WO2025208333A1

WO2025208333A1 - Artificial intelligence model indications among cells

Info

Publication number: WO2025208333A1
Application number: PCT/CN2024/085524
Authority: WO
Inventors: Qiaoyu Li; Tao Luo; Mahmoud Taherzadeh Boroujeni; Hamed Pezeshki
Original assignee: Qualcomm Inc
Current assignee: Qualcomm Inc
Priority date: 2024-04-02
Filing date: 2024-04-02
Publication date: 2025-10-09
Anticipated expiration: 2026-10-02

Abstract

Some wireless communication systems may utilize artificial intelligence (AI) models for performing one or more functionalities. Some examples of the techniques described herein may reduce overhead resource consumption while protecting network-side information (e.g., proprietary setup information). To avoid use of an explicit model identification, a UE may receive reference signaling and evaluate the performance of one or more candidate models for an initial cell. For instance, the UE may generate a metric by inputting one or more reference signals to one or more candidate AI models. In some approaches, a metric may be generated for each candidate AI model. Metrics may be compared to select an AI model associated with a best metric. The selected AI model may be utilized for receiving a signal. For instance, the AI model may be utilized to perform beam prediction, where a signal is communicated via the selected beam.

Description

ARTIFICIAL INTELLIGENCE MODEL INDICATIONS AMONG CELLS

INTRODUCTION

The following relates to wireless communication, including managing indications among cells.

Wireless communication systems are widely deployed to provide various types of communication content such as voice, video, packet data, messaging, broadcast, and so on. These systems may be capable of supporting communication with multiple users by sharing the available system resources (e.g., time, frequency, and power) . Examples of such multiple-access systems include fourth generation (4G) systems such as Long Term Evolution (LTE) systems, LTE-Advanced (LTE-A) systems, or LTE-A Pro systems, and fifth generation (5G) systems which may be referred to as New Radio (NR) systems. These systems may employ technologies such as code division multiple access (CDMA) , time division multiple access (TDMA) , frequency division multiple access (FDMA) , orthogonal FDMA (OFDMA) , or discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM) . A wireless multiple-access communication system may include one or more base stations, each supporting wireless communication for communication devices, which may be known as user equipment (UE) .

SUMMARY

A method for wireless communications by a user equipment (UE) is described. The method may include receiving one or more first reference signals via at least one downlink beam, generating a metric based on the one or more first reference signals input to one or more candidate artificial intelligence (AI) models for beam prediction, receiving a first signal from a first cell based on a first AI model, the first AI model selected from the one or more candidate AI models based on the metric, receiving an indication associated with an applicability of the first AI model for a second cell, and receiving, based on the indication, a second signal from the second cell based on the first AI model.

A UE for wireless communications is described. The UE may include one or more memories and one or more processors coupled with the one or more memories. The one or more processors may be configured to cause the UE to receive one or more first reference signals via at least one downlink beam, generate a metric based on the one or more first reference signals input to one or more candidate AI models for beam prediction, receive a first signal from a first cell based on a first AI model, the first AI model selected from the one or more candidate AI models based on the metric, receive an indication associated with an applicability of the first AI model for a second cell, and receive, based on the indication, a second signal from the second cell based on the first AI model.

Another UE for wireless communications is described. The UE may include means for receiving one or more first reference signals via at least one downlink beam, means for generating a metric based on the one or more first reference signals input to one or more candidate AI models for beam prediction, means for receiving a first signal from a first cell based on a first AI model, the first AI model selected from the one or more candidate AI models based on the metric, means for receiving an indication associated with an applicability of the first AI model for a second cell, and means for receiving, based on the indication, a second signal from the second cell based on the first AI model.

A non-transitory computer-readable medium storing code for wireless communications is described. The code may include instructions executable by one or more processors to receive one or more first reference signals via at least one downlink beam, generate a metric based on the one or more first reference signals input to one or more candidate AI models for beam prediction, receive a first signal from a first cell based on a first AI model, the first AI model selected from the one or more candidate AI models based on the metric, receive an indication associated with an applicability of the first AI model for a second cell, and receive, based on the indication, a second signal from the second cell based on the first AI model.

Some examples of the method, UEs, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for switching from the first cell to the second cell, where the first signal may be received before the switch and the second signal may be received after the switch.

Some examples of the method, UEs, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for receiving the indication from the first cell before the switch from the first cell to the second cell.

Some examples of the method, UEs, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for receiving the indication from the second cell after the switch from the first cell to the second cell.

Some examples of the method, UEs, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for receiving the indication via a radio resource control (RRC) configuration message or a medium access control-control element (MAC-CE) message, where the indication identifies one or more cells, identifies one or more functionalities associated with the first AI model, or both, the one or more functionalities including spatial beam prediction, temporal beam prediction, spatial-temporal beam prediction, channel state information (CSI) compression, CSI prediction, or a combination thereof.

Some examples of the method, UEs, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for receiving the indication based on a first quantity of beams of the first cell that may be equal to a second quantity of beams of the second cell.

Some examples of the method, UEs, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for receiving a configuration message that identifies one or more cells associated with the indication, where the indication identifies one or more functionalities associated with the first AI model and transmitting information based on the configuration message to the first cell, the second cell, or both.

Some examples of the method, UEs, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for receiving the indication based on a first functionality that may be active for the first cell and for the second cell, the first functionality associated with the first AI model.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the indication may include an identification of a functionality associated with the first AI model and a command for utilization of the first AI model for the second cell.

Some examples of the method, UEs, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for detecting an absence of a second indication associated with the applicability of the first AI model for a third cell, receiving, based on the detection, one or more second reference signals via a second downlink beam, generating a second metric based on the one or more second reference signals input to the one or more candidate AI models for beam prediction, and receiving a third signal from the third cell based on a second AI model, the second AI model selected based on the second metric.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the indication may include an identification of a zone or a cell identifier or both for application of the first AI model in the second cell.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the indication may include an identification of a dataset, a configuration, a codebook, a deployment, or a combination thereof for a second cell that is identical to a corresponding identification for a first cell.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the indication may be agnostic to the first AI model.

Some examples of the method, UEs, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for deactivating the first AI model at a first time that the UE may be connected to the first cell and activating the first AI model based on a condition that the UE may be connected to the first cell at a second time subsequent to the first time.

Some examples of the method, UEs, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for receiving a first virtual identifier for the first AI model, deactivating the first AI model, and activating the first AI model based on a condition that the first virtual identifier matches the indication.

Some examples of the method, UEs, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for receiving a first report configuration associated with a first cell for information associated with a functionality of the first AI model, deactivating the first AI model, and activating the first AI model based on a condition that the indication may be a second report configuration associated with the first cell for information associated with the functionality of the first AI model.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of a wireless communication system that supports artificial intelligence (AI) model indications among cells in accordance with one or more aspects of the present disclosure.

FIG. 2 shows an example of a network architecture that supports AI model indications among cells in accordance with one or more aspects of the present disclosure.

FIG. 3 shows an example of a wireless communication system that supports AI model indications among cells in accordance with one or more aspects of the present disclosure.

FIG. 4 shows an example of a block diagram that supports AI model indications among cells in accordance with one or more aspects of the present disclosure.

FIG. 5 shows an example of a block diagram that supports AI model indications among cells in accordance with one or more aspects of the present disclosure.

FIG. 6 shows an example of a process flow that supports AI model indications among cells in accordance with one or more aspects of the present disclosure.

FIG. 7 shows an example of a machine learning process that supports AI model indications among cells in accordance with one or more aspects of the present disclosure.

FIGs. 8 and 9 show block diagrams of devices that support AI model indications among cells in accordance with one or more aspects of the present disclosure.

FIG. 10 shows a block diagram of a communications manager that supports AI model indications among cells in accordance with one or more aspects of the present disclosure.

FIG. 11 shows a diagram of a system including a device that supports AI model indications among cells in accordance with one or more aspects of the present disclosure.

FIGs. 12 and 13 show flowcharts illustrating methods that support AI model indications among cells in accordance with one or more aspects of the present disclosure.

FIG. 14 shows a flowchart illustrating a method that support AI model indications among cells in accordance with one or more aspects of the present disclosure.

DETAILED DESCRIPTION

Some wireless communication systems may utilize artificial intelligence (AI) (e.g., machine learning (ML) ) models for performing one or more functionalities. An AI model or ML model (which may be referred to herein as a “model” or an “AI model” ) is a structure (e.g., data structure, program, or algorithmic structure) capable of being trained using data (e.g., training input data, ground truth data) to predict one or more outputs. For instance, training input data and corresponding ground truth data that represents one or more target outputs may be utilized for a training scenario. In a training scenario, the AI model or ML model may be executed using the training input data to predict outputs, where the AI model or ML model is adjusted to reduce a difference between the predicted outputs and the ground truth data. In an application scenario, the AI model or ML model may be executed using input data (e.g., real-world data or runtime data that is different from the training input data) . In some approaches, prediction performance may include determining a degree of prediction accuracy. For instance, predicted outputs may be compared with measured performance to determine the prediction performance of a model. In some examples, prediction performance may be expressed by one or more metrics, which may indicate a degree of prediction accuracy. To be effective, models may be deployed in application scenarios that are similar to one or more training scenarios. For wireless communication systems, for example, a model may provide accurate outputs (e.g., predictions, inferences) when the model is utilized in an application scenario that is similar to a training scenario. For uniform performance of a model, for instance, some parameters (e.g., beamforming codebooks, quasi co-location (QCL) relationships, quantity and ordering of beams, among other examples) , may be maintained from a training scenario to an application (e.g., runtime or prediction) scenario.

Some approaches to utilizing models for wireless communication may train various models for application scenarios with varying parameters, where the models may be explicitly identified for global use. For example, a network may identify models, where the models may be utilized in any cell throughout a network or across networks, where “global use” indicates that an identified application could be selected for use anywhere within a network or across networks. In some cases, global model identification could be utilized to reverse engineer network-side information. Because one or more network characteristics (e.g., antenna type, network entity setup, among other examples) may correspond to the use of a model, model identification might reveal one or more network characteristics. If models are identified by networks, for example, model identifiers may be utilized to determine one or more regions or locations with network entities that are deployed based on the same codebooks, antenna panels, or radio algorithms, among other examples. Accordingly, explicit and globally applicable model identifiers may indirectly expose one or more characteristics of a network. Because some network characteristics may be proprietary (e.g., proprietary setup information) , protection of network characteristics may be sought.

Some other approaches may avoid explicitly identifying models by relying on a user equipment (UE) to receive reference signaling from the network for performance monitoring. A reference signal is a signal with established properties (e.g., frequency, amplitude, timing, codes, among other examples) communicated between devices. Reference signals may be utilized to perform resource allocation or selection, power control, tracking, channel estimation, or one or more other functions. In some examples, one or more reference signals may communicated via a downlink beam. A beam may be radio frequency (RF) energy that is directionally shaped or focused. A downlink beam is a beam provided from a cell or network entity to a UE.

The UE may execute candidate models using the reference signaling to evaluate the performance of one or more candidate models, and may select a best performing model. A candidate model is an AI model that may be selected to perform one or more functionalities. For instance, a UE may receive one or more reference signals via one or more beams such that the UE can derive prediction performance metrics for one or more candidate models to determine whether to activate an AI-based functionality (e.g., beam prediction) or whether to select a model (s) for use. A metric may be a value, quantity, or indication associated with model performance. For instance, a metric may indicate a channel quality when performing beam prediction or another operation.

In some approaches, a UE may utilize the performance monitoring (e.g., one or more metrics) to perform model activation or model selection. Model activation may be an operation where a UE or a network entity activates usage of one or more AI models. Model selection may be an operation where a UE or a network entity selects a model from a set of candidate models for use. For instance, in a case that a metric satisfies a threshold (e.g., accuracy threshold, quality threshold, among other examples) , the UE may activate model usage or may select a model (e.g., a best-performing model) to perform one or more operations (e.g., beam prediction or compression, among other examples) . In some cases, performance monitoring (e.g., model activation or model selection) may be utilized by a UE to avoid identifying one or more models on the network side.

In some aspects, reference signaling and performance monitoring (e.g., model activation or model selection) may be performed for each cell that a UE communicates with. Using reference signaling and performance monitoring (e.g., metric evaluation) of candidate models for UE-side model activation or model selection may consume a relatively large amount of overhead signaling, processing resources, or time (e.g., latency) if the reference signaling or monitoring is performed repetitively for multiple cells. A cell may refer to a logical communication entity (e.g., communication resource) for communication with a network entity. For example, a cell may provide transmission or reception of one or more wireless signals. In some cases, a cell may be associated with a geographic area (e.g., a coverage area) . In some commercial deployments, a significant amount (e.g., 15%, 25%, 40%, 50%, 75%, or 100%, among other examples) of neighboring cells may use similar setups including antenna height, down-tilt angle, antenna panels, or beamforming codebooks. As a UE moves away from a first network entity that provides a first cell and towards a second network entity that provides a second cell, link attenuation or degradation may occur in the first cell, while a potential link in the second cell may strengthen, which may eventually cause the UE to switch from the first cell to the second cell. If the UE carries out performance monitoring based model activation or selection each time a cell switch occurs, resources may be wasted because a model that is applicable in one cell may also be applicable across one or more neighboring cells. Accordingly, explicit identification of models may expose network information, and using performance monitoring for model selection (to avoid explicit model identification) may utilize or consume a significant quantity of processing, communication resources, or time.

Some examples of the techniques described herein may reduce resource usage from performance monitoring while protecting network-side information (e.g., proprietary setup information) . To avoid use of a model identification, a UE may receive reference signaling and evaluate the performance of one or more candidate models for an initial cell. For instance, the UE may generate a metric by inputting one or more reference signals to one or more candidate AI models. In an example, each candidate AI model may predict a beam based on the reference signal (s) . In some approaches, a metric may be generated for each candidate AI model (e.g., each predicted beam) . Metrics may be compared to select an AI model associated with a best metric (e.g., smallest difference between a value of a predicted beam and a measured value, highest channel quality value, highest signal-to-noise ratio (SNR) , lowest noise, or other metric) . The selected AI model may be utilized for receiving a signal. For instance, the AI model may be utilized to perform beam prediction, where a signal is communicated (e.g., received) via the selected beam.

In some cases, a UE may switch between cells (e.g., from a source cell to a target cell) . Switching between cells may occur when a UE establishes a connection with a target cell. In some examples, cell switching may be performed via an upper-layer handover procedure (e.g., handover via signaling performed at layer 3 (L3) ) or via a lower-layer switch procedure (e.g., a lower-layer triggered mobility (LTM) switch procedure or a cell switch performed at L1 or layer 2 (L2) ) .

When switching between cells, the UE may utilize (e.g., reuse) the same model for one or more consecutive cells. For example, the UE may receive an indication associated with an applicability of the selected AI model for one or more cells. For instance, a selected AI model may be applicable to one or more cells (e.g., all cells) in a network or in a region with similar setups. In some examples, model applicability for a cell may occur if the model functions or performs similarly for the cell relative to a cell in which the AI model was monitored or selected (or if the AI model would have been selected for the cell based on performance monitoring) . The indication associated with the applicability may be a value, code, or signal that indicates whether an AI model may be applicable to one or more cells (e.g., whether the AI model that is applicable for a cell may be applicable to one or more other cells) . The indication may be agnostic to the AI model. For example, the indication may be agnostic to the AI model in that the indication may not globally identify an AI model.

In some examples, the indication may indicate whether the AI model is applicable to another cell, may identify one or more cells where the AI model is applicable, may identify one or more functionalities associated with the AI model, may include a zone identifier for the applicability of the AI model, may include a command for utilization of the model, may include a virtual identifier (e.g., an identifier that is not associated with any model by the network) , may identify information (e.g., a dataset, a configuration, a codebook, or a deployment, among other examples) that may indicate applicability, may be based on whether a quantity of beams is identical between cells, may be based on whether functionalities are active between cells, or a combination thereof. A functionality may be a functionality provided by an AI model. Examples of functionality may include spatial beam prediction, temporal beam prediction, spatial-temporal beam prediction, channel state information (CSI) compression, CSI prediction, or a combination thereof. In some examples, a configuration message (e.g., a message indicating a configuration for CSI reporting) may be utilized to determine whether to reuse the model after a switch (e.g., when the CSI reporting configuration of a target cell matches the CSI reporting configuration of a source cell) . When the UE switches to a cell where the AI model is applicable (in accordance with the indication) , the UE may reuse the AI model in the target cell without reevaluating the candidate AI model (s) .

In some approaches, the presence or absence of an indication may be utilized to determine whether an AI model may be used (e.g., reused) in another cell. For example, an indication may not be presented in some cases (e.g., may not be presented for one or more AI functionalities) . If the indication is presented, the indication may indicate whether the UE may use the (same) first AI model in the second cell.

In some example, a UE may utilize a virtual identifier to determine whether an AI model may be reused in another cell. A virtual identifier is an identifier that is not associated with any model by the network. For instance, the UE may receive a virtual identifier corresponding to an AI model. If an indication for a subsequent cell matches the virtual identifier, the AI model may be reused for the subsequent cell.

Reusing a selected AI model for one or more cells may provide network information protection while reducing signaling overhead or power consumption. Some approaches may avoid revealing whether the same network implementations are used for cells that are distant from each other. For instance, globally defined model or dataset identifiers may not be utilized, or model training may be based on datasets collected in various regions or countries via UE vendor implementation choices, rather than worldwide model training. Some examples of the techniques described may allow network vendors flexibility to change codebooks over time (because performance monitoring based model initialization may be used for any cell) , while overhead or power consumption can be reduced. Some examples of the techniques may be applied to cases where new cells are supplied (e.g., dual carrier-carrier aggregation (DC-CA) ) without completely switching to another cell. For instance, a new cell may be supplied by aggregating carriers, where a same model may be applied to the new cell as was used in a previous cell. Reusing the same model without using performance monitoring may reduce communication and processing resource consumption.

Aspects of the disclosure are initially described in the context of wireless communication systems. Aspects of the disclosure are also described in the context of block diagrams and a process flow. Aspects of the disclosure are further illustrated by and described with reference to apparatus diagrams, system diagrams, and flowcharts that relate to AI model indications among cells.

FIG. 1 shows an example of a wireless communication system 100 that supports AI model indications among cells in accordance with one or more aspects of the present disclosure. The wireless communication system 100 may include one or more devices, such as one or more network devices (e.g., network entities 105) , one or more UEs 115, and a core network 130. In some examples, the wireless communication system 100 may be a Long Term Evolution (LTE) network, an LTE-Advanced (LTE-A) network, an LTE-A Pro network, a New Radio (NR) network, or a network operating in accordance with other systems and radio technologies, including future systems and radio technologies not explicitly mentioned herein.

The network entities 105 may be dispersed throughout a geographic area to form the wireless communication system 100 and may include devices in different forms or having different capabilities. In various examples, a network entity 105 may be referred to as a network element, a mobility element, a radio access network (RAN) node, or network equipment, among other nomenclature. In some examples, network entities 105 and UEs 115 may wirelessly communicate via communication link (s) 125 (e.g., a RF access link) . For example, a network entity 105 may support a coverage area 110 (e.g., a geographic coverage area) over which the UEs 115 and the network entity 105 may establish the communication link (s) 125. The coverage area 110 may be an example of a geographic area over which a network entity 105 and a UE 115 may support the communication of signals according to one or more radio access technologies (RATs) .

The UEs 115 may be dispersed throughout a coverage area 110 of the wireless communication system 100, and each UE 115 may be stationary, or mobile, or both at different times. The UEs 115 may be devices in different forms or having different capabilities. Some example UEs 115 are illustrated in FIG. 1. The UEs 115 described herein may be capable of supporting communications with various types of devices in the wireless communication system 100 (e.g., other wireless communication devices, including UEs 115 or network entities 105) , as shown in FIG. 1.

As described herein, a node of the wireless communication system 100, which may be referred to as a network node, or a wireless node, may be a network entity 105 (e.g., any network entity described herein) , a UE 115 (e.g., any UE described herein) , a network controller, an apparatus, a device, a computing system, one or more components, or another suitable processing entity configured to perform any of the techniques described herein. For example, a node may be a UE 115. As another example, a node may be a network entity 105. As another example, a first node may be configured to communicate with a second node or a third node. In one aspect of this example, the first node may be a UE 115, the second node may be a network entity 105, and the third node may be a UE 115. In another aspect of this example, the first node may be a UE 115, the second node may be a network entity 105, and the third node may be a network entity 105. In yet other aspects of this example, the first, second, and third nodes may be different relative to these examples. Similarly, reference to a UE 115, network entity 105, apparatus, device, computing system, or the like may include disclosure of the UE 115, network entity 105, apparatus, device, computing system, or the like being a node. For example, disclosure that a UE 115 is configured to receive information from a network entity 105 also discloses that a first node is configured to receive information from a second node.

In some examples, network entities 105 may communicate with a core network 130, or with one another, or both. For example, network entities 105 may communicate with the core network 130 via backhaul communication link (s) 120 (e.g., in accordance with an S1, N2, N3, or other interface protocol) . In some examples, network entities 105 may communicate with one another via backhaul communication link (s) 120 (e.g., in accordance with an X2, Xn, or other interface protocol) either directly (e.g., directly between network entities 105) or indirectly (e.g., via the core network 130) . In some examples, network entities 105 may communicate with one another via a midhaul communication link 162 (e.g., in accordance with a midhaul interface protocol) or a fronthaul communication link 168 (e.g., in accordance with a fronthaul interface protocol) , or any combination thereof. The backhaul communication link (s) 120, midhaul communication links 162, or fronthaul communication links 168 may be or include one or more wired links (e.g., an electrical link, an optical fiber link) or one or more wireless links (e.g., a radio link, a wireless optical link) , among other examples or various combinations thereof. A UE 115 may communicate with the core network 130 via a communication link 155.

One or more of the network entities 105 or network equipment described herein may include or may be referred to as a base station 140 (e.g., a base transceiver station, a radio base station, an NR base station, an access point, a radio transceiver, a NodeB, an eNodeB (eNB) , a next-generation NodeB or giga-NodeB (either of which may be referred to as a gNB) , a 5G NB, a next-generation eNB (ng-eNB) , a Home NodeB, a Home eNodeB, or other suitable terminology) . In some examples, a network entity 105 (e.g., a base station 140) may be implemented in an aggregated (e.g., monolithic, standalone) base station architecture, which may be configured to utilize a protocol stack that is physically or logically integrated within one network entity (e.g., a network entity 105 or a single RAN node, such as a base station 140) .

In some examples, a network entity 105 may be implemented in a disaggregated architecture (e.g., a disaggregated base station architecture, a disaggregated RAN architecture) , which may be configured to utilize a protocol stack that is physically or logically distributed among multiple network entities (e.g., network entities 105) , such as an integrated access and backhaul (IAB) network, an open RAN (O-RAN) (e.g., a network configuration sponsored by the O-RAN Alliance) , or a virtualized RAN (vRAN) (e.g., a cloud RAN (C-RAN) ) . For example, a network entity 105 may include one or more of a central unit (CU) , such as a CU 160, a distributed unit (DU) , such as a DU 165, a radio unit (RU) , such as an RU 170, a RAN Intelligent Controller (RIC) , such as an RIC 175 (e.g., a Near-Real Time RIC (Near-RT RIC) , a Non-Real Time RIC (Non-RT RIC) ) , a Service Management and Orchestration (SMO) system, such as an SMO system 180, or any combination thereof. An RU 170 may also be referred to as a radio head, a smart radio head, a remote radio head (RRH) , a remote radio unit (RRU) , or a transmission reception point (TRP) . One or more components of the network entities 105 in a disaggregated RAN architecture may be co-located, or one or more components of the network entities 105 may be located in distributed locations (e.g., separate physical locations) . In some examples, one or more of the network entities 105 of a disaggregated RAN architecture may be implemented as virtual units (e.g., a virtual CU (VCU) , a virtual DU (VDU) , a virtual RU (VRU) ) .

The split of functionality between a CU 160, a DU 165, and an RU 170 is flexible and may support different functionalities depending on which functions (e.g., network layer functions, protocol layer functions, baseband functions, RF functions, or any combinations thereof) are performed at a CU 160, a DU 165, or an RU 170. For example, a functional split of a protocol stack may be employed between a CU 160 and a DU 165 such that the CU 160 may support one or more layers of the protocol stack and the DU 165 may support one or more different layers of the protocol stack. In some examples, the CU 160 may host upper protocol layer (e.g., L3, L2) functionality and signaling (e.g., Radio Resource Control (RRC) , service data adaptation protocol (SDAP) , Packet Data Convergence Protocol (PDCP) ) . The CU 160 (e.g., one or more CUs) may be connected to a DU 165 (e.g., one or more DUs) or an RU 170 (e.g., one or more RUs) , or some combination thereof, and the DUs 165, RUs 170, or both may host lower protocol layers, such as L1 (e.g., physical (PHY) layer) or L2 (e.g., radio link control (RLC) layer, medium access control (MAC) layer) functionality and signaling, and may each be at least partially controlled by the CU 160. Additionally, or alternatively, a functional split of the protocol stack may be employed between a DU 165 and an RU 170 such that the DU 165 may support one or more layers of the protocol stack and the RU 170 may support one or more different layers of the protocol stack. The DU 165 may support one or multiple different cells (e.g., via one or multiple different RUs, such as an RU 170) . In some cases, a functional split between a CU 160 and a DU 165 or between a DU 165 and an RU 170 may be within a protocol layer (e.g., some functions for a protocol layer may be performed by one of a CU 160, a DU 165, or an RU 170, while other functions of the protocol layer are performed by a different one of the CU 160, the DU 165, or the RU 170) . A CU 160 may be functionally split further into CU control plane (CU-CP) and CU user plane (CU-UP) functions. A CU 160 may be connected to a DU 165 via a midhaul communication link 162 (e.g., F1, F1-c, F1-u) , and a DU 165 may be connected to an RU 170 via a fronthaul communication link 168 (e.g., open fronthaul (FH) interface) . In some examples, a midhaul communication link 162 or a fronthaul communication link 168 may be implemented in accordance with an interface (e.g., a channel) between layers of a protocol stack supported by respective network entities (e.g., one or more of the network entities 105) that are in communication via such communication links.

In some wireless communication systems (e.g., the wireless communication system 100) , infrastructure and spectral resources for radio access may support wireless backhaul link capabilities to supplement wired backhaul connections, providing an IAB network architecture (e.g., to a core network 130) . In some cases, in an IAB network, one or more of the network entities 105 (e.g., network entities 105 or IAB node (s) 104) may be partially controlled by each other. The IAB node (s) 104 may be referred to as a donor entity or an IAB donor. A DU 165 or an RU 170 may be partially controlled by a CU 160 associated with a network entity 105 or base station 140 (such as a donor network entity or a donor base station) . The one or more donor entities (e.g., IAB donors) may be in communication with one or more additional devices (e.g., IAB node (s) 104) via supported access and backhaul links (e.g., backhaul communication link (s) 120) . IAB node (s) 104 may include an IAB mobile termination (IAB-MT) controlled (e.g., scheduled) by one or more DUs (e.g., DUs 165) of a coupled IAB donor. An IAB-MT may be equipped with an independent set of antennas for relay of communications with UEs 115 or may share the same antennas (e.g., of an RU 170) of IAB node (s) 104 used for access via the DU 165 of the IAB node (s) 104 (e.g., referred to as virtual IAB-MT (vIAB-MT) ) . In some examples, the IAB node (s) 104 may include one or more DUs (e.g., DUs 165) that support communication links with additional entities (e.g., IAB node (s) 104, UEs 115) within the relay chain or configuration of the access network (e.g., downstream) . In such cases, one or more components of the disaggregated RAN architecture (e.g., the IAB node (s) 104 or components of the IAB node (s) 104) may be configured to operate according to the techniques described herein.

For instance, an access network (AN) or RAN may include communications between access nodes (e.g., an IAB donor) , IAB node (s) 104, and one or more UEs 115. The IAB donor may facilitate connection between the core network 130 and the AN (e.g., via a wired or wireless connection to the core network 130) . That is, an IAB donor may refer to a RAN node with a wired or wireless connection to the core network 130. The IAB donor may include one or more of a CU 160, a DU 165, and an RU 170, in which case the CU 160 may communicate with the core network 130 via an interface (e.g., a backhaul link) . The IAB donor and IAB node (s) 104 may communicate via an F1 interface according to a protocol that defines signaling messages (e.g., an F1 AP protocol) . Additionally, or alternatively, the CU 160 may communicate with the core network 130 via an interface, which may be an example of a portion of a backhaul link, and may communicate with other CUs (e.g., including a CU 160 associated with an alternative IAB donor) via an Xn-C interface, which may be an example of another portion of a backhaul link.

IAB node (s) 104 may refer to RAN nodes that provide IAB functionality (e.g., access for UEs 115, wireless self-backhauling capabilities) . A DU 165 may act as a distributed scheduling node towards child nodes associated with the IAB node (s) 104, and the IAB-MT may act as a scheduled node towards parent nodes associated with IAB node (s) 104. That is, an IAB donor may be referred to as a parent node in communication with one or more child nodes (e.g., an IAB donor may relay transmissions for UEs through other IAB node (s) 104) . Additionally, or alternatively, IAB node (s) 104 may also be referred to as parent nodes or child nodes to other IAB node (s) 104, depending on the relay chain or configuration of the AN. The IAB-MT entity of IAB node (s) 104 may provide a Uu interface for a child IAB node (e.g., the IAB node (s) 104) to receive signaling from a parent IAB node (e.g., the IAB node (s) 104) , and a DU interface (e.g., a DU 165) may provide a Uu interface for a parent IAB node to signal to a child IAB node or UE 115.

For example, IAB node (s) 104 may be referred to as parent nodes that support communications for child IAB nodes, or may be referred to as child IAB nodes associated with IAB donors, or both. An IAB donor may include a CU 160 with a wired or wireless connection (e.g., backhaul communication link (s) 120) to the core network 130 and may act as a parent node to IAB node (s) 104. For example, the DU 165 of an IAB donor may relay transmissions to UEs 115 through IAB node (s) 104, or may directly signal transmissions to a UE 115, or both. The CU 160 of the IAB donor may signal communication link establishment via an F1 interface to IAB node (s) 104, and the IAB node (s) 104 may schedule transmissions (e.g., transmissions to the UEs 115 relayed from the IAB donor) through one or more DUs (e.g., DUs 165) . That is, data may be relayed to and from IAB node (s) 104 via signaling via an NR Uu interface to MT of IAB node (s) 104 (e.g., other IAB node (s) ) . Communications with IAB node (s) 104 may be scheduled by a DU 165 of the IAB donor or of IAB node (s) 104.

In the case of the techniques described herein applied in the context of a disaggregated RAN architecture, one or more components of the disaggregated RAN architecture may be configured to support test as described herein. For example, some operations described as being performed by a UE 115 or a network entity 105 (e.g., a base station 140) may additionally, or alternatively, be performed by one or more components of the disaggregated RAN architecture (e.g., components such as an IAB node, a DU 165, a CU 160, an RU 170, an RIC 175, an SMO system 180) .

A UE 115 may include or may be referred to as a mobile device, a wireless device, a remote device, a handheld device, or a subscriber device, or some other suitable terminology, where the “device” may also be referred to as a unit, a station, a terminal, or a client, among other examples. A UE 115 may also include or may be referred to as a personal electronic device such as a cellular phone, a personal digital assistant (PDA) , a tablet computer, a laptop computer, or a personal computer. In some examples, a UE 115 may include or be referred to as a wireless local loop (WLL) station, an Internet of Things (IoT) device, an Internet of Everything (IoE) device, or a machine type communications (MTC) device, among other examples, which may be implemented in various objects such as appliances, vehicles, or meters, among other examples.

The UEs 115 described herein may be able to communicate with various types of devices, such as UEs 115 that may sometimes operate as relays, as well as the network entities 105 and the network equipment including macro eNBs or gNBs, small cell eNBs or gNBs, or relay base stations, among other examples, as shown in FIG. 1.

The UEs 115 and the network entities 105 may wirelessly communicate with one another via the communication link (s) 125 (e.g., one or more access links) using resources associated with one or more carriers. The term “carrier” may refer to a set of RF spectrum resources having a defined PHY layer structure for supporting the communication link (s) 125. For example, a carrier used for the communication link (s) 125 may include a portion of an RF spectrum band (e.g., a bandwidth part (BWP) ) that is operated according to one or more PHY layer channels for a given RAT (e.g., LTE, LTE-A, LTE-A Pro, NR) . Each PHY layer channel may carry acquisition signaling (e.g., synchronization signals, system information) , control signaling that coordinates operation for the carrier, user data, or other signaling. The wireless communication system 100 may support communication with a UE 115 using carrier aggregation or multi-carrier operation. A UE 115 may be configured with multiple downlink component carriers and one or more uplink component carriers according to a carrier aggregation configuration. Carrier aggregation may be used with both frequency division duplexing (FDD) and time division duplexing (TDD) component carriers. Communication between a network entity 105 and other devices may refer to communication between the devices and any portion (e.g., entity, sub-entity) of a network entity 105. For example, the terms “transmitting, ” “receiving, ” or “communicating, ” when referring to a network entity 105, may refer to any portion of a network entity 105 (e.g., a base station 140, a CU 160, a DU 165, a RU 170) of a RAN communicating with another device (e.g., directly or via one or more other network entities, such as one or more of the network entities 105) .

In some examples, such as in a carrier aggregation configuration, a carrier may have acquisition signaling or control signaling that coordinates operations for other carriers. A carrier may be associated with a frequency channel (e.g., an evolved universal mobile telecommunication system terrestrial radio access (E-UTRA) absolute RF channel number (EARFCN) ) and may be identified according to a channel raster for discovery by the UEs 115. A carrier may be operated in a standalone mode, in which case initial acquisition and connection may be conducted by the UEs 115 via the carrier, or the carrier may be operated in a non-standalone mode, in which case a connection is anchored using a different carrier (e.g., of the same or a different RAT) .

The communication link (s) 125 of the wireless communication system 100 may include downlink transmissions (e.g., forward link transmissions) from a network entity 105 to a UE 115, uplink transmissions (e.g., return link transmissions) from a UE 115 to a network entity 105, or both, among other configurations of transmissions. Carriers may carry downlink or uplink communications (e.g., in an FDD mode) or may be configured to carry downlink and uplink communications (e.g., in a TDD mode) .

A carrier may be associated with a particular bandwidth of the RF spectrum and, in some examples, the carrier bandwidth may be referred to as a “system bandwidth” of the carrier or the wireless communication system 100. For example, the carrier bandwidth may be one of a set of bandwidths for carriers of a particular RAT (e.g., 1.4, 3, 5, 10, 15, 20, 40, or 80 megahertz (MHz) ) . Devices of the wireless communication system 100 (e.g., the network entities 105, the UEs 115, or both) may have hardware configurations that support communications using a particular carrier bandwidth or may be configurable to support communications using one of a set of carrier bandwidths. In some examples, the wireless communication system 100 may include network entities 105 or UEs 115 that support concurrent communications using carriers associated with multiple carrier bandwidths. In some examples, each served UE 115 may be configured for operating using portions (e.g., a sub-band, a BWP) or all of a carrier bandwidth.

Signal waveforms transmitted via a carrier may be made up of multiple subcarriers (e.g., using multi-carrier modulation (MCM) techniques such as orthogonal frequency division multiplexing (OFDM) or discrete Fourier transform spread OFDM (DFT-S-OFDM) ) . In a system employing MCM techniques, a resource element may refer to resources of one symbol period (e.g., a duration of one modulation symbol) and one subcarrier, in which case the symbol period and subcarrier spacing may be inversely related. The quantity of bits carried by each resource element may depend on the modulation scheme (e.g., the order of the modulation scheme, the coding rate of the modulation scheme, or both) , such that a relatively higher quantity of resource elements (e.g., in a transmission duration) and a relatively higher order of a modulation scheme may correspond to a relatively higher rate of communication. A wireless communications resource may refer to a combination of an RF spectrum resource, a time resource, and a spatial resource (e.g., a spatial layer, a beam) , and the use of multiple spatial resources may increase the data rate or data integrity for communications with a UE 115.

One or more numerologies for a carrier may be supported, and a numerology may include a subcarrier spacing (Δf) and a cyclic prefix. A carrier may be divided into one or more BWPs having the same or different numerologies. In some examples, a UE 115 may be configured with multiple BWPs. In some examples, a single BWP for a carrier may be active at a given time and communications for the UE 115 may be restricted to one or more active BWPs.

The time intervals for the network entities 105 or the UEs 115 may be expressed in multiples of a basic time unit which may, for example, refer to a sampling period of T_s=1/ (Δf_max·N_f) seconds, for which Δf_max may represent a supported subcarrier spacing, and N_f may represent a supported discrete Fourier transform (DFT) size. Time intervals of a communications resource may be organized according to radio frames each having a specified duration (e.g., 10 milliseconds (ms) ) . Each radio frame may be identified by a system frame number (SFN) (e.g., ranging from 0 to 1023) .

Each frame may include multiple consecutively-numbered subframes or slots, and each subframe or slot may have the same duration. In some examples, a frame may be divided (e.g., in the time domain) into subframes, and each subframe may be further divided into a quantity of slots. Alternatively, each frame may include a variable quantity of slots, and the quantity of slots may depend on subcarrier spacing. Each slot may include a quantity of symbol periods (e.g., depending on the length of the cyclic prefix prepended to each symbol period) . In some wireless communication systems, such as the wireless communication system 100, a slot may further be divided into multiple mini-slots associated with one or more symbols. Excluding the cyclic prefix, each symbol period may be associated with one or more (e.g., N_f) sampling periods. The duration of a symbol period may depend on the subcarrier spacing or frequency band of operation.

A subframe, a slot, a mini-slot, or a symbol may be the smallest scheduling unit (e.g., in the time domain) of the wireless communication system 100 and may be referred to as a transmission time interval (TTI) . In some examples, the TTI duration (e.g., a quantity of symbol periods in a TTI) may be variable. Additionally, or alternatively, the smallest scheduling unit of the wireless communication system 100 may be dynamically selected (e.g., in bursts of shortened TTIs (sTTIs) ) .

Physical channels may be multiplexed for communication using a carrier according to various techniques. A physical control channel and a physical data channel may be multiplexed for signaling via a downlink carrier, for example, using one or more of time division multiplexing (TDM) techniques, frequency division multiplexing (FDM) techniques, or hybrid TDM-FDM techniques. A control region (e.g., a control resource set (CORESET) ) for a physical control channel may be defined by a set of symbol periods and may extend across the system bandwidth or a subset of the system bandwidth of the carrier. One or more control regions (e.g., CORESETs) may be configured for a set of the UEs 115. For example, one or more of the UEs 115 may monitor or search control regions for control information according to one or more search space sets, and each search space set may include one or multiple control channel candidates in one or more aggregation levels arranged in a cascaded manner. An aggregation level for a control channel candidate may refer to an amount of control channel resources (e.g., control channel elements (CCEs) ) associated with encoded information for a control information format having a given payload size. Search space sets may include common search space sets configured for sending control information to UEs 115 (e.g., one or more UEs) or may include UE-specific search space sets for sending control information to a UE 115 (e.g., a specific UE) .

A network entity 105 may provide communication coverage via one or more cells, for example a macro cell, a small cell, a hot spot, or other types of cells, or any combination thereof. The term “cell” may refer to a logical communication entity used for communication with a network entity 105 (e.g., using a carrier) and may be associated with an identifier for distinguishing neighboring cells (e.g., a physical cell identifier (PCID) , a virtual cell identifier (VCID) ) . In some examples, a cell also may refer to a coverage area 110 or a portion of a coverage area 110 (e.g., a sector) over which the logical communication entity operates. Such cells may range from smaller areas (e.g., a structure, a subset of structure) to larger areas depending on various factors such as the capabilities of the network entity 105. For example, a cell may be or include a building, a subset of a building, or exterior spaces between or overlapping with coverage areas 110, among other examples.

A macro cell generally covers a relatively large geographic area (e.g., several kilometers in radius) and may allow unrestricted access by the UEs 115 with service subscriptions with the network provider supporting the macro cell. A small cell may be associated with a network entity 105 operating with lower power (e.g., a base station 140 operating with lower power) relative to a macro cell, and a small cell may operate using the same or different (e.g., licensed, unlicensed) frequency bands as macro cells. Small cells may provide unrestricted access to the UEs 115 with service subscriptions with the network provider or may provide restricted access to the UEs 115 having an association with the small cell (e.g., the UEs 115 in a closed subscriber group (CSG) , the UEs 115 associated with users in a home or office) . A network entity 105 may support one or more cells and may also support communications via the one or more cells using one or multiple component carriers.

In some examples, a carrier may support multiple cells, and different cells may be configured according to different protocol types (e.g., MTC, narrowband IoT (NB-IoT) , enhanced mobile broadband (eMBB) ) that may provide access for different types of devices.

In some examples, a network entity 105 (e.g., a base station 140, an RU 170) may be movable and therefore provide communication coverage for a moving coverage area, such as the coverage area 110. In some examples, coverage areas 110 (e.g., different coverage areas) associated with different technologies may overlap, but the coverage areas 110 (e.g., different coverage areas) may be supported by the same network entity (e.g., a network entity 105) . In some other examples, overlapping coverage areas, such as a coverage area 110, associated with different technologies may be supported by different network entities (e.g., the network entities 105) . The wireless communication system 100 may include, for example, a heterogeneous network in which different types of the network entities 105 support communications for coverage areas 110 (e.g., different coverage areas) using the same or different RATs.

The wireless communication system 100 may support synchronous or asynchronous operation. For synchronous operation, network entities 105 (e.g., base stations 140) may have similar frame timings, and transmissions from different network entities (e.g., different ones of the network entities 105) may be approximately aligned in time. For asynchronous operation, network entities 105 may have different frame timings, and transmissions from different network entities (e.g., different ones of network entities 105) may, in some examples, not be aligned in time. The techniques described herein may be used for either synchronous or asynchronous operations.

Some UEs 115, such as MTC or IoT devices, may be relatively low cost or low complexity devices and may provide for automated communication between machines (e.g., via Machine-to-Machine (M2M) communication) . M2M communication or MTC may refer to data communication technologies that allow devices to communicate with one another or a network entity 105 (e.g., a base station 140) without human intervention. In some examples, M2M communication or MTC may include communications from devices that integrate sensors or meters to measure or capture information and relay such information to a central server or application program that uses the information or presents the information to humans interacting with the application program. Some UEs 115 may be designed to collect information or enable automated behavior of machines or other devices. Examples of applications for MTC devices include smart metering, inventory monitoring, water level monitoring, equipment monitoring, healthcare monitoring, wildlife monitoring, weather and geological event monitoring, fleet management and tracking, remote security sensing, physical access control, and transaction-based business charging.

Some UEs 115 may be configured to employ operating modes that reduce power consumption, such as half-duplex communications (e.g., a mode that supports one-way communication via transmission or reception, but not transmission and reception concurrently) . In some examples, half-duplex communications may be performed at a reduced peak rate. Other power conservation techniques for the UEs 115 may include entering a power saving deep sleep mode when not engaging in active communications, operating using a limited bandwidth (e.g., according to narrowband communications) , or a combination of these techniques. For example, some UEs 115 may be configured for operation using a narrowband protocol type that is associated with a defined portion or range (e.g., set of subcarriers or resource blocks (RBs) ) within a carrier, within a guard-band of a carrier, or outside of a carrier.

The wireless communication system 100 may be configured to support ultra-reliable communications or low-latency communications, or various combinations thereof. For example, the wireless communication system 100 may be configured to support ultra-reliable low-latency communications (URLLC) . The UEs 115 may be designed to support ultra-reliable, low-latency, or critical functions. Ultra-reliable communications may include private communication or group communication and may be supported by one or more services such as push-to-talk, video, or data. Support for ultra-reliable, low-latency functions may include prioritization of services, and such services may be used for public safety or general commercial applications. The terms ultra-reliable, low-latency, and ultra-reliable low-latency may be used interchangeably herein.

In some examples, a UE 115 may be configured to support communicating directly with other UEs (e.g., one or more of the UEs 115) via a device-to-device (D2D) communication link, such as a D2D communication link 135 (e.g., in accordance with a peer-to-peer (P2P) , D2D, or sidelink protocol) . In some examples, one or more UEs 115 of a group that are performing D2D communications may be within the coverage area 110 of a network entity 105 (e.g., a base station 140, an RU 170) , which may support aspects of such D2D communications being configured by (e.g., scheduled by) the network entity 105. In some examples, one or more UEs 115 of such a group may be outside the coverage area 110 of a network entity 105 or may be otherwise unable to or not configured to receive transmissions from a network entity 105. In some examples, groups of the UEs 115 communicating via D2D communications may support a one-to-many (1: M) system in which each UE 115 transmits to one or more of the UEs 115 in the group. In some examples, a network entity 105 may facilitate the scheduling of resources for D2D communications. In some other examples, D2D communications may be carried out between the UEs 115 without an involvement of a network entity 105.

In some systems, a D2D communication link 135 may be an example of a communication channel, such as a sidelink communication channel, between vehicles (e.g., UEs 115) . In some examples, vehicles may communicate using vehicle-to-everything (V2X) communications, vehicle-to-vehicle (V2V) communications, or some combination of these. A vehicle may signal information related to traffic conditions, signal scheduling, weather, safety, emergencies, or any other information relevant to a V2X system. In some examples, vehicles in a V2X system may communicate with roadside infrastructure, such as roadside units, or with the network via one or more network nodes (e.g., network entities 105, base stations 140, RUs 170) using vehicle-to-network (V2N) communications, or with both.

The core network 130 may provide user authentication, access authorization, tracking, Internet Protocol (IP) connectivity, and other access, routing, or mobility functions. The core network 130 may be an evolved packet core (EPC) or 5G core (5GC) , which may include at least one control plane entity that manages access and mobility (e.g., a mobility management entity (MME) , an access and mobility management function (AMF) ) and at least one user plane entity that routes packets or interconnects to external networks (e.g., a serving gateway (S-GW) , a Packet Data Network (PDN) gateway (P-GW) , or a user plane function (UPF) ) . The control plane entity may manage non-access stratum (NAS) functions such as mobility, authentication, and bearer management for the UEs 115 served by the network entities 105 (e.g., base stations 140) associated with the core network 130. User IP packets may be transferred through the user plane entity, which may provide IP address allocation as well as other functions. The user plane entity may be connected to IP services 150 for one or more network operators. The IP services 150 may include access to the Internet, Intranet (s) , an IP Multimedia Subsystem (IMS) , or a Packet-Switched Streaming Service.

The wireless communication system 100 may operate using one or more frequency bands, which may be in the range of 300 megahertz (MHz) to 300 gigahertz (GHz) . Generally, the region from 300 MHz to 3 GHz is known as the ultra-high frequency (UHF) region or decimeter band because the wavelengths range from approximately one decimeter to one meter in length. UHF waves may be blocked or redirected by buildings and environmental features, which may be referred to as clusters, but the waves may penetrate structures sufficiently for a macro cell to provide service to the UEs 115 located indoors. Communications using UHF waves may be associated with smaller antennas and shorter ranges (e.g., less than one hundred kilometers) compared to communications using the smaller frequencies and longer waves of the high frequency (HF) or very high frequency (VHF) portion of the spectrum below 300 MHz.

The wireless communication system 100 may also operate using a super high frequency (SHF) region, which may be in the range of 3 GHz to 30 GHz, also known as the centimeter band, or using an extremely high frequency (EHF) region of the spectrum (e.g., from 30 GHz to 300 GHz) , also known as the millimeter band. In some examples, the wireless communication system 100 may support millimeter wave (mmW) communications between the UEs 115 and the network entities 105 (e.g., base stations 140, RUs 170) , and EHF antennas of the respective devices may be smaller and more closely spaced than UHF antennas. In some examples, such techniques may facilitate using antenna arrays within a device. The propagation of EHF transmissions, however, may be subject to even greater attenuation and shorter range than SHF or UHF transmissions. The techniques disclosed herein may be employed across transmissions that use one or more different frequency regions, and designated use of bands across these frequency regions may differ by country or regulating body.

The wireless communication system 100 may utilize both licensed and unlicensed RF spectrum bands. For example, the wireless communication system 100 may employ License Assisted Access (LAA) , LTE-Unlicensed (LTE-U) RAT, or NR technology using an unlicensed band such as the 5 GHz industrial, scientific, and medical (ISM) band. While operating using unlicensed RF spectrum bands, devices such as the network entities 105 and the UEs 115 may employ carrier sensing for collision detection and avoidance. In some examples, operations using unlicensed bands may be based on a carrier aggregation configuration in conjunction with component carriers operating using a licensed band (e.g., LAA) . Operations using unlicensed spectrum may include downlink transmissions, uplink transmissions, P2P transmissions, or D2D transmissions, among other examples.

A network entity 105 (e.g., a base station 140, an RU 170) or a UE 115 may be equipped with multiple antennas, which may be used to employ techniques such as transmit diversity, receive diversity, multiple-input multiple-output (MIMO) communications, or beamforming. The antennas of a network entity 105 or a UE 115 may be located within one or more antenna arrays or antenna panels, which may support MIMO operations or transmit or receive beamforming. For example, one or more base station antennas or antenna arrays may be co-located at an antenna assembly, such as an antenna tower. In some examples, antennas or antenna arrays associated with a network entity 105 may be located at diverse geographic locations. A network entity 105 may include an antenna array with a set of rows and columns of antenna ports that the network entity 105 may use to support beamforming of communications with a UE 115. Likewise, a UE 115 may include one or more antenna arrays that may support various MIMO or beamforming operations. Additionally, or alternatively, an antenna panel may support RF beamforming for a signal transmitted via an antenna port.

The network entities 105 or the UEs 115 may use MIMO communications to exploit multipath signal propagation and increase spectral efficiency by transmitting or receiving multiple signals via different spatial layers. Such techniques may be referred to as spatial multiplexing. The multiple signals may, for example, be transmitted by the transmitting device via different antennas or different combinations of antennas. Likewise, the multiple signals may be received by the receiving device via different antennas or different combinations of antennas. Each of the multiple signals may be referred to as a separate spatial stream and may carry information associated with the same data stream (e.g., the same codeword) or different data streams (e.g., different codewords) . Different spatial layers may be associated with different antenna ports used for channel measurement and reporting. MIMO techniques include single-user MIMO (SU-MIMO) , for which multiple spatial layers are transmitted to the same receiving device, and multiple-user MIMO (MU-MIMO) , for which multiple spatial layers are transmitted to multiple devices.

Beamforming, which may also be referred to as spatial filtering, directional transmission, or directional reception, is a signal processing technique that may be used at a transmitting device or a receiving device (e.g., a network entity 105, a UE 115) to shape or steer an antenna beam (e.g., a transmit beam, a receive beam) along a spatial path between the transmitting device and the receiving device. Beamforming may be achieved by combining the signals communicated via antenna elements of an antenna array such that some signals propagating along particular orientations with respect to an antenna array experience constructive interference while others experience destructive interference. The adjustment of signals communicated via the antenna elements may include a transmitting device or a receiving device applying amplitude offsets, phase offsets, or both to signals carried via the antenna elements associated with the device. The adjustments associated with each of the antenna elements may be defined by a beamforming weight set associated with a particular orientation (e.g., with respect to the antenna array of the transmitting device or receiving device, or with respect to some other orientation) .

A network entity 105 or a UE 115 may use beam sweeping techniques as part of beamforming operations. For example, a network entity 105 (e.g., a base station 140, an RU 170) may use multiple antennas or antenna arrays (e.g., antenna panels) to conduct beamforming operations for directional communications with a UE 115. Some signals (e.g., synchronization signals, reference signals, beam selection signals, or other control signals) may be transmitted by a network entity 105 multiple times along different directions. For example, the network entity 105 may transmit a signal according to different beamforming weight sets associated with different directions of transmission. Transmissions along different beam directions may be used to identify (e.g., by a transmitting device, such as a network entity 105, or by a receiving device, such as a UE 115) a beam direction for later transmission or reception by the network entity 105.

Some signals, such as data signals associated with a particular receiving device, may be transmitted by a transmitting device (e.g., a network entity 105 or a UE 115) along a single beam direction (e.g., a direction associated with the receiving device, such as another network entity 105 or UE 115) . In some examples, the beam direction associated with transmissions along a single beam direction may be determined based on a signal that was transmitted along one or more beam directions. For example, a UE 115 may receive one or more of the signals transmitted by the network entity 105 along different directions and may report to the network entity 105 an indication of the signal that the UE 115 received with a highest signal quality or an otherwise acceptable signal quality.

In some examples, transmissions by a device (e.g., by a network entity 105 or a UE 115) may be performed using multiple beam directions, and the device may use a combination of digital precoding or beamforming to generate a combined beam for transmission (e.g., from a network entity 105 to a UE 115) . The UE 115 may report feedback that indicates precoding weights for one or more beam directions, and the feedback may correspond to a configured set of beams across a system bandwidth or one or more sub-bands. The network entity 105 may transmit a reference signal (e.g., a cell-specific reference signal (CRS) , a channel state information reference signal (CSI-RS) ) , which may be precoded or unprecoded. The UE 115 may provide feedback for beam selection, which may be a precoding matrix indicator (PMI) or codebook-based feedback (e.g., a multi-panel type codebook, a linear combination type codebook, a port selection type codebook) . Although these techniques are described with reference to signals transmitted along one or more directions by a network entity 105 (e.g., a base station 140, an RU 170) , a UE 115 may employ similar techniques for transmitting signals multiple times along different directions (e.g., for identifying a beam direction for subsequent transmission or reception by the UE 115) or for transmitting a signal along a single direction (e.g., for transmitting data to a receiving device) .

A receiving device (e.g., a UE 115) may perform reception operations in accordance with multiple receive configurations (e.g., directional listening) when receiving various signals from a transmitting device (e.g., a network entity 105) , such as synchronization signals, reference signals, beam selection signals, or other control signals. For example, a receiving device may perform reception in accordance with multiple receive directions by receiving via different antenna subarrays, by processing received signals according to different antenna subarrays, by receiving according to different receive beamforming weight sets (e.g., different directional listening weight sets) applied to signals received at multiple antenna elements of an antenna array, or by processing received signals according to different receive beamforming weight sets applied to signals received at multiple antenna elements of an antenna array, any of which may be referred to as “listening” according to different receive configurations or receive directions. In some examples, a receiving device may use a single receive configuration to receive along a single beam direction (e.g., when receiving a data signal) . The single receive configuration may be aligned along a beam direction determined based on listening according to different receive configuration directions (e.g., a beam direction determined to have a highest signal strength, highest SNR, or otherwise acceptable signal quality based on listening according to multiple beam directions) .

The wireless communication system 100 may be a packet-based network that operates according to a layered protocol stack. In the user plane, communications at the bearer or PDCP layer may be IP-based. An RLC layer may perform packet segmentation and reassembly to communicate via logical channels. A MAC layer may perform priority handling and multiplexing of logical channels into transport channels. The MAC layer also may implement error detection techniques, error correction techniques, or both to support retransmissions to improve link efficiency. In the control plane, an RRC layer may provide establishment, configuration, and maintenance of an RRC connection between a UE 115 and a network entity 105 or a core network 130 supporting radio bearers for user plane data. A PHY layer may map transport channels to physical channels.

The UEs 115 and the network entities 105 may support retransmissions of data to increase the likelihood that data is received successfully. Hybrid automatic repeat request (HARQ) feedback is one technique for increasing the likelihood that data is received correctly via a communication link (e.g., the communication link (s) 125, a D2D communication link 135) . HARQ may include a combination of error detection (e.g., using a cyclic redundancy check (CRC) ) , forward error correction (FEC) , and retransmission (e.g., automatic repeat request (ARQ) ) . HARQ may improve throughput at the MAC layer in relatively poor radio conditions (e.g., low signal-to-noise conditions) . In some examples, a device may support same-slot HARQ feedback, in which case the device may provide HARQ feedback in a specific slot for data received via a previous symbol in the slot. In some other examples, the device may provide HARQ feedback in a subsequent slot, or according to some other time interval.

As described herein, a node, which may be referred to as a node, a network node, a network entity, or a wireless node, may be a base station (e.g., any base station described herein) , a UE (e.g., any UE described herein) , a network controller, an apparatus, a device, a computing system, one or more components, and/or another suitable processing entity configured to perform any of the techniques described herein. For example, a network node may be a UE. As another example, a network node may be a base station. As another example, a first network node may be configured to communicate with a second network node or a third network node. In one aspect of this example, the first network node may be a UE, the second network node may be a base station, and the third network node may be a UE. In another aspect of this example, the first network node may be a UE, the second network node may be a base station, and the third network node may be a base station. In yet other aspects of this example, the first, second, and third network nodes may be different relative to these examples. Similarly, reference to a UE, base station, apparatus, device, computing system, or the like may include disclosure of the UE, base station, apparatus, device, computing system, or the like being a network node. For example, disclosure that a UE is configured to receive information from a base station also discloses that a first network node is configured to receive information from a second network node. Consistent with this disclosure, once a specific example is broadened in accordance with this disclosure (e.g., a UE is configured to receive information from a base station also discloses that a first network node is configured to receive information from a second network node) , the broader example of the narrower example may be interpreted in the reverse, but in a broad open-ended way. In the example above where a UE being configured to receive information from a base station also discloses that a first network node being configured to receive information from a second network node, the first network node may refer to a first UE, a first base station, a first apparatus, a first device, a first computing system, a first one or more components, a first processing entity, or the like configured to receive the information; and the second network node may refer to a second UE, a second base station, a second apparatus, a second device, a second computing system, a second one or more components, a second processing entity, or the like.

As described herein, communication of information (e.g., any information, signal, or the like) may be described in various aspects using different terminology. Disclosure of one communication term includes disclosure of other communication terms. For example, a first network node may be described as being configured to transmit information to a second network node. In this example and consistent with this disclosure, disclosure that the first network node is configured to transmit information to the second network node includes disclosure that the first network node is configured to provide, send, output, communicate, or transmit information to the second network node. Similarly, in this example and consistent with this disclosure, disclosure that the first network node is configured to transmit information to the second network node includes disclosure that the second network node is configured to receive, obtain, or decode the information that is provided, sent, output, communicated, or transmitted by the first network node.

The electromagnetic spectrum is often subdivided, based on frequency/wavelength, into various classes, bands, channels, etc. In 5G NR two initial operating bands have been identified as frequency range designations FR1 (410 MHz –7.125 GHz) and FR2 (24.25 GHz –52.6 GHz) . It should be understood that although a portion of FR1 is greater than 6 GHz, FR1 is often referred to (interchangeably) as a “Sub-6 GHz” band in various documents and articles. A similar nomenclature issue sometimes occurs with regard to FR2, which is often referred to (interchangeably) as a “millimeter wave” band in documents and articles, despite being different from the extremely high frequency (EHF) band (30 GHz –300 GHz) which is identified by the International Telecommunications Union (ITU) as a “millimeter wave” band.

The frequencies between FR1 and FR2 are often referred to as mid-band frequencies. Recent 5G NR studies have identified an operating band for these mid-band frequencies as frequency range designation FR3 (7.125 GHz –24.25 GHz) . Frequency bands falling within FR3 may inherit FR1 characteristics and/or FR2 characteristics, and thus may effectively extend features of FR1 and/or FR2 into mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operation beyond 52.6 GHz. For example, three higher operating bands have been identified as frequency range designations FR4a or FR4–1 (52.6 GHz –71 GHz) , FR4 (52.6 GHz –114.25 GHz) , and FR5 (114.25 GHz –300 GHz) . Each of these higher frequency bands falls within the EHF band.

With the above aspects in mind, unless specifically stated otherwise, it should be understood that the term “sub-6 GHz” or the like if used herein may broadly represent frequencies that may be less than 6 GHz, may be within FR1, or may include mid-band frequencies. Further, unless specifically stated otherwise, it should be understood that the term “millimeter wave” or the like if used herein may broadly represent frequencies that may include mid-band frequencies, may be within FR2, FR4, FR4-a or FR4–1, and/or FR5, or may be within the EHF band.

Techniques described herein, in addition to or as an alternative to be carried out between UEs 115 and network entities 105, may be implemented via additional or alternative wireless devices, including IAB nodes 104, distributed units (DUs) 165, centralized units (CUs) 160, radio units (RUs) 170, and the like. For example, in some implementations, aspects described herein may be implemented in the context of a disaggregated radio access network (RAN) architecture (e.g., open RAN architecture) . In a disaggregated architecture, the RAN may be split into three areas of functionality corresponding to the CU 160, the DU 165, and the RU 170. The split of functionality between the CU 160, DU 165, and RU 170 is flexible and as such gives rise to numerous permutations of different functionalities depending upon which functions (e.g., MAC functions, baseband functions, radio frequency functions, and any combinations thereof) are performed at the CU 160, DU 165, and RU 170. For example, a functional split of the protocol stack may be employed between a DU 165 and an RU 170 such that the DU 165 may support one or more layers of the protocol stack and the RU 170 may support one or more different layers of the protocol stack.

Some wireless communication systems (e.g., wireless communication system 100) , infrastructure and spectral resources for NR access may additionally support wireless backhaul link capabilities in supplement to wireline backhaul connections, providing an IAB network architecture. One or more network entities 105 may include CUs 160, DUs 165, and RUs 170 and may be referred to as donor network entities 105 or IAB donors. One or more DUs 165 (e.g., and/or RUs 170) associated with a donor network entity 105 may be partially controlled by CUs 160 associated with the donor network entity 105. The one or more donor network entities 105 (e.g., IAB donors) may be in communication with one or more additional network entities 105 (e.g., IAB nodes 104) via supported access and backhaul links. IAB nodes 104 may support mobile terminal (MT) functionality controlled and/or scheduled by DUs 165 of a coupled IAB donor. In addition, the IAB nodes 104 may include DUs 165 that support communication links with additional entities (e.g., IAB nodes 104, UEs 115, etc. ) within the relay chain or configuration of the access network (e.g., downstream) . In such cases, one or more components of the disaggregated RAN architecture (e.g., one or more IAB nodes 104 or components of IAB nodes 104) may be configured to operate according to the techniques described herein.

In some examples, the wireless communication system 100 may include a core network 130 (e.g., a next generation core network (NGC) ) , one or more IAB donors, IAB nodes 104, and UEs 115, where IAB nodes 104 may be partially controlled by each other and/or the IAB donor. The IAB donor and IAB nodes 104 may be examples of aspects of network entities 105. IAB donor and one or more IAB nodes 104 may be configured as (e.g., or in communication according to) some relay chain.

For instance, an access network (AN) or RAN may refer to communications between access nodes (e.g., IAB donor) , IAB nodes 104, and one or more UEs 115. The IAB donor may facilitate connection between the core network 130 and the AN (e.g., via a wireline or wireless connection to the core network 130) . That is, an IAB donor may refer to a RAN node with a wireline or wireless connection to core network 130. The IAB donor may include a CU 160 and at least one DU 165 (e.g., and RU 170) , where the CU 160 may communicate with the core network 130 over an NG interface (e.g., some backhaul link) . The CU 160 may host L3 (e.g., RRC, service data adaption protocol (SDAP) , PDCP, etc. ) functionality and signaling. The at least one DU 165 and/or RU 170 may host lower layer, such as L1 and L2 (e.g., RLC, MAC, physical (PHY) , etc. ) functionality and signaling, and may each be at least partially controlled by the CU 160. The DU 165 may support one or multiple different cells. IAB donor and IAB nodes 104 may communicate over an F1 interface according to some protocol that defines signaling messages (e.g., F1 AP protocol) . Additionally, CU 160 may communicate with the core network over an NG interface (which may be an example of a portion of backhaul link) , and may communicate with other CUs 160 (e.g., a CU 160 associated with an alternative IAB donor) over an Xn-C interface (which may be an example of a portion of a backhaul link) .

IAB nodes 104 may refer to a RAN node that provides IAB functionality (e.g., access for UEs 115, wireless self-backhauling capabilities, etc. ) . IAB nodes 104 may include a DU 165 and an MT. A DU 165 may act as a distributed scheduling node towards child nodes associated with the IAB node 104, and the MT may act as a scheduled node towards parent nodes associated with the IAB node 104. That is, an IAB donor may be referred to as a parent node in communication with one or more child nodes (e.g., an IAB donor may relay transmissions for UEs through one or more other IAB nodes 104) . Additionally, an IAB node 104 may also be referred to as a parent node or a child node to other IAB nodes 104, depending on the relay chain or configuration of the AN. Therefore, the MT entity of IAB nodes 104 (e.g., MTs) may provide a Uu interface for a child node to receive signaling from a parent IAB node 104, and the DU interface (e.g., DUs 165) may provide a Uu interface for a parent node to signal to a child IAB node 104 or UE 115.

For example, IAB node 104 may be referred to a parent node associated with IAB node, and a child node associated with IAB donor. The IAB donor may include a CU 160 with a wireline (e.g., optical fiber) or wireless connection to the core network and may act as parent node to IAB nodes 104. For example, the DU 165 of IAB donor may relay transmissions to UEs 115 through IAB nodes 104, and may directly signal transmissions to a UE 115. The CU 160 of IAB donor may signal communication link establishment via an F1 interface to IAB nodes 104, and the IAB nodes 104 may schedule transmissions (e.g., transmissions to the UEs 115 relayed from the IAB donor) through the DUs 165. That is, data may be relayed to and from IAB nodes 104 via signaling over an NR Uu interface to MT of the IAB node 104. Communications with IAB node 104 may be scheduled by DU 165 of IAB donor and communications with IAB node 104 may be scheduled by DU 165 of IAB node 104.

In the case of the techniques described herein applied in the context of a disaggregated RAN architecture, one or more components of the disaggregated RAN architecture (e.g., one or more IAB nodes 104 or components of IAB nodes 104) may be configured to support techniques for large round trip times in random access channel procedures as described herein. For example, some operations described as being performed by a UE 115 or a network entity 105 may additionally or alternatively be performed by components of the disaggregated RAN architecture (e.g., IAB nodes, DUs, CUs, etc. ) .

Some wireless communication systems may utilize AI (e.g., ML) models for performing one or more functionalities. An AI model (e.g., ML model) is a structure capable of being trained using data (e.g., input data, ground truth data) to predict one or more outputs. To be effective, models may be deployed in application scenarios that are similar to one or more training scenarios. For wireless communication systems, for example, a model may provide accurate outputs (e.g., predictions, inferences) when the model is utilized in an application scenario that is similar to a training scenario. For uniform performance of a model, for instance, some parameters (e.g., beamforming codebooks, QCL relationships, quantity and ordering of beams, among other examples) , may be maintained from a training scenario to an application (e.g., runtime or prediction) scenario.

Some approaches to utilizing models for wireless communication may train various models for different application scenarios with varying parameters, where the models may be explicitly identified for global use. For example, data collected from a cell may be utilized to train an identified model, which model could be utilized for inferencing at another cell that is located in a different region or location relatively far from the cell utilized to train the model, and parameter consistency may be ensured by network vendors for the same model identification. In some cases, global model identification could be utilized to reverse engineer network-side information. If models are explicitly identified by networks, model identifiers may be utilized to determine one or more regions or locations that are deployed based on the same codebooks, antenna panels, or radio algorithms, among other examples. Accordingly, explicit and globally applicable model identifiers may indirectly expose one or more characteristics of a network.

Some other approaches may avoid explicitly identifying models by relying on a UE 115 to receive reference signaling from the network (e.g., network entity 105) . A reference signal is a signal with established properties (e.g., frequency, amplitude, timing, codes, among other examples) communicated between devices. Reference signals may be utilized to perform resource allocation or selection, power control, tracking, channel estimation, or one or more other functions. In some examples, one or more reference signals may communicated via a downlink beam. A beam may be RF energy that is directionally shaped or focused. A downlink beam is a beam provided from a cell or network entity 105 to a UE 115.

The UE 115 may execute candidate models using the reference signaling to evaluate the performance of one or more candidate models, and may select a best performing model. A candidate model is an AI model that may be selected to perform one or more functionalities. For instance, a UE 115 may receive auxiliary reference signals via one or more beams such that the UE 115 can derive prediction performance metrics for one or more candidate models to determine whether to activate an AI-based functionality (e.g., beam prediction) or whether to select a model (s) for use. A metric may be a value, quantity, or indication associated with model performance. For instance, a metric may indicate a channel quality when performing beam prediction or another operation.

In some approaches, reference signals may be transmitted from beams (e.g., set A beams and set B beams) relatively frequently before model activation. A UE may determine measurements (e.g., L1-RSRPs) from the reference signals. The reference signals (e.g., measurements of the reference signals from set B beams, L1-RSRPs, among other examples) may be input to the candidate models to produce beam predictions (e.g., set A beam predictions) . Measurements from the set A beams may be compared with predictions of the set A beams to determine metrics (e.g., differences) for each of the candidate models. The metrics may be utilized to select a candidate model with the best performance. The selected model may be activated for communication with (e.g., for receiving downlink signals from) the cell.

Using reference signaling and performance monitoring (e.g., metric evaluation) of candidate models for UE-side model activation or model selection may consume a relatively large amount of overhead signaling, processing resources, or time (e.g., latency) if the reference signaling or monitoring is performed repetitively for multiple cells. A cell is a communication resource provided by a network entity. For example, a cell may provide transmission or reception of one or more wireless signals. In some cases, a cell may be associated with a geographic area (e.g., a coverage area 110) . In some commercial deployments, a significant amount of neighboring cells may use similar setups including antenna height, down-tilt angle, antenna panels, or beamforming codebooks (e.g., identical codebooks and panels) . If the UE 115 carries out performance monitoring based model initialization or selection each time a cell switch occurs, resources may be wasted because a model may be applicable across such neighboring cells. Accordingly, explicit identification of models may expose network information, and using performance monitoring for model selection (to avoid explicit model identification) may waste a significant quantity of processing, communication resources, or time.

Some examples of the techniques described herein may reduce overhead resource consumption while protecting network-side information (e.g., proprietary setup information) . To avoid use of an explicit model identification, a UE 115 may receive reference signaling and evaluate the performance of one or more candidate models for an initial cell. For instance, the UE 115 may generate a metric by inputting one or more reference signals to one or more candidate AI models. In an example, each candidate AI model may predict a beam based on the reference signal (s) . In some approaches, a metric may be generated for each candidate AI model (e.g., each predicted beam) . Metrics may be compared to select an AI model associated with a best metric (e.g., highest channel quality value, highest SNR, lowest noise, or other metric) . The selected AI model may be utilized for receiving a signal. For instance, the AI model may be utilized to perform beam prediction, where a signal is communicated (e.g., received) via the selected beam.

In some cases, a UE 115 may switch between cells (e.g., from a source cell to a target cell) . Switching between cells may occur when a UE 115 establishes a connection with a target cell. In some examples, cell switching may be performed via an upper-layer handover procedure (e.g., handover via signaling performed at L3) or via a lower-layer switch procedure (e.g., an LTM switch procedure or a cell switch performed at L1 or L2) . In LTM, lower-layer (e.g., L1 or L2) signaling may be utilized to coordinate, command, or indicate when the UE 115 switches between cells.

When switching between cells, the UE 115 may utilize (e.g., reuse) the same model for one or more consecutive cells. For example, the UE 115 may receive an indication associated with an applicability of the selected AI model for one or more cells. In some examples, model applicability for a cell may occur if the model functions or performs similarly for the cell relative to a cell in which the AI model was monitored or selected (or if the AI model would have been selected for the cell based on performance monitoring) . The indication associated with the applicability may be a value, code, or signal that indicates whether an AI model for a cell may be applicable to one or more other cells. The indication may be agnostic to the AI model. For example, the indication may be agnostic to the AI model in that the indication may not explicitly identify an AI model.

In some examples, the indication may indicate whether the AI model is applicable to another cell, may identify one or more cells where the AI model is applicable, may identify one or more functionalities associated with the AI model, may include a zone identifier for the applicability of the AI model, may include a command for utilization of the model, may include a virtual identifier (e.g., an identifier that is not associated with a particular model) , may identify information (e.g., a dataset, a configuration, a codebook, or a deployment, among other examples) that may indicate applicability, may be based on whether a quantity of beams is identical between cells, may be based on whether functionalities are active between cells, or a combination thereof. A functionality may be a functionality provided by an AI model. Examples of functionality may include spatial beam prediction, temporal beam prediction, spatial-temporal beam prediction, CSI compression, CSI prediction, or a combination thereof. In some examples, a configuration message (e.g., a message indicating a configuration for CSI reporting) may be utilized to determine whether to reuse the model after a switch (e.g., when the CSI reporting configuration of a target cell matches the CSI reporting configuration of a source cell) . When the UE 115 switches to a cell where the AI model is applicable (in accordance with the indication) , the UE 115 may reuse the AI model in the target cell without reevaluating the candidate AI model (s) .

Reusing a selected AI model for one or more cells may provide a good tradeoff between network information protection and signaling overhead or power consumption. Some approaches may avoid revealing whether the same network implementations are used for cells that are distant from each other. For instance, globally defined model or dataset identifiers may not be utilized, or model training may be based on datasets collected in various regions or countries via UE 115 vendor implementation choices, rather than worldwide model training. Some examples of the techniques described may allow network vendors flexibility to change codebooks over time (because performance monitoring based model initialization may be used for any cell) , while overhead or power consumption can be reduced. Some examples of the techniques may be applied to cases where new cells are supplied (e.g., DC-CA) without completely switching to another cell.

Some examples of the techniques described herein may account for conditions across cells (e.g., for cell switch examples or examples where a new cell is supplied) . In some approaches, one or more models may be utilized for beam prediction, and signaling procedures (which may be related to CSI reporting procedures in some examples) are provided to inform the UE 115 regarding model continuity across cells. In some examples, one or more models may be implemented on the UE side (e.g., not on the network side) without prior geographical zone identification information. For example, network side signaling may indicate whether the same model can be utilized, while the network is unaware (e.g., does not have identifying information) of the model.

In some examples of the techniques described herein, a model may be trained to perform beam prediction (e.g., spatial domain downlink beam prediction, temporal downlink beam prediction, or spatial-temporal downlink beam prediction) to generate a predicted set of beams (e.g., set A beams) based on measurement results from another set of beams (e.g., set B beams) . For instance, the set B beams may be a set of beams from which measurements may be taken as inputs for the model. In some cases, beams in the set A beams and in the set B beams may be in the same frequency range or in different frequency ranges.

In some approaches, model training and inferencing may be performed on the UE 115 side. In some examples, the set A beams and the set B beams may be different (e.g., the set B beam may not be a subset of the set A beams or the set B beams may be a subset of the set A beams) . In some examples, the set A beams and the set B beams may be the same. The set A beams may be utilized for downlink beam prediction. Codebook construction for the set A beams and the set B beams may vary. In some examples, model input may include measurement results (e.g., measurement results of K (K≥1) latest measurements instances) , L1 reference signal received power (RSRP) measurement based on the set B beams, L1-RSRP measurement based on the set B beams and assistance information, channel impulse response (CIR) based on the set B beams, or L1-RSRP measurement based on the set B beams and the corresponding downlink transmit beam identifier or the receive beam identifier. In some approaches, F predictions for F (F≥1) future time instances may be obtained based on the output of the model, where each prediction may correspond to each time instance.

Some examples of beam prediction may be utilized for beam management. For instance, a UE 115 may perform downlink transmit beam prediction for a UE-side model. In some approaches, spatial-domain downlink transmit beam prediction may be performed for the set A beams based on measurement results from the set B beams. In some examples, temporal downlink transmit beam prediction may be performed for the set A beams based on the historic measurement results of the set B beams. Some examples of the techniques described herein may provide signaling or mechanism (s) to facilitate life cycle management (LCM) operations for beam management use cases. For instance, some of the techniques may be utilized to improve cohesion between training and inferencing for one or more network-side conditions for inferencing at the UE 115.

In some examples, a UE 115 may include a communications manager 108. In accordance with some of the techniques described herein, the communications manager 108 may receive one or more first reference signals via at least one downlink beam. The communications manager 108 may generate a metric based on the one or more first reference signals input to one or more candidate AI models for beam prediction. The communications manager 108 may receive a first signal from a first cell based on a first AI model, the first AI model selected from the one or more candidate AI models based on the metric. The communications manager 108 may receive an indication associated with an applicability of the first AI model for a second cell. The communications manager 108 may receive, based on the indication, a second signal from the second cell based on the first AI model. In some examples, the communications manager 108 may omit or perform one or more other operations described with reference to FIGs. 1–14.

In some examples, a network entity 105 may include a communications manager 102. In accordance with some of the techniques described herein, the communications manager 102 may output one or more first reference signals via at least one downlink beam. The communications manager 102 may output a first signal from a first cell for reception by a UE 115 based on a first AI model for beam prediction. The communications manager 102 may output an indication associated with an applicability of the first AI model for a second cell. In some examples, the communications manager 108 may omit or perform one or more other operations described with reference to FIGs. 1–14.

FIG. 2 shows an example of a network architecture 200 (e.g., a disaggregated base station architecture, a disaggregated RAN architecture) that supports AI model indications among cells in accordance with one or more aspects of the present disclosure. The network architecture 200 may illustrate an example for implementing one or more aspects of the wireless communication system 100. The network architecture 200 may include one or more CUs 160-a that may communicate directly with a core network 130-a via a backhaul communication link 120-a, or indirectly with the core network 130-a through one or more disaggregated network entities 105 (e.g., a Near-RT RIC 175-b via an E2 link, or a Non-RT RIC 175-a associated with an SMO 180-a (e.g., an SMO Framework) , or both) . A CU 160-a may communicate with one or more DUs 165-a via respective midhaul communication links 162-a (e.g., an F1 interface) . The DUs 165-a may communicate with one or more RUs 170-a via respective fronthaul communication links 168-a. The RUs 170-a may be associated with respective coverage areas 110-a and may communicate with UEs 115-a via one or more communication links 125-a. In some implementations, a UE 115-a may be simultaneously served by multiple RUs 170-a.

Each of the network entities 105 of the network architecture 200 (e.g., CUs 160-a, DUs 165-a, RUs 170-a, Non-RT RICs 175-a, Near-RT RICs 175-b, SMOs 180-a, Open Clouds (O-Clouds) 205, Open eNBs (O-eNBs) 210) may include one or more interfaces or may be coupled with one or more interfaces configured to receive or transmit signals (e.g., data, information) via a wired or wireless transmission medium. Each network entity 105, or an associated processor (e.g., controller) providing instructions to an interface of the network entity 105, may be configured to communicate with one or more of the other network entities 105 via the transmission medium. For example, the network entities 105 may include a wired interface configured to receive or transmit signals over a wired transmission medium to one or more of the other network entities 105. Additionally, or alternatively, the network entities 105 may include a wireless interface, which may include a receiver, a transmitter, or transceiver (e.g., an RF transceiver) configured to receive or transmit signals, or both, over a wireless transmission medium to one or more of the other network entities 105.

In some examples, a CU 160-a may host one or more higher layer control functions. Such control functions may include RRC, PDCP, SDAP, or the like. Each control function may be implemented with an interface configured to communicate signals with other control functions hosted by the CU 160-a. A CU 160-a may be configured to handle user plane functionality (e.g., CU-UP) , control plane functionality (e.g., CU-CP) , or a combination thereof. In some examples, a CU 160-a may be logically split into one or more CU-UP units and one or more CU-CP units. A CU-UP unit may communicate bidirectionally with the CU-CP unit via an interface, such as an E1 interface when implemented in an O-RAN configuration. A CU 160-a may be implemented to communicate with a DU 165-a, as necessary, for network control and signaling.

A DU 165-a may correspond to a logical unit that includes one or more functions (e.g., base station functions, RAN functions) to control the operation of one or more RUs 170-a. In some examples, a DU 165-a may host, at least partially, one or more of an RLC layer, a MAC layer, and one or more aspects of a PHY layer (e.g., a high PHY layer, such as modules for 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 3rd Generation Partnership Project (3GPP) . In some examples, a DU 165-a may further host one or more low PHY layers. Each layer may be implemented with an interface configured to communicate signals with other layers hosted by the DU 165-a, or with control functions hosted by a CU 160-a.

In some examples, lower-layer functionality may be implemented by one or more RUs 170-a. For example, an RU 170-a, controlled by a DU 165-a, may correspond to a logical node that hosts RF processing functions, or low-PHY layer functions (e.g., 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, an RU 170-a may be implemented to handle over the air (OTA) communication with one or more UEs 115-a. In some implementations, real-time and non-real-time aspects of control and user plane communication with the RU (s) 170-a may be controlled by the corresponding DU 165-a. In some examples, such a configuration may enable a DU 165-a and a CU 160-a to be implemented in a cloud-based RAN architecture, such as a vRAN architecture.

The SMO 180-a may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network entities 105. For non-virtualized network entities 105, the SMO 180-a 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 (e.g., an O1 interface) . For virtualized network entities 105, the SMO 180-a may be configured to interact with a cloud computing platform (e.g., an O-Cloud 205) to perform network entity life cycle management (e.g., to instantiate virtualized network entities 105) via a cloud computing platform interface (e.g., an O2 interface) . Such virtualized network entities 105 can include, but are not limited to, CUs 160-a, DUs 165-a, RUs 170-a, and Near-RT RICs 175-b. In some implementations, the SMO 180-a may communicate with components configured in accordance with a 4G RAN (e.g., via an O1 interface) . Additionally, or alternatively, in some implementations, the SMO 180-a may communicate directly with one or more RUs 170-a via an O1 interface. The SMO 180-a also may include a Non-RT RIC 175-a configured to support functionality of the SMO 180-a.

The Non-RT RIC 175-a may be configured to include a logical function that enables non-real-time control and optimization of RAN elements and resources, Artificial Intelligence (AI) or Machine Learning (ML) workflows including model training and updates, or policy-based guidance of applications/features in the Near-RT RIC 175-b. The Non-RT RIC 175-a may be coupled to or communicate with (e.g., via an A1 interface) the Near-RT RIC 175-b. The Near-RT RIC 175-b 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 (e.g., via an E2 interface) connecting one or more CUs 160-a, one or more DUs 165-a, or both, as well as an O-eNB 210, with the Near-RT RIC 175-b.

In some examples, to generate AI/ML models to be deployed in the Near-RT RIC 175-b, the Non-RT RIC 175-a may receive parameters or external enrichment information from external servers. Such information may be utilized by the Near-RT RIC 175-b and may be received at the SMO 180-a or the Non-RT RIC 175-a from non-network data sources or from network functions. In some examples, the Non-RT RIC 175-a or the Near-RT RIC 175-b may be configured to tune RAN behavior or performance. For example, the Non-RT RIC 175-a may monitor long-term trends and patterns for performance and employ AI or ML models to perform corrective actions through the SMO 180-a (e.g., reconfiguration via O1) or via generation of RAN management policies (e.g., A1 policies) .

Some examples of the techniques described herein may be implemented or performed using the network architecture 200. For example, an RU 170-a may output (e.g., transmit) one or more reference signals via one or more downlink beams. The UE 115-a may generate a metric based on the reference signals (e.g., measurements of the reference signals, L1-RSRPs, among other examples) input to one or more candidate AI models for beam prediction. In some cases, the UE 115-a may activate an AI model that is selected from the candidate AI model (s) based on the metric. For instance, the UE 115-a may utilize the selected AI model to receive a signal from the RU 170-a. A CU 160-a, DU 165-a, or RU 170-a may output (e.g., transmit) an indication associated with an applicability of the selected AI model for another cell (e.g., another cell corresponding to another coverage area 110-a or served by another RU 170-a) . The UE 115-a may utilize the selected AI model to receive a signal in the other cell. In some examples, the network architecture 200 may be utilized to perform one or more other techniques as described with reference to FIG. 1 or any of FIGs. 3–13.

FIG. 3 shows an example of a wireless communication system 300 that supports AI model indications among cells in accordance with one or more aspects of the present disclosure. The wireless communication system 300 may implement aspects of or may be implemented by aspects of the wireless communication system 100 or the network architecture 200. For example, the wireless communication system 300 includes a UE 115-b, which may be an example of a UE 115 described with reference to FIG. 1 or the UE 115-a described with reference to FIG. 2. The wireless communication system 300 also includes a network entity 105-a, network entity 105-b, network entity 105-c, which may be examples of a network entity 105 as described with reference to FIG. 1, or which may be examples of a CU 160-a, DU 165-a, or RU 170-a as described with reference to FIG. 2.

The UE 115-b may communicate with the network entity 105-a using a communication link. For example, the network entity 105-a may provide a first cell 305-a for the UE 115-b. In some examples, the first cell 305-a may initially be a serving cell for the UE 115-b. The network entity 105-b may provide a second cell 305-b, and network entity 105-c may provide a third cell 305-c. In some examples, the second cell 305-b and the third cell 305-c may initially be candidate cells for cell switching. Each of the network entities 105-a, 105-b, 105-c, or cells 305-a, 305-b, 305-c, may provide one or more respective beams 310-a, 310-b, 310-c for communication with the UE 115-b. In some examples, the UE 115-b may utilize one or more first beams 313-a for communication (s) with the network entity 105-a, one or more second beams 313-b for communication (s) with the network entity 105-b, or one or more third beams 313-c for communication (s) with the network entity 105-c. For instance, the first beams 313-a, the second beams 313-b, or the third beams 313-c may be uplink beams for uplink transmissions. In some examples, the UE 115-b may utilize one or more receive beams for receiving signals from one or more of the network entity 105-a, network entity 105-b, or network entity 105-c. As used herein, a “communication resource” may refer to a cell or beam. A “cell” may refer to a serving cell, a candidate cell, or a target cell. While a first cell 305-a, a second cell 305-b, and a third cell 305-c are shown in the example of FIG. 3, a different quantity of serving cells or a different quantity of candidate cells may be utilized in some examples.

A candidate cell may be a cell that is a candidate for communication with a UE (e.g., UE 115-b) . For example, a candidate cell may be a cell that may provide one or more communication resources to a UE (e.g., UE 115-b) . A candidate cell may be evaluated by a UE (e.g., the UE 115-b) or a network entity (e.g., the network entity 105-a) for handover or cell switching of the UE. For example, one or more of the second cell 305-b or the third cell 305-c may be an LTM candidate cell or a non-serving cell. In an example, the first cell 305-a may be a serving cell, and the second cell 305-b and third cell 305-c may be non-serving cells that are candidate cells (e.g., candidate cells for handover or cell switching) .

A target cell may be a cell (e.g., a candidate cell) that is selected for communication with a UE (e.g., UE 115-b) . The term “candidate cell” may denote a candidate cell or a target cell (e.g., a candidate cell that has been selected for communication with a UE) . In some examples, one or more candidate cells may be LTM candidate cells (which may be serving cells or non-serving cells) , may be a single serving cell, or may be multiple serving cells. In some examples, a cell may be a primary cell (PCell) , secondary cell (SCell) , or special cell (SpCell) . For example, the first cell 305-a, the second cell 305-b, and the third cell 305-c may be SpCells included in a configured candidate SpCell set.

The UE 115-b may establish one or more communication links with one or more of the network entities 105-a, 105-b, 105-c. In some examples, a communication link may be an example of an NR or LTE link between the UE 115-b and a network entity 105-a, network entity 105-b, or network entity 105-c. In some examples, a communication link may be a bi-directional links that enable both uplink and downlink communications. For example, the UE 115-b may transmit uplink signals (e.g., uplink transmissions) , such as uplink control signals or uplink data signals, to one or more of network entity 105-a, network entity 105-b, or network entity 105-c. One or more of network entity 105-a, network entity 105-b, or network entity 105-c may transmit downlink signals (e.g., downlink transmissions) , such as downlink control signals or downlink data signals, to the UE 115-b using a communication link. In the example of FIG. 3, a first communication link 125-a between the network entity 105-a and the UE 115-b, a second communication link 125-b between the network entity 105-b and the UE 115-b, and a third communication link 125-c between the network entity 105-c and the UE 115-b are shown.

The network entity 105-a may output (e.g., transmit) , or the UE 115-b may receive, one or more first reference signals 315 via at least one downlink beam 310-a. For example, the UE 115-b may receive one or more channel state information reference signals (CSI-RSs) , cell-specific reference signals (CRSs) , or one or more other reference signals from the network entity 105-a.

The UE 115-b may utilize the one or more reference signals 315 to autonomously select a first AI model from the candidate AI model (s) 345. In some examples, the UE 115-b may generate a metric 350 based on the one or more first reference signals input to one or more candidate AI models 345 for beam prediction. For instance, the UE 115-b may determine one or more measurements (e.g., L1-RSRPs) based on the reference signals 315. In some examples, inputting one or more reference signals (e.g., reference signal (s) 315) to an AI model (e.g., candidate AI model (s) 345) may mean that the reference signal (s) may be input directly to the AI model or that information (e.g., measurement (s) ) based on the reference signal (s) may be input to the AI model. For instance, the one or more reference signals 315 or one or more measurements (e.g., L1-RSRPs) based on the reference signal (s) 315 may be input to the one or more candidate models 345. In some examples, one or more candidate AI models 345 may generate an output. In some approaches, each candidate AI model 345 may generate a beam prediction, CSI compression, CSI prediction, a metric 350, or other output.

The UE 115-b may utilize the output to generate the metric 350, or the output may be the metric 350. In some examples, the metric 350 may be a difference between a value of a predicted beam (e.g., set A beam) and a beam measurement value (e.g., measurement from a set A beam) , a channel quality, SNR, RSRP, or other metric. In some examples, a metric 350 may be generated for each of the candidate AI model (s) 345 (or prediction (s) , such as predicted beam (s) , for instance) .

The UE 115-b may receive a first signal 320 from a first cell 305-a based on a first AI model, where the first AI model selected from the one or more candidate AI models 345 based on the metric 350. For instance, the UE 115-b may perform a selection 355 to produce one or more selected AI models 353. In some cases, the first AI model may be selected from the candidate AI model (s) 345 as the AI model associated with a best metric among one or more metrics 350 associated with other candidate AI models, if any. For example, the first AI model may be associated with a smallest difference between a value of a predicted beam and a measured value, a highest channel quality value, a highest SNR, a lowest noise, or other best metric.

The UE 115-b may utilize (e.g., activate) the first AI model to receive the first signal 320. For instance, the first AI model may perform a prediction (e.g., beam prediction, CSI prediction, or CSI compression, among other examples) that may be utilized to receive the first signal 320. In an example, a predicted beam from the first AI model may be utilized to control beamforming (e.g., precoding) to receive the first signal 320.

Some examples of the techniques described herein may be utilized in the context of cell switching. In one example, the UE 115-b may be connected to the first cell 305-a and may have activated the first AI model with one or more functionalities, where the first AI model has been selected without an indication or exchange of a model identifier, dataset identifier, or configuration identifier associated with the one or more functionalities. As described herein, candidate AI model functionalities may include one or more of beam prediction (e.g., spatial beam prediction, temporal beam prediction, or spatial-temporal beam prediction) , CSI compression, (temporal) CSI prediction, or one or more other functionalities in some approaches.

The UE 115-b may receive an indication 325 associated with an applicability of the first AI model for a second cell 305-b. The indication 325 may be the indication 325-a or the indication 325-b illustrated in FIG. 3, or may be a combination of both. For instance, the UE 115-b may receive network signaling indicating whether the same AI functionality for the first cell 305-a may be activated in the second cell 305-b or information regarding whether the UE 115-b may use the AI model that was last used for the activated AI functionality in the first cell 305-a.

For beam prediction or one or more other AL functionalities, if the indication 325 indicates that the first AI model may be used (e.g., reused) in the second cell 305-b, the UE 115-b may be activated with the AI functionality in the second cell 305-b without performance monitoring to initialize (e.g., select) an AI model. For instance, the UE 115-b may reuse the first AI model for the second cell 305-b without receiving reference signaling to determine (or to assist the network with determining) whether the AI functionality may be activated. If the indication 325 indicates that the first AI model may not be reused without performance monitoring-based AI model initialization, the UE 115-b may engage in performance monitoring-based AI model initialization in the second cell 305-b to determine whether an AI model may be activated or which AI model may be utilized in the second cell 305-b.

As described herein, the indication 325 associated with the applicability may be a value, code, or signal that indicates whether an AI model (e.g., the first AI model) may be applicable to one or more cells. The indication may be agnostic to the AI model (s) (e.g., the first AI model or the candidate AI model (s) ) . For example, the indication 325 may be agnostic to the AI model in that the indication 325 may not globally identify an AI model.

In some examples, the indication 325 may include an identification of a functionality associated with the first AI model and a command for utilization of the first AI model for the second cell 305-b. For instance, the indication 325 may be presented and may include respective AI functionality identifiers with the command (e.g., information regarding whether the UE 115-b may use the same AI model in the second cell 305-b) .

In some examples, the indication 325 may include an identification of a zone, a cell identifier, or both for application of the first AI model in the second cell 305-b. For instance, the indication 325 signaled from the network may indicate one or more cells within a zone (e.g., geographical zone) for which the same AI model for an AI functionality may be reused by the UE 115-b without performance monitoring-based AI model initialization, if performance monitoring based AI model initialization has been performed by the UE 115-b in a cell within the zone. In some examples, the identification of a zone may be a regional indication, information referring to a geographical zone, or a zone identifier. Additionally, or alternatively, the identification of a zone may refer to a series of cell identifiers (whether the associated cells are configured for the UE 115-b or not yet configured for the UE 115-b, for instance) . In some examples, if the first cell 305-a and the second cell 305-b are both within the indicated geographical zone, and if the UE 115-b has already been activated with an AI functionality with the first AI model in the first cell 305-a, the UE 115-b may use (e.g., reuse) the first AI model for the same AI functionality in the second cell 305-b. In some examples, the first AI model reuse in a zone may be conditioned on the quantities of measurement resources (e.g., set B beams) and prediction target resources (e.g., set A beams) being the same for the AI functionality (e.g., beam prediction) to be activated in the second cell 305-b relative to the AI functionality activated in the first cell 305-a.

In some examples, the indication 325 may provide information other than directly indicating whether the same model may be used (e.g., reused) . For instance, the indication 325 may be an implicit indication rather than directly indication that the same model may be used. In some aspects, the indication 325 may include an identification of a dataset, a configuration, a codebook, a deployment, or a combination thereof for a second cell 305-b that is identical to a corresponding identification for a first cell 305-a. for example, the information may be specified as a dataset, configuration, codebook, or deployment identified for AI functionality to be activated in the second cell 305-b that is the same as in the first cell 305-a.

In some examples, network entities (e.g., the network entity 105-a and the network entity 105-b) may communicate (e.g., via a backhaul link or a wireless link) to communicate information indicating whether an AI model utilized for one network entity may be utilized for another network entity. For instance, the information may indicate active AI functionality (ies) , a quantity of one or more beams, a codebook identifier (s) , a dataset, a configuration, a zone identifier (s) , a cell identifier (s) , or other information for the network entities to coordinate whether an AI model utilized for one network entity may be applicable to another network entity.

In some examples, the UE 115-b may switch from the first cell 305-a to the second cell 305-b. For instance, the UE 115-b may perform a switching procedure to execute the switch 340 (e.g., cell switch) . The example of FIG. 3 illustrates an example of a switch 340 from the first cell 305-a to the second cell 305-b. The switch 340 may occur in one or more examples, including a cell change without CA (e.g., standalone) , CA, DC-CA, or new radio dual connectivity (NR-DC) , among other examples. In some examples, the UE 115-b may utilize information to switch communications from the first cell 305-a to the second cell 305-b. For instance, the information that may be utilized for the switch 340 may include QCL information, reference signal information, beam information, or a combination thereof, relative to the second cell 305-b. QCL information may indicate one or more QCL relationships of one or more reference signals associated with the second cell 305-b. Reference signal information may indicate one or more types of reference signals (e.g., CSI-RS, among other examples) associated with the second cell 305-b. Beam information may indicate one or more beams 310-b for communication.

The UE 115-b may receive, based on the indication 325, a second signal 330 from the second cell 305-b based on the first AI model. For instance, the first signal 320 may be received before the switch 340 and the second signal 330 may be received after the switch 340. After the switch 340, the UE 115-b may utilize the first AI model to receive the second signal 330 based on the indication 325. For example, if the indication 325 indicates that the first AI model has applicability to the second cell 305-b, the UE 115-b may utilize the first AI model to receive the second signal 330. The first AI model may be utilized for the second cell 305-b without performance monitoring-based AI model initialization in the second cell 305-b if the indication 325 indicates that the first AI model is applicable to the second cell 305-b.

In some examples, the indication 325 may be received before or after the switch 340 to the second cell 305-b, or before or after the UE 115-b is supplied with a second cell 305-b (while a connection with the first cell 305-a may still be maintained) . For instance, the UE 115-b may receive the indication 325-a from the first cell 305-a before the switch 340 from the first cell 305-a to the second cell 305-b. An example of a first example 405-a where an indication is received before a switch is given with reference to FIG. 4. In some examples, the UE 115-b may receive the indication 325-b from the second cell 305-b after the switch 340 from the first cell 305-a to the second cell 305-b. An example of a second example 405-b where an indication is received after a switch is given with reference to FIG. 4.

In some aspects, the UE 115-b may receive the indication 325 via a radio resource control (RRC) configuration message or a medium access control-control element (MAC-CE) message. The indication 325 may identify one or more cells (e.g., may include a cell identifier for the first cell 305-a or the second cell 305-b) , may identify one or more functionalities associated with the first AI model, or both. For instance, the indication may identify one or more functionalities including spatial beam prediction, temporal beam prediction, spatial-temporal beam prediction, CSI compression, CSI prediction, another functionality (ies) , or a combination thereof. A more detailed example of an indication 425-a is given with reference to FIG. 4.

In some approaches, the UE 115-b may receive the indication 325 based on a first quantity of beams 310-a (e.g., downlink beams) of the first cell 305-a being equal to a second quantity of beams 310-b (e.g., downlink beams) of the second cell 305-b. For instance, the UE 115-b may receive the indication 325 (from the first cell 305-a or the second cell 305-b) if the first quantity of beams 310-a is equal to the second quantity of beams 310-b. In some examples, the network entity 105-a and the network entity 105-b may communicate (via a backhaul link or a direct wireless link, for instance) to indicate or exchange information indicating the first quantity of beams 310-a or the second quantity of beams 310-b. If the first quantity is equal to the second quantity, the first cell 305-a may provide the indication 325-a or the second cell 305-b may provide the indication 325-b.

In some examples, receiving the indication 325 may include receiving a configuration message that identifies one or more cells (e.g., the first cell 305-a or the second cell 305-b) associated with the indication 325. In some aspects, the configuration message may be the indication 325, the configuration message may be included in the indication 325, the indication 325 may be included in the configuration message, or the configuration message may be communicated separately from the indication 325.

In some aspects, the configuration message may indicate a CSI report setting. For instance, the UE 115-b may be configured to feedback beam prediction results via a CSI report to the first cell 305-a. Additionally, or alternatively, the UE 115-b may be configured to feedback beam prediction results via a CSI report to the second cell 305-b. In some examples, the configuration message (e.g., a CSI report setting associated with a CSI report) , may indicate one or more cell identifiers (e.g., a cell identifier (s) of the first cell 305-a, of the second cell 305-b or of other cell (s) that are different from the cell identifier corresponding to the CSI report setting) . The indicated cell identifier (s) may correspond to one or more cells that may use the same AI model (e.g., the first AI model) identified for a CSI report setting, if the UE 115-b is scheduled with another CSI report to feedback beam prediction results in the indicated cells. In some examples, the configuration message or the indication 325 may identify one or more functionalities associated with the first AI model or a CSI report setting used in a cell (e.g., in the first cell 305-a) such that the UE 115-b may utilize the first AI model that has been used for the cell (e.g., first cell 305-a) for the functionality or CSI report.

In some aspects, one or more conditions may be satisfied for one or more cells to be referred to as the first cell 305-a, which may reduce UE buffering. For example, one condition may be that first cell 305-a may (e.g., must) be a source cell when the UE 115-b is switched to the second cell 305-b. Another example of a condition may be that the first cell 305-a may (e.g., must) be an active serving cell.

In some examples, the configuration message or the first AI model reuse may be conditioned on the quantities of measurement resources (e.g., set B beams) and prediction target resources (e.g., set A beams) being the same for a CSI report scheduled in the second cell 305-b compared with the quantities of measurement resources and prediction target resources for a CSI report scheduled in the first cell 305-a.

In some examples, the UE 115-b may receive the indication 325 based on a first functionality that is active for the first cell 305-a and for the second cell 305-b, where the first functionality is associated with the first AI model. For instance, the configuration message or the first AI model reuse may be conditioned on having the same AI functionality activated for the CSI report in the first cell 305-a and for the CSI report in the second cell 305-b.

In some examples, the UE 115-b may transmit information (based on the configuration message) to the first cell 305-a, the second cell 305-b, or both. For instance, the UE 115-b may transmit a CSI report (e.g., a CSI report indicating beam prediction results) to the first cell 305-a, to the second cell 305-b, or to both.

In some approaches, the presence or absence of an indication may be utilized to determine whether the first AI model may be used (e.g., reused) in another cell. For example, an indication may not be presented in some cases (e.g., may not be presented for one or more AI functionalities) . If the indication 325 is presented, the indication 325 may indicate whether the UE 115-b may use the (same) first AI model in the second cell 305-b. The first AI model may be an AI model that was utilized in a previous cell or that was determined using reference signaling or performance monitoring. In some approaches, the indication 325, if presented, may be limited to indicating that the first AI model may be reused for the indicated AI functionality (e.g., spatial beam prediction, temporal beam prediction, spatial-temporal beam prediction, CSI compression, CSI prediction, or a combination thereof) . If an indication is not presented, the UE 115-b may determine that the AI functionality (ies) of the first AI model may not be reused in another cell without performance monitoring-based AI model initialization.

FIG. 3 illustrates an example where the UE 115-b may perform a switch 360 to the third cell 305-c. In this example, the UE 115-b may detect an absence of a second indication associated with the applicability of the first AI model for the third cell 305-c. The UE 115-b may utilize performance monitoring to select an AI model for the third cell 305-c. For example, the UE 115-b may receive, based on the detection, one or more second reference signals 365 via a second downlink beam of the beams 310-c. The UE 115-b may generate a second metric 350 based on the one or more second reference signals 365 input to the one or more candidate AI models 345 for beam prediction. In this example, a selected AI model 353 may be the second AI model. The UE 115-b may receive a third signal 370 from the third cell based on a second AI model, where the second AI model is selected based on the second metric.

Some examples of the techniques described herein may be utilized for intra-cell cases. For instance, AI functionality for the first cell 305-a may be activated for the UE 115-b and later deactivated. For instance, AI functionality may be deactivated due to a moving speed of the UE 115-a, where the moving speed is too fast to ensure prediction accuracy. Without a global model identifier, the UE 115-b may lack an indication regarding whether to reuse the previously-used first AI model.

Some of the techniques described herein may be utilized to provide an indication to the UE 115-b regarding whether the same AI functionality may be reactivated. In some examples, the indication 325-a may be provided to the UE 115-b from the network to indicate whether the same first AI model may be used when AI functionality is reactivated.

In an example, the UE 115-b may deactivate the first AI model at a first time that the UE 115-b is connected to the first cell 305-a. For instance, the UE 115-b may detect motion that satisfies a threshold or may receive an instruction from the first cell 305-a to deactivate the first AI model. The UE 115-b may activate (e.g., reactivate) the first AI model based on a condition that the UE 115-b is connected to the first cell 305-a at a second time subsequent to the first time. For example, if AI functionality is activated and deactivated in a given cell, if the same AI functionality may be reactivated in the same cell, the UE 115-b may use the same AI model for the reactivated AI functionality. The AI model (e.g., the first AI model) last used may be reactivated when the AI functionality is activated in the same cell (e.g., the first cell 305-a) .

In another example, the UE 115-a may utilize a virtual identifier. A virtual identifier is an identifier that is not associated with any model by the network. For instance, the UE 115-b may receive a first virtual identifier for the first AI model. The UE 115-b may deactivate the first AI model. The UE 115-b may activate (e.g., reactivate) the first AI model based on a condition that the first virtual identifier matches the indication 325-a. For example, when an AI functionality is activated in the first cell 305-a, the UE 115-b may receive a first virtual identifier (via an RRC message, MAC-CE message, or downlink control information (DCI) , for instance) from the first cell 305-a. If the same AI functionality is deactivated but later reactivated, the UE 115-b may receive further network signaling (e.g., the indication 325-a) indicating a second virtual identifier. If the first virtual identifier and the second virtual identifier are identical, the UE 115-b may use (e.g., reuse) the same AI model (e.g., the first AI model) for the reactivated AI functionality. The AI model (e.g., the first AI model) last used may be reactivated when the AI functionality is activated in the same cell (e.g., the first cell 305-a) . An example of AI functionality reactivation is provided with reference to FIG. 5. In some examples, the UE 115-b may store or maintain a set (e.g., table) of the virtual identifiers historically linked with one or more previously activated AI functionalities. The UE 115-b may compare the set of virtual identifiers with one or more newly reactivated AI functionalities to determine whether the same model may be used.

Some examples of the techniques described may be utilized in conjunction with CSI reports, where the use of the AI model is controlled by the network. For instance, the UE 115-b may receive a first report configuration associated with a first cell 305-a for information associated with a functionality of the first AI model. The UE 115-b may deactivate the first AI model. The UE 115-b may activate the first AI model based on a condition that the indication 325 is a second report configuration associated with the first cell 305-a for information associated with the functionality of the first AI model. For example, the UE 115-b may be activated or triggered with a semi-persistent or aperiodic CSI report to feedback beam prediction results associated with an AI functionality using an AI model. If the semi-persistent or aperiodic CSI report is deactivated, and then reactivated or re-triggered in the same cell (e.g., the first cell 305-a) , the UE 115-b may use the same AI model that was most recently used when the same semi-persistent or aperiodic CSI report was activated or triggered.

FIG. 4 shows an example of a block diagram 400 that supports AI model indications among cells in accordance with one or more aspects of the present disclosure. The block diagram 400 illustrates a first example 405-a and a second example 405-b. The first example 405-a illustrates a UE 115-c and the second example 405-b illustrates a UE 115-d. The UE 115-c or the UE 115-d may be examples of a UE 115 described with reference to FIG. 1, a UE 115-a described with reference to FIG. 2, or a UE 115-b described with reference to FIG. 3.

In the first example 405-a, the UE 115-c performs a switch 420-a from a first cell 470-a to a second cell 475-a. When the UE 115-c is in the first cell 470-a, the UE 115-c may perform physical downlink control channel (PDCCH) monitoring in the first cell 470-a. For instance, the UE 115-c may be connected to the first cell 470-a. When the UE 115-c performs the switch 420-a to the second cell 475-a, the UE 115-c may perform PDCCH monitoring in the second cell 475-a. For instance, the UE 115-c may be connected to the second cell 475-a.

In the first example 405-a, the UE 115-c may receive an indication 425-a from the first cell 470-a before the switch 420-a to the second cell 475-a. The indication 425-a may be an example of the indication 325-a described with reference to FIG. 3.

As illustrated in FIG. 4, the indication 425-a may be sent via an RRC message or a MAC-CE message. The indication 425-a may include one or more cell identifiers 430-a, functionality identifiers 435-a, zone identifiers 440-a, commands 445-a, identifiers 450-a (e.g., for a dataset, configuration, codebook, or deployment, among other examples) , virtual identifiers 455-a, configuration messages 460-a (e.g., CSI report setting (s) ) , or other information as described with reference to FIG. 3. In some examples, the configuration message (s) 460-a may be received in conjunction with the indication 425-a or separately from the indication 425-a.

When the UE 115-c is located in the first cell 470-a, an AI functionality 465-a of a first AI model may be activated. As described with reference to FIG. 3, the UE 115-c may utilize the indication 425-a (e.g., one or more of the indication 425-a contents) to determine whether to use (e.g., reuse) the first AI model for communication in the second cell 475-a. As shown in the first example 405-a, for instance, the UE 115-c may receive an indication 425-a of a functionality identifier 435-a. If an AI functionality 465-a is active in the first cell 470-a and matches an active AI functionality 465-a in the second cell 475-a, the UE 115-c may reuse the first AI model to perform the AI functionality 465-a in the second cell 475-a. Other indication 425-a contents may be utilized to determine whether to use the first AI model in the second cell 475-a as described with reference to FIG. 3 in other examples.

As described with reference to FIG. 3, the indication 425-a may be provided from the first cell 470-a. For example, the indication 425-a may be indicated by the network from the first cell 470-a (e.g., a source cell) before the UE 115-c is eventually switched to (e.g., before the UE 115-c monitors the PDCCH in the second cell 475-a) the second cell 475-a (e.g., a target cell) , or before the UE 115-c is supplied with the second cell 475-a.

In some examples, the UE 115-c may receive one or more RRC configurations or MAC-CEs from the first cell 470-a that may include one or more cell identifiers 430-a with respect to the second cell 475-a and one or more associated AI functionality identifiers 435-a, together with a command 445-a indicating whether the same AI model under an AI functionality may be used for the second cell 475-a. In L3-based mobility, where the first cell 470-a is a source cell and the second cell 475-a is a target cell, the indication 425-a (e.g., indication content) may be included in one or more RRC configuration messages for the first cell 470-a or the second cell 475-a.

In LTM, the indication 425-a (e.g., indication content) may be included in one or more LTM configuration messages associated with LTM candidate cells (if the first cell 470-a is an active serving cell and the second cell 475-a is one of the LTM candidate cells) , or may be included in a MAC-CE cell switch command to switch the UE 115-c from the first cell 470-a to the second cell 475-a.

For beam prediction, reuse of the first AI model may be further conditioned on the quantities of measurement resources (e.g., set B beams) and prediction target resources (e.g., set A beams) being the same for the AI functionality activated in the second cell 475-a relative to the corresponding quantities of the first cell 470-a.

In the second example 405-b, the UE 115-d performs a switch 420-b from a first cell 470-b to a second cell 475-b. When the UE 115-d is in the first cell 470-b, the UE 115-d may perform PDCCH monitoring in the first cell 470-b. For instance, the UE 115-d may be connected to the first cell 470-b. When the UE 115-d performs the switch 420-b to the second cell 475-b, the UE 115-d may perform PDCCH monitoring in the second cell 475-b. For instance, the UE 115-d may be connected to the second cell 475-b.

In the second example 405-b, the UE 115-d may receive an indication 425-b from the second cell 475-b after the switch 420-b to the second cell 475-b. The indication 425-b may be an example of the indication 325-b described with reference to FIG. 3.

The indication 425-b may be sent via an RRC message or a MAC-CE message. The indication 425-b may include one or more of the contents similarly described for the indication 425-a or as described with reference to FIG. 3.

When the UE 115-d is located in the first cell 470-b, an AI functionality 465-b of a first AI model may be activated. As described with reference to FIG. 3, the UE 115-d may utilize the indication 425-b (e.g., one or more of the indication 425-b contents) to determine whether to use (e.g., reuse) the first AI model for communication in the second cell 475-b. As shown in the second example 405-b, for instance, the UE 115-d may receive an indication 425-b of a functionality identifier. If an AI functionality 465-b is active in the first cell 470-b and matches an active AI functionality 465-b in the second cell 475-b, the UE 115-d may reuse the first AI model to perform the AI functionality 465-b in the second cell 475-b. Other indication 425-b contents may be utilized to determine whether to use the first AI model in the second cell 475-b as described with reference to FIG. 3 in other examples.

As described with reference to FIG. 3, the indication 425-b may be provided from the second cell 475-b. For example, the indication 425-b may be indicated by the network from the second cell 475-b (e.g., a target cell) after the UE 115-d is eventually switched to (e.g., after the UE 115-d begins monitoring the PDCCH in the second cell 475-b) the second cell 475-b (e.g., target cell) , or after the UE 115-d is supplied with the second cell 475-b.

In some examples, the UE 115-d may receive RRC configurations or MAC-CEs from the second cell 475-b that may include a cell identifier with respect to the first cell 470-b and one or more associated AI functionality identifiers, together with a command indicating whether the same AI model under an AI functionality may be used for the second cell 475-b. In L3-based mobility or LTM, where the first cell 470-b is a source cell and the second cell 475-b is a target cell, the indication 425-b (e.g., indication content) may be included in one or more RRC configuration messages for the second cell 475-b. In some examples, the indicated first cell 470-b can be a serving cell that was active within a threshold period.

For beam prediction, reuse of the first AI model may be further conditioned on the quantities of measurement resources (e.g., set B beams) and prediction target resources (e.g., set A beams) being the same for the AI functionality activated in the second cell 475-b relative to the corresponding quantities of the first cell 470-b.

FIG. 5 shows an example of a block diagram 500 that supports AI model indications among cells in accordance with one or more aspects of the present disclosure. In the example of FIG. 5, UE 115-e traverses a first cell 525-a, a second cell 525-b, a third cell 525-c, a fourth cell 525-d, and a fifth cell 525-e. The UE 115-e may be an example of a UE 115 described with reference to FIG. 1, a UE 115-a described with reference to FIG. 2, a UE 115-b described with reference to FIG. 3, or a UE 115-c described with reference to FIG. 4.

In this example, AI initialization 505 may be performed when the UE 115-e is located in the first cell 525-a. For instance, the UE 115-e may use performance monitoring to evaluate and select a first AI model as described with reference to FIG. 3.

When the UE 115-e switches to the second cell 525-b, the UE 115-e may continue 510-a to use the first AI model for communication in the second cell 525-b as described with reference to FIG. 3. For instance, the UE 115-e may receive an indication that the first AI model is applicable in the second cell 525-b.

When the UE 115-e switches to the third cell 525-c, the UE 115-e may continue 510-b to use the first AI model for communication in the third cell 525-c as described with reference to FIG. 3. For instance, the UE 115-e may receive an indication that the first AI model is applicable in the third cell 525-c.

When the UE 115-e switches to the fourth cell 525-d, the UE 115-e may discontinue 515 using the first AI model for communication in the fourth cell 525-d as described with reference to FIG. 3. For instance, the UE 115-e may detect an absence of an indication or may receive an indication that the first AI model is not applicable in the fourth cell 525-d. In response to the first AI model being inapplicable to the fourth cell 525-d, AI initialization 520 may be performed. For instance, the UE 115-e may use performance monitoring to evaluate and select a second AI model as described with reference to FIG. 3.

When the UE 115-e switches to the fifth cell 525-e, the UE 115-e may continue 530 to use the second AI model for communication in the fifth cell 525-e as described with reference to FIG. 3. For instance, the UE 115-e may receive an indication that the second AI model is applicable in the fifth cell 525-e.

While in the fifth cell 525-e, the UE 115-e may deactivate 535 the second AI model. For instance, due to a high moving speed of the UE 115-e, the second AI model may be deactivated. Subsequently, the UE 115-e may activate 537 the second AI model. For instance, the UE 115-e may receive an indication that the second AI model may be reactivated due to the UE 115-e being located in the same fifth cell 525-e where the second AI model was previously utilized. In some examples, the activation 537 may be based on the use of a virtual identifier. For instance, the UE 115-e may receive a first virtual identifier from the cell 525-e (or the cell 525-d) . After deactivation 535 and activation 537, the UE 115-e may receive a second virtual identifier. If the first virtual identifier and the second virtual identifier are identical, the UE 115-e may use (e.g., reuse) the same AI model (e.g., the second AI model) for the reactivated AI functionality.

FIG. 6 shows an example of a process flow 600 that supports AI model indications among cells in accordance with one or more aspects of the present disclosure. The process flow 600 includes a UE 115-f. The UE 115-f may be an example of a UE 115 described with reference to FIG. 1, a UE 115-a described with reference to FIG. 2, a UE 115-b described with reference to FIG. 3, a UE 115-c described with reference to FIG. 4, or a UE 115-e described with reference to FIG. 5. The process flow 600 may also include a network entity 105-d and a network entity 105-e, which may be examples of one or more of the network entities 105, 105-a, 105-b, 105-c, CU 160-a, DU 165-a, or RU 170-a, as described with reference to FIG. 1, FIG. 2, or FIG. 3. The network entity 105-d may provide a first cell and the network entity 105-e may provide a second cell for a cell switching procedure.

In the following description of the process flow 600, the operations between the network entity 105-d, the network entity 105-e, and the UE 115-f may be performed in a different order than the example order shown, or the operations performed by the network entity 105-d, the network entity 105-e, and the UE 115-f may be performed in different orders or at different times. One or more operations may be omitted from the process flow 600, or one or more operations may be added to the process flow 600.

At 605, the UE 115-f may receive one or more reference signals from the network entity 105-d. The reference signal (s) may be received as described with reference to FIG. 3.

At 610, the UE 115-f may generate a metric (s) based on the reference signal (s) . The metric (s) may be generated as described with reference to FIG. 3. For instance, the UE 115-f may input the reference signal (s) or one or more measurement (s) of the reference signal (s) to one or more candidate AI models. The candidate AI model (s) output (s) may be the metric (s) or may be utilized to determine the metric (s) .

At 615, the UE 115-f may utilize the metric to select an AI model. The UE 115-f may select the AI model as described with reference to FIG. 3.

At 620, the UE 115-f may receive a first signal from the network entity 105-d. For example, the UE 115-f may receive the first signal using the selected AI model as described with reference to FIG. 3.

At 625, the UE 115-f may receive an indication from the network entity 105-d. For example, the UE 115-f may receive the indication as described with reference to FIG. 3.

At 630, the UE 115-f may receive a cell switch command from the network entity 105-d. For example, the UE 115-f may receive an RRC message or MAC-CE message commanding the UE 115-f to switch from the network entity 105-d to the network entity 105-e. In some examples, the indication and the cell switch command may be received in the same message or in different messages.

At 635, the UE 115-f may determine to use the AI model based on the indication. For example, the UE 115-f may determine to use the previous AI model as described with reference to FIG. 3.

At 640, the UE 115-f may receive a second signal from the network entity 105-e. For example, the UE 115-f may receive the second signal using the AI model as described with reference to FIG. 3.

FIG. 7 shows an example of a machine learning process 700 that supports AI model indications among cells in accordance with one or more aspects of the present disclosure. The machine learning process 700 may be implemented at a network entity 105, or a UE 115, or both as described with reference to FIGs. 1 through 6.

The machine learning process 700 may include a machine learning algorithm 710. In some examples, one or more of the AI models described herein may be structured in accordance with the machine learning algorithm 710 for beam prediction or other AI functionality described herein. As illustrated, the machine learning algorithm 710 may be an example of a neural network, such as a feed forward (FF) or deep feed forward (DFF) neural network, a recurrent neural network (RNN) , a long/short term memory (LSTM) neural network, or any other type of neural network. However, any other machine learning algorithms may be supported. For example, the machine learning algorithm 710 may implement a nearest neighbor algorithm, a linear regression algorithm, a Bayes algorithm, a random forest algorithm, or any other machine learning algorithm. Furthermore, the machine learning process 700 may involve supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, or any combination thereof.

The machine learning algorithm 710 may include an input layer 715, one or more hidden layers 720, and an output layer 725. In a fully connected neural network with one hidden layer 720, each hidden layer node 735 may receive a value from each input layer node 730 as input, where each input may be weighted. These neural network weights may be based on a cost function that is revised during training of the machine learning algorithm 710. Similarly, each output layer node 740 may receive a value from each hidden layer node 735 as input, where the inputs are weighted. If post-deployment training (e.g., online training) is supported, memory may be allocated to store errors and/or gradients for reverse matrix multiplication. These errors and/or gradients may support updating the machine learning algorithm 710 based on output feedback. Training the machine learning algorithm 710 may support computation of the weights (e.g., connecting the input layer nodes 730 to the hidden layer nodes 735 and the hidden layer nodes 735 to the output layer nodes 740) to map an input pattern to a desired output outcome. This training may result in a device-specific machine learning algorithm 710 based on the historic application data and data transfer for a specific network entity 105 or UE 115.

In some examples, input values 705 may be sent to the machine learning algorithm 710 for processing. In some examples, preprocessing may be performed according to a sequence of operations on the input values 705 such that the input values 705 may be in a format that is compatible with the machine learning algorithm 710. The input values 705 may be converted into a set of k input layer nodes 730 at the input layer 715. In some cases, different measurements may be input at different input layer nodes 730 of the input layer 715. Some input layer nodes 730 may be assigned default values (e.g., values of 0) if the number of input layer nodes 730 exceeds the number of inputs corresponding to the input values 705. As illustrated, the input layer 715 may include three input layer nodes 730-a, 730-b, and 730-c. However, it is to be understood that the input layer 715 may include any number of input layer nodes 730 (e.g., 20 input nodes) .

The machine learning algorithm 710 may convert the input layer 715 to a hidden layer 720 based on a number of input-to-hidden weights between the k input layer nodes 730 and the n hidden layer nodes 735. The machine learning algorithm 710 may include any number of hidden layers 720 as intermediate steps between the input layer 715 and the output layer 725. Additionally, each hidden layer 720 may include any number of nodes. For example, as illustrated, the hidden layer 720 may include four hidden layer nodes 735-a, 735-b, 735-c, and 735-d. However, it is to be understood that the hidden layer 720 may include any number of hidden layer nodes 735 (e.g., 10 input nodes) . In a fully connected neural network, each node in a layer may be based on each node in the previous layer. For example, the value of hidden layer node 735-a may be based on the values of input layer nodes 730-a, 730-b, and 730-c (e.g., with different weights applied to each node value) .

The machine learning algorithm 710 may determine values for the output layer nodes 740 of the output layer 725 following one or more hidden layers 720. For example, the machine learning algorithm 710 may convert the hidden layer 720 to the output layer 725 based on a number of hidden-to-output weights between the n hidden layer nodes 735 and the m output layer nodes 740. In some cases, n=m. Each output layer node 740 may correspond to a different output value 745 of the machine learning algorithm 710. As illustrated, the machine learning algorithm 710 may include three output layer nodes 740-a, 740-b, and 740-c, supporting three different threshold values. However, it is to be understood that the output layer 725 may include any number of output layer nodes 740. In some examples, post-processing may be performed on the output values 745 according to a sequence of operations such that the output values 745 may be in a format that is compatible with reporting the output values 745.

FIG. 8 shows a block diagram 800 of a device 805 that supports AI model indications among cells in accordance with one or more aspects of the present disclosure. The device 805 may be an example of aspects of a UE 115 as described herein. The device 805 may include a receiver 810, a transmitter 815, and a communications manager 820. The device 805, or one or more components of the device 805 (e.g., the receiver 810, the transmitter 815, the communications manager 820) , may include at least one processor, which may be coupled with at least one memory, to, individually or collectively, support or enable the described techniques. Each of these components may be in communication with one another (e.g., via one or more buses) .

The receiver 810 may provide a means for receiving information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to AI model indications among cells) . Information may be passed on to other components of the device 805. The receiver 810 may utilize a single antenna or a set of multiple antennas.

The transmitter 815 may provide a means for transmitting signals generated by other components of the device 805. For example, the transmitter 815 may transmit information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to AI model indications among cells) . In some examples, the transmitter 815 may be co-located with a receiver 810 in a transceiver module. The transmitter 815 may utilize a single antenna or a set of multiple antennas.

The communications manager 820, the receiver 810, the transmitter 815, or various combinations or components thereof may be examples of means for performing various aspects of AI model indications among cells as described herein. For example, the communications manager 820, the receiver 810, the transmitter 815, or various combinations or components thereof may be capable of performing one or more of the functions described herein.

In some examples, the communications manager 820, the receiver 810, the transmitter 815, or various combinations or components thereof may be implemented in hardware (e.g., in communications management circuitry) . The hardware may include at least one of a processor, a digital signal processor (DSP) , a central processing unit (CPU) , an application-specific integrated circuit (ASIC) , a field-programmable gate array (FPGA) or other programmable logic device, a microcontroller, discrete gate or transistor logic, discrete hardware components, or any combination thereof configured as or otherwise supporting, individually or collectively, a means for performing the functions described in the present disclosure. In some examples, at least one processor and at least one memory coupled with the at least one processor may be configured to perform one or more of the functions described herein (e.g., by one or more processors, individually or collectively, executing instructions stored in the at least one memory) .

Additionally, or alternatively, the communications manager 820, the receiver 810, the transmitter 815, or various combinations or components thereof may be implemented in code (e.g., as communications management software or firmware) executed by at least one processor (e.g., referred to as a processor-executable code) . If implemented in code executed by at least one processor, the functions of the communications manager 820, the receiver 810, the transmitter 815, or various combinations or components thereof may be performed by a general-purpose processor, a DSP, a CPU, an ASIC, an FPGA, a microcontroller, or any combination of these or other programmable logic devices (e.g., configured as or otherwise supporting, individually or collectively, a means for performing the functions described in the present disclosure) .

In some examples, the communications manager 820 may be configured to perform various operations (e.g., receiving, obtaining, monitoring, outputting, transmitting) using or otherwise in cooperation with the receiver 810, the transmitter 815, or both. For example, the communications manager 820 may receive information from the receiver 810, send information to the transmitter 815, or be integrated in combination with the receiver 810, the transmitter 815, or both to obtain information, output information, or perform various other operations as described herein.

The communications manager 820 may support wireless communications in accordance with examples as disclosed herein. For example, the communications manager 820 is capable of, configured to, or operable to support a means for receiving one or more first reference signals via at least one downlink beam. The communications manager 820 is capable of, configured to, or operable to support a means for generating a metric based on the one or more first reference signals input to one or more candidate AI models for beam prediction. The communications manager 820 is capable of, configured to, or operable to support a means for receiving a first signal from a first cell based on a first AI model, the first AI model selected from the one or more candidate AI models based on the metric. The communications manager 820 is capable of, configured to, or operable to support a means for receiving an indication associated with an applicability of the first AI model for a second cell. The communications manager 820 is capable of, configured to, or operable to support a means for receiving, based on the indication, a second signal from the second cell based on the first AI model.

By including or configuring the communications manager 820 in accordance with examples as described herein, the device 805 (e.g., at least one processor controlling or otherwise coupled with the receiver 810, the transmitter 815, the communications manager 820, or a combination thereof) may support techniques for reduced processing, reduced power consumption, or more efficient utilization of communication resources.

FIG. 9 shows a block diagram 900 of a device 905 that supports AI model indications among cells in accordance with one or more aspects of the present disclosure. The device 905 may be an example of aspects of a device 805 or a UE 115 as described herein. The device 905 may include a receiver 910, a transmitter 915, and a communications manager 920. The device 905, or one or more components of the device 905 (e.g., the receiver 910, the transmitter 915, the communications manager 920) , may include at least one processor, which may be coupled with at least one memory, to support the described techniques. Each of these components may be in communication with one another (e.g., via one or more buses) .

The receiver 910 may provide a means for receiving information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to AI model indications among cells) . Information may be passed on to other components of the device 905. The receiver 910 may utilize a single antenna or a set of multiple antennas.

The transmitter 915 may provide a means for transmitting signals generated by other components of the device 905. For example, the transmitter 915 may transmit information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to AI model indications among cells) . In some examples, the transmitter 915 may be co-located with a receiver 910 in a transceiver module. The transmitter 915 may utilize a single antenna or a set of multiple antennas.

The device 905, or various components thereof, may be an example of means for performing various aspects of AI model indications among cells as described herein. For example, the communications manager 920 may include a reference signal component 925, a metric generation component 930, a model component 935, an indication component 940, or any combination thereof. The communications manager 920 may be an example of aspects of a communications manager 820 as described herein. In some examples, the communications manager 920, or various components thereof, may be configured to perform various operations (e.g., receiving, obtaining, monitoring, outputting, transmitting) using or otherwise in cooperation with the receiver 910, the transmitter 915, or both. For example, the communications manager 920 may receive information from the receiver 910, send information to the transmitter 915, or be integrated in combination with the receiver 910, the transmitter 915, or both to obtain information, output information, or perform various other operations as described herein.

The communications manager 920 may support wireless communications in accordance with examples as disclosed herein. The reference signal component 925 is capable of, configured to, or operable to support a means for receiving one or more first reference signals via at least one downlink beam. The metric generation component 930 is capable of, configured to, or operable to support a means for generating a metric based on the one or more first reference signals input to one or more candidate AI models for beam prediction. The model component 935 is capable of, configured to, or operable to support a means for receiving a first signal from a first cell based on a first AI model, the first AI model selected from the one or more candidate AI models based on the metric. The indication component 940 is capable of, configured to, or operable to support a means for receiving an indication associated with an applicability of the first AI model for a second cell. The model component 935 is capable of, configured to, or operable to support a means for receiving, based on the indication, a second signal from the second cell based on the first AI model.

FIG. 10 shows a block diagram 1000 of a communications manager 1020 that supports AI model indications among cells in accordance with one or more aspects of the present disclosure. The communications manager 1020 may be an example of aspects of a communications manager 820, a communications manager 920, or both, as described herein. The communications manager 1020, or various components thereof, may be an example of means for performing various aspects of AI model indications among cells as described herein. For example, the communications manager 1020 may include a reference signal component 1025, a metric generation component 1030, a model component 1035, an indication component 1040, a switch component 1045, a virtual identifier component 1050, a configuration component 1055, an information transmission component 1060, or any combination thereof. Each of these components, or components or subcomponents thereof (e.g., one or more processors, one or more memories) , may communicate, directly or indirectly, with one another (e.g., via one or more buses) .

The communications manager 1020 may support wireless communications in accordance with examples as disclosed herein. The reference signal component 1025 is capable of, configured to, or operable to support a means for receiving one or more first reference signals via at least one downlink beam. The metric generation component 1030 is capable of, configured to, or operable to support a means for generating a metric based on the one or more first reference signals input to one or more candidate AI models for beam prediction. The model component 1035 is capable of, configured to, or operable to support a means for receiving a first signal from a first cell based on a first AI model, the first AI model selected from the one or more candidate AI models based on the metric. The indication component 1040 is capable of, configured to, or operable to support a means for receiving an indication associated with an applicability of the first AI model for a second cell. In some examples, the model component 1035 is capable of, configured to, or operable to support a means for receiving, based on the indication, a second signal from the second cell based on the first AI model.

In some examples, the switch component 1045 is capable of, configured to, or operable to support a means for switching from the first cell to the second cell, where the first signal is received before the switch and the second signal is received after the switch.

In some examples, to support receiving the indication, the indication component 1040 is capable of, configured to, or operable to support a means for receiving the indication from the first cell before the switch from the first cell to the second cell.

In some examples, to support receiving the indication, the indication component 1040 is capable of, configured to, or operable to support a means for receiving the indication from the second cell after the switch from the first cell to the second cell.

In some examples, to support receiving the indication, the indication component 1040 is capable of, configured to, or operable to support a means for receiving the indication via an RRC configuration message or a medium access control-control element (MAC-CE) message, where the indication identifies one or more cells, identifies one or more functionalities associated with the first AI model, or both, the one or more functionalities including spatial beam prediction, temporal beam prediction, spatial-temporal beam prediction, CSI compression, CSI prediction, or a combination thereof.

In some examples, to support receiving the indication, the indication component 1040 is capable of, configured to, or operable to support a means for receiving the indication based on a first quantity of beams of the first cell that is equal to a second quantity of beams of the second cell.

In some examples, to support receiving the indication, the configuration component 1055 is capable of, configured to, or operable to support a means for receiving a configuration message that identifies one or more cells associated with the indication, where the indication identifies one or more functionalities associated with the first AI model. In some examples, to support receiving the indication, the information transmission component 1060 is capable of, configured to, or operable to support a means for transmitting information based on the configuration message to the first cell, the second cell, or both.

In some examples, to support receiving the indication, the indication component 1040 is capable of, configured to, or operable to support a means for receiving the indication based on a first functionality that is active for the first cell and for the second cell, the first functionality associated with the first AI model.

In some examples, the indication includes an identification of a functionality associated with the first AI model and a command for utilization of the first AI model for the second cell.

In some examples, the indication component 1040 is capable of, configured to, or operable to support a means for detecting an absence of a second indication associated with the applicability of the first AI model for a third cell. In some examples, the reference signal component 1025 is capable of, configured to, or operable to support a means for receiving, based on the detection, one or more second reference signals via a second downlink beam. In some examples, the metric generation component 1030 is capable of, configured to, or operable to support a means for generating a second metric based on the one or more second reference signals input to the one or more candidate AI models for beam prediction. In some examples, the model component 1035 is capable of, configured to, or operable to support a means for receiving a third signal from the third cell based on a second AI model, the second AI model selected based on the second metric.

In some examples, the indication includes an identification of a zone or a cell identifier or both for application of the first AI model in the second cell.

In some examples, the indication includes an identification of a dataset, a configuration, a codebook, a deployment, or a combination thereof for a second cell that is identical to a corresponding identification for a first cell.

In some examples, the indication is agnostic to the first AI model.

In some examples, the model component 1035 is capable of, configured to, or operable to support a means for deactivating the first AI model at a first time that the UE is connected to the first cell. In some examples, the model component 1035 is capable of, configured to, or operable to support a means for activating the first AI model based on a condition that the UE is connected to the first cell at a second time subsequent to the first time.

In some examples, the virtual identifier component 1050 is capable of, configured to, or operable to support a means for receiving a first virtual identifier for the first AI model. In some examples, the model component 1035 is capable of, configured to, or operable to support a means for deactivating the first AI model. In some examples, the model component 1035 is capable of, configured to, or operable to support a means for activating the first AI model based on a condition that the first virtual identifier matches the indication.

In some examples, the configuration component 1055 is capable of, configured to, or operable to support a means for receiving a first report configuration associated with a first cell for information associated with a functionality of the first AI model. In some examples, the model component 1035 is capable of, configured to, or operable to support a means for deactivating the first AI model. In some examples, the model component 1035 is capable of, configured to, or operable to support a means for activating the first AI model based on a condition that the indication is a second report configuration associated with the first cell for information associated with the functionality of the first AI model.

FIG. 11 shows a diagram of a system 1100 including a device 1105 that supports AI model indications among cells in accordance with one or more aspects of the present disclosure. The device 1105 may be an example of or include components of a device 805, a device 905, or a UE 115 as described herein. The device 1105 may communicate (e.g., wirelessly) with one or more other devices (e.g., network entities 105, UEs 115, or a combination thereof) . The device 1105 may include components for bi-directional voice and data communications including components for transmitting and receiving communications, such as a communications manager 1120, an input/output (I/O) controller, such as an I/O controller 1110, a transceiver 1115, one or more antennas 1125, at least one memory 1130, code 1135, and at least one processor 1140. These components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more buses (e.g., a bus 1145) .

The I/O controller 1110 may manage input and output signals for the device 1105. The I/O controller 1110 may also manage peripherals not integrated into the device 1105. In some cases, the I/O controller 1110 may represent a physical connection or port to an external peripheral. In some cases, the I/O controller 1110 may utilize an operating system such as or another known operating system. Additionally, or alternatively, the I/O controller 1110 may represent or interact with a modem, a keyboard, a mouse, a touchscreen, or a similar device. In some cases, the I/O controller 1110 may be implemented as part of one or more processors, such as the at least one processor 1140. In some cases, a user may interact with the device 1105 via the I/O controller 1110 or via hardware components controlled by the I/O controller 1110.

In some cases, the device 1105 may include a single antenna. However, in some other cases, the device 1105 may have more than one antenna, which may be capable of concurrently transmitting or receiving multiple wireless transmissions. The transceiver 1115 may communicate bi-directionally via the one or more antennas 1125 using wired or wireless links as described herein. For example, the transceiver 1115 may represent a wireless transceiver and may communicate bi-directionally with another wireless transceiver. The transceiver 1115 may also include a modem to modulate the packets, to provide the modulated packets to one or more antennas 1125 for transmission, and to demodulate packets received from the one or more antennas 1125. The transceiver 1115, or the transceiver 1115 and one or more antennas 1125, may be an example of a transmitter 815, a transmitter 915, a receiver 810, a receiver 910, or any combination thereof or component thereof, as described herein.

The at least one memory 1130 may include random access memory (RAM) and read-only memory (ROM) . The at least one memory 1130 may store computer-readable, computer-executable, or processor-executable code, such as the code 1135. The code 1135 may include instructions that, when executed by the at least one processor 1140, cause the device 1105 to perform various functions described herein. The code 1135 may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some cases, the code 1135 may not be directly executable by the at least one processor 1140 but may cause a computer (e.g., when compiled and executed) to perform functions described herein. In some cases, the at least one memory 1130 may include, among other things, a basic I/O system (BIOS) which may control basic hardware or software operation such as the interaction with peripheral components or devices.

The at least one processor 1140 may include one or more intelligent hardware devices (e.g., one or more general-purpose processors, one or more DSPs, one or more CPUs, one or more graphics processing units (GPUs) , one or more neural processing units (NPUs) (also referred to as neural network processors or deep learning processors (DLPs) ) , one or more microcontrollers, one or more ASICs, one or more FPGAs, one or more programmable logic devices, discrete gate or transistor logic, one or more discrete hardware components, or any combination thereof) . In some cases, the at least one processor 1140 may be configured to operate a memory array using a memory controller. In some other cases, a memory controller may be integrated into the at least one processor 1140. The at least one processor 1140 may be configured to execute computer-readable instructions stored in a memory (e.g., the at least one memory 1130) to cause the device 1105 to perform various functions (e.g., functions or tasks supporting AI model indications among cells) . For example, the device 1105 or a component of the device 1105 may include at least one processor 1140 and at least one memory 1130 coupled with or to the at least one processor 1140, the at least one processor 1140 and the at least one memory 1130 configured to perform various functions described herein.

In some examples, the at least one processor 1140 may include multiple processors and the at least one memory 1130 may include multiple memories. One or more of the multiple processors may be coupled with one or more of the multiple memories, which may, individually or collectively, be configured to perform various functions described herein. In some examples, the at least one processor 1140 may be a component of a processing system, which may refer to a system (such as a series) of machines, circuitry (including, for example, one or both of processor circuitry (which may include the at least one processor 1140) and memory circuitry (which may include the at least one memory 1130) ) , or components, that receives or obtains inputs and processes the inputs to produce, generate, or obtain a set of outputs. The processing system may be configured to perform one or more of the functions described herein. For example, the at least one processor 1140 or a processing system including the at least one processor 1140 may be configured to, configurable to, or operable to cause the device 1105 to perform one or more of the functions described herein. Further, as described herein, being “configured to, ” being “configurable to, ” and being “operable to” may be used interchangeably and may be associated with a capability, when executing code 1135 (e.g., processor-executable code) stored in the at least one memory 1130 or otherwise, to perform one or more of the functions described herein.

The communications manager 1120 may support wireless communications in accordance with examples as disclosed herein. For example, the communications manager 1120 is capable of, configured to, or operable to support a means for receiving one or more first reference signals via at least one downlink beam. The communications manager 1120 is capable of, configured to, or operable to support a means for generating a metric based on the one or more first reference signals input to one or more candidate AI models for beam prediction. The communications manager 1120 is capable of, configured to, or operable to support a means for receiving a first signal from a first cell based on a first AI model, the first AI model selected from the one or more candidate AI models based on the metric. The communications manager 1120 is capable of, configured to, or operable to support a means for receiving an indication associated with an applicability of the first AI model for a second cell. The communications manager 1120 is capable of, configured to, or operable to support a means for receiving, based on the indication, a second signal from the second cell based on the first AI model.

By including or configuring the communications manager 1120 in accordance with examples as described herein, the device 1105 may support techniques for improved communication reliability, reduced latency, improved user experience related to reduced processing, reduced power consumption, more efficient utilization of communication resources, improved coordination between devices, longer battery life, or improved utilization of processing capability.

In some examples, the communications manager 1120 may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise in cooperation with the transceiver 1115, the one or more antennas 1125, or any combination thereof. Although the communications manager 1120 is illustrated as a separate component, in some examples, one or more functions described with reference to the communications manager 1120 may be supported by or performed by the at least one processor 1140, the at least one memory 1130, the code 1135, or any combination thereof. For example, the code 1135 may include instructions executable by the at least one processor 1140 to cause the device 1105 to perform various aspects of AI model indications among cells as described herein, or the at least one processor 1140 and the at least one memory 1130 may be otherwise configured to, individually or collectively, perform or support such operations.

FIG. 12 shows a flowchart illustrating a method 1200 that supports AI model indications among cells in accordance with one or more aspects of the present disclosure. The operations of the method 1200 may be implemented by a UE or its components as described herein. For example, the operations of the method 1200 may be performed by a UE 115 as described with reference to FIGs. 1 through 11. In some examples, a UE may execute a set of instructions to control the functional elements of the UE to perform the described functions. Additionally, or alternatively, the UE may perform aspects of the described functions using special-purpose hardware.

At 1205, the method may include receiving one or more first reference signals via at least one downlink beam. The operations of 1205 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1205 may be performed by a reference signal component 1025 as described with reference to FIG. 10.

At 1210, the method may include generating a metric based on the one or more first reference signals input to one or more candidate AI models for beam prediction. The operations of 1210 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1210 may be performed by a metric generation component 1030 as described with reference to FIG. 10.

At 1215, the method may include receiving a first signal from a first cell based on a first AI model, the first AI model selected from the one or more candidate AI models based on the metric. The operations of 1215 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1215 may be performed by a model component 1035 as described with reference to FIG. 10.

At 1220, the method may include receiving an indication associated with an applicability of the first AI model for a second cell. The operations of 1220 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1220 may be performed by an indication component 1040 as described with reference to FIG. 10.

At 1225, the method may include receiving, based on the indication, a second signal from the second cell based on the first AI model. The operations of 1225 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1225 may be performed by a model component 1035 as described with reference to FIG. 10.

FIG. 13 shows a flowchart illustrating a method 1300 that supports AI model indications among cells in accordance with one or more aspects of the present disclosure. The operations of the method 1300 may be implemented by a UE or its components as described herein. For example, the operations of the method 1300 may be performed by a UE 115 as described with reference to FIGs. 1 through 11. In some examples, a UE may execute a set of instructions to control the functional elements of the UE to perform the described functions. Additionally, or alternatively, the UE may perform aspects of the described functions using special-purpose hardware.

At 1305, the method may include receiving one or more first reference signals via at least one downlink beam. The operations of 1305 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1305 may be performed by a reference signal component 1025 as described with reference to FIG. 10.

At 1310, the method may include generating a metric based on the one or more first reference signals input to one or more candidate AI models for beam prediction. The operations of 1310 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1310 may be performed by a metric generation component 1030 as described with reference to FIG. 10.

At 1315, the method may include receiving a first signal from a first cell based on a first AI model, the first AI model selected from the one or more candidate AI models based on the metric. The operations of 1315 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1315 may be performed by a model component 1035 as described with reference to FIG. 10.

At 1320, the method may include receiving an indication associated with an applicability of the first AI model for a second cell. The operations of 1320 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1320 may be performed by an indication component 1040 as described with reference to FIG. 10.

At 1325, the method may include switching from the first cell to the second cell, where the first signal is received before the switch. The operations of 1325 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1325 may be performed by a switch component 1045 as described with reference to FIG. 10.

At 1330, the method may include receiving, based on the indication, a second signal from the second cell based on the first AI model, where the second signal is received after the switch. The operations of 1330 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1330 may be performed by a model component 1035 as described with reference to FIG. 10.

FIG. 14 shows a flowchart illustrating a method 1200 that supports AI model indications among cells in accordance with one or more aspects of the present disclosure. The operations of the method 1200 may be implemented by a network entity or its components as described herein. For example, the operations of the method 1200 may be performed by a network entity 105 as described with reference to FIGs. 1 through 13. In some examples, a network entity may execute a set of instructions to control the functional elements of the network entity to perform the described functions. Additionally, or alternatively, the network entity may perform aspects of the described functions using special-purpose hardware.

At 1205, the method may include outputting one or more first reference signals via at least one downlink beam. The operations of 1205 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1205 may be performed by a communications manager 102 as described with reference to FIG. 1.

At 1210, the method may include outputting a first signal from a first cell for reception by UE based at least in part of a first AI model for beam prediction. The operations of 1210 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1210 may be performed by a communications manager 102 as described with reference to FIG. 1.

At 1215, the method may include outputting an indication associated with an applicability of the first AI model for a second cell. The operations of 1215 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1215 may be performed by a communications manager 102 as described with reference to FIG. 1.

The following provides an overview of aspects of the present disclosure:

Aspect 1: A method for wireless communications at a UE, comprising: receiving one or more first reference signals via at least one downlink beam; generating a metric based at least in part on the one or more first reference signals input to one or more candidate AI models for beam prediction; receiving a first signal from a first cell based at least in part on a first AI model, the first AI model selected from the one or more candidate AI models based at least in part on the metric; receiving an indication associated with an applicability of the first AI model for a second cell; and receiving, based at least in part on the indication, a second signal from the second cell based at least in part on the first AI model.

Aspect 2: The method of aspect 1, further comprising: switching from the first cell to the second cell, wherein the first signal is received before the switch and the second signal is received after the switch.

Aspect 3: The method of aspect 2, the receiving the indication comprising: receiving the indication from the first cell before the switch from the first cell to the second cell.

Aspect 4: The method of aspect 1, the receiving the indication comprising: receiving the indication from the second cell after the switch from the first cell to the second cell.

Aspect 5: The method of any of aspects 2 through 4, the receiving the indication comprising: receiving the indication via an RRC configuration message or a MAC-CE message, wherein the indication identifies one or more cells, identifies one or more functionalities associated with the first AI model, or both, the one or more functionalities comprising spatial beam prediction, temporal beam prediction, spatial-temporal beam prediction, CSI compression, CSI prediction, or a combination thereof.

Aspect 6: The method of any of aspects 2 through 5, the receiving the indication comprising: receiving the indication based at least in part on a first quantity of beams of the first cell that is equal to a second quantity of beams of the second cell.

Aspect 7: The method of any of aspects 2 through 6, the receiving the indication comprising: receiving a configuration message that identifies one or more cells associated with the indication, wherein the indication identifies one or more functionalities associated with the first AI model; and transmitting information based at least in part on the configuration message to the first cell, the second cell, or both.

Aspect 8: The method of any of aspects 2 through 7, the receiving the indication comprising: receiving the indication based at least in part on a first functionality that is active for the first cell and for the second cell, the first functionality associated with the first AI model.

Aspect 9: The method of any of aspects 1 through 8, the indication comprising an identification of a functionality associated with the first AI model and a command for utilization of the first AI model for the second cell.

Aspect 10: The method of any of aspects 1 through 9, further comprising: detecting an absence of a second indication associated with the applicability of the first AI model for a third cell; receiving, based at least in part on the detection, one or more second reference signals via a second downlink beam; generating a second metric based at least in part on the one or more second reference signals input to the one or more candidate AI models for beam prediction; and receiving a third signal from the third cell based at least in part on a second AI model, the second AI model selected based at least in part on the second metric.

Aspect 11: The method of any of aspects 1 through 10, the indication comprising an identification of a zone or a cell identifier or both for application of the first AI model in the second cell.

Aspect 12: The method of any of aspects 1 through 11, the indication comprising an identification of a dataset, a configuration, a codebook, a deployment, or a combination thereof for a second cell that is identical to a corresponding identification for a first cell.

Aspect 13: The method of any of aspects 1 through 12, the indication agnostic to the first AI model.

Aspect 14: The method of any of aspects 1 through 13, further comprising: deactivating the first AI model at a first time that the UE is connected to the first cell; and activating the first AI model based at least in part on a condition that the UE is connected to the first cell at a second time subsequent to the first time.

Aspect 15: The method of any of aspects 1 through 14, further comprising: receiving a first virtual identifier for the first AI model; deactivating the first AI model; and activating the first AI model based at least in part on a condition that the first virtual identifier matches the indication.

Aspect 16: The method of any of aspects 1 through 15, further comprising: receiving a first report configuration associated with a first cell for information associated with a functionality of the first AI model; deactivating the first AI model; and activating the first AI model based at least in part on a condition that the indication is a second report configuration associated with the first cell for information associated with the functionality of the first AI model.

Aspect 17: An apparatus for wireless communications at a UE, comprising one or more memories storing processor-executable code, and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the UE to perform a method of any of aspects 1 through 16.

Aspect 18: An apparatus for wireless communications, comprising at least one means for performing a method of any of aspects 1 through 16.

Aspect 19: A non-transitory computer-readable medium storing code for wireless communications, the code comprising instructions executable by one or more processors to perform a method of any of aspects 1 through 16.

Aspect 20: A UE or wireless station, comprising a processing system that includes processor circuitry and memory circuitry that stores code and is coupled with the processor circuitry, the processing system configured to cause the UE or wireless station to perform a method of any of aspects 1 through 16.

It should be noted that the methods described herein describe possible implementations. The operations and the steps may be rearranged or otherwise modified and other implementations are possible. Further, aspects from two or more of the methods may be combined.

Although aspects of an LTE, LTE-A, LTE-A Pro, or NR system may be described for purposes of example, and LTE, LTE-A, LTE-A Pro, or NR terminology may be used in much of the description, the techniques described herein are applicable beyond LTE, LTE-A, LTE-A Pro, or NR networks. For example, the described techniques may be applicable to various other wireless communication systems such as Ultra Mobile Broadband (UMB) , Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi) , IEEE 802.16 (WiMAX) , IEEE 802.20, Flash-OFDM, as well as other systems and radio technologies not explicitly mentioned herein.

Information and signals described herein may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

The various illustrative blocks and components described in connection with the disclosure herein may be implemented or performed using a general-purpose processor, a DSP, an ASIC, a CPU, a graphics processing unit (GPU) , a neural processing unit (NPU) , an FPGA or other programmable logic device, 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 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, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration) . Any functions or operations described herein as being capable of being performed by a processor may be performed by multiple processors that, individually or collectively, are capable of performing the described functions or operations.

The functions described herein may be implemented using hardware, software executed by a processor, firmware, or any combination thereof. If implemented using software executed by a processor, the functions may be stored as or transmitted using one or more instructions or code of a computer-readable medium. Other examples and implementations are within the scope of the disclosure and appended claims. For example, due to the nature of software, functions described herein may be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations.

Computer-readable media includes both non-transitory computer storage media and communication media including any medium that facilitates transfer of a computer program from one location to another. A non-transitory storage medium may be any available medium that may be accessed by a general-purpose or special-purpose computer. By way of example, and not limitation, non-transitory computer-readable media may include RAM, ROM, electrically erasable programmable ROM (EEPROM) , flash memory, compact disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that may be used to carry or store desired program code means in the form of instructions or data structures and that may be accessed by a general-purpose or special-purpose computer or a general-purpose or special-purpose processor. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) , or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of computer-readable medium. Disk and disc, as used herein, include CD, laser disc, optical disc, digital versatile disc (DVD) , floppy disk, and Blu-ray disc. Disks may reproduce data magnetically, and discs may reproduce data optically using lasers. Combinations of the above are also included within the scope of computer-readable media. Any functions or operations described herein as being capable of being performed by a memory may be performed by multiple memories that, individually or collectively, are capable of performing the described functions or operations.

As used herein, including in the claims, “or” as used in a list of items (e.g., a list of items prefaced by a phrase such as “at least one of” or “one or more of” ) indicates an inclusive list such that, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C) . Also, as used herein, the phrase “based on” shall not be construed as a reference to a closed set of conditions. For example, an example step that is described as “based on condition A” may be based on both a condition A and a condition B without departing from the scope of the present disclosure. In other words, as used herein, the phrase “based on” shall be construed in the same manner as the phrase “based at least in part on. ”

As used herein, including in the claims, the article “a” before a noun is open-ended and understood to refer to “at least one” of those nouns or “one or more” of those nouns. Thus, the terms “a, ” “at least one, ” “one or more, ” and “at least one of one or more” may be interchangeable. For example, if a claim recites “acomponent” that performs one or more functions, each of the individual functions may be performed by a single component or by any combination of multiple components. Thus, the term “acomponent” having characteristics or performing functions may refer to “at least one of one or more components” having a particular characteristic or performing a particular function. Subsequent reference to a component introduced with the article “a” using the terms “the” or “said” may refer to any or all of the one or more components. For example, a component introduced with the article “a” may be understood to mean “one or more components, ” and referring to “the component” subsequently in the claims may be understood to be equivalent to referring to “at least one of the one or more components. ” Similarly, subsequent reference to a component introduced as “one or more components” using the terms “the” or “said” may refer to any or all of the one or more components. For example, referring to “the one or more components” subsequently in the claims may be understood to be equivalent to referring to “at least one of the one or more components. ”

The term “determine” or “determining” encompasses a variety of actions and, therefore, “determining” can include calculating, computing, processing, deriving, investigating, looking up (such as via looking up in a table, a database, or another data structure) , ascertaining, and the like. Also, “determining” can include receiving (e.g., receiving information) , accessing (e.g., accessing data stored in memory) , and the like. Also, “determining” can include resolving, obtaining, selecting, choosing, establishing, and other such similar actions.

In the appended figures, similar components or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If just the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label or other subsequent reference label.

The description set forth herein, in connection with the appended drawings, describes example configurations and does not represent all the examples that may be implemented or that are within the scope of the claims. The term “example” used herein means “serving as an example, instance, or illustration” and not “preferred” or “advantageous over other examples. ” The detailed description includes specific details for the purpose of providing an understanding of the described techniques. These techniques, however, may be practiced without these specific details. In some figures, known structures and devices are shown in block diagram form in order to avoid obscuring the concepts of the described examples.

The description herein is provided to enable a person having ordinary skill in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to a person having ordinary skill in the art, and the generic principles defined herein may be applied to other variations without departing from the scope of the disclosure. Thus, the disclosure is not limited to the examples and designs described herein but is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.

Claims

An apparatus for wireless communication at a user equipment (UE) , comprising:

one or more memories; and

one or more processors coupled with the one or more memories and configured to cause the UE to:

receive one or more first reference signals via at least one downlink beam;

generate a metric based at least in part on the one or more first reference signals input to one or more candidate artificial intelligence models for beam prediction;

receive a first signal from a first cell based at least in part on a first artificial intelligence model, the first artificial intelligence model selected from the one or more candidate artificial intelligence models based at least in part on the metric;

receive an indication associated with an applicability of the first artificial intelligence model for a second cell; and

receive, based at least in part on the indication, a second signal from the second cell based at least in part on the first artificial intelligence model.
The apparatus of claim 1, wherein the one or more processors are configured to cause the UE to:

switch from the first cell to the second cell, wherein the first signal is received before the switch and the second signal is received after the switch.
The apparatus of claim 2, wherein, to receive the indication, the one or more processors are configured to cause the UE to:

receive the indication from the first cell before the switch from the first cell to the second cell.
The apparatus of claim 2, wherein, to receive the indication, the one or more processors are configured to cause the UE to:

receive the indication from the second cell after the switch from the first cell to the second cell.
The apparatus of claim 2, wherein, to receive the indication, the one or more processors are configured to cause the UE to:

receive the indication via a radio resource control (RRC) configuration message or a medium access control-control element (MAC-CE) message, wherein the indication identifies one or more cells, identifies one or more functionalities associated with the first artificial intelligence model, or both, the one or more functionalities comprising spatial beam prediction, temporal beam prediction, spatial-temporal beam prediction, channel state information (CSI) compression, CSI prediction, or a combination thereof.
The apparatus of claim 2, wherein, to receive the indication, the one or more processors are configured to cause the UE to:

receive the indication based at least in part on a first quantity of beams of the first cell that is equal to a second quantity of beams of the second cell.
The apparatus of claim 2, wherein, to receive the indication, the one or more processors are configured to cause the UE to:

receive a configuration message that identifies one or more cells associated with the indication, wherein the indication identifies one or more functionalities associated with the first artificial intelligence model; and

transmit information based at least in part on the configuration message to the first cell, the second cell, or both.
The apparatus of claim 2, wherein, to receive the indication, the one or more processors are configured to cause the UE to:

receive the indication based at least in part on a first functionality that is active for the first cell and for the second cell, the first functionality associated with the first artificial intelligence model.
The apparatus of claim 1, the indication comprising an identification of a functionality associated with the first artificial intelligence model and a command for utilization of the first artificial intelligence model for the second cell.
The apparatus of claim 1, wherein the one or more processors are configured to cause the UE to:

detect an absence of a second indication associated with the applicability of the first artificial intelligence model for a third cell;

receive, based at least in part on the detection, one or more second reference signals via a second downlink beam;

generate a second metric based at least in part on the one or more second reference signals input to the one or more candidate artificial intelligence models for beam prediction; and

receive a third signal from the third cell based at least in part on a second artificial intelligence model, the second artificial intelligence model selected based at least in part on the second metric.
The apparatus of claim 1, the indication comprising an identification of a zone or a cell identifier or both for application of the first artificial intelligence model in the second cell.
The apparatus of claim 1, the indication comprising an identification of a dataset, a configuration, a codebook, a deployment, or a combination thereof for a second cell that is identical to a corresponding identification for a first cell.
The apparatus of claim 1, the indication agnostic to the first artificial intelligence model.
The apparatus of claim 1, wherein the one or more processors are configured to cause the UE to:

deactivate the first artificial intelligence model at a first time that the UE is connected to the first cell; and

activate the first artificial intelligence model based at least in part on a condition that the UE is connected to the first cell at a second time subsequent to the first time.
The apparatus of claim 1, wherein the one or more processors are configured to cause the UE to:

receive a first virtual identifier for the first artificial intelligence model;

deactivate the first artificial intelligence model; and

activate the first artificial intelligence model based at least in part on a condition that the first virtual identifier matches the indication.
The apparatus of claim 1, wherein the one or more processors are configured to cause the UE to:

receive a first report configuration associated with a first cell for information associated with a functionality of the first artificial intelligence model;

deactivate the first artificial intelligence model; and

activate the first artificial intelligence model based at least in part on a condition that the indication is a second report configuration associated with the first cell for information associated with the functionality of the first artificial intelligence model.
A method for wireless communications at a user equipment (UE) , comprising:

receiving one or more first reference signals via at least one downlink beam;

generating a metric based at least in part on the one or more first reference signals input to one or more candidate artificial intelligence models for beam prediction;

receiving a first signal from a first cell based at least in part on a first artificial intelligence model, the first artificial intelligence model selected from the one or more candidate artificial intelligence models based at least in part on the metric;

receiving an indication associated with an applicability of the first artificial intelligence model for a second cell; and

receiving, based at least in part on the indication, a second signal from the second cell based at least in part on the first artificial intelligence model.
The method of claim 17, further comprising:

switching from the first cell to the second cell, wherein the first signal is received before the switch and the second signal is received after the switch.
A non-transitory computer-readable medium storing code for wireless communications, the code comprising instructions executable by one or more processors to cause a user equipment (UE) to:

receive one or more first reference signals via at least one downlink beam;

generate a metric based at least in part on the one or more first reference signals input to one or more candidate artificial intelligence models for beam prediction;

receive a first signal from a first cell based at least in part on a first artificial intelligence model, the first artificial intelligence model selected from the one or more candidate artificial intelligence models based at least in part on the metric;

receive an indication associated with an applicability of the first artificial intelligence model for a second cell; and

receive, based at least in part on the indication, a second signal from the second cell based at least in part on the first artificial intelligence model.
The non-transitory computer-readable medium of claim 19, wherein the instructions are further executable by the one or more processors to cause the UE to:

switch from the first cell to the second cell, wherein the first signal is received before the switch and the second signal is received after the switch.