate such CSI report configuration, CSI resource configuration, CSI-RS resource set configuration, or SSB resource set configuration with the first total number and/or the second total number along with the identifier.

In certain aspects, the identifier is signaled by network entity 902 to UE 904 separately from a CSI report configuration (e.g., CSI report configuration 802 of FIG. 8) , a CSI resource configuration (e.g., CSI resource setting 804 of FIG. 8) , a CSI-RS resource set configuration (e.g., measurement resource set 806 of FIG. 8) , or a SSB resource set configuration (e.g., measurement resource set 806 of FIG. 8) , for example, that schedules/associates reference signal (s) and/or prediction target (s) for the first set of beams and/or the second set of beams, such as for collecting training data for training the beam prediction technique or for collecting measurement data to perform inference by the beam prediction technique to predict the first set of beams. In certain such aspects, the identifier is signaled along with an indication of such CSI report configuration, CSI resource configuration, CSI-RS resource set configuration, or SSB resource set configuration, such as to associate such CSI report configuration, CSI resource configuration, CSI-RS resource set configuration, or SSB resource set configuration with the identifier. In certain such aspects, the indication of the first total number and/or the second total number is signaled along with the identifier, such as to associate the first total number and/or the second total number with the identifier.

In certain aspects, such as where the UE 904 obtains the indication of the first total number and/or the second total number from prediction entity 906, the prediction entity 906 sends, and the UE 904 obtains, the indication (e.g., via one or more packets) . For example, the prediction entity 906 may manage the identifier associated with the beam prediction technique, and also the first total number and/or the second total number. In certain such aspects, the UE 904 may obtain the identifier from the prediction entity 906 or the network entity 902 (e.g., which may obtain the identifier from the prediction entity 906) .

At 910, UE 904 obtains, from network entity 902 (or alternatively prediction entity 906) beam identifier information (e.g., associated with the identifier) .

In certain aspects, the beam identifier information includes at least one of: (1) first information associating each of a plurality of first reference signals with a respective beam identifier of a respective beam of the first set of beams (e.g., wherein each respective beam identifier of each respective beam of the first set of beams has a respective value less than or equal to the first total number) ; or (2) second information associating each of a plurality of second reference signals with a respective beam identifier of a respective beam of the second set of beams (e.g., wherein each respective beam identifier of each respective beam of the second set of beams has a respective value less than or equal to the second total number) . For example, the beam identifier information may be for training data collection for training of the beam prediction technique and/or for measurement collection for prediction by the beam prediction technique, such as during inference.

For example, the first information may associate each of the reference signals (e.g., during training data collection) or prediction targets (e.g., during inference) associated with the first set of beams (e.g., in a CSI report configuration) to a respective beam identifier having a value within the range defined by the first total number N_A of the first set of beams. For example, each respective beam identifier may have a value n_A, where 1≤n_A≤N_A. The second information may associate each of the reference signals (e.g., during training data collection or during inference) associated with the second set of beams (e.g., in a CSI report configuration) to a respective beam identifier having a value within the range defined by the second total number N_B of the second set of beams. For example, each respective beam identifier may have a value n_B, where 1≤n_B≤N_B.

In certain aspects, the first information includes, for each of the plurality of first reference signals, a respective explicit indication of the respective beam identifier. In certain aspects, the second information comprises, for each of the plurality of second reference signals, a respective explicit indication of the respective beam identifier. For example, in certain aspects, connections or associations between beam identifier (s) of the first and/or second set of beams and reference signal (s) are explicitly indicated by the first information for each beam individually. For example, where there are four beams (e.g., of the first and/or second set of beams) such as beams A, B, C, and D, the first information may explicitly indicate which beam is associated with which beam identifier, such as A with 3, B with 1, C with 4, and D with 2.

In certain such aspects where each beam identifier is explicitly indicated, the UE 904 receives at least one of: (1) a first resource set configuration (e.g., measurement resource set 806 of FIG. 8) associating each of the plurality of first reference signals with a respective resource entry identifier of the first resource set configuration; or (2) a second resource set configuration (e.g., measurement resource set 806 of FIG. 8) associating each of the plurality of second reference signals with a respective resource entry identifier of the second resource set configuration. In certain aspects, the first information associates each respective resource entry identifier of the first resource set configuration with a respective beam identifier of a respective beam of the first set of beams. In certain aspects, the second information associates each respective resource entry identifier of the second resource set configuration with a respective beam identifier of a respective beam of the second set of beams.

In certain aspects, the UE 904 receives the at least one of the first resource set configuration or the second resource set configuration in RRC signaling and receives the first information and/or second information in the RRC signaling. For example, for the first resource set configuration (e.g., for persistent, semi-persistent, or aperiodic RSs) and/or the second resource set configuration (e.g., for persistent, semi-persistent, or aperiodic RSs) , a flag indicating whether the respective resource set is scheduled for the first set of beams (e.g., Set-Abeams) or the second set of beams (e.g., Set-B beams) may be RRC signaled, such as in the respective resource set, or separately for the respective resource set (e.g., in a CSI resource report configuration (e.g., CSI report configuration 802 of FIG. 8) or CSI resource setting (e.g., CSI resource setting 804 of FIG. 8) scheduling the respective resource set) . Each resource entry identifier may be further RRC signaled (e.g., in the respective resource set, or separately from the respective resource set) along with an associated value of a beam identifier, thereby associating each reference signal associated with a resource entry identifier with a respective beam identifier.

In certain aspects, the UE 904 receives the at least one of the first resource set configuration or the second resource set configuration in RRC signaling, receives an activation of the at least one of the first resource set configuration or the second resource set configuration in a MAC-CE, and receives the first information and/or second information in the MAC-CE. For example, the first resource set configuration or the second resource set configuration may be for a semi-persistent resource set (e.g., for CSI-RS) . For example, for the first resource set configuration (e.g., for semi-persistent RSs) and/or the second resource set configuration (e.g., for semi-persistent RSs) , a flag indicating whether the respective resource set is scheduled for the first set of beams (e.g., Set-A beams) or the second set of beams (e.g., Set-B beams) may be: (1) RRC signaled, such as in the respective resource set, or separately for the respective resource set (e.g., in a CSI resource report configuration (e.g., CSI report configuration 802 of FIG. 8) or CSI resource setting (e.g., CSI resource setting 804 of FIG. 8) scheduling the respective resource set) ; or (2) signaled in the MAC-CE. Each resource entry identifier may be further included in the MAC-CE along with an associated value of a beam identifier, thereby associating each reference signal associated with a resource entry identifier with a respective beam identifier.

In certain aspects, the UE 904 receives the at least one of the first resource set configuration or the second resource set configuration in RRC signaling, receives a DCI triggering the at least one of the first resource set configuration or the second resource set configuration, and receives the first information and/or second information in the DCI. For example, the first resource set configuration or the second resource set configuration may be for an aperiodic resource set (e.g., for CSI-RS) . In certain aspects, for the first resource set configuration (e.g., for aperiodic RSs) and/or the second resource set configuration (e.g., for aperiodic RSs) , a flag indicating whether the respective resource set is scheduled for the first set of beams (e.g., Set-Abeams) or the second set of beams (e.g., Set-B beams) may be: (1) RRC signaled, such as in the respective resource set, separately for the respective resource set (e.g., in a CSI resource report configuration (e.g., CSI report configuration 802 of FIG. 8) or CSI resource setting (e.g., CSI resource setting 804 of FIG. 8) scheduling the respective resource set) , or in a CSI-AssociatedReportConfigInfo associated with the respective resource set; or (2) signaled in the DCI. Each resource entry identifier may be further included in the RRC signaling or DCI (e.g., the RRC signaling or DCI including the flag) , such as along with an associated value of a beam identifier, thereby associating each reference signal associated with a resource entry identifier with a respective beam identifier.

In certain aspects, the at least one of the first information or the second information is received by UE 904 in a CSI sub-configuration associated with at least one of the plurality of first reference signals or the plurality of second reference signals. For example, UE 904 may receive a CSI report configuration (e.g., CSI report configuration 802 of FIG. 8) that includes or is otherwise associated with a plurality of CSI sub-configurations, and each of the CSI-sub configurations may be associated with different reference signals associated with the first and/or second set of beams. The beam identifiers for the first and/or second set of beams may be signaled within the respective CSI-sub configurations. For example, if a first CSI-sub configuration is associated with three RSs associated with three beams of the first set of beams or the second set of beams, the CSI-sub configuration may include, for each of the three RSs, the respective beam identifier of the associated beam, such as mapped to an identifier of the respective RS.

In certain aspects, the first information or second information may not include a respective explicit indication of the respective beam identifier for each beam. Rather, in certain aspects, the first information comprises a first starting beam identifier. In certain aspects, the second information comprises a second starting beam identifier. For example, UE 904 may receive a measurement resource set (e.g., measurement resource set 806, such as SSB or CSI-RS) for the first or second set of beams. The reference signals indicated in the measurement resource set may be associated with corresponding resource entry identifiers, as discussed. UE 904 may further receive (e.g., in the measurement resource set or through separate signaling also indicating the measurement resource set) a starting beam identifier for the measurement resource set. UE 904 may assign beam identifiers to the references signals indicated in the measurement resource set in order of their corresponding resource entry identifiers (e.g., in increasing or decreasing order) . For example, where the measurement resource set associates reference signals X, Y, Z with resource identifiers 8, 15, 10, and the starting beam identifier is 2, then reference signal X may be associated with beam identifier 2, reference signal Z may be associated with beam identifier 3, and reference signal Y may be associated with beam identifier 4. In certain aspects, such indication of a starting beam identifier may be used with any of the techniques discussed with respect to including a respective explicit indication of the respective beam identifier for each beam, such as by including a respective explicit indication of the starting beam identifier for each group of a plurality of beams, such as in any of the signaling mentioned.

