WO2025118232A1

WO2025118232A1 - Correspondence between artificial intelligence models and channel state information reporting

Info

Publication number: WO2025118232A1
Application number: PCT/CN2023/137071
Authority: WO
Inventors: Qiaoyu Li; Mahmoud Taherzadeh Boroujeni; Hamed Pezeshki
Original assignee: Qualcomm Inc
Current assignee: Qualcomm Inc
Priority date: 2023-12-07
Filing date: 2023-12-07
Publication date: 2025-06-12
Anticipated expiration: 2026-06-07

Abstract

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive from a network entity a first message that indicates a correspondence between one or more artificial intelligence (AI) /machine learning (ML) models or functionalities, and one or more channel state information (CSI) reports. In some cases, the correspondence may be between a single CSI report and one or more AI models or functionalities. In some cases, the correspondence may between a single AI model or functionality and one or more CSI reports. The UE may generate a set of measured values associated with a set of reference signals, where a set of predicted values is generated using a first AI model or functionality in accordance with the correspondence and based on the set of measured values. The UE may transmit to the network entity a CSI report message that indicates the set of predicted values.

Description

CORRESPONDENCE BETWEEN ARTIFICIAL INTELLIGENCE MODELS AND CHANNEL STATE INFORMATION REPORTING

TECHNICAL FIELD

The following relates to wireless communications, including correspondence between artificial intelligence (AI) models and channel state information (CSI) reporting.

BACKGROUND

Wireless communications 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 communications system may include one or more base stations, each supporting wireless communication for communication devices, which may be known as user equipment (UE) .

SUMMARY

The described techniques relate to improved methods, systems, devices, and apparatuses that support correspondence between artificial intelligence (AI) models and channel state information (CSI) reporting. For example, a user equipment (UE) may support AI and/or machine learning (ML) -based beam prediction. Such a UE may predict measurements (e.g., reference signal received power (RSRP) measurements, signal-to-interference-plus-noise ratio (SINR) measurements, channel impulse response (CIR) measurements) for a set of directional beams based on measurements of synchronization system blocks (SSBs) or CSI reference signals (CSI-RSs) via one or more directional beams (e.g., SSB beams, CSI-RS beams) . The UE may train a given AI/ML model/functionality using measurements of a first set of beams (e.g., set-B beams) to predict measurements for a set of future beams (e.g., set-A beams) . Further, a trained AI/ML model/functionality may use measurements of a third set of beams (e.g., set-B beams) to predict measurements for a fourth set of beams (e.g., set-A beams) (e.g., which process may be referred to as beam inference) , which the UE 115 may report in a beam measurement report. The mapping of the beam measurements to inputs of the AI model may affect the output of the AI model (e.g., the predicted measurements) .

In some examples, the UE may receive from the network entity a message indicating a correspondence between one or more AI/ML models/functionalities and one or more CSI reports. In some cases, such correspondence may follow a combination of one or more correspondence techniques. In a first technique, the UE may receive an indication of a correspondence (e.g., a linkage) between one or more AI/ML models/functionalities and a given CSI report, based on signaling associated with the CSI report (e.g., one or multiple AI/ML models/functionalities may be associated with a single CSI report) . In accordance with the first technique, the UE may autonomously select an AI/ML model/functionality from the one or more AI/ML models/functionalities and use the selected AI/ML model/functionality to generate a set of predicted values to include in the CSI report. In some examples, the UE may receive the indication of the correspondence between the one or more AI/ML models/functionalities and the CSI report via a radio resource control (RRC) message, a medium access control-control element (MAC-CE) message, a downlink control information (DCI) message, or any combination thereof.

In a second technique, the UE may receive an indication of a correspondence (e.g., a linkage) between one or more CSI reports and one or more AI/ML models/functionalities (e.g., one or multiple CSI reports may be associated with a single AI/ML model/functionality) . For instance, a given geographic coverage area or set of cells of the network entity may support the use of some AI/ML model/functionality, and as such, the network entity may indicate which one or more CSI reports are applicable for the AI/ML model/functionality. In some examples, the UE may receive the indication of the correspondence between one or more CSI reports and the AI/ML model/functionality via an RRC message or a MAC-CE message, or both.

A method for wireless communications by a UE is described. The method may include receiving a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports, generating a set of measured values associated with a set of reference signals, where a set of predicted values is generated using a first AI model or functionality, or both, in accordance with the correspondence and based on the set of measured values, and transmitting a CSI report message that indicates the set of predicted values.

A UE for wireless communications is described. The UE may include one or more memories storing processor executable code, and one or more processors coupled with (e.g., operatively, communicatively, functionally, electronically, or electrically) the one or more memories. The one or more processors may individually or collectively operable to execute the code (e.g., directly, indirectly, after pre-processing, without pre-processing) to cause the UE to receive a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports, generate a set of measured values associated with a set of reference signals, where a set of predicted values is generated using a first AI model or functionality, or both, in accordance with the correspondence and based on the set of measured values, and transmit a CSI report message that indicates the set of predicted values.

Another UE for wireless communications is described. The UE may include means for receiving a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports, means for generating a set of measured values associated with a set of reference signals, where a set of predicted values is generated using a first AI model or functionality, or both, in accordance with the correspondence and based on the set of measured values, and means for transmitting a CSI report message that indicates the set of predicted values.

A non-transitory computer-readable medium storing code for wireless communications is described. The code may include instructions executable by one or more processors (e.g., directly, indirectly, after pre-processing, without pre-processing) to receive a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports, generate a set of measured values associated with a set of reference signals, where a set of predicted values is generated using a first AI model or functionality, or both, in accordance with the correspondence and based on the set of measured values, and transmit a CSI report message that indicates the set of predicted values.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the correspondence indicates that a set of AI models or functionalities and the method, apparatuses, and non-transitory computer-readable medium may include further operations, features, means, or instructions for determining the first AI model or functionality, or both, from the set of AI models or functionalities, or both based on an autonomous selection procedure, where the set of predicted values that may be generated using the first AI model or functionality, or both, based on the autonomous selection procedure.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, transmitting the CSI report message may include operations, features, means, or instructions for transmitting, as part of the CSI report message, an indication of a first model or functionality identifier associated with the first AI model or functionality, 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 a second message indicating a first mapping of respective first identifiers of a first set of resources to respective output feature identifiers of an AI model or functionality, or both, and a second mapping of respective second identifiers of a second set of resources to respective input feature identifiers of the AI model or functionality, or both, for each AI model or functionality, or both, of the one or more AI models or functionalities, or both.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the first mapping and the second mapping may be based on a CSI report quantity associated with the CSI report message being associated with directional beam prediction.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the first message indicating the correspondence may be a RRC message including a CSI report setting indicating that the one or more AI models or functionalities, or both, may be associated with a single CSI report.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the first message indicating the correspondence may be a RRC message including an aperiodic CSI report setting associated with a set of CSI report configurations for a single CSI report, each CSI report configuration of the set of CSI report configurations indicates a respective set of resources associated with a respective AI model or functionality, or both, and each of the respective AI models or functionalities, or both, may be associated with the single CSI report.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the first message indicating the correspondence may be a MAC-CE message for activation of a single semi-persistent CSI report and indicates that the one or more AI models or functionalities, or both, may be associated with the single semi-persistent CSI report.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the first message indicating the correspondence may be a MAC-CE message indicating a change from one or more first AI models or functionalities, or both, being associated with a single CSI report to one or more second AI models or functionalities, or both, being associated with the single CSI report.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the first message indicating the correspondence may be a DCI message including one or more fields and at least one field of the one or more fields indicates one or more respective AI models or functionalities, or both, being associated with a respective CSI report.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the first message indicates a respective model or functionality identifier associated with each of the one or more AI models or functionalities, or both.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the first message indicates a subset of model or functionality identifiers from a set of model or functionality identifiers configured via one or more control messages and each model or functionality identifier of the subset of model or functionality identifiers may be associated with a respective AI model or functionality, or both, of the one or more AI models or functionalities, 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 a first RRC message that indicates one or more model or functionality identifiers each associated with a respective AI model or functionality, or both, supported for a geographic coverage area or one or more cells, or any combination thereof, associated with a network entity.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the first message indicating the correspondence may be a second RRC message that indicates one or more respective CSI reports associated with each respective AI model or functionality, or both, indicated in the first RRC message.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the first message indicating the correspondence may be a MAC-CE message that activates one or more of the respective AI models or functionalities, or both, indicated in the first RRC message and further indicates one or more respective CSI reports associated with each of the activated AI models or functionalities, or both.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the first message indicates a subset of model or functionality identifiers of the one or more model or functionality identifiers indicated in the first RRC message, the subset of model or functionality identifiers associated with a subset of AI models or functionalities, or both.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the subset of model or functionality identifiers may be indicated via a set of indices each associated with a respective model or functionality identifier of the one or more model or functionality identifiers and a first value for an index of the set of indices indicates that an associated model or functionality identifier may be included in the subset of model or functionality identifiers.

In some examples of the method, UEs, and non-transitory computer-readable medium described herein, the subset of model or functionality identifiers may be indicated via a list of model or functionality identifiers that includes the subset of model or functionality identifiers and an ordering of the list of model or functionality identifiers may be based on an order of AI models or functionalities, or both, for generating the set of predicted values.

A method for wireless communications by a network entity is described. The method may include outputting a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports, outputting a set of reference signals, and obtaining a CSI report message that indicates a set of predicted values that are based on a set of measured values associated with the set of reference signals, where the set of predicted values of the CSI report message is associated with a first AI model or functionality, or both, in accordance with the correspondence.

A network entity for wireless communications is described. The network entity may include one or more memories storing processor executable code, and one or more processors coupled with (e.g., operatively, communicatively, functionally, electronically, or electrically) the one or more memories. The one or more processors may individually or collectively operable to execute the code (e.g., directly, indirectly, after pre-processing, without pre-processing) to cause the network entity to output a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports, output a set of reference signals, and obtain a CSI report message that indicates a set of predicted values that are based on a set of measured values associated with the set of reference signals, where the set of predicted values of the CSI report message is associated with a first AI model or functionality, or both, in accordance with.

Another network entity for wireless communications is described. The network entity may include means for outputting a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports, means for outputting a set of reference signals, and means for obtaining a CSI report message that indicates a set of predicted values that are based on a set of measured values associated with the set of reference signals, where the set of predicted values of the CSI report message is associated with a first AI model or functionality, or both, in accordance with the correspondence.

A non-transitory computer-readable medium storing code for wireless communications is described. The code may include instructions executable by one or more processors (e.g., directly, indirectly, after pre-processing, without pre-processing) to output a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports, output a set of reference signals, and obtain a CSI report message that indicates a set of predicted values that are based on a set of measured values associated with the set of reference signals, where the set of predicted values of the CSI report message is associated with a first AI model or functionality, or both, in accordance with the correspondence.

In some examples of the method, network entities, and non-transitory computer-readable medium described herein, obtaining the CSI report message may include operations, features, means, or instructions for obtaining, as part of the CSI report message, an indication of a first model or functionality identifier associated with the first AI model or functionality, or both.

Some examples of the method, network entities, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for outputting a second message indicating a first mapping of respective first identifiers of a first set of resources to respective output feature identifiers of an AI model or functionality, or both, and a second mapping of respective second identifiers of a second set of resources to respective input feature identifiers of the AI model or functionality, or both, for each AI model or functionality, or both, of the one or more AI models or functionalities, or both.

In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the first mapping and the second mapping may be based on a CSI report quantity associated with the CSI report message being associated with directional beam prediction.

In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the first message indicating the correspondence may be a RRC message including a CSI report setting indicating that the one or more AI models or functionalities, or both, may be associated with a single CSI report.

In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the first message indicating the correspondence may be a RRC message including an aperiodic CSI report setting associated with a set of CSI report configurations for a single CSI report, each CSI report configuration of the set of CSI report configurations indicates a respective set of resources associated with a respective AI model or functionality, or both, and each of the respective AI models or functionalities, or both, may be associated with the single CSI report.

In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the first message indicating the correspondence may be a MAC-CE message for activation of a single semi-persistent CSI report and indicates that the one or more AI models or functionalities, or both, may be associated with the single semi-persistent CSI report.

In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the first message indicating the correspondence may be a MAC-CE message indicating a change from one or more first AI models or functionalities, or both, being associated with a single CSI report to one or more second AI models or functionalities, or both, being associated with the single CSI report.

In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the first message indicating the correspondence may be a DCI message including one or more fields and at least one field of the one or more fields indicates one or more respective AI models or functionalities, or both, being associated with a respective CSI report.

In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the first message indicates a respective model or functionality identifier associated with each of the one or more AI models or functionalities, or both.

In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the first message indicates a subset of model or functionality identifiers from a set of model or functionality identifiers configured via one or more control messages, where each model or functionality identifier of the subset of model or functionality identifiers may be associated with a respective AI model or functionality, or both, of the one or more AI models or functionalities, or both.

Some examples of the method, network entities, and non-transitory computer-readable medium described herein may further include operations, features, means, or instructions for outputting a first RRC message that indicates one or more model or functionality identifiers each associated with a respective AI model or functionality, or both, supported for a geographic coverage area or one or more cells, or any combination thereof, associated with the network entity.

In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the first message indicating the correspondence may be a second RRC message that indicates one or more respective CSI reports associated with each respective AI model or functionality, or both, indicated in the first RRC message.

In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the first message indicating the correspondence may be a MAC-CE message that activates one or more of the respective AI models or functionalities, or both, indicated in the first RRC message and further indicates one or more respective CSI reports associated with each of the activated AI models or functionalities, or both.

In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the first message indicates a subset of model or functionality identifiers of the one or more model or functionality identifiers indicated in the first RRC message, the subset of model or functionality identifiers associated with a subset of AI models or functionalities, or both.

In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the subset of model or functionality identifiers may be indicated via a set of indices each associated with a respective model or functionality identifier of the one or more model or functionality identifiers and a first value for an index of the set of indices indicates that an associated model or functionality identifier may be included in the subset of model or functionality identifiers.

In some examples of the method, network entities, and non-transitory computer-readable medium described herein, the subset of model or functionality identifiers may be indicated via a list of model or functionality identifiers that includes the subset of model or functionality identifiers and an ordering of the list of model or functionality identifiers may be based on an order of AI models or functionalities, or both, for generating the set of predicted values.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of a wireless communications system that supports correspondence between artificial intelligence (AI) models and channel state information (CSI) reporting in accordance with one or more aspects of the present disclosure.

FIG. 2 shows an example of a wireless communications system that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure.

FIG. 3 shows an example of a process flow that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure.

FIGs. 4 and 5 show block diagrams of devices that support correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure.

FIG. 6 shows a block diagram of a communications manager that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure.

FIG. 7 shows a diagram of a system including a device that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure.

FIGs. 8 and 9 show block diagrams of devices that support correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure.

FIG. 10 shows a block diagram of a communications manager that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure.

FIG. 11 shows a diagram of a system including a device that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure.

FIGs. 12 through 15 show flowcharts illustrating methods that support correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure.

DETAILED DESCRIPTION

A user equipment (UE) may support artificial intelligence (AI) and/or machine learning (ML) -based beam prediction. Such a UE may collect data measurements (e.g., reference signal received power (RSRP) measurements, signal-to-interference-plus-noise-ratio (SINR) measurements, channel impulse response (CIR) measurements) for one or more directional beams based on measurements of synchronization system blocks (SSBs) or channel state information (CSI) reference signals (CSI-RSs) , for example, via SSB beams (e.g., directional beams via which SSBs are transmitted/received) and/or via CSI-RS beams (e.g., directional beams via which CSI-RSs are transmitted/received) . The UE may train a given AI/ML model/functionality using measurements of a first set of beams (e.g., set-B beams) to predict measurements for a set of future beams (e.g., set-A beams) . A trained AI/ML model/functionality may use measurements of a third set of beams (e.g., set-B beams) to predict measurements for a fourth set of beams (e.g., set-A beams) (e.g., which process may be referred to as beam inference) , which the UE 115 may report in a beam measurement report. The mapping of the beam measurements to inputs of the AI/ML model/functionality may affect the output of the AI/ML model/functionality (e.g., the predicted measurements) . Thus, for accurate use of an AI/ML model/functionality, the UE and network entity (e.g., the gNB) may agree on a mapping and order of channel measurement resources (CMRs) or interference measurement resources (IMRs) to AI/ML model/functionality inputs.

To allow for flexibility of using multiple beamforming codebooks with reference to mapping set-B beams and set-A beams to the input/output features of an AI/ML model/functionality, a set of trained AI/ML models/functionalities may be standardized or predefined. For example, there may be a defined mapping or ordering of beam measurements (e.g., which SSB or CSI-RS index) to AI/ML model/functionality inputs and/or AI/ML model/functionality outputs for some AI/ML models/functionalities. Thus, assuming either offline-or online-based parameter consistency across model training and inference, it may be advantageous for the UE to identify a correspondence between applicable AI/ML models/functionalities from the set of AI/ML models/functionalities and one or more CSI reports that carry beam predication results (e.g., during model/functionality inference procedures) .

According to the techniques described herein, the UE may receive, from the network entity, a message indicating a correspondence (e.g., a linkage) between one or more AI/ML models/functionalities and one or more CSI reports (e.g., one or more CSI report configuration identifiers (IDs) ) . In some cases, such correspondence may be indicating in accordance with one or a combination of techniques. In a first technique, the UE may receive an indication regarding a linkage between one or more AI/ML models/functionalities and a given CSI report, based on signaling associated with the given CSI report (e.g., one or multiple AI/ML models/functionalities may be associated with a single CSI report) . In accordance with the first technique, the UE may autonomously select an AI/ML model/functionality from the one or more AI/ML models/functionalities and use the selected AI/ML model/functionality to generate a set of predicted values to include in the single CSI report. In some examples, the UE may receive the indication of the linkage between one or more AI/ML models/functionalities and the single CSI report via a radio resource control (RRC) message, a medium access control-control element (MAC-CE) message, a downlink control information (DCI) message, or any combination thereof.

In a second technique, the UE may receive an indication regarding a correspondence (e.g., a linkage) between one or more CSI reports and one or more AI/ML models/functionalities (e.g., one or multiple CSI reports associated with a single AI/ML model/functionality ID) . For instance, a given geographic coverage area or set of cells of the network entity may support the use of a given AI/ML model/functionality, and as such, the network entity may indicate which one or more CSI reports are applicable for the given AI/ML model/functionality. In some examples, the UE may receive the indication of the linkage between one or more CSI reports and the single AI/ML model/functionality via an RRC message or a MAC-CE message.

Agreement of correspondence between AI/ML models/functionalities and CSI reports may enable latency and overhead reduction, as well as beam selection accuracy improvement for beam management purposes (e.g., including beam prediction in the time and/or spatial domain) . Agreement of the correspondence may further enable life cycle management of AI/ML models/functionalities, including model training, model deployment, model inference, model monitoring, and model updating.

Aspects of the disclosure are initially described in the context of wireless communications systems 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 correspondence between AI/ML models/functionalities and CSI reporting.

FIG. 1 shows an example of a wireless communications system 100 that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure. The wireless communications system 100 may include one or more network entities 105, one or more UEs 115, and a core network 130. In some examples, the wireless communications system 100 may be a Long Term Evolution (LTE) network, an LTE-Advanced (LTE-A) network, an LTE-APro 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 communications 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 one or more communication links 125 (e.g., a radio frequency (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 one or more communication links 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 communications 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, such as other UEs 115 or network entities 105, as shown in FIG. 1. In some examples, a UE 115 may support AI and/or ML models and/or functionalities, which the UE 115 may use to perform various wireless communications procedures (e.g., CSI prediction, beam selection and/or beam prediction, among other examples) . For example, the UE 115 may generate inference data associated with one or more AI/ML models/functionalities. Additionally, or alternatively, the UE 115 may perform life cycle management (LCM) operations for a given AI/ML model and/or functionality (e.g., model or functionality selection, activation, deactivation, switching, and fallback, among other examples) in accordance with one or more AI/ML models/functionalities. As described herein, an AI functionality or AI model may be referred to as an ML functionality or ML model, or vice versa. In some aspects, LCM may be model-based or functionality-based LCM procedures. The terms “AI” and “ML” may, in some examples, be used interchangeably to refer to similar technologies, models, functions, algorithms, or any combination thereof. Likewise, the terms “model” and “functionality” may be used interchangeably. In some examples, ML operations may be considered a subset of AI operations. In any case, aspects of the features described herein may be referred to as AI functionalities, AI functions, AI models, AI services, AI operations, among other examples, but these aspects may be similarly applicable to ML functionalities, ML functions, ML models, ML services, ML operations, or any combination thereof. Thus, reference to either “ML” or “AI” herein may refer to ML, AI, or both, and the terms “AI” or “ML” should not be considered limiting to the scope of the claims or the disclosure.

As described herein, a node of the wireless communications 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, among other examples, may include disclosure of the UE 115, network entity 105, apparatus, device, computing system, among other examples, 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 the core network 130, or with one another, or both. For example, network entities 105 may communicate with the core network 130 via one or more backhaul communication links 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 a backhaul communication link 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 a 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 links 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) , 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 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 a 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 a single network entity 105 (e.g., 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 two or more network entities 105, such as an integrated access 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) 160, a distributed unit (DU) 165, a radio unit (RU) 170, a RAN Intelligent Controller (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) 180 system, 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 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, and 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., layer 3 (L3) , layer 2 (L2) ) functionality and signaling (e.g., RRC, service data adaption protocol (SDAP) , Packet Data Convergence Protocol (PDCP) ) . The CU 160 may be connected to one or more DUs 165 or RUs 170, and the one or more DUs 165 or RUs 170 may host lower protocol layers, such as layer 1 (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 more RUs 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 one or more DUs 165 via a midhaul communication link 162 (e.g., F1, F1-c, F1-u) , and a DU 165 may be connected to one or more RUs 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 105 that are in communication via such communication links.

In wireless communications systems (e.g., wireless communications 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 network entities 105 (e.g., IAB nodes 104) may be partially controlled by each other. One or more IAB nodes 104 may be referred to as a donor entity or an IAB donor. One or more DUs 165 or one or more RUs 170 may be partially controlled by one or more CUs 160 associated with a donor network entity 105 (e.g., a donor base station 140) . 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 (e.g., backhaul communication links 120) . IAB nodes 104 may include an IAB mobile termination (IAB-MT) controlled (e.g., scheduled) by DUs 165 of a coupled IAB donor. An IAB-MT may include 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 an IAB node 104 used for access via the DU 165 of the IAB node 104 (e.g., referred to as virtual IAB-MT (vIAB-MT) ) . In some examples, the IAB nodes 104 may include DUs 165 that support communication links with additional entities (e.g., IAB nodes 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., one or more IAB nodes 104 or components of IAB nodes 104) may be configured to operate according to the techniques described herein.

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 correspondence between AI models and CSI reporting 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., IAB nodes 104, DUs 165, CUs 160, RUs 170, RIC 175, SMO 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 multimedia/entertainment device (e.g., a radio, a MP3 player, or a video device) , a camera, a gaming device, a navigation/positioning device (e.g., GNSS (global navigation satellite system) devices based on, for example, GPS (global positioning system) , Beidou, GLONASS, or Galileo, or a terrestrial-based device) , a tablet computