WO2025096667A1 - Beam management aspects of life cycle management - Google Patents

Abstract

A user equipment (UE) includes a transceiver, an antenna coupled with the transceiver, and a processor. In one or more embodiments, the processor is configured to cause the UE to receive an indication of a configuration for a first set of beams used by a network device for reference signal measurements, and measure the reference signals. A set of training data based on the measurements is provided to a machine learning engine (internal or external to the UE), which provides a beam estimation/prediction (BEP) model in response. The UE may then use the BEP model to infer or otherwise determine a beam prediction for the first set of beams based on measurements of a second set of beams for an indicated same configuration of the first set of beams. A report indicating the beam prediction may then be transmitted to the network.

Description

BEAM MANAGEMENT ASPECTS OF LIFE CYCLE MANAGEMENT

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This Patent Cooperation Treaty patent application claims priority to U.S. Provisional Patent Application No. 63/547,310, filed November 3, 2023, and titled “Beam Management Aspects of Life Cycle Management,” the contents of which are incorporated herein by reference as if fully disclosed herein in its entirety.

TECHNICAL FIELD

[0002] This application relates generally to wireless communication systems, including systems, apparatuses, and methods for beam management aspects of life cycle management.

BACKGROUND

[0003] Wireless mobile communication technology uses various standards and protocols to transmit data between a network device (e.g., a base station, a radio head, etc.) and a wireless communication device. Wireless communication system standards and protocols can include, for example, 3rd Generation Partnership Project (3GPP) long-term evolution (LTE) (e.g., 4G), 3GPP new radio (NR) (e.g., 5G), and IEEE 802. 11 standard for wireless local area networks (WLAN) (commonly known to industry groups as Wi-Fi®).

[0004] As contemplated by the 3GPP, different wireless communication systems standards and protocols can use various radio access networks (RANs) for communicating between a network device of the RAN (which may also sometimes be referred to generally as a RAN node, a network node, or simply a node) and a wireless communication device known as a UE (user equipment). 3GPP RANs can include, for example, global systems for mobile communications (GSM), enhanced data rates for GSM evolution (EDGE) RAN (GERAN), Universal Terrestrial Radio Access Network (UTRAN), Evolved Universal Terrestrial Radio Access Network (E- UTRAN), and/or Next-Generation Radio Access Network (NG-RAN).

[0005] Each RAN may use one or more radio access technologies (RATs) to perform communication between the network device and the UE. For example, the GERAN implements GSM and/or EDGE RAT, the UTRAN implements universal mobile telecommunication system (UMTS) RAT or other 3 GPP RAT, the E-UTRAN implements LTE RAT (sometimes simply referred to as LTE), and NG-RAN implements NR RAT (sometimes referred to herein as 5G RAT, 5G NR RAT, or simply NR). Tn certain deployments, the E-UTRAN may also implement NR RAT. In certain deployments, NG-RAN may also implement LTE RAT.

[0006] A network device used by a RAN may correspond to that RAN. One example of an E- UTRAN network device is an Evolved Universal Terrestrial Radio Access Network (E-UTRAN)

Node B (also commonly denoted as evolved Node B, enhanced Node B, eNodeB, or eNB). One example of an NG-RAN network device is a next generation Node B (also sometimes referred to as a g Node B or gNB).

[0007] A RAN provides its communication services with external entities through its connection to a core network (CN). For example, E-UTRAN may utilize an Evolved Packet Core (EPC), while NG-RAN may utilize a 5G Core Network (5GC).

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0008] To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced. [0009] FIG. 1 shows an example wireless communication system, according to embodiments described herein.

[0010] FIG. 2 shows an example signaling diagram, according to one or more aspects described herein.

[0011] FIG. 3 shows an example signaling diagram, according to one or more aspects described herein.

[0012] FIG. 4 shows an example method of wireless communication, according to one or more aspects described herein.

[0013] FIG. 5 shows another example method of wireless communication, according to one or more aspects described herein. [0014] FIG. 6 illustrates an example architecture of a wireless communication system, according to embodiments described herein.

[0015] FIG. 7 illustrates an example system for performing signaling between a wireless device and a network device, according to embodiments described herein.

DETAILED DESCRIPTION

[0016] Various embodiments are described with regard to a user equipment (UE). However, reference to a UE is merely provided for illustrative purposes. The example embodiments may be utilized with any electronic component that may establish a connection to a network and is configured with the hardware, software, and/or firmware to exchange information and data with a network. Therefore, the UE as described herein is used to represent any appropriate electronic device.

[0017] Beamforming may be used in a wireless communications system in an effort to optimize wireless communications with a UE by focusing signals at the UE, enhancing efficiency and reliability. Beamforming by a network device may employ multiple antennas and advanced algorithms to create precise, concentrated communication links. This targeted approach boosts signal strength, quality, and data rates, supporting multiple-input multipleoutput (MIMO) configurations. By adapting signal direction based on UE location and surroundings, beamforming overcomes obstacles to enable faster speeds and lower latency for users in diverse scenarios and applications. A network device (e.g., a base station or gNB) serving UEs in a coverage area may use a set of beams, each beam of which is associated with a particular set of antenna parameters for transmission and/or reception using one or more sets of antenna arrays at the network device. Different beams may generally be associated with communications in a particular vertical and horizontal direction. For example, a base station may be deployed and configured such that a beam is horizontally associated with some number of radial degrees (e.g., radial angles of five, ten, or twenty degrees), while one or more beams may be present in the vertical direction and associated with different vertical angles (e.g., two, three, or four beams in the vertical direction). In some cases, the distribution of beams may be uniform (e.g., vertically and/or horizontally uniform or evenly distribution). In other cases, however, beams may be irregularly sized and/or distributed in the spatial domain.

[0018] Additionally, network operators perform life cycle management (LCM) to oversee different stages of the network’ s operation, including managing the beams used by the network for communication with served UEs. LCM for beams may include performance monitoring, data collection, UE assistance monitoring, and configuration. Various aspects of LCM include planning and design, testing, deployment, installation, configuration, integration, and ongoing operation and maintenance to ensure optimal network performance. In the context of beam management, communication beams used by a network device may be reconfigured or otherwise modified during operation, and different network devices may use different beam configurations for communication. However, a network operator may be incentivized or otherwise not desire to disclose information about the beam configurations used by the network device. For example, a network operator may seek to avoid disclosing proprietary or confidential information, such as beam patterns (e.g., how wide or narrow are the beams), or different parameters for beamforming.

[0019] A UE in communication with a network device uses resources to track or otherwise determine a beam configuration of a network device. A network device may utilize a relatively large number of communication beams. As such, a UE may expend increased resources and/or power to monitor and measure the large number of beams. To mitigate this expenditure of resources and/or power, the network may use one set of beams for communication and/or control in its operation (e.g., a Set A), but the UE may measure a different set of beams (e.g., a Set B, which may be a subset of Set A). The UE may then deduce or otherwise determine parameters for one or more of the Set A beams based on the measurements of the Set B beams.

[0020] The UE may use or rely on machine learning with reference to various management and control tasks, such as channel state information (CSI), beam management, or positioning. In one or more embodiments, the UE uses machine learning to determine parameters for one or more beams of the Set A beams based on the Set B beam measurements. Machine learning may also include artificial intelligence. Processors, systems, servers, or other devices or components, or groups of any of these, that implement or perform machine learning may be referred to as a machine learning engine.

[0021] A machine learning engine may include and/or generate a machine learning model to find patterns or make decisions from a previously unseen dataset. In some examples, a machine learning model may refer to a program (algorithm, code, process). Additionally, or alternatively, a machine learning model may refer to parameters, values, data, or other inputs provided to a machine learning engine (e.g., a program) that define or otherwise control the operation of the machine learning engine. The machine learning model may be trained using a dataset, where the program is optimized to find certain patterns or outputs from the dataset. The output of the training is the machine learning model.

[0022] In some cases, there may be variation in UE features and capabilities, and UEs may be able to handle different management setups. For example, a network entity may supply 32 beams for measurement, while a UE has the capability to measure 8 beams and deduce from those measurements the 32 beams. As a second example, a UE may have the capability to measure 16 beams and deduce the 32 beams from those 16 measurements. The UE may report that the UE is capable of deducing measurement for a set of beams based on beam measurements for a subset of those beams, but different UEs may have different capabilities to actually deduce those beams (e.g., different UEs may need to measure different quantities of beams to deduce the set of beams). Moreover, network entities (e.g., base stations) may have different beamforming configurations, capabilities, or both.

[0023] In light of the above, a common understanding between a UE and the network (e.g., network entities of the network) regarding a beam configuration for a network entity and a UE capability for a machine learning model regarding the beam configuration, for example so that inference of the operational beams of the network entity can be performed by the UE using the machine learning model. As further discussed herein, a machine learning model may be trained using channel information or channel state information (CSI) as training data, and may be, include, or be referred to as a beam estimation/prediction (BEP) model. In one or more embodiments, the network (e.g., via a network entity) provides a configuration to the UE indicating a configuration of the beams of a network entity. In one or more embodiments, in order to allow for the network to avoid disclosing proprietary information reading the beam configuration, the indicated beam configuration may be an index of a set of indices available for communication. The network may then configure or reconfigure the network entity according to one of the indexed beam configurations, and the UE may have at a future time an indication that a beam configuration is the same or different than a prior beam configuration. The UE can then collect data to train and deploy a BEP model based on the indicated configuration for the beams (e.g., Set A beams, and set B beams optionally for some cases) of the network entity. For example, such a BEP model may be used in connection with LCM. The UE may collect the data for the model, then provide such data to a UE-side server that trains the model. The UE-side server may then provide the model back to the UE. In some cases, the UE handshakes with the network entity so that the network entity knows that the UE has a BEP model that is correct (right, appropriate) for the current situation (e.g., for the UE served by a network entity of the network with the network entities’ beam configuration). In one or more aspects, it is beneficial for there to be consistency between training and configuration (e.g., inference) stages for Set A and Set B. The UE can then use the BEP model to measure reference signals from a network entity for beams of the Set B, and use the BEP model to infer channel state information for one or more beams of the Set A. The UE can provide to the network, for example in a channel state information report or a beam report that includes an indication of measurements, a prediction, a ranking, or a combination of these, for one or more beams of the Set A.

[0024] FIG. 1 shows an example wireless communication system 100, according to one or more aspects described herein. In one or more embodiments, wireless communication system 100, supports one or more aspects of beam management aspects of life cycle management, as further described herein.

[0025] Wireless communication system 100 includes a UE 102, network device 104, and a machine learning engine 106. One or more UEs including the UE 102 may be being served by (e.g., has an established radio resource control (RRC) connection with) the network device 104 via communication link 120. Coverage area 110 (e.g., a cell or serving cell) is the service area for the RF spectrum band utilized by network device 104. In one or more embodiments, communication link 120 may include a downlink connection and/or uplink connection.

[0026] In one or more embodiments, network device 104 utilizes beam steering, which may also be, include, or be referred to as electronic beam steering. Additionally, in one or more embodiments, UE 102 utilizes beam steering to receive signals, transmit signals, or both. As used herein, electronic beam steering refers, without limitation, to the ability of a device (e.g., network device 104) to perform beamforming, beam shaping, or other multiple antenna or multiple antenna-element techniques that control, direct, or otherwise shape electromagnetic energy radiated from the network device 104 in different directions and with different magnitudes or amplitudes. Electronic beam steering also refers to the network device 104 adjusting antennas or antenna elements to increase or decrease the ability to receive electromagnetic radiation from a particular direction. Such reception beamforming may be referred to as a “receive beams,” as opposed to transmit beamforming using “transmit beams.” A network device 104 uses beam steering for the transmission of signals to UEs (e.g., UE 102) served by the network device 104. Such signals can include data signals, control signals, or both. Control signal may include reference signals, synchronization signals, or control channels, or combinations of these.

[0027] In one or more embodiments, network device 104 utilities beam steering to transmit reference signals on a set of beams 130. In some embodiments, the reference signals include a channel state information reference signal (CSI-RS) or a synchronization signal block (SSB) (e.g., including a primary synchronization signal (PSS), secondary synchronization signal (SSS), physical broadcast channel (PBCH) carrying control information, and a demodulation reference signal (DMRS) for the PBCH). As an example configuration, a network device 104 may transmit reference signals (e.g., CSI-RS, SSB, or both) on a set of 32 transmit beams that are spatially distributed in both vertical and horizontal directions. For example, the set of beams 130 may include 8 of transmit beams 132 at a first vertical beam angle, 8 of transmit beams 134 at a second vertical beam angle, 8 of transmit beams 136 at a third vertical beam angle, and 8 of transmit beams 138 at a fourth vertical beam angle. The beams of the set of beams 130 may be spread horizontally as well.

[0028] In the example of wireless communication system 100, the set of beams 130 illustrates an example of a Set A beams, and may also be referred to as operational beams. A subset of the set of beams 130 includes 8 beams (beam 140, beam 141, beam 142, beam 143, beam 144, beam 145, beam 146, and beam 147) that collectively may be an example of a Set B beams. As illustrated with reference to the set of beams 130, Set B is a subset of Set A in the illustration. In one or more embodiments, the set of beams 1 0 may each correspond to a beam used for the transmission of SSBs by the network device 104. In some embodiments, each beam of the set of beams 130 may be transmitted on a different set of time and frequency resources, in addition to being transmitted on different spatial resources as illustrated with reference to the set of beams 130.

[0029] In one or more embodiments, machine learning engine 106 may be a UE-side server in communication with UE 102. Machine learning engine 106 may implement or otherwise perform one or more machine learning tasks, such as training a BEP model for UE 102 based on a set of measurements taken by UE 102 of reference signals received from network device 104 on one or more beams of the set of beams 130 (e.g., Set A beams, Set B beams). Following training, machine learning engine 106 may then provide the BEP model back to UE 102 via communication link 122, which may be a wired or wireless connection.

[0030] Machine learning engine 106 may be or include one or more computing components, such as a processor and memory. In one or more embodiments, the machine learning engine 106 is a device external to UE 102, but in communication with UE 102 (e.g., directly, or via a network device such as network device 104, such as a server). In other embodiments, the machine learning engine 106 is a server (e.g., a software-based server) for a machine learning engine that is internal to UE 102 or otherwise collocated with UE 102, for example within a same mobile device, vehicle, and so on.

[0031] In some examples, the design (e.g., configuration, parameter values, analog beam selection such as SSB or narrow beam resources) for a network operator controlling and/or configuring network device 104 are part of a core implementation and proprietary (e.g., to the network operator, network entity manufacturer, etc.). Such design may consider various network conditions, such as UE mobility (e.g., how frequently the best transmission beam for a UE changes), UE distribution (e.g., where the targeted UEs are physically), a hierarchy among SSB beams and/or narrow beams, and so on. A network operator may wish to provide high quality of service (QoS) for UEs and also reduce power consumption by the network, including by network entities. As such, a network operator may desire to utilize different beam designs at different times. As an example, a key performance indicator for the network may be system capacity during rush hour, but in the middle of the night a key performance indicator may be power consumption. In other examples, additional or different key performance indicators may be desired.

[0032] Information regarding the beam configuration for network entities of the network may be shared differently. In one or more embodiments, the network (e.g., via network device 104) may share data regarding the beam configuration with the UE 102 for data collection at training and inference, and training and inference are performed on the UE side (e.g., on the side of the wireless connection opposite network device 104, such as at UE 102 and/or machine learning engine 106). Providing beam design information to UE 102 may enable the network (e.g., a vendor of network device 104) to benefit from machine learning (e.g., artificial intelligence) without implementing machine learning on the network side, while also making it easier for UE 102 (e.g., vendors of chipsets or other components of UE 102) to implement machine learning. In some embodiments, providing the beam design information may effectively offload the hard work (e.g., processing time, power consumption) from the network device 104 to the UE 102. In other embodiment, a performance of a single BEP model across different scenarios and/or configurations may be sufficient (e.g., exceed a threshold value) such that the network may opt to use functionality-based LCM. In one or more embodiments, a network operator may not desire to share beam design information (e.g., with UE 102), and the UE 102 and network device 104 may exchange signaling to identify the beam configuration of the network device 104 and UE capability information for UE 102 according to signaling mechanisms that may be specific to the network that includes network device 104.

[0033] In one or more embodiments, the performance of the wireless communication system 100 that includes the network device 104 serving UEs, including UE 102, are affected by differences (e.g., mismatches) in the design for the set of beams 130, including Set A and/or Set B design. For example, when there is a mismatch for Set A design for the set of beams 130, the machine learning inference performance may be worse than a conventional approach (e.g., an approach that does not use a BEP model for inference). In some examples, the mismatch for Set A may be an analog beam codebook design mismatch between training and inference. As another example, when there is a mismatch for Set B design the BEP model inference performance may be worse than the conventional approach or can be degraded compared to the case without mismatch. [0034] FIG. 2 shows an example signaling diagram 200, according to one or more aspects described herein. In one or more embodiments, signaling diagram 200, supports one or more aspects of beam management aspects of life cycle management, as further described herein. In one or more embodiments, one or more features or aspects of the signaling diagram 200 may be performed in or implemented by wireless communication system 100.

[0035] The network device 104 can transmit to UE 102 indications of a configuration 210 for a first set of beams 130 used by a network device for transmission of reference signals 212 for measurement. In one or more embodiments, a second set of beams (e.g., when set B is not a subset of set A) may optionally (e.g., additionally or alternatively ) be used for the transmission of reference signals 212. The configuration 210 may be a measurement resource configuration, and the indication providing an indication to the UE 102 of a configuration of the first set of beams 130 of the network device 104. The UE 102 may then use the configuration 210 to collect training data. In one or more embodiments, the UE 102 measures, during a first time duration, the reference signals 212 according to the configuration for the first set of beams 130.

[0036] The UE 102 may then provide a set of training data 214 to the machine learning engine 106. In one or more embodiments, the set of training data 214 is based on the measurements of the reference signals 212, and is for both the first set of beams 130 and a second set of beams that has a relationship with (e.g., maps to, corresponds to, or is otherwise associated with) the first set of beams. As further described herein, the first set of beams may be a Set A and the second set of beams may be a Set B. In some embodiments, the second set of beams is a subset of the first set of beams 130. In other embodiments, the second set of beams is wider or otherwise different from the first set of beams. For example, the second set of beams may be one or more SSB beams, while the first set of beams are beams (e.g., narrower communication beams, or otherwise different from the first set of beams).

[0037] The UE 102 obtains, from the machine learning engine 106, responsive to providing the set of training data 214, a BEP model 218. The machine learning engine 106 performs BEP model training 216 based on the set of training data 214 provided by the UE 102, as further described herein. The machine learning engine 106 then provides the BEP model 218 to UE 102.

[0038] The UE 102 receives another indication of a configuration 220 for the first set of beams 130 used by the network device 104 for transmission of reference signals for measurement. In one or more embodiments, the network device 104 is the same network device as the network device 104 from which UE 102 received the configuration 210. For example, UE 102 may still be within the coverage area of the network device 104. In some embodiments, the network device 104 is a different network device from the network device 104 from which UE 102 received the configuration 210. For example, UE 102 may have moved to another coverage area. The configuration 220 may indicate a configuration of the first set of beams 130 that is compatible with the BEP model. In one or more embodiments, UE 102 measures, the reference signals 222 during a second time duration and according to the configuration 220 (e.g., or configuration 210, for example where the indicated configuration is the same).

[0039] In one or more embodiments, using the BEP model, the UE 102 performs beam determination 224 for one or more beams of the first set of beams 130 (e.g., Set A beams) based at least in part on measuring the reference signals corresponding to (e.g., that map to or are otherwise associated with) the second set of beams (e.g., Set B beams). In one or more embodiments, beam determination 224 includes determining a beam (e.g., a best beam for the UE 102, or a set of best beams).

[0040] The UE 102 then transmits, to the network device 104, a report 226 that indicates the results of beam determination 224. In one or more embodiments, the report 226 is a CSI report. In some embodiments, the report 226 is a beam report.

[0041] FIG. 3 shows an example signaling diagram 300, according to one or more aspects described herein. In one or more embodiments, signaling diagram 300, supports one or more aspects of beam management aspects of life cycle management, as further described herein. In one or more embodiments, signaling diagram 300 may include or be an example of one or more features or aspects of the signaling diagram 200. In some embodiments, one or more features or aspects of the signaling diagram 300 may be performed in or implemented by wireless communication system 100.

[0042] In one or more embodiments, different signaling occurs during different sessions, including a first session 332, a second session 334, and a third session 336. In one or more embodiments the first session 332 and the third session 336 corresponds to a connected mode (e.g., an RRC connected mode), and the second session 334 corresponds to an idle mode (e.g., an RRC idle mode). In other embodiments, all signaling of signaling diagram 300 occurs while the UE 102 is in a connected mode. In still other embodiments, different combinations of connected mode and idle mode signaling are possible, such that the discussed signaling may occur in a different one of the first session 332, the second session 334, or the third session 336.

[0043] The UE 102 may provide UE capability signaling 308 to network device 104. In one or more embodiments, the UE 102 transmits the UE capability signaling 308 that includes an indication of a capability of the UE for collection of training data to train a BEP model. In some embodiments, the UE 102 transmits the UE capability signaling 308 that includes an indication of a capability of the UE to infer one or more beams of a first set of beams (e.g., Set A) from the second set of beams 130 (e.g., Set B). In some embodiments, a single indication may indicate both support for collection of training data and model inference. The UE capability signaling for collection of training data and for model inference may be provided in a same set of signaling or message, or may be provided in separate signaling or messages.

[0044] In one or more embodiments, the UE capability signaling 308 may indicate whether the UE 102 can perform model inference (or inference). In some embodiments, the UE capability signaling indicates a quantity or range of quantities of beams for which the UE 102 can perform inference, for example a quantity of beams for Set A, or Set B, or both.

[0045] The network device 104 can transmit to UE 102 an indication of a configuration 310 for the first set of beams 130 used by a network device for transmission of reference signals for measurement. The configuration 310 may be a measurement resource configuration, and the indication providing an indication to the UE 102 of a configuration of the first set of beams 130 of the network device 104.

[0046] In one or more embodiments, the information for the design for the first set of beams (e.g., Set A) can be carried in a measurement resource configuration, or a measurement resource set configuration. For examples, a parameter set_A_config_index may be present. For example, the indication of the configuration for the first set of beams is an index of a set of indices. In some embodiments, each index of the set of indices is for (e.g., corresponds to, maps to, or is otherwise associated with) a respective configuration of a plurality of configurations for the first set of beams. In some embodiments, the index values may be from 0 to 127. In some embodiments, the indication of the configuration for the first set of beams is received in a configuration for a reference signal measurement resource set (e.g., a non- zero power channel state information reference signal resource set, such as NZP-CSI-RS-ResourceSet). In some embodiments, such information may be applicable to data collection for training, inference, and performance monitoring. In some embodiments, for example in the case Set B is a subset of a Set A, the set_A_config_index may be replaced by set_A_set_B_config_index, which indicates an index for Set A configuration and Set B configuration. In some embodiments, the set_A_config_index may be replaced by set_A_config_index and set B config index. which indicate an index for Set A configuration and another index for Set B configuration.

[0047] In one or more embodiments, the information regarding the first set of beams 130 (e.g., Set A) design can be carried in a report configuration. In some examples, the reporting configuration is a channel state information reporting configuration. For example, as further described herein, the parameter set_A_config_index may be present in a reporting configuration (e.g., a channel state information reporting configuration, such as CSI-ReportConfigf In some embodiments, such information may be applicable to inference and performance monitoring. In some embodiments, for example in the case Set B is a subset of a Set A, the set_A_config_index may be replaced by set_A_set_B_config_index, which indicates an index for Set A configuration and Set B configuration. In some embodiments, the set_A_config_index may be replaced by set_A_config_index and set_B_config_index, which indicate an index for Set A configuration and another index for Set B configuration.

[0048] In one or more embodiments, the beam information (e.g., analog beam information) for each beam in the first set of beams 130 (e.g., Set A) is explicitly provided by the network (e.g., via network device 104). In some embodiments, for example to ensure the consistency between training and inference, the indication of the configuration (e.g., set_A_config_index) may be a global index (which may also be referred to or be a “global variable” or “persistent variable”), so that any such indication (e.g., set_A_config_index with the same value refers to the same configuration (e.g., Set A design) across sessions. In some embodiments, if the indication were not global, as is typical for RRC signaling, the indication (e.g., set_A_config_index) would be a “local variable.” In some embodiments, together with the indication for the configuration, the configuration signaling includes beam information for each beam in the set of beams corresponding to the indication (e.g., information for each of set_A_beam_informationJor_beam_0, set_A_beam_information^or_beam_l , set_A_beam_inforniation_for_beam_2, and so on).

[0049] In one or more embodiments, the configuration for the first set of beams includes one or more of a network vendor identifier and/or a scenario identifier and/or configuration identifier. In some embodiments, set_A_config_index may contain any or all from a network vendor-ID, or a scenario-ID, or a configuration-ID, as a subfield from its composition.

[0050] In one or more embodiments, the second set of beams (e.g., Set B) is a subset of the first set of beams 130 (e.g., Set A). For example, the first set of beams can be 32 beams, and the second set of beams can be 8 beams. The indication of the second set of beams out of the first set of beams 130 may be alternatively conducted different ways.

[0051] In one or more embodiments, an indication of the second set of beams for the BEP model is a bitmap or a combinatorial indexing selection from the first set of beams 130. For examples, with a 32-bit bitmap to select the second set of beams (e.g., Set B) out of the first set of beams 130 (e.g., Set A) beams [0000, 1000, ..., 0001], then it means Beams 4, 5, ..., 31 are selected for set B, and measurement resources for the second set of beams (e.g., Set B) are arranged in that order. [0052] In one or more embodiments, the selection of set B beams out of set A beams may allow re-ordering of the beams. Tn a first example, the second set of beams {4, . .., 31 } is used, and the inputs to the BEP model (e.g., Al model) are in the order starting with 4, proceeding to 5, and so on, to 31. In a second examples, the second set of beam {31, . .. , 4 } is used, and the first measurement resource is beam 31 in the first set of beams 130 (e.g., Set A), such that the inputs to the BEP model (e.g., Al model) are in the order starting with 31, proceeding to 30, and so on, to 4.

[0053] The UE 102 uses the configuration 310 to collect training data 314. In one or more embodiments, the UE 102 measures, during a first time duration, the reference signals 312 according to the configuration for the first set of beams 130. In one or more embodiments, the first set of beams 130 is used for the transmission of reference signals 212. In some embodiments, a second set of beams (e.g., when set B is not a subset of set A) may optionally (e.g., additionally or alternatively ) be used for the transmission of reference signals 312.

[0054] In one or more embodiments, the set of training data 314 includes CST. The UE 102 may then provide the set of training data 314 to the machine learning engine 106, as further described herein. The machine learning engine 106 performs model training 316 based on the set of training data 314 provided by the UE 102, as further described herein. The UE 102 then obtains, from the machine learning engine 106, responsive to providing the set of training data 314, a BEP model 318.

[0055] In one or more embodiments, the UE 102 may provide UE capability signaling 328 to network device 104. The UE capability signaling 328 may be similar to the UE capability signaling 308 described herein. In some embodiments, the UE 102 may provide UE capability signaling 308 during a first session 332 and the UE capability signaling 328 during a third session 336.

[0056] The UE 102 receives another indication of a configuration 320 for the first set of beams 130 used by the network device 104 for transmission of reference signals for measurement. In some embodiments, the indication of the configuration 320 may include aspects or be an example of the indication of the configuration 310, as further described herein. In one or more embodiments, the indication of the configuration 320 is used by UE 102 for the BEP model 318 to perform measurements of reference signals 322, as further described herein. In one or more embodiments, using the BEP model 318, the UE 102 performs beam determination 324 for one or more beams of the first set of beams (e.g., Set A beams) based at least in part on measuring the reference signals 322 corresponding to (e.g., mapping to communication resources associated with) the second set of beams (e.g., Set B beams). In one or more embodiments, beam determination 324 includes determining CSI, which may include an indication of one or more beams determined for beam determination 324.

[0057] The UE 102 then transmits, to the network device 104, a beam report 326 that indicates the CSI. In one or more embodiments, the beam report 326 is a CSI report.

[0058] FIG. 4 shows an example method 400 of wireless communication by a UE. In one or more embodiments, method 400, supports one or more aspects of beam management aspects of life cycle management, as further described herein. In some cases, the UE may be the UE 102, wireless device 702, or one of the other UEs described herein. The method 400 may be performed using a processor, a transceiver (or a main radio), or other components of the UE.

[0059] At 402, the method 400 includes receiving an indication of a configuration for a first set and optionally a second set (under certain cases, e.g., for Set B) of beams used by a network device for transmission of reference signals for measurement. In one or more embodiments, the indication of the configuration is for a first set of beams. In some embodiments, the indication of the configuration is for both the first set of beams and a second set of beams.

[0060] At 404, the method 400 includes measuring, during a first time duration, the reference signals according to the configuration for the first set and optionally a second set (under certain cases) of beams. In one or more embodiments, the method 400 includes measuring, during the first time duration, the reference signals according to the configuration for the first set. In some embodiments, the method 400 includes measuring, during the first time duration, the reference signals according to the configuration for both the first set of beams and the second set of beams.

[0061] At 406, the method 400 includes obtaining a BEP model to be used to determine CSI for a second set of beams, the second set of beams having a relationship with the first set of beams, and the BEP model being based at least in part on the measurement of the reference signals during the first time duration.

[0062] At 408, the method 400 includes measuring, during a second time duration and according to the configuration, the reference signals.

[0063] At 410, the method 400 includes determining, using the BEP model, beam prediction for one or more beams of the first set of beams based at least in part on the measurement of the reference signals during the second time duration. In one or more embodiments a beam prediction may be or include a beam ranking. Under certain conditions, the UE may perform measurements for the predicted or top-ranked one or more beams of the first set of beams.

[0064] At 412, the method 400 includes transmitting, to the network device, a report (e.g., a CSI report or beam report) based at least in part on the beam prediction. In one or more embodiments the beam prediction is or includes beam information which can include the ranking of one or more beams in set A, or a predicted strength of one or more beams in set A, or both.

[0065] In one or more embodiments, the method 400 further includes providing, to a machine learning engine, a set of training data based at least in part on the measurement of the reference signals, the set of training data for both the first set of beams and the second set of beams. The method 400 may further include obtaining, from the machine learning engine, responsive to providing the set of training data, the BEP model.

[0066] In one or more embodiments, the method 400 further includes transmitting UE capability signaling comprising a first indication of a capability of the UE for collection of training data to train a BEP model. In some embodiments, the method 400 further includes transmitting UE capability signaling comprising a second indication of a capability of the UE to determine (e.g., infer) the one or more beams of the first set of beams from the second set of beams using the BEP model. In some embodiments, the method 400 further includes transmitting UE capability signaling that includes both the first indication and the second indication. In one or more embodiments, the indication of the configuration is received responsive to the UE capability signaling.

[0067] In one or more embodiments, the indication of the configuration for the first set of beams is an index of a set of indices, each index of the set of indices corresponding to a respective configuration of a plurality of configurations for the first set of beams.

[0068] In one or more embodiments, the indication of the configuration for the first set of beams is received in a configuration for a reference signal measurement resource set. In some embodiments, the configuration for the reference signal measurement resource set comprises a non- zero power channel state information reference signal resource set.

[0069] In one or more embodiments, the indication of the configuration for the first set of beams is received in a reporting configuration. In some embodiments, the reporting configuration comprises a channel state information reporting configuration.

[0070] In one or more embodiments, the second set of beams is a subset of the first set of beams. In one or more embodiments, the second set of beams has at least one beam different from the first set of beams. In some embodiments each beam of the second set of beams is different from each beam of the first set of beams.

[0071] In one or more embodiments, the indication of the configuration for the first set of beams comprises beam information for each beam of the first set of beams. [0072] In one or more embodiments, the configuration for the first set of beams further comprises one or more of a network vendor identifier and/or a scenario identifier and/or configuration identifier.

[0073] In one or more embodiments, an indication of the second set of beams for the BEP model comprises a bitmap or a combinatorial indexing selection from the first set of beams (e.g., [1100 1100 1100 1100] to choose 8 Set B beams out of 16 Set A beams). In some embodiments, an indication of the second set of beams for the BEP model is a reordered subset of the first set of beams.

[0074] In one or more embodiments, the first time duration is for a first radio resource control connection session, and the second time duration is for a second radio resource control connection session. In some embodiments, one or more of 402 through 414 occur during a first session (e.g., during an RRC active mode), and one or more of 402 through 414 occur during a second session (e.g., during an RRC active mode). In some embodiments, one or more of 402 through 414 occur during an RRC idle mode or an RRC inactive mode for the UE.

[0075] In one or more embodiments, the indication of the configuration is for both the first set of beams and the second set of beams, and the UE measures, during the first time duration, the reference signals received via the transceiver according to the configuration for the first set of beams and the second set of beams.

[0076] The method 400 may be variously embodied, extended, or adapted, as described in the following paragraphs and elsewhere in this description.

[0077] FIG. 5 shows an example method 500 of wireless communication by a network device. In one or more embodiments, method 500 supports one or more aspects of beam management aspects of life cycle management, as further described herein. In some cases, the network device may be the network device 104, network device 720, or one of the other network devices described herein. The method 500 may be performed using a processor, a transceiver (or main radio), or other components of the network device.

[0078] At 502, the method 500 includes transmitting reference signals using a first set of beams. In some embodiments, (e.g., when set B is not a subset of set A), at 502, the method 500 may optionally (e.g., additionally or alternatively) include transmitting references signals using a second set of beams.

[0079] At 504, the method 500 includes receiving, from a UE, UE capability signaling comprising an indication of a capability of the UE for training for the BEP model (e.g., the collection of training data for a BEP model) or inference using the BEP model to determine a beam prediction for the first set of beams using measurements of a second set of beams.

[0080] At 506, the method 500 includes transmitting, responsive to the UE capability signaling, an indication of a configuration for the first set of beams. Additionally, or alternatively, in some embodiments, at 506, the method 500 includes transmitting an indication of a configuration for the second set of beams.

[0081] At 508, the method 500 includes receiving, from the UE, a report (e.g., a channel state information report or beam report) that indicates the beam prediction.

[0082] In one or more embodiments, the indication of the configuration for the first set of beams is an index of a set of indices, each index of the set of indices corresponding to a respective configuration of a plurality of configurations for the first set of beams. In some embodiments, the indication of the configuration for the first set of beams is transmitted in a configuration for a reference signal measurement resource set. In some embodiments, the indication of the configuration for the first set of beams is transmitted in a channel state information reporting configuration. In some embodiments, the indication of the configuration for the first set of beams comprises beam information for each beam of the first set of beams.

[0083] Embodiments contemplated herein include one or more non-transitory computer- readable media storing instructions to cause an electronic device, upon execution of the instructions by one or more processors of the electronic device, to perform one or more elements of the method 400 or 500. In the context of method 400, this non-transitory computer-readable media may be, for example, a memory of a UE (such as a memory 706 of a wireless device 702 that is a UE, as described herein). In the context of method 500, this non-transitory computer- readable media may be, for example, a memory of a network device (such as a memory 724 of a network device 720, as described herein).

[0084] Embodiments contemplated herein include an apparatus having logic, modules, or circuitry to perform one or more elements of the method 400 or 500. In the context of method 400, this apparatus may be, for example, an apparatus of a UE (such as a wireless device 702 that is a UE). In the context of method 500, this apparatus may be, for example, an apparatus of a network device (such as a network device 720, as described herein).

[0085] Embodiments contemplated herein include an apparatus having one or more processors and one or more computer-readable media, using or storing instructions that, when executed by the one or more processors, cause the one or more processors to perform one or more elements of the method 400 or 500. In the context of method 400, this apparatus may be, for example, an apparatus of a UE (such as a wireless device 702 that is a UE, as described herein). In the context of the method 500, this apparatus may be, for example, an apparatus of a network device (such as a network device 720, as described herein).

[0086] Embodiments contemplated herein include a signal as described in or related to one or more elements of the method 400 or 500.

[0087] Embodiments contemplated herein include a computer program or computer program product having instructions, wherein execution of the program by a processor causes the processor to carry out one or more elements of the method 400 or 500. In the context of method 400, the processor may be a processor of a UE (such as a processor(s) 704 of a wireless device 702 that is a UE, as described herein), and the instructions may be, for example, located in the processor and/or on a memory of the UE (such as a memory 706 of a wireless device 702 that is a UE, as described herein). In the context of method 500, the processor may be a processor of a network device (such as a processor(s) 722 of a network device 720, as described herein), and the instructions may be, for example, located in the processor and/or on a memory of the network device (such as a memory 724 of a network device 720, as described herein).

[0088] FIG. 6 illustrates an example architecture of a wireless communication system, according to embodiments described herein. The following description is provided for an example wireless communication system 600 that operates in conjunction with the LTE system standards or specifications and/or 5G or NR system standards or specifications, as provided by 3GPP technical specifications.

[0089] As shown by FIG. 6, the wireless communication system 600 includes UE 602 and UE 604 (although any number of UEs may be used). In this example, the UE 602 and the UE 604 are illustrated as smartphones (e.g., handheld touchscreen mobile computing devices connectable to one or more cellular networks) but may also comprise any mobile or non-mobile computing device configured for wireless communication.

[0090] The UE 602 and UE 604 may be configured to communicatively couple with a RAN 606. In some embodiments, the RAN 606 may be NG-RAN, E-UTRAN, etc. The UE 602 and UE 604 utilize connections (or channels) (shown as connection 608 and connection 610, respectively) with the RAN 606, each of which comprises a physical communications interface. The RAN 606 can include one or more network devices, such as base station 612 and base station 614, that enable the connection 608 and connection 610. [0091] In this example, the connection 608 and connection 610 are air interfaces to enable such communicative coupling and may be consistent with RAT(s) used by the RAN 606, such as, for example, an LTE and/or NR.

[0092] In some embodiments, the UE 602 and UE 604 may also directly exchange communication data via a sidelink interface 616. The UE 604 is shown to be configured to an access point (shown as AP 618) via connection 620. By way of example, the connection 620 can comprise a local wireless connection, such as a connection consistent with any IEEE 802.11 protocol, wherein the AP 618 may comprise a Wi-Fi® router. In this example, the AP 618 may be connected to another network (for example, the Internet) without going through a core network (CN) 624.

[0093] In some embodiments, the UE 602 and UE 604 can be configured to communicate using orthogonal frequency division multiplexing (OFDM) communication signals with each other or with the base station 612 and/or the base station 614 over a multicarrier communication channel in accordance with various communication techniques, such as, but not limited to, an orthogonal frequency division multiple access (OFDMA) communication technique (e.g., for downlink communications) or a single carrier frequency division multiple access (SC-FDMA) communication technique (e.g., for uplink and ProSe or sidelink communications), although the scope of the embodiments is not limited in this respect. The OFDM signals can comprise a plurality of orthogonal subcarriers.

[0094] In some embodiments, all or parts of the base station 612 or base station 614 may be implemented as one or more software entities running on server computers as part of a virtual network. In addition, or in other embodiments, the base station 612 or base station 614 may be configured to communicate with one another via interface 622. In embodiments where the wireless communication system 600 is an LTE system (e.g., when the CN 624 is an EPC), the interface 622 may be an X2 interface. The X2 interface may be defined between two or more network devices of a RAN (e.g., two or more eNBs and the like) that connect to an EPC, and/or between two eNBs connecting to the EPC. In embodiments where the wireless communication system 600 is an NR system (e.g., when CN 624 is a 5GC), the interface 622 may be an Xn interface. The Xn interface is defined between two or more network devices of a RAN (e.g., two or more gNBs and the like) that connect to the 5GC, between a base station 612 (e.g., a gNB) connecting to the 5GC and an eNB, and/or between two eNBs connecting to the 5GC (e.g., CN 624).

[0095] The RAN 606 is shown to be communicatively coupled to the CN 624 via interface 628. The CN 624 may comprise one or more network elements 626, which are configured to offer various data and telecommunications services to customers/subscribers (e.g., users of UE 602 and UE 604) who are connected to the CN 624 via the RAN 606. The components of the CN 624 may be implemented in one physical device or separate physical devices including components to read and execute instructions from a machine-readable or computer-readable medium (e.g., a non-transitory machine-readable storage medium).

[0096] In some embodiments, the CN 624 may be an EPC, and the RAN 606 may be connected with the CN 624 via an SI interface 628. In some embodiments, the SI interface 628 may he split into two parts, an SI user plane (SI -U) interface, which carries traffic data between the base station 612 or base station 614 and a serving gateway (S-GW), and the Sl-MME interface, which is a signaling interface between the base station 612 or base station 614 and mobility management entities (MMEs).

[0097] In some embodiments, the CN 624 may be a 5GC, and the RAN 606 may be connected with the CN 624 via an NG interface 628. In embodiments, the NG interface 628 may be split into two parts, an NG user plane (NG-U) interface, which carries traffic data between the base station 612 or base station 614 and a user plane function (UPF), and the S 1 control plane (NG-C) interface, which is a signaling interface between the base station 612 or base station 614 and access and mobility management functions (AMFs).

[0098] Generally, an application server 630 may be an element offering applications that use internet protocol (IP) bearer resources with the CN 624 (e.g., packet switched data services). The application server 630 can also be configured to support one or more communication services (e.g., VoIP sessions, group communication sessions, etc.) for the UE 602 and UE 604 via the CN 624. The application server 630 may communicate with the CN 624 through an IP communications interface 632.

[0099] FIG. 7 illustrates an example system 700 for performing the signaling 738 between a wireless device 702 and a network device 720, according to embodiments described herein. The system 700 may he a portion of a wireless communication system as herein described. The wireless device 702 may be, for example, a UE of a wireless communication system. The network device 720 may be, for example, a base station (e.g., an eNB or a gNB) or a radio head of a wireless communication system.

[00100] The wireless device 702 may include one or more processor(s) 704. The processor(s) 704 may execute instructions such that various operations of the wireless device 702 are performed, as described herein. The processor(s) 704 may include one or more baseband processors implemented using, for example, a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a controller, a field programmable gate array (FPGA) device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.

[00101] The wireless device 702 may include a memory 706. The memory 706 may be a non- transitory computer-readable storage medium that stores instructions 708 (which may include, for example, the instructions being executed by the processor(s) 704). The instructions 708 may also be referred to as program code or a computer program. The memory 706 may also store data used by, and results computed by, the processor(s) 704.

[00102] The wireless device 702 may include one or more transceiver(s) 710 (also collectively referred to as a transceiver 710) that may include radio frequency (RF) transmitter and/or receiver circuitry that use the antenna(s) 712 of the wireless device 702 to facilitate signaling (e.g., the signaling 738) to and/or from the wireless device 702 and other devices (e.g., the network device 720) according to corresponding RATs. The wireless device 702 may also include or communicate with a machine learning engine 718. As further discussed herein (e.g., with reference to UE 102), in some embodiments, machine learning engine 718 may be a part or portion of wireless device 702. In some embodiments, machine learning engine 718 may be external to and in wired or wireless communication with wireless device 702 (e.g., in wireless communication with beam manager 716 via transceiver(s) 710). In one or more embodiments, machine learning engine 718 is a part of beam manager 716.

[00103] The wireless device 702 may include one or more antenna(s) 712 (e.g., one, two, four, eight, or more). For embodiments with multiple antenna(s) 712, the wireless device 702 may leverage the spatial diversity of such multiple antenna(s) 712 to send and/or receive multiple different data streams on the same time and frequency resources. This behavior may be referred to as, for example, MIMO behavior (referring to the multiple antennas used at each of a transmitting device and a receiving device that enable this aspect). MIMO transmissions by the wireless device 702 may be accomplished according to precoding (or digital beamforming) that is applied at the wireless device 702 that multiplexes the data streams across the antenna(s) 712 according to known or assumed channel characteristics such that each data stream is received with an appropriate signal strength relative to other streams and at a desired location in the spatial domain (e.g., the location of a receiver associated with that data stream). Some embodiments may use single user MIMO (SU-MIMO) methods (where the data streams are all directed to a single receiver) and/or multi-user MIMO (MU-MIMO) methods (where individual data streams may be directed to individual (different) receivers in different locations in the spatial domain). [00104] In some embodiments having multiple antennas, the wireless device 702 may implement analog beamforming techniques, whereby phases of the signals sent by the antenna(s) 712 are relatively adjusted such that the (joint) transmission of the antenna(s) 712 can be directed (this is sometimes referred to as beam steering).

[00105] The wireless device 702 may include one or more interface(s) 714. The interface(s) 714 may be used to provide input to or output from the wireless device 702. For example, a wireless device 702 that is a UE may include interface(s) 714 such as microphones, speakers, a touchscreen, buttons, and the like in order to allow for input and/or output to the UE by a user of the UE. Other interfaces of such a UE may be made up of transmitters, receivers, and other circuitry (e.g., other than the transceiver(s) 710/antenna(s) 712 already described) that allow for communication between the UE and other devices and may operate according to known protocols (e.g., Wi-Fi®, Bluetooth®, and the like).

[00106] The wireless device 702 may include beam manager 716. The beam manager 716 may be implemented via hardware, software, or combinations thereof. For example, the beam manager 716 may be implemented as a processor, circuit, and/or instructions 708 stored in the memory 706 and executed by the processor(s) 704. In some examples, the beam manager 716 may be integrated within the processor(s) 704 and/or the transceiver(s) 710. For example, the beam manager 716 may be implemented by a combination of software components (e.g., executed by a DSP or a general processor) and hardware components (e.g., logic gates and circuitry) within the processor(s) 704 or the transceiver(s) 710.

[00107] The beam manager 716 may be used for various aspects of the present disclosure, for example, aspects of FIGs. 1-7, from a wireless device or UE perspective. The beam manager 716 may be configured to, for example, receive, via the transceiver, an indication of a configuration for a first set of beams used by a network device for transmission of reference signals for measurement; measure, during a first time duration, the reference signals received via the transceiver according to the configuration for the first set of beams; obtain a beam estimation/prediction (BEP) model to be used to determine a beam prediction for a second set of beams , the second set of beams having a relationship with the first set of beams, and the BEP model being based at least in part on the measurement of the reference signals during the first time duration; measure, during a second time duration and according to the configuration, the reference signals received via the transceiver; determine, using the CSI BEP model, the beam prediction for one or more beams of the second set of beams based at least in part on the measurement of the reference signals during the second time duration; and transmit, to the network device via the transceiver, a report based at least in part on the beam prediction. [00108] The network device 720 may include one or more processor(s) 722. The processor(s) 722 may execute instructions such that various operations of the network device 720 are performed, as described herein. The processor(s) 722 may include one or more baseband processors implemented using, for example, a CPU, a DSP, an ASIC, a controller, an FPGA device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein.

[00109] The network device 720 may include a memory 724. The memory 724 may be a non- transitory computer-readable storage medium that stores instructions 726 (which may include, for example, the instructions being executed by the processor(s) 722). The instructions 726 may also be referred to as program code or a computer program. The memory 724 may also store data used by, and results computed by, the processor(s) 722.

[00110] The network device 720 may include one or more transceiver(s) 728 (also collectively referred to as a transceiver 728) that may include RF transmitter and/or receiver circuitry that use the antenna(s) 730 of the network device 720 to facilitate signaling (e.g., the signaling 738) to and/or from the network device 720 with other devices (e.g., the wireless device 702) according to corresponding RATs.

[00111] The network device 720 may include one or more antenna(s) 730 (e.g., one, two, four, or more). In embodiments having multiple antenna(s) 730, the network device 720 may perform MIMO, digital beamforming, analog beamforming, beam steering, etc., as has been described.

[00112] The network device 720 may include one or more interface(s) 732. The interface(s) 732 may be used to provide input to or output from the network device 720. For example, a network device 720 of a RAN (e.g., a base station, a radio head, etc.) may include interface(s) 732 made up of transmitters, receivers, and other circuitry (e.g., other than the transceiver(s) 728/antenna(s) 730 already described) that enables the network device 720 to communicate with other equipment in a network, and/or that enables the network device 720 to communicate with external networks, computers, databases, and the like for purposes of operations, administration, and maintenance of the network device 720 or other equipment operably connected thereto.

[00113] The network device 720 may include at least one beam manager 736. The beam manager 736 may be implemented via hardware, software, or combinations thereof. For example, the beam manager 736 may be implemented as a processor, circuit, and/or instructions 726 stored in the memory 724 and executed by the processor(s) 722. In some examples, the beam manager 736 may be integrated within the processor(s) 722 and/or the transceiver(s) 728. For example, the beam manager 736 may be implemented by a combination of software components (e.g., executed by a DSP or a general processor) and hardware components (e.g., logic gates and circuitry) within the processor(s) 722 or the transceiver(s) 728.

[00114] The beam manager 736 may be used for various aspects of the present disclosure, for example, aspects of FIGs. 1-7, from a network device perspective. The beam manager 736 may be configured to, for example, transmit, via a transceiver, reference signals using a first set of beams; receive, from a user equipment (UE), UE capability signaling comprising an indication of a capability of the UE for training a beam estimation/prediction (BEP) model or using the BEP model to determine a beam prediction for the first set of beams using measurements of a second set of beams; transmit, responsive to the UE capability signaling, an indication of a configuration for the first set of beams; and receive, from the UE, a report that indicates the beam prediction.

[00115] For one or more embodiments, at least one of the components set forth in one or more of the preceding figures may be configured to perform one or more operations, techniques, processes, and/or methods as set forth herein. For example, a baseband processor (or processor) as described herein in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth herein. For another example, circuitry associated with a UE, network device, network element, etc. as described above in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth herein.

[00116] Any of the above described embodiments may be combined with any other embodiment (or combination of embodiments), unless explicitly stated otherwise. The foregoing description of one or more implementations provides illustration and description but is not intended to be exhaustive or to limit the scope of embodiments to the precise form described. Modifications and variations are possible in light of the above teachings or may be acquired from practice of various embodiments.

[00117] Embodiments and implementations of the systems and methods described herein may include various operations, which may be embodied in machine-executable instructions to be executed by a computer system. A computer system may include one or more general-purpose or special-purpose computers (or other electronic devices). The computer system may include hardware components that include specific logic for performing the operations or may include a combination of hardware, software, and/or firmware.

[00118] The systems described herein pertain to specific embodiments but are provided as examples. These embodiments can be combined into single systems, partially combined into other systems, split into multiple systems or divided or combined in other ways. In addition, it is contemplated that parameters, attributes, aspects, etc. of any one embodiment can be used in another embodiment. The parameters, attributes, aspects, etc. are merely described in one or more embodiments for clarity, and it is recognized that the parameters, attributes, aspects, etc. can be combined with or substituted for parameters, attributes, aspects, etc. of another embodiment unless specifically disclaimed herein. [00119] Although the foregoing has been described in some detail for purposes of clarity, it will be apparent that changes and modifications may be made without departing from the principles thereof. It should be noted that there are many alternative ways of implementing both the processes and apparatuses described herein. Accordingly, the present embodiments are to be considered illustrative and not restrictive, and the description is not to be limited to the details given herein but may be modified within the scope and equivalents of the appended claims.

Claims

1. A u ser equipment ( UE), comprising : a transceiver; and a processor configured to cause the UE to, receive, via the transceiver, an indication of a configuration for a first set of beams used by a network device for transmission of reference signals for measurement; measure, during a first time duration, the reference signals received via the transceiver according to the configuration for the first set of beams; obtain a beam estimation/prediction (BEP) model to be used to determine a beam prediction for a second set of beams, the second set of beams having a relationship with the first set of beams, and the BEP model being based at least in part on the measurement of the reference signals during the first time duration; measure, during a second time duration and according to the configuration, the reference signals received via the transceiver; determine, using the BEP model, the beam prediction for one or more beams of the second set of beams based at least in part on the measurement of the reference signals during the second time duration; and transmit, to the network device via the transceiver, a report based at least in part on the beam prediction.

2. The UE of claim 1, wherein the processor configured to obtain the BEP model is further configured to: provide, to a machine learning engine, a set of training data based at least in part on the measurement of the reference signals, the set of training data for both the first set of beams and the second set of beams; and obtain, from the machine learning engine, responsive to providing the set of training data, the BEP model.

3. The UE of claim 1, wherein the processor is further configured to cause the UE to: transmit UE capability signaling comprising a first indication of a capability of the UE for training the BEP model, a second indication of a capability of the UE to using the BEP model to determine the beam prediction, or both, wherein the indication of the configuration is received responsive to the UE capability signaling.

4. The UE of claim 1 , wherein the indication of the configuration for the first set of beams is an index of a set of indices, each index of the set of indices corresponding to a respective configuration of a plurality of configurations for the first set of beams.

5. The UE of claim 1, wherein the indication of the configuration for the first set of beams is received in a configuration for a reference signal measurement resource set.

6. The UE of claim 5, wherein the configuration for the reference signal measurement resource set comprises a non-zero power CSI reference signal resource set.

7. The UE of claim 1, wherein the indication of the configuration for the first set of beams is received in a reporting configuration.

8. The UE of claim 7, wherein the reporting configuration comprises a CSI reporting configuration.

9. The UE of claim 1 , wherein the second set of beams is a subset of the first set of beams.

10. The UE of claim 1, wherein the indication of the configuration for the first set of beams comprises beam information for each beam of the first set of beams.

11. The UE of claim 1 , wherein the configuration for the first set of beams further comprises one or more of a network vendor identifier or a scenario identifier or a configuration identifier.

12. The UE of claim 1, wherein an indication of the second set of beams for the BEP model comprises a bitmap or a combinatorial indexing selection from the first set of beams.

13. The UE of claim 1, wherein an indication of the second set of beams for the BEP model is a reordered subset of the first set of beams.

14. The UE of claim 1, wherein the first time duration is for a first radio resource control connection session, and the second time duration is for a second radio resource control connection session.

15. The UE of claim 1, wherein: the indication of the configuration is for both the first set of beams and the second set of beams; and the processor configured to measure the reference signals comprises the processor configured to cause the UE to measure, during the first time duration, the reference signals received via the transceiver according to the configuration for the first set of beams and the second set of beams.

16. A network device, comprising: a transceiver; and a processor configured to cause the network device to, transmit, via the transceiver, reference signals using a first set of beams; receive, from a user equipment (UE), UE capability signaling comprising an indication of a capability of the UE for training a beam estimation/prediction (BEP) model or using the BEP model to determine a beam prediction for the first set of beams using measurements of a second set of beams; transmit, responsive to the UE capability signaling, an indication of a configuration for the first set of beams; and receive, from the UE, a report that indicates the beam prediction.

17. The network device of claim 16, wherein the indication of the configuration for the first set of beams is an index of a set of indices, each index of the set of indices corresponding to a respective configuration of a plurality of configurations for the first set of beams.

18. The network device of claim 16, wherein the indication of the configuration for the first set of beams is transmitted in a configuration for a reference signal measurement resource set, or is transmitted in a CSI reporting configuration.

19. The network device of claim 16, wherein the indication of the configuration for the first set of beams comprises beam information for each beam of the first set of beams.

20. A method of wireless communication at a user equipment (UE), comprising: receiving an indication of a configuration for a first set of beams used by a network device for transmission of reference signals for measurement; measuring, during a first time duration, the reference signals according to the configuration for the first set of beams; obtaining a beam estimation/prediction (BEP) model to be used to determine a beam prediction for a second set of beams, the second set of beams having a relationship with the first set of beams, and the BEP model being based at least in part on the measurement of the reference signals during the first time duration; measuring, during a second time duration and according to the configuration, the reference signals; determining, using the BEP model, the beam prediction for one or more beams of the second set of beams based at least in part on the measurement of the reference signals during the second time duration; and transmitting, to the network device, a report based at least in part on the beam prediction.