In certain aspects, the UE 904 may obtain, from network entity 902 (or alternatively prediction entity 906) , additional or alternative beam identifier information (e.g., associated with the identifier) (such as where the above beam identifier information is for training data collection, and the additional or alternative beam identifier information is for inference) . The additional or alternative beam identifier information may include at least one of: (1) third information associating each of a plurality of prediction targets with a respective beam identifier of a respective beam of a third set of beams to be predicted using the beam prediction technique (e.g., wherein each respective beam identifier of each respective beam of the third set of beams has a respective value less than or equal to the first total number) ; or (2) fourth information associating each of a plurality of reference signals with a respective beam identifier of a respective beam of a fourth set of beams used for predicting the third set of beams (e.g., wherein each respective beam identifier of each respective beam of the fourth set of beams has a respective value less than or equal to the second total number) . For example, during inference, measurement (s) of the fourth set of beams may be used to predict measurement (s) of the third set of beams.

For example, the third information may associate each of the prediction targets associated with the third set of beams (e.g., in a CSI report configuration, for which L1 prediction results are requested, such as by network entity 902, etc. ) to a respective beam identifier having a value within the range defined by the first total number N_A of the first set of beams (and/or third set of beams) . For example, each respective beam identifier may have a value n_A, where 1≤n_A≤N_A. The fourth information may associate each of the reference signals associated with the fourth set of beams (e.g., in a CSI report configuration) to a respective beam identifier having a value within the range defined by the second total number N_B of the second set of beams (and/or fourth set of beams) . For example, each respective beam identifier may have a value n_B, where 1≤n_B≤N_B.

In certain aspects, the fourth information includes, for each of the plurality of reference signals, a respective explicit indication of the respective beam identifier. For example, the fourth information may include similar or the same type of information as discussed with respect to various aspects for the first and/or second information, but for the plurality of reference signals and the fourth set of beams, instead of the first plurality of reference signals and the first set of beams or the second plurality of reference signals and the second set of beams. Further, the UE 904 may receive the fourth information in a similar or same type of way as discussed with respect to various aspects for the first and/or second information.

In certain aspects, the fourth information may not include a respective explicit indication of the respective beam identifier for each beam. Rather, in certain aspects, the fourth information includes a starting beam identifier. For example, the fourth information may include similar or the same type of information as discussed with respect to various aspects for the first and/or second information, but for the plurality of reference signals and the fourth set of beams, instead of the first plurality of reference signals and the first set of beams or the second plurality of reference signals and the second set of beams. Further, the UE 904 may receive the fourth information in a similar or same type of way as discussed with respect to various aspects for the first and/or second information.

In certain aspects, the third information includes, for each of the plurality of prediction targets, a respective explicit indication of the respective beam identifier. For example, in certain aspects, connections or associations between beam identifier (s) of the third set of beams and prediction target (s) are explicitly indicated by the third information for each beam individually. For example, where there are four in the third set of beams such as beams A, B, C, and D, the third information may explicitly indicate which beam is associated with which beam identifier, such as A with 3, B with 1, C with 4, and D with 2.

In certain such aspects where each beam identifier is explicitly indicated, the UE 904 receives a prediction target set configuration (e.g., in CSI report configuration 802 measurement resource set 806 of FIG. 8) associating each of the plurality of prediction targets with a respective target entry identifier of the prediction target set configuration. In certain aspects, the third information associates each respective target entry identifier of the prediction target set configuration with a respective beam identifier of a respective beam of the third set of beams.

In certain aspects, the UE 904 receives the prediction target set configuration (e.g., for persistent, semi-persistent, or aperiodic CSI reporting) in RRC signaling and receives the third information in the RRC signaling. For example, each target entry identifier may be RRC signaled (e.g., in the prediction target set configuration, or separately from the prediction target set configuration) along with an associated value of a beam identifier, thereby associating each prediction target associated with a target entry identifier with a respective beam identifier.

In certain aspects, the UE 904 receives the prediction target set configuration (e.g., for semi-persistent CSI reporting) in RRC signaling, receives an activation of CSI reporting associated with the third set of beams in a MAC-CE (e.g., a MAC-CE activating semi-persistent CSI reporting carrying prediction results for the third set of beams) , and receives the third information in the MAC-CE. For example, each target entry identifier may be further included in the MAC-CE along with an associated value of a beam identifier, thereby associating each prediction target associated with a target entry identifier with a respective beam identifier.

In certain aspects, the UE 904 receives the prediction target set configuration in RRC signaling, receives a DCI triggering a CSI report associated with the third set of beams (e.g., a CSI report carrying prediction results for the third set of beams) , and receives the third information in the DCI. Each target entry identifier may be included in the RRC signaling (e.g., in CSI-AssociatedReportConfigInfo) or the DCI, such as along with an associated value of a beam identifier, thereby associating each prediction target associated with a target entry identifier with a respective beam identifier.

In certain aspects, the third information may not include a respective explicit indication of the respective beam identifier for each beam. Rather, in certain aspects, the third information comprises a starting beam identifier. For example, UE 904 may receive a prediction target set configuration (e.g., in CSI report configuration 802 measurement resource set 806 of FIG. 8) for the third set of beams. The prediction targets indicated in the prediction target set may be associated with corresponding target entry identifiers, as discussed. UE 904 may further receive (e.g., in the prediction target set or through separate signaling also indicating the prediction target set) a starting beam identifier for the prediction target set. UE 904 may assign beam identifiers to the prediction targets indicated in the prediction target set in order of their corresponding target entry identifiers (e.g., in increasing or decreasing order) . For example, where the prediction target set associates prediction targets X, Y, Z with target entry identifiers 8, 15, 10, and the starting beam identifier is 2, then prediction target X may be associated with beam identifier 2, prediction target Z may be associated with beam identifier 3, and prediction target Y may be associated with beam identifier 4. In certain aspects, such indication of a starting beam identifier may be used with any of the techniques discussed with respect to including a respective explicit indication of the respective beam identifier for each beam, such as by including a respective explicit indication of the starting beam identifier for each group of a plurality of beams, such as in any of the signaling mentioned.

In certain aspects, UE 904 may not obtain beam identifier information at 910. For example, instead of UE 904 receiving explicit signaling of the associations between beam identifier (s) of the first set of beams, second set of beams, third set of beams, and/or fourth set of beams and corresponding reference signal (s) or prediction target (s) of the beam (s) , the beam identifier of a beam associated with the corresponding reference signal or prediction target may be determined by UE 904 (e.g., as a random variable associated with) based on a temporal resource (e.g., symbol, slot, subframe, frame ID, etc. ) corresponding to the reference signal (e.g., temporal resource occupied by the reference signal) or prediction target (e.g., temporal resource assumed as associated with the prediction target) .

In certain aspects, each beam of the first set of beams, second set of beams, third set of beams, and/or fourth set of beams may be associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated. For example, for a reference signal associated with the first set of beams, second set of beams, third set of beams or fourth set of beams, the temporal resource in which the reference signal is communicated (e.g., identifier or order in time of the temporal resource) may be used to determine the beam identifier associated with the reference signal. For example, beam identifiers may be assigned in order of the identifiers or in order of time of the temporal resources. As another example, the identifier of the temporal resource itself may be used as the beam identifier, or a seed in a random number generator to generate a random beam identifier.

In certain aspects, each beam of the third set of beams may be associated with a respective beam identifier based on a respective prediction target associated with the beam. For example, in certain aspects, for a prediction target associated with the third set of beams, where a reference signal is not actually communicated for the prediction target, the temporal resource associated with the prediction target (e.g., identifier or order in time of the temporal resource) may be used to determine the beam identifier associated with the prediction target. For example, beam identifiers may be assigned in order of the identifiers or in order of time of the temporal resources. As another example, the identifier of the temporal resource itself may be used as the beam identifier, or a seed in a random number generator to generate a random beam identifier.

In certain aspects, each beam of the third set of beams is associated with a respective beam identifier based on a temporal resource in which a CSI report associated with the beam is communicated. For example, the actually occupied temporal resource for CSI report carrying prediction results for the third set of beams may be used to determine the beam identifiers for the prediction targets. In another example, if the third set of beams are not actually transmitted, then randomized beam identifiers may be requested (e.g., from network entity 902 or prediction entity 906) for temporal resources where the CSI report carrying the prediction results are communicated. In other words, the temporal resources corresponding to prediction targets associated with the third set of beams may impact which beams are requested to be predicted and reported as part of the third set of beams.

In certain aspects, instead of UE 904 receiving explicit signaling of the associations between beam identifier (s) of the first set of beams, second set of beams, third set of beams, and/or fourth set of beams and corresponding reference signal (s) or prediction target (s) of the beam (s) , the beam identifier of a beam associated with the corresponding reference signal or prediction target may be determined by UE 904 (e.g., as a random variable associated with) based on a resource entry identifier or target entry identifier associated with the reference signal or prediction target. For example, if the UE 904 is configured with a single resource set or target set for Set-Aor Set-B beams, and the number of reference signals or prediction targets indicated in the resource set or target set is equal to the total number of Set-Aor Set-B beams, the reference signals or prediction targets may be associated with beam identifiers in order (e.g., decreasing or increasing) of their associated resource entry identifier or target entry identifier.

Accordingly, in certain aspects, each beam of the first set of beams, second set of beams, and/or fourth set of beams is associated with a respective beam identifier based on a respective resource entry identifier of a respective reference signal associated with the beam. In certain aspects, each beam of a third set of beams is associated with a respective beam identifier based on a respective target entry identifier of a respective prediction target associated with the beam.

For example, in certain aspects, each beam of the first set of beams is associated with a respective beam identifier based on a respective resource entry identifier, in a first resource set configuration, of a respective reference signal associated with the beam, based on the first resource set configuration including a number of resource entry identifiers equal to the first total number. In certain aspects, each beam of the second set of beams is associated with a respective beam identifier based on a respective resource entry identifier, in a second resource set configuration, of a respective reference signal associated with the beam, based on the second resource set configuration including a number of resource entry identifiers equal to the second total number. In certain aspects, each beam of the first set of beams is associated with a respective beam identifier based on a respective target entry identifier, in a prediction target set configuration, of a respective prediction target associated with the beam, based on the first resource set configuration including a number of target entry identifiers equal to the first total number.

At 912, network entity 902 sends one or more reference signals to UE 904, such as using one or more of the first set of beams, second set of beams, third set of beams, or fourth set of beams.

At 914, UE 904 sends to network entity 902 or prediction entity 906, one or more measurements of the one or more reference signals sent at 912, and/or one or more predictions based on the one or more measurements of the one or more reference signals sent at 912.

For example, in certain aspects, the one or more measurements include training data for the beam prediction technique and the one or more measurements are associated with both the first set of beams and the second set of beams. For example, the one or more reference signals may be references signals for both the first set of beams and the second set of beams.

In certain aspects, the one or more measurements are associated with the second set of beams, and the one or more measurements are used for predicting the first set of beams. For example, the one or more reference signals may be reference signals for the second set of beams, and the prediction entity may predict the first set of beams based on UE 904 sending the one or more measurements for the second set of beams. The prediction entity 906 may send the predictions for the first set of beams (or a subset thereof) to the network entity 902 (e.g., via UE 904, where UE 904 may also send the one or more measurements (or a subset thereof) to network entity 902) . In another example, the one or more reference signals may be reference signals for the second set of beams, and the UE 904 may predict the first set of beams based on measurements of the one or more reference signals. The UE 904 may report the one or more measurements (or a subset thereof) and/or one or more predictions for the first set of beams (or a subset thereof) to the network entity 902.

In certain aspects, the one or more measurements are associated with the fourth set of beams, and the one or more measurements are used for predicting the third set of beams. For example, the one or more reference signals may be reference signals for the fourth set of beams, and the prediction entity may predict the third set of beams based on UE 904 sending the one or more measurements for the fourth set of beams. The prediction entity 906 may send the predictions for the third set of beams (or a subset thereof) to the network entity 902 (e.g., via UE 904, where UE 904 may also send the one or more measurements (or a subset thereof) to network entity 902) . In another example, the one or more reference signals may be reference signals for the fourth set of beams, and the UE 904 may predict the third set of beams based on measurements of the one or more reference signals. The UE 904 may report the one or more measurements (or a subset thereof) and/or one or more predictions for the third set of beams (or a subset thereof) to the network entity 902.

Note that the process flow illustrated in FIG. 9 is an example of signaling of beam information for beam prediction, and aspects of the present disclosure may be applied to signaling of beam information for beam prediction. Note that the process flow illustrated in FIG. 9 is described herein to facilitate an understanding of signaling of beam information for beam prediction, and aspects of the present disclosure may be performed in various manners via alternative or additional signaling and/or operations. In certain aspects, the operations and/or signaling of FIG. 9 may occur in an order different from that described or depicted, and various actions, operations, and/or signaling may be added, omitted, or combined.

Example Operations

FIG. 10 shows a method 1000 for wireless communications by an apparatus, such as UE 104 of FIGS. 1 and 3.

In certain aspects, method 1000 provides for communication of a total number of a first set of beams and/or a total number of a second set of beams, such as from a network entity to a UE. In certain aspects, the UE may use information about the total number of the first set of beams and/or the total number of the second set of beams to determine input/output feature dimensions of an ML model or beam prediction technique used by the entity for beam prediction. The UE may send such information to an entity, which may utilize such information, to determine when there is sufficient training data, the determination of which is a technical problem, which may allow the entity to continue training until there is sufficient training data, leading to improved prediction performance. Such improved prediction performance may provide a technical benefit of improved communications, such as between the UE and the network entity, as then an appropriate beam can be selected for communication between the UE and the network entity, which may improve throughout, error rate, etc.

Method 1000 begins at block 1005 with receiving an identifier associated with a beam prediction technique.

Method 1000 then proceeds to block 1010 with receiving an indication of one or more of: a first total number of a first set of beams to be predicted using the beam prediction technique; or a second total number of a second set of beams used for predicting the first set of beams.

Method 1000 then proceeds to block 1015 with sending one or more measurements associated with the first set of beams, the second set of beams, or both.

In one aspect, the beam prediction technique comprises a machine learning model.

In one aspect, the one or more of the first total number or the second total number comprises the first total number and the second total number.

In one aspect, the one or more measurements comprise training data for the beam prediction technique; and the one or more measurements are associated with both the first set of beams and the second set of beams.

In one aspect, the one or more measurements are associated with the second set of beams; and the one or more measurements are used for predicting the first set of beams.

In one aspect, method 1000 further includes receiving at least one of: first information associating each of a plurality of first reference signals with a respective beam identifier of a respective beam of the first set of beams; or second information associating each of a plurality of second reference signals with a respective beam identifier of a respective beam of the second set of beams.

In one aspect, at least one of: each respective beam identifier of each respective beam of the first set of beams has a respective value less than or equal to the first total number; or each respective beam identifier of each respective beam of the second set of beams has a respective value less than or equal to the second total number.

In one aspect, the at least one of the first information or the second information comprises the first information and the second information.

In one aspect, the one or more measurements are based on at least one reference signal of the plurality of first reference signals or the plurality of second reference signals.

In one aspect, at least one of: the first information comprises, for each of the plurality of first reference signals, a respective explicit indication of the respective beam identifier; or the second information comprises, for each of the plurality of second reference signals, a respective explicit indication of the respective beam identifier.

In one aspect, method 1000 further includes receiving at least one of: a first resource set configuration associating each of the plurality of first reference signals with a respective resource entry identifier of the first resource set configuration; or a second resource set configuration associating each of the plurality of second reference signals with a respective resource entry identifier of the second resource set configuration; and at least one of: the first information associates each respective resource entry identifier of the first resource set configuration with a respective beam identifier of a respective beam of the first set of beams; or the second information associates each respective resource entry identifier of the second resource set configuration with a respective beam identifier of a respective beam of the second set of beams.

In one aspect, receiving the at least one of the first resource set configuration or the second resource set configuration comprises receiving the at least one of the first resource set configuration or the second resource set configuration in RRC signaling; and receiving the at least one of the first information or the second information comprises receiving the at least one of the first information or the second information in the RRC signaling.

In one aspect, receiving the at least one of the first resource set configuration or the second resource set configuration comprises receiving the at least one of the first resource set configuration or the second resource set configuration in RRC signaling; the method 1000 further includes receiving an activation of the at least one of the first resource set configuration or the second resource set configuration in a MAC-CE; and receiving the at least one of the first information or the second information comprises receiving the at least one of the first information or the second information in the MAC-CE.

In one aspect, receiving the at least one of the first resource set configuration or the second resource set configuration comprises receiving the at least one of the first resource set configuration or the second resource set configuration in RRC signaling; the method 1000 further includes receiving a DCI triggering the at least one of the first resource set configuration or the second resource set configuration; and receiving the at least one of the first information or the second information comprises receiving the at least one of the first information or the second information in the DCI.

In one aspect, receiving the at least one of the first information or the second information comprises receiving the at least one of the first information or the second information in a CSI sub-configuration associated with at least one of the plurality of first reference signals or the plurality of second reference signals.

In one aspect, at least one of: the first information comprises a first starting beam identifier; or the second information comprises a second starting beam identifier.

In one aspect, method 1000 further includes receiving at least one of: first information associating each of a plurality of prediction targets with a respective beam identifier of a respective beam of a third set of beams to be predicted using the beam prediction technique; or second information associating each of a plurality of reference signals with a respective beam identifier of a respective beam of a fourth set of beams used for predicting the third set of beams.

In one aspect, at least one of: each respective beam identifier of each respective beam of the third set of beams has a respective value less than or equal to the first total number; or each respective beam identifier of each respective beam of the fourth set of beams has a respective value less than or equal to the second total number.

In one aspect, method 1000 further includes sending one or more second measurements associated with the fourth set of beams.

In one aspect, the one or more second measurements are used for predicting the third set of beams.

In one aspect, the at least one of the first information or the second information comprises the second information; and the second information comprises, for each of the plurality of reference signals, a respective explicit indication of the respective beam identifier.

In one aspect, method 1000 further includes receiving a resource set configuration associating each of the plurality of reference signals with a respective resource entry identifier of the resource set configuration; and the second information associates each respective resource entry identifier of the resource set configuration with a respective beam identifier of a respective beam of the fourth set of beams.

In one aspect, receiving the resource set configuration comprises receiving the resource set configuration in RRC signaling; and receiving the second information comprises receiving the second information in the RRC signaling.

In one aspect, receiving the resource set configuration comprises receiving the resource set configuration in RRC signaling; the method 1000 further includes receiving an activation of the resource set configuration in a MAC-CE; and receiving the second information comprises receiving the second information in the MAC-CE.

In one aspect, receiving the resource set configuration comprises receiving the resource set configuration in RRC signaling; the method 1000 further includes receiving a DCI triggering the resource set configuration; and receiving the second information comprises receiving the second information in the DCI.

In one aspect, receiving the second information comprises receiving the second information in a CSI sub-configuration associated with the plurality of reference signals.

In one aspect, the at least one of the first information or the second information comprises the second information; and the second information comprises a starting beam identifier.

In one aspect, the at least one of the first information or the second information comprises the first information; and the first information comprises, for each of the plurality of prediction targets, a respective explicit indication of the respective beam identifier.

In one aspect, method 1000 further includes receiving a prediction target set configuration associating each of the plurality of prediction targets with a respective target entry identifier of the prediction target set configuration; and the first information associates each respective target entry identifier of the prediction target set configuration with a respective beam identifier of a respective beam of the third set of beams.

In one aspect, receiving the prediction target set configuration comprises receiving the prediction target set configuration in RRC signaling; and receiving the first information comprises receiving the first information in the RRC signaling.

In one aspect, receiving the prediction target set configuration comprises receiving the prediction target set configuration in RRC signaling; the method 1000 further includes receiving an activation of CSI reporting associated with the third set of beams in a MAC-CE; and receiving the first information comprises receiving the first information in the MAC-CE.

In one aspect, receiving the prediction target set configuration comprises receiving the prediction target set configuration in RRC signaling; the method 1000 further includes receiving a DCI triggering a CSI report associated with the third set of beams; and receiving the first information comprises receiving the first information in the DCI.

In one aspect, the at least one of the first information or the second information comprises the first information; and the first information comprises a starting beam identifier.

In one aspect, block 1010 includes receiving the indication in one or more of: a CSI report configuration indicating the identifier; a CSI resource configuration indicating the identifier; a CSI-RS resource set configuration indicating the identifier; a SSB resource set configuration indicating the identifier; or signaling indicating at least one of the CSI report configuration, the CSI resource configuration, the CSI-RS resource set, the SSB resource set, or the identifier.

In one aspect, block 1010 includes receiving the indication via RRC signaling.

In one aspect, block 1010 includes receiving the indication from an entity configured to train the beam prediction technique; and block 1015 includes sending the one or more measurements to the entity.

In one aspect, block 1005 includes receiving the identifier from the entity.

In one aspect, at least one of: each beam of the first set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated; each beam of the second set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated; each beam of a third set of beams is associated with a respective beam identifier based on a temporal resource in which a CSI report associated with the beam is communicated, wherein the third set of beams is to be predicted using the beam prediction technique; each beam of the third set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated; each beam of the third set of beams is associated with a respective beam identifier based on a respective prediction target associated with the beam; or each beam of a fourth set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated, wherein the fourth set of beams is used for predicting the third set of beams.

In one aspect, at least one of: each beam of the first set of beams is associated with a respective beam identifier based on a respective resource entry identifier of a respective reference signal associated with the beam; each beam of the second set of beams is associated with a respective beam identifier based on a respective resource entry identifier of a respective reference signal associated with the beam; each beam of a third set of beams is associated with a respective beam identifier based on a respective target entry identifier of a respective prediction target associated with the beam, wherein the third set of beams is to be predicted using the beam prediction technique; or each beam of a fourth set of beams is associated with a respective beam identifier based on a respective resource entry identifier of a respective reference signal associated with the beam, wherein the fourth set of beams is used for predicting the third set of beams.

In one aspect, at least one of: each beam of the first set of beams is associated with a respective beam identifier based on a respective resource entry identifier, in a first resource set configuration, of a respective reference signal associated with the beam, based on the first resource set configuration including a number of resource entry identifiers equal to the first total number; each beam of the second set of beams is associated with a respective beam identifier based on a respective resource entry identifier, in a second resource set configuration, of a respective reference signal associated with the beam, based on the second resource set configuration including a number of resource entry identifiers equal to the second total number; or each beam of the first set of beams is associated with a respective beam identifier based on a respective target entry identifier, in a prediction target set configuration, of a respective prediction target associated with the beam, based on the first resource set configuration including a number of target entry identifiers equal to the first total number.

In one aspect, method 1000, or any aspect related to it, may be performed by an apparatus, such as communications device 1200 of FIG. 12, which includes various components operable, configured, or adapted to perform the method 1000. Communications device 1200 is described below in further detail.

Note that FIG. 10 is just one example of a method, and other methods including fewer, additional, or alternative operations are possible consistent with this disclosure.

FIG. 11 shows a method 1100 for wireless communications by an apparatus, such as BS 102 of FIGS. 1 and 3, or a disaggregated base station as discussed with respect to FIG. 2.

In certain aspects, method 1100 provides for communication of a total number of a first set of beams and/or a total number of a second set of beams, such as from a network entity to a UE. In certain aspects, the UE may use information about the total number of the first set of beams and/or the total number of the second set of beams to determine input/output feature dimensions of an ML model or beam prediction technique used by the entity for beam prediction. The UE may send such information to an entity, which may utilize such information, to determine when there is sufficient training data, the determination of which is a technical problem, which may allow the entity to continue training until there is sufficient training data, leading to improved prediction performance. Such improved prediction performance may provide a technical benefit of improved communications, such as between the UE and the network entity, as then an appropriate beam can be selected for communication between the UE and the network entity, which may improve throughout, error rate, etc.

Method 1100 begins at block 1105 with sending an identifier associated with a beam prediction technique.

Method 1100 then proceeds to block 1110 with sending an indication of one or more of: a first total number of a first set of beams to be predicted using the beam prediction technique; or a second total number of a second set of beams used for predicting the first set of beams.

Method 1100 then proceeds to block 1115 with receiving one or more measurements associated with the first set of beams, the second set of beams, or both.

In certain aspects, method 1100 further includes sending at least one of: first information associating each of a plurality of first reference signals with a respective beam identifier of a respective beam of the first set of beams; or second information associating each of a plurality of second reference signals with a respective beam identifier of a respective beam of the second set of beams.

In certain aspects, method 1100 further includes sending at least one of: a first resource set configuration associating each of the plurality of first reference signals with a respective resource entry identifier of the first resource set configuration; or a second resource set configuration associating each of the plurality of second reference signals with a respective resource entry identifier of the second resource set configuration; and at least one of: the first information associates each respective resource entry identifier of the first resource set configuration with a respective beam identifier of a respective beam of the first set of beams; or the second information associates each respective resource entry identifier of the second resource set configuration with a respective beam identifier of a respective beam of the second set of beams.

In one aspect, sending the at least one of the first resource set configuration or the second resource set configuration comprises sending the at least one of the first resource set configuration or the second resource set configuration in RRC signaling; and sending the at least one of the first information or the second information comprises sending the at least one of the first information or the second information in the RRC signaling.

In one aspect, sending the at least one of the first resource set configuration or the second resource set configuration comprises sending the at least one of the first resource set configuration or the second resource set configuration in RRC signaling; the method 1100 further includes sending an activation of the at least one of the first resource set configuration or the second resource set configuration in a MAC-CE; and sending the at least one of the first information or the second information comprises sending the at least one of the first information or the second information in the MAC-CE.

In one aspect, sending the at least one of the first resource set configuration or the second resource set configuration comprises sending the at least one of the first resource set configuration or the second resource set configuration in RRC signaling; the method 1100 further includes sending a DCI triggering the at least one of the first resource set configuration or the second resource set configuration; and sending the at least one of the first information or the second information comprises sending the at least one of the first information or the second information in the DCI.

In one aspect, sending the at least one of the first information or the second information comprises sending the at least one of the first information or the second information in a CSI sub-configuration associated with at least one of the plurality of first reference signals or the plurality of second reference signals.

In certain aspects, method 1100 further includes sending at least one of: first information associating each of a plurality of prediction targets with a respective beam identifier of a respective beam of a third set of beams to be predicted using the beam prediction technique; or second information associating each of a plurality of reference signals with a respective beam identifier of a respective beam of a fourth set of beams used for predicting the third set of beams.

In certain aspects, method 1100 further includes receiving one or more second measurements associated with the fourth set of beams.

In certain aspects, method 1100 further includes sending a resource set configuration associating each of the plurality of reference signals with a respective resource entry identifier of the resource set configuration; and the second information associates each respective resource entry identifier of the resource set configuration with a respective beam identifier of a respective beam of the fourth set of beams.

In one aspect, sending the resource set configuration comprises sending the resource set configuration in RRC signaling; and sending the second information comprises sending the second information in the RRC signaling.

In one aspect, sending the resource set configuration comprises sending the resource set configuration in RRC signaling; the method 1100 further includes sending an activation of the resource set configuration in a MAC-CE; and sending the second information comprises sending the second information in the MAC-CE.

In one aspect, sending the resource set configuration comprises sending the resource set configuration in RRC signaling; the method 1100 further includes sending a DCI triggering the resource set configuration; and sending the second information comprises sending the second information in the DCI.

In one aspect, sending the second information comprises sending the second information in a CSI sub-configuration associated with the plurality of reference signals.

In certain aspects, method 1100 further includes sending a prediction target set configuration associating each of the plurality of prediction targets with a respective target entry identifier of the prediction target set configuration; and the first information associates each respective target entry identifier of the prediction target set configuration with a respective beam identifier of a respective beam of the third set of beams.

In one aspect, sending the prediction target set configuration comprises sending the prediction target set configuration in RRC signaling; and sending the first information comprises sending the first information in the RRC signaling.

In one aspect, sending the prediction target set configuration comprises sending the prediction target set configuration in RRC signaling; the method 1100 further includes sending an activation of CSI reporting associated with the third set of beams in a MAC-CE; and sending the first information comprises sending the first information in the MAC-CE.

In one aspect, sending the prediction target set configuration comprises sending the prediction target set configuration in RRC signaling; the method 1100 further includes sending a DCI triggering a CSI report associated with the third set of beams; and sending the first information comprises sending the first information in the DCI.

In one aspect, block 1110 includes sending the indication in one or more of: a CSI report configuration indicating the identifier; a CSI resource configuration indicating the identifier; a CSI-RS resource set configuration indicating the identifier; a SSB resource set configuration indicating the identifier; or signaling indicating at least one of the CSI report configuration, the CSI resource configuration, the CSI-RS resource set, the SSB resource set, or the identifier.

In one aspect, block 1110 includes sending the indication via RRC signaling.

In one aspect, method 1100, or any aspect related to it, may be performed by an apparatus, such as communications device 1300 of FIG. 13, which includes various components operable, configured, or adapted to perform the method 1100. Communications device 1300 is described below in further detail.

Note that FIG. 11 is just one example of a method, and other methods including fewer, additional, or alternative operations are possible consistent with this disclosure.

Example Communications Device

FIG. 12 depicts aspects of an example communications device 1200. In some aspects, communications device 1200 is a user equipment, such as UE 104 described above with respect to FIGS. 1 and 3.

The communications device 1200 includes a processing system 1205 coupled to a transceiver 1245 (e.g., a transmitter and/or a receiver) . The transceiver 1245 is configured to transmit and receive signals for the communications device 1200 via an antenna 1250, such as the various signals as described herein. The processing system 1205 may be configured to perform processing functions for the communications device 1200, including processing signals received and/or to be transmitted by the communications device 1200.

The processing system 1205 includes one or more processors 1210. In various aspects, the one or more processors 1210 may be representative of one or more of receive processor 358, transmit processor 364, TX MIMO processor 366, and/or controller/processor 380, as described with respect to FIG. 3. The one or more processors 1210 are coupled to a computer-readable medium/memory 1225 via a bus 1240. In certain aspects, the computer-readable medium/memory 1225 is configured to store instructions (e.g., computer-executable code) , including code 1230 and 1235, that when executed by the one or more processors 1210, enable and cause the one or more processors 1210 to perform the method 1000 described with respect to FIG. 10, or any aspect related to it, including any operations described in relation to FIG. 10. Note that reference to a processor performing a function of communications device 1200 may include one or more processors performing that function of communications device 1200, such as in a distributed fashion.

In the depicted example, computer-readable medium/memory 1225 stores code for receiving 1230 and code for sending 1235. Processing of the code 1230 and 1235 may enable and cause the communications device 1200 to perform the method 1000 described with respect to FIG. 10, or any aspect related to it.

The one or more processors 1210 include circuitry configured to implement (e.g., execute) the code (e.g., executable instructions) stored in the computer-readable medium/memory 1225, including circuitry for receiving 1215 and circuitry for sending 1220. Processing with circuitry 1215 and 1220 may enable and cause the communications device 1200 to perform the method 1000 described with respect to FIG. 10, or any aspect related to it.

Various components of the communications device 1200 may provide means for performing the method 1000 described with respect to FIG. 10, or any aspect related to it. More generally, means for communicating, transmitting, sending or outputting for transmission may include the transceivers 354, antenna (s) 352, transmit processor 364, TX MIMO processor 366, AI processor 370, and/or controller/processor 380 of the UE 104 illustrated in FIG. 3, transceiver 1245 and/or antenna 1250 of the communications device 1200 in FIG. 12, and/or one or more processors 1210 of the communications device 1200 in FIG. 12. Means for communicating, receiving or obtaining may include the transceivers 354, antenna (s) 352, receive processor 358, AI processor 370, and/or controller/processor 380 of the UE 104 illustrated in FIG. 3, transceiver 1245 and/or antenna 1250 of the communications device 1200 in FIG. 12, and/or one or more processors 1210 of the communications device 1200 in FIG. 12.

FIG. 13 depicts aspects of an example communications device 1300. In some aspects, communications device 1300 is a network entity, such as BS 102 of FIGS. 1 and 3, or a disaggregated base station as discussed with respect to FIG. 2.

The communications device 1300 includes a processing system 1305 coupled to a transceiver 1345 (e.g., a transmitter and/or a receiver) and/or a network interface 1355. The transceiver 1345 is configured to transmit and receive signals for the communications device 1300 via an antenna 1350, such as the various signals as described herein. The network interface 1355 is configured to obtain and send signals for the communications device 1300 via communications link (s) , such as a backhaul link, midhaul link, and/or fronthaul link as described herein, such as with respect to FIG. 2. The processing system 1305 may be configured to perform processing functions for the communications device 1300, including processing signals received and/or to be transmitted by the communications device 1300.

The processing system 1305 includes one or more processors 1310. In various aspects, one or more processors 1310 may be representative of one or more of receive processor 338, transmit processor 320, TX MIMO processor 330, and/or controller/processor 340, as described with respect to FIG. 3. The one or more processors 1310 are coupled to a computer-readable medium/memory 1325 via a bus 1340. In certain aspects, the computer-readable medium/memory 1325 is configured to store instructions (e.g., computer-executable code) , including code 1330 and 1335, that when executed by the one or more processors 1310, enable and cause the one or more processors 1310 to perform the method 1100 described with respect to FIG. 11, or any aspect related to it, including any operations described in relation to FIG. 11. Note that reference to a processor of communications device 1300 performing a function may include one or more processors of communications device 1300 performing that function, such as in a distributed fashion.

In the depicted example, the computer-readable medium/memory 1325 stores code for sending 1330 and code for receiving 1335. Processing of the code 1330 and 1335 may enable and cause the communications device 1300 to perform the method 1100 described with respect to FIG. 11, or any aspect related to it.

The one or more processors 1310 include circuitry configured to implement (e.g., execute) the code (e.g., executable instructions) stored in the computer-readable medium/memory 1325, including circuitry for sending 1315 and circuitry for receiving 1320. Processing with circuitry 1315 and 1320 may enable and cause the communications device 1300 to perform the method 1100 described with respect to FIG. 11, or any aspect related to it.

Various components of the communications device 1300 may provide means for performing the method 1100 described with respect to FIG. 11, or any aspect related to it. Means for communicating, transmitting, sending or outputting for transmission may include the transceivers 332, antenna (s) 334, transmit processor 320, TX MIMO processor 330, AI processor 318, and/or controller/processor 340 of the BS 102 illustrated in FIG. 3, transceiver 1345, antenna 1350, and/or network interface 1355 of the communications device 1300 in FIG. 13, and/or one or more processors 1310 of the communications device 1300 in FIG. 13. Means for communicating, receiving or obtaining may include the transceivers 332, antenna (s) 334, receive processor 338, AI processor 318, and/or controller/processor 340 of the BS 102 illustrated in FIG. 3, transceiver 1345, antenna 1350, and/or network interface 1355 of the communications device 1300 in FIG. 13, and/or one or more processors 1310 of the communications device 1300 in FIG. 13.

Example Clauses

Implementation examples are described in the following numbered clauses:

Clause 1: A method for wireless communications by an apparatus comprising: receiving an identifier associated with a beam prediction technique; receiving an indication of one or more of: a first total number of a first set of beams to be predicted using the beam prediction technique; or a second total number of a second set of beams used for predicting the first set of beams; and sending one or more measurements associated with the first set of beams, the second set of beams, or both.

Clause 2: The method of Clause 1, wherein the beam prediction technique comprises a machine learning model.

Clause 3: The method of any one of Clauses 1-2, wherein the one or more of the first total number or the second total number comprises the first total number and the second total number.

Clause 4: The method of any one of Clauses 1-3, wherein: the one or more measurements comprise training data for the beam prediction technique; and the one or more measurements are associated with both the first set of beams and the second set of beams.

Clause 5: The method of any one of Clauses 1-4, wherein: the one or more measurements are associated with the second set of beams; and the one or more measurements are used for predicting the first set of beams.

Clause 6: The method of any one of Clauses 1-5, further comprising receiving at least one of: first information associating each of a plurality of first reference signals with a respective beam identifier of a respective beam of the first set of beams; or second information associating each of a plurality of second reference signals with a respective beam identifier of a respective beam of the second set of beams.

Clause 7: The method of Clause 6, wherein at least one of: each respective beam identifier of each respective beam of the first set of beams has a respective value less than or equal to the first total number; or each respective beam identifier of each respective beam of the second set of beams has a respective value less than or equal to the second total number.

Clause 8: The method of any one of Clauses 6-7, wherein the at least one of the first information or the second information comprises the first information and the second information.

Clause 9: The method of any one of Clauses 6-8, wherein the one or more measurements are based on at least one reference signal of the plurality of first reference signals or the plurality of second reference signals.

Clause 10: The method of any one of Clauses 6-9, wherein at least one of: the first information comprises, for each of the plurality of first reference signals, a respective explicit indication of the respective beam identifier; or the second information comprises, for each of the plurality of second reference signals, a respective explicit indication of the respective beam identifier.

Clause 11: The method of Clause 10, further comprising receiving at least one of: a first resource set configuration associating each of the plurality of first reference signals with a respective resource entry identifier of the first resource set configuration; or a second resource set configuration associating each of the plurality of second reference signals with a respective resource entry identifier of the second resource set configuration; and at least one of: the first information associates each respective resource entry identifier of the first resource set configuration with a respective beam identifier of a respective beam of the first set of beams; or the second information associates each respective resource entry identifier of the second resource set configuration with a respective beam identifier of a respective beam of the second set of beams.

Clause 12: The method of Clause 11, wherein: receiving the at least one of the first resource set configuration or the second resource set configuration comprises receiving the at least one of the first resource set configuration or the second resource set configuration in RRC signaling; and receiving the at least one of the first information or the second information comprises receiving the at least one of the first information or the second information in the RRC signaling.

Clause 13: The method of Clause 11, wherein: receiving the at least one of the first resource set configuration or the second resource set configuration comprises receiving the at least one of the first resource set configuration or the second resource set configuration in RRC signaling; the method further comprises receiving an activation of the at least one of the first resource set configuration or the second resource set configuration in a MAC-CE; and receiving the at least one of the first information or the second information comprises receiving the at least one of the first information or the second information in the MAC-CE.

Clause 14: The method of Clause 11, wherein: receiving the at least one of the first resource set configuration or the second resource set configuration comprises receiving the at least one of the first resource set configuration or the second resource set configuration in RRC signaling; the method further comprises receiving a DCI triggering the at least one of the first resource set configuration or the second resource set configuration; and receiving the at least one of the first information or the second information comprises receiving the at least one of the first information or the second information in the DCI.

Clause 15: The method of Clause 10, wherein receiving the at least one of the first information or the second information comprises receiving the at least one of the first information or the second information in a CSI sub-configuration associated with at least one of the plurality of first reference signals or the plurality of second reference signals.

Clause 16: The method of any one of Clauses 6-15, wherein at least one of: the first information comprises a first starting beam identifier; or the second information comprises a second starting beam identifier.

Clause 17: The method of any one of Clauses 1-16, further comprising receiving at least one of: first information associating each of a plurality of prediction targets with a respective beam identifier of a respective beam of a third set of beams to be predicted using the beam prediction technique; or second information associating each of a plurality of reference signals with a respective beam identifier of a respective beam of a fourth set of beams used for predicting the third set of beams.

Clause 18: The method of Clause 17, wherein at least one of: each respective beam identifier of each respective beam of the third set of beams has a respective value less than or equal to the first total number; or each respective beam identifier of each respective beam of the fourth set of beams has a respective value less than or equal to the second total number.

Clause 19: The method of any one of Clauses 17-18, further comprising: sending one or more second measurements associated with the fourth set of beams.

Clause 20: The method of Clause 19, wherein the one or more second measurements are used for predicting the third set of beams.

Clause 21: The method of any one of Clauses 17-20, wherein the at least one of the first information or the second information comprises the first information and the second information.

Clause 22: The method of any one of Clauses 17-20, wherein: the at least one of the first information or the second information comprises the second information; and the second information comprises, for each of the plurality of reference signals, a respective explicit indication of the respective beam identifier.

Clause 23: The method of Clause 22, further comprising receiving a resource set configuration associating each of the plurality of reference signals with a respective resource entry identifier of the resource set configuration; and the second information associates each respective resource entry identifier of the resource set configuration with a respective beam identifier of a respective beam of the fourth set of beams.

Clause 24: The method of Clause 23, wherein: receiving the resource set configuration comprises receiving the resource set configuration in RRC signaling; and receiving the second information comprises receiving the second information in the RRC signaling.

Clause 25: The method of Clause 23, wherein: receiving the resource set configuration comprises receiving the resource set configuration in RRC signaling; the method further comprises receiving an activation of the resource set configuration in a MAC-CE; and receiving the second information comprises receiving the second information in the MAC-CE.

Clause 26: The method of Clause 23, wherein: receiving the resource set configuration comprises receiving the resource set configuration in RRC signaling; the method further comprises receiving a DCI triggering the resource set configuration; and receiving the second information comprises receiving the second information in the DCI.

Clause 27: The method of Clause 22, wherein receiving the second information comprises receiving the second information in a CSI sub-configuration associated with the plurality of reference signals.

Clause 28: The method of Clause 20, wherein: the at least one of the first information or the second information comprises the second information; and the second information comprises a starting beam identifier.

Clause 29: The method of Clause 20, wherein: the at least one of the first information or the second information comprises the first information; and the first information comprises, for each of the plurality of prediction targets, a respective explicit indication of the respective beam identifier.

Clause 30: The method of Clause 29, further comprising receiving a prediction target set configuration associating each of the plurality of prediction targets with a respective target entry identifier of the prediction target set configuration; and the first information associates each respective target entry identifier of the prediction target set configuration with a respective beam identifier of a respective beam of the third set of beams.

Clause 31: The method of Clause 30, wherein: receiving the prediction target set configuration comprises receiving the prediction target set configuration in RRC signaling; and receiving the first information comprises receiving the first information in the RRC signaling.

Clause 32: The method of Clause 30, wherein: receiving the prediction target set configuration comprises receiving the prediction target set configuration in RRC signaling; the method further comprises receiving an activation of CSI reporting associated with the third set of beams in a MAC-CE; and receiving the first information comprises receiving the first information in the MAC-CE.

Clause 33: The method of Clause 30, wherein: receiving the prediction target set configuration comprises receiving the prediction target set configuration in RRC signaling; the method further comprises receiving a DCI triggering a CSI report associated with the third set of beams; and receiving the first information comprises receiving the first information in the DCI.

Clause 34: The method of Clause 33, wherein: the at least one of the first information or the second information comprises the first information; and the first information comprises a starting beam identifier.

Clause 35: The method of any one of Clauses 1-34, wherein receiving the indication comprises receiving the indication in one or more of: a CSI report configuration indicating the identifier; a CSI resource configuration indicating the identifier; a CSI-RS resource set configuration indicating the identifier; a SSB resource set configuration indicating the identifier; or signaling indicating at least one of the CSI report configuration, the CSI resource configuration, the CSI-RS resource set, the SSB resource set, or the identifier.

Clause 36: The method of Clause 35, wherein receiving the indication comprises receiving the indication via RRC signaling.

Clause 37: The method of any one of Clauses 1-34, wherein: receiving the indication comprises receiving the indication from an entity configured to train the beam prediction technique; and sending the one or more measurements comprises sending the one or more measurements to the entity.

Clause 38: The method of Clause 37, wherein receiving the identifier comprises receiving the identifier from the entity.

Clause 39: The method of any one of Clauses 1-5, wherein at least one of: each beam of the first set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated; each beam of the second set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated; each beam of a third set of beams is associated with a respective beam identifier based on a temporal resource in which a CSI report associated with the beam is communicated, wherein the third set of beams is to be predicted using the beam prediction technique; each beam of the third set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated; each beam of the third set of beams is associated with a respective beam identifier based on a respective prediction target associated with the beam; or each beam of a fourth set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated, wherein the fourth set of beams is used for predicting the third set of beams.

Clause 40: The method of any one of Clauses 1-5, wherein at least one of: each beam of the first set of beams is associated with a respective beam identifier based on a respective resource entry identifier of a respective reference signal associated with the beam; each beam of the second set of beams is associated with a respective beam identifier based on a respective resource entry identifier of a respective reference signal associated with the beam; each beam of a third set of beams is associated with a respective beam identifier based on a respective target entry identifier of a respective prediction target associated with the beam, wherein the third set of beams is to be predicted using the beam prediction technique; or each beam of a fourth set of beams is associated with a respective beam identifier based on a respective resource entry identifier of a respective reference signal associated with the beam, wherein the fourth set of beams is used for predicting the third set of beams.

Clause 41: The method of any one of Clauses 1-5, wherein at least one of: each beam of the first set of beams is associated with a respective beam identifier based on a respective resource entry identifier, in a first resource set configuration, of a respective reference signal associated with the beam, based on the first resource set configuration including a number of resource entry identifiers equal to the first total number; each beam of the second set of beams is associated with a respective beam identifier based on a respective resource entry identifier, in a second resource set configuration, of a respective reference signal associated with the beam, based on the second resource set configuration including a number of resource entry identifiers equal to the second total number; or each beam of the first set of beams is associated with a respective beam identifier based on a respective target entry identifier, in a prediction target set configuration, of a respective prediction target associated with the beam, based on the first resource set configuration including a number of target entry identifiers equal to the first total number.

Clause 42: A method for wireless communications by an apparatus comprising: sending an identifier associated with a beam prediction technique; sending an indication of one or more of: a first total number of a first set of beams to be predicted using the beam prediction technique; or a second total number of a second set of beams used for predicting the first set of beams; and receiving one or more measurements associated with the first set of beams, the second set of beams, or both.

Clause 43: The method of Clause 42, wherein the beam prediction technique comprises a machine learning model.

Clause 44: The method of any one of Clauses 42-43, wherein the one or more of the first total number or the second total number comprises the first total number and the second total number.

Clause 45: The method of any one of Clauses 42-44, wherein: the one or more measurements comprise training data for the beam prediction technique; and the one or more measurements are associated with both the first set of beams and the second set of beams.

Clause 46: The method of any one of Clauses 42-45, wherein: the one or more measurements are associated with the second set of beams; and the one or more measurements are used for predicting the first set of beams.

Clause 47: The method of any one of Clauses 42-46, further comprising sending at least one of: first information associating each of a plurality of first reference signals with a respective beam identifier of a respective beam of the first set of beams; or second information associating each of a plurality of second reference signals with a respective beam identifier of a respective beam of the second set of beams.

Clause 48: The method of Clause 47, wherein at least one of: each respective beam identifier of each respective beam of the first set of beams has a respective value less than or equal to the first total number; or each respective beam identifier of each respective beam of the second set of beams has a respective value less than or equal to the second total number.

Clause 49: The method of any one of Clauses 47-48, wherein the at least one of the first information or the second information comprises the first information and the second information.

Clause 50: The method of any one of Clauses 47-49, wherein the one or more measurements are based on at least one reference signal of the plurality of first reference signals or the plurality of second reference signals.

Clause 51: The method of any one of Clauses 47-50, wherein at least one of: the first information comprises, for each of the plurality of first reference signals, a respective explicit indication of the respective beam identifier; or the second information comprises, for each of the plurality of second reference signals, a respective explicit indication of the respective beam identifier.

Clause 52: The method of Clause 51, further comprising sending at least one of: a first resource set configuration associating each of the plurality of first reference signals with a respective resource entry identifier of the first resource set configuration; or a second resource set configuration associating each of the plurality of second reference signals with a respective resource entry identifier of the second resource set configuration; and at least one of: the first information associates each respective resource entry identifier of the first resource set configuration with a respective beam identifier of a respective beam of the first set of beams; or the second information associates each respective resource entry identifier of the second resource set configuration with a respective beam identifier of a respective beam of the second set of beams.

Clause 53: The method of Clause 52, wherein: sending the at least one of the first resource set configuration or the second resource set configuration comprises sending the at least one of the first resource set configuration or the second resource set configuration in RRC signaling; and sending the at least one of the first information or the second information comprises sending the at least one of the first information or the second information in the RRC signaling.

Clause 54: The method of Clause 52, wherein: sending the at least one of the first resource set configuration or the second resource set configuration comprises sending the at least one of the first resource set configuration or the second resource set configuration in RRC signaling; the method further comprises sending an activation of the at least one of the first resource set configuration or the second resource set configuration in a MAC-CE; and sending the at least one of the first information or the second information comprises sending the at least one of the first information or the second information in the MAC-CE.

Clause 55: The method of Clause 52, wherein: sending the at least one of the first resource set configuration or the second resource set configuration comprises sending the at least one of the first resource set configuration or the second resource set configuration in RRC signaling; the method further comprises sending a DCI triggering the at least one of the first resource set configuration or the second resource set configuration; and sending the at least one of the first information or the second information comprises sending the at least one of the first information or the second information in the DCI.

Clause 56: The method of Clause 51, wherein sending the at least one of the first information or the second information comprises sending the at least one of the first information or the second information in a CSI sub-configuration associated with at least one of the plurality of first reference signals or the plurality of second reference signals.

Clause 57: The method of any one of Clauses 47-56, wherein at least one of: the first information comprises a first starting beam identifier; or the second information comprises a second starting beam identifier.

Clause 58: The method of any one of Clauses 42-57, further comprising sending at least one of: first information associating each of a plurality of prediction targets with a respective beam identifier of a respective beam of a third set of beams to be predicted using the beam prediction technique; or second information associating each of a plurality of reference signals with a respective beam identifier of a respective beam of a fourth set of beams used for predicting the third set of beams.

Clause 59: The method of Clause 58, wherein at least one of: each respective beam identifier of each respective beam of the third set of beams has a respective value less than or equal to the first total number; or each respective beam identifier of each respective beam of the fourth set of beams has a respective value less than or equal to the second total number.

Clause 60: The method of any one of Clauses 58-59, further comprising: receiving one or more second measurements associated with the fourth set of beams.

Clause 61: The method of Clause 60, wherein the one or more second measurements are used for predicting the third set of beams.

Clause 62: The method of any one of Clauses 58-61, wherein the at least one of the first information or the second information comprises the first information and the second information.

Clause 63: The method of any one of Clauses 58-61, wherein: the at least one of the first information or the second information comprises the second information; and the second information comprises, for each of the plurality of reference signals, a respective explicit indication of the respective beam identifier.

Clause 64: The method of Clause 63, further comprising sending a resource set configuration associating each of the plurality of reference signals with a respective resource entry identifier of the resource set configuration; and the second information associates each respective resource entry identifier of the resource set configuration with a respective beam identifier of a respective beam of the fourth set of beams.

Clause 65: The method of Clause 64, wherein: sending the resource set configuration comprises sending the resource set configuration in RRC signaling; and sending the second information comprises sending the second information in the RRC signaling.

Clause 66: The method of Clause 64, wherein: sending the resource set configuration comprises sending the resource set configuration in RRC signaling; the method further comprises sending an activation of the resource set configuration in a MAC-CE; and sending the second information comprises sending the second information in the MAC-CE.

Clause 67: The method of Clause 64, wherein: sending the resource set configuration comprises sending the resource set configuration in RRC signaling; the method further comprises sending a DCI triggering the resource set configuration; and sending the second information comprises sending the second information in the DCI.

Clause 68: The method of Clause 63, wherein sending the second information comprises sending the second information in a CSI sub-configuration associated with the plurality of reference signals.

Clause 69: The method of any one of Clauses 58-61, wherein: the at least one of the first information or the second information comprises the second information; and the second information comprises a starting beam identifier.

Clause 70: The method of any one of Clauses 58-61, wherein: the at least one of the first information or the second information comprises the first information; and the first information comprises, for each of the plurality of prediction targets, a respective explicit indication of the respective beam identifier.

Clause 71: The method of Clause 70, further comprising sending a prediction target set configuration associating each of the plurality of prediction targets with a respective target entry identifier of the prediction target set configuration; and the first information associates each respective target entry identifier of the prediction target set configuration with a respective beam identifier of a respective beam of the third set of beams.

Clause 72: The method of Clause 71, wherein: sending the prediction target set configuration comprises sending the prediction target set configuration in RRC signaling; and sending the first information comprises sending the first information in the RRC signaling.

Clause 73: The method of Clause 71, wherein: sending the prediction target set configuration comprises sending the prediction target set configuration in RRC signaling; the method further comprises sending an activation of CSI reporting associated with the third set of beams in a MAC-CE; and sending the first information comprises sending the first information in the MAC-CE.

Clause 74: The method of Clause 71, wherein: sending the prediction target set configuration comprises sending the prediction target set configuration in RRC signaling; the method further comprises sending a DCI triggering a CSI report associated with the third set of beams; and sending the first information comprises sending the first information in the DCI.

Clause 75: The method of any one of Clauses 58-61, wherein: the at least one of the first information or the second information comprises the first information; and the first information comprises a starting beam identifier.

Clause 76: The method of any one of Clauses 42-75, wherein sending the indication comprises sending the indication in one or more of: a CSI report configuration indicating the identifier; a CSI resource configuration indicating the identifier; a CSI-RS resource set configuration indicating the identifier; a SSB resource set configuration indicating the identifier; or signaling indicating at least one of the CSI report configuration, the CSI resource configuration, the CSI-RS resource set, the SSB resource set, or the identifier.

Clause 77: The method of Clause 76, wherein sending the indication comprises sending the indication via RRC signaling.

Clause 78: The method of any one of Clauses 42-46, wherein at least one of: each beam of the first set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated; each beam of the second set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated; each beam of a third set of beams is associated with a respective beam identifier based on a temporal resource in which a CSI report associated with the beam is communicated, wherein the third set of beams is to be predicted using the beam prediction technique; each beam of the third set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated; each beam of the third set of beams is associated with a respective beam identifier based on a respective prediction target associated with the beam; or each beam of a fourth set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated, wherein the fourth set of beams is used for predicting the third set of beams.

Clause 79: The method of any one of Clauses 42-46, wherein at least one of: each beam of the first set of beams is associated with a respective beam identifier based on a respective resource entry identifier of a respective reference signal associated with the beam; each beam of the second set of beams is associated with a respective beam identifier based on a respective resource entry identifier of a respective reference signal associated with the beam; each beam of a third set of beams is associated with a respective beam identifier based on a respective target entry identifier of a respective prediction target associated with the beam, wherein the third set of beams is to be predicted using the beam prediction technique; or each beam of a fourth set of beams is associated with a respective beam identifier based on a respective resource entry identifier of a respective reference signal associated with the beam, wherein the fourth set of beams is used for predicting the third set of beams.

Clause 80: The method of any one of Clauses 42-46, wherein at least one of: each beam of the first set of beams is associated with a respective beam identifier based on a respective resource entry identifier, in a first resource set configuration, of a respective reference signal associated with the beam, based on the first resource set configuration including a number of resource entry identifiers equal to the first total number; each beam of the second set of beams is associated with a respective beam identifier based on a respective resource entry identifier, in a second resource set configuration, of a respective reference signal associated with the beam, based on the second resource set configuration including a number of resource entry identifiers equal to the second total number; or each beam of the first set of beams is associated with a respective beam identifier based on a respective target entry identifier, in a prediction target set configuration, of a respective prediction target associated with the beam, based on the first resource set configuration including a number of target entry identifiers equal to the first total number.

Clause 81: One or more apparatuses, comprising: one or more memories comprising executable instructions; and one or more processors configured to execute the executable instructions and cause the one or more apparatuses to perform a method in accordance with any one of Clauses 1-80.

Clause 82: One or more apparatuses, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to cause the one or more apparatuses to perform a method in accordance with any one of Clauses 1-80.

Clause 83: One or more apparatuses, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to perform a method in accordance with any one of Clauses 1-80.

Clause 84: One or more apparatuses, comprising means for performing a method in accordance with any one of Clauses 1-80.

Clause 85: One or more non-transitory computer-readable media comprising executable instructions that, when executed by one or more processors of one or more apparatuses, cause the one or more apparatuses to perform a method in accordance with any one of Clauses 1-80.

Clause 86: One or more computer program products embodied on one or more computer-readable storage media comprising code for performing a method in accordance with any one of Clauses 1-80.

Additional Considerations

The preceding description is provided to enable any person skilled in the art to practice the various aspects described herein. The examples discussed herein are not limiting of the scope, applicability, or aspects set forth in the claims. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects. For example, changes may be made in the function and arrangement of elements discussed without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as appropriate. For instance, the methods described may be performed in an order different from that described, and various actions may be added, omitted, or combined. Also, features described with respect to some examples may be combined in some other examples. For example, an apparatus may be implemented or a method may be practiced using any number of the aspects set forth herein. In addition, the scope of the disclosure is intended to cover such an apparatus or method that is practiced using other structure, functionality, or structure and functionality in addition to, or other than, the various aspects of the disclosure set forth herein. It should be understood that any aspect of the disclosure disclosed herein may be embodied by one or more elements of a claim.

The various illustrative logical blocks, modules and circuits described in connection with the present disclosure may be implemented or performed with a general purpose processor, an AI processor, a digital signal processor (DSP) , an ASIC, a field programmable gate array (FPGA) or other programmable logic device (PLD) , discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any commercially available processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, a system on a chip (SoC) , or any other such configuration.

As used herein, a phrase referring to “at least one of” a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination with multiples of the same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b, b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c) .

As used herein, the term “determining” encompasses a wide variety of actions. For example, “determining” may include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure) , ascertaining and the like. Also, “determining” may include receiving (e.g., receiving information) , accessing (e.g., accessing data in a memory) and the like. Also, “determining” may include resolving, selecting, choosing, establishing and the like.

As used herein, “coupled to” and “coupled with” generally encompass direct coupling and indirect coupling (e.g., including intermediary coupled aspects) unless stated otherwise. For example, stating that a processor is coupled to a memory allows for a direct coupling or a coupling via an intermediary aspect, such as a bus.

The methods disclosed herein comprise one or more actions for achieving the methods. The method actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of actions is specified, the order and/or use of specific actions may be modified without departing from the scope of the claims. Further, the various operations of methods described above may be performed by any suitable means capable of performing the corresponding functions. The means may include various hardware and/or software component (s) and/or module (s) , including, but not limited to a circuit, an application specific integrated circuit (ASIC) , or processor.

The following claims are not intended to be limited to the aspects shown herein, but are to be accorded the full scope consistent with the language of the claims. Reference to an element in the singular is not intended to mean only one unless specifically so stated, but rather “one or more. ” The subsequent use of a definite article (e.g., “the” or “said” ) with an element (e.g., “the processor” ) is not intended to invoke a singular meaning (e.g., “only one” ) on the element unless otherwise specifically stated. For example, reference to an element (e.g., “aprocessor, ” “acontroller, ” “amemory, ” “atransceiver, ” “an antenna, ” “the processor, ” “the controller, ” “the memory, ” “the transceiver, ” “the antenna, ” etc. ) , unless otherwise specifically stated, should be understood to refer to one or more elements (e.g., “one or more processors, ” “one or more controllers, ” “one or more memories, ” “one more transceivers, ” etc. ) . The terms “set” and “group” are intended to include one or more elements, and may be used interchangeably with “one or more. ” Where reference is made to one or more elements performing functions (e.g., steps of a method) , one element may perform all functions, or more than one element may collectively perform the functions. When more than one element collectively performs the functions, each function need not be performed by each of those elements (e.g., different functions may be performed by different elements) and/or each function need not be performed in whole by only one element (e.g., different elements may perform different sub-functions of a function) . Similarly, where reference is made to one or more elements configured to cause another element (e.g., an apparatus) to perform functions, one element may be configured to cause the other element to perform all functions, or more than one element may collectively be configured to cause the other element to perform the functions. Unless specifically stated otherwise, the term “some” refers to one or more. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims.

Claims

An apparatus configured for wireless communications, comprising:

one or more memories; and

one or more processors coupled to the one or more memories, the one or more processors being configured to cause the apparatus to:

receive an identifier associated with a beam prediction technique;

receive an indication of one or more of:

a first total number of a first set of beams to be predicted using the beam prediction technique; or

a second total number of a second set of beams used for predicting the first set of beams; and

send one or more measurements associated with the first set of beams, the second set of beams, or both.
The apparatus of claim 1, wherein:

the one or more measurements are associated with the second set of beams; and

the one or more measurements are used for predicting the first set of beams.
The apparatus of claim 1, wherein the one or more processors are configured to cause the apparatus to receive at least one of:

first information associating each of a plurality of first reference signals with a respective beam identifier of a respective beam of the first set of beams; or

second information associating each of a plurality of second reference signals with a respective beam identifier of a respective beam of the second set of beams.
The apparatus of claim 3, wherein at least one of:

each respective beam identifier of each respective beam of the first set of beams has a respective value less than or equal to the first total number; or

each respective beam identifier of each respective beam of the second set of beams has a respective value less than or equal to the second total number.
The apparatus of claim 3, wherein the one or more measurements are based on at least one reference signal of the plurality of first reference signals or the plurality of second reference signals.
The apparatus of claim 3, wherein at least one of:

the first information comprises, for each of the plurality of first reference signals, a respective explicit indication of the respective beam identifier; or

the second information comprises, for each of the plurality of second reference signals, a respective explicit indication of the respective beam identifier.
The apparatus of claim 6, wherein:

the one or more processors are configured to cause the apparatus to receive at least one of:

a first resource set configuration associating each of the plurality of first reference signals with a respective resource entry identifier of the first resource set configuration; or

a second resource set configuration associating each of the plurality of second reference signals with a respective resource entry identifier of the second resource set configuration; and

at least one of:

the first information associates each respective resource entry identifier of the first resource set configuration with a respective beam identifier of a respective beam of the first set of beams; or

the second information associates each respective resource entry identifier of the second resource set configuration with a respective beam identifier of a respective beam of the second set of beams.
The apparatus of claim 7, wherein:

to receive the at least one of the first resource set configuration or the second resource set configuration comprises to receive the at least one of the first resource set configuration or the second resource set configuration in radio resource control (RRC) signaling; and

to receive the at least one of the first information or the second information comprises to receive the at least one of the first information or the second information in the RRC signaling.
The apparatus of claim 7, wherein:

to receive the at least one of the first resource set configuration or the second resource set configuration comprises to receive the at least one of the first resource set configuration or the second resource set configuration in radio resource control (RRC) signaling;

the one or more processors are configured to cause the apparatus to receive an activation of the at least one of the first resource set configuration or the second resource set configuration in a medium access control (MAC) control element (CE) ; and

to receive the at least one of the first information or the second information comprises to receive the at least one of the first information or the second information in the MAC-CE.
The apparatus of claim 7, wherein:

to receive the at least one of the first resource set configuration or the second resource set configuration comprises to receive the at least one of the first resource set configuration or the second resource set configuration in radio resource control (RRC) signaling;

the one or more processors are configured to cause the apparatus to receive a downlink control information (DCI) triggering the at least one of the first resource set configuration or the second resource set configuration; and

to receive the at least one of the first information or the second information comprises to receive the at least one of the first information or the second information in the DCI.
The apparatus of claim 6, wherein to receive the at least one of the first information or the second information comprises to receive the at least one of the first information or the second information in a channel state information (CSI) sub-configuration associated with at least one of the plurality of first reference signals or the plurality of second reference signals.
The apparatus of claim 3, wherein at least one of:

the first information comprises a first starting beam identifier; or

the second information comprises a second starting beam identifier.
The apparatus of claim 1, wherein the one or more processors are configured to cause the apparatus to receive at least one of:

first information associating each of a plurality of prediction targets with a respective beam identifier of a respective beam of a third set of beams to be predicted using the beam prediction technique; or

second information associating each of a plurality of reference signals with a respective beam identifier of a respective beam of a fourth set of beams used for predicting the third set of beams.
The apparatus of claim 13, wherein at least one of:

each respective beam identifier of each respective beam of the third set of beams has a respective value less than or equal to the first total number; or

each respective beam identifier of each respective beam of the fourth set of beams has a respective value less than or equal to the second total number.
The apparatus of claim 13, wherein:

the at least one of the first information or the second information comprises the second information; and

the second information comprises, for each of the plurality of reference signals, a respective explicit indication of the respective beam identifier.
The apparatus of claim 15, wherein:

the one or more processors are configured to cause the apparatus to receive a resource set configuration associating each of the plurality of reference signals with a respective resource entry identifier of the resource set configuration; and

the second information associates each respective resource entry identifier of the resource set configuration with a respective beam identifier of a respective beam of the fourth set of beams.
The apparatus of claim 15, wherein to receive the second information comprises to receive the second information in a channel state information (CSI) sub-configuration associated with the plurality of reference signals.
The apparatus of claim 13, wherein:

the at least one of the first information or the second information comprises the second information; and

the second information comprises a starting beam identifier.
The apparatus of claim 13, wherein:

the at least one of the first information or the second information comprises the first information; and

the first information comprises, for each of the plurality of prediction targets, a respective explicit indication of the respective beam identifier.
The apparatus of claim 19, wherein:

the one or more processors are configured to cause the apparatus to receive a prediction target set configuration associating each of the plurality of prediction targets with a respective target entry identifier of the prediction target set configuration; and

the first information associates each respective target entry identifier of the prediction target set configuration with a respective beam identifier of a respective beam of the third set of beams.
The apparatus of claim 13, wherein:

the at least one of the first information or the second information comprises the first information; and

the first information comprises a starting beam identifier.
The apparatus of claim 1, wherein to receive the indication comprises to receive the indication in one or more of:

a channel state information (CSI) report configuration indicating the identifier;

a CSI resource configuration indicating the identifier;

a CSI reference signal (CSI-RS) resource set configuration indicating the identifier;

a synchronization signal block (SSB) resource set configuration indicating the identifier; or

signaling indicating at least one of the CSI report configuration, the CSI resource configuration, the CSI-RS resource set, the SSB resource set, or the identifier.
The apparatus of claim 1, wherein:

to receive the indication comprises to receive the indication from an entity configured to train the beam prediction technique; and

to send the one or more measurements to send the one or more measurements to the entity.
The apparatus of claim 1, wherein at least one of:

each beam of the first set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated;

each beam of the second set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated;

each beam of a third set of beams is associated with a respective beam identifier based on a temporal resource in which a channel state information (CSI) report associated with the beam is communicated, wherein the third set of beams is to be predicted using the beam prediction technique;

each beam of the third set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated;

each beam of the third set of beams is associated with a respective beam identifier based on a respective prediction target associated with the beam; or

each beam of a fourth set of beams is associated with a respective beam identifier based on a respective temporal resource in which a respective reference signal associated with the beam is communicated, wherein the fourth set of beams is used for predicting the third set of beams.
The apparatus of claim 1, wherein at least one of:

each beam of the first set of beams is associated with a respective beam identifier based on a respective resource entry identifier of a respective reference signal associated with the beam;

each beam of the second set of beams is associated with a respective beam identifier based on a respective resource entry identifier of a respective reference signal associated with the beam;

each beam of a third set of beams is associated with a respective beam identifier based on a respective target entry identifier of a respective prediction target associated with the beam, wherein the third set of beams is to be predicted using the beam prediction technique; or

each beam of a fourth set of beams is associated with a respective beam identifier based on a respective resource entry identifier of a respective reference signal associated with the beam, wherein the fourth set of beams is used for predicting the third set of beams.
The apparatus of claim 1, wherein at least one of:

each beam of the first set of beams is associated with a respective beam identifier based on a respective resource entry identifier, in a first resource set configuration, of a respective reference signal associated with the beam, based on the first resource set configuration including a number of resource entry identifiers equal to the first total number;

each beam of the second set of beams is associated with a respective beam identifier based on a respective resource entry identifier, in a second resource set configuration, of a respective reference signal associated with the beam, based on the second resource set configuration including a number of resource entry identifiers equal to the second total number; or

each beam of the first set of beams is associated with a respective beam identifier based on a respective target entry identifier, in a prediction target set configuration, of a respective prediction target associated with the beam, based on the first resource set configuration including a number of target entry identifiers equal to the first total number.
An apparatus configured for wireless communications, comprising:

one or more memories; and

one or more processors coupled to the one or more memories, the one or more processors being configured to cause the apparatus to:

send an identifier associated with a beam prediction technique;

send an indication of one or more of:

a first total number of a first set of beams to be predicted using the beam prediction technique; or

a second total number of a second set of beams used for predicting the first set of beams; and

receive one or more measurements associated with the first set of beams, the second set of beams, or both.
The apparatus of claim 27, wherein the one or more processors are configured to cause the apparatus to send at least one of:

first information associating each of a plurality of first reference signals with a respective beam identifier of a respective beam of the first set of beams; or

second information associating each of a plurality of second reference signals with a respective beam identifier of a respective beam of the second set of beams.
A method for wireless communications, comprising:

receiving an identifier associated with a beam prediction technique;

receiving an indication of one or more of:

a first total number of a first set of beams to be predicted using the beam prediction technique; or

a second total number of a second set of beams used for predicting the first set of beams; and

sending one or more measurements associated with the first set of beams, the second set of beams, or both.
A method for wireless communications, comprising:

sending an identifier associated with a beam prediction technique;

sending an indication of one or more of:

a first total number of a first set of beams to be predicted using the beam prediction technique; or

a second total number of a second set of beams used for predicting the first set of beams; and

receiving one or more measurements associated with the first set of beams, the second set of beams, or both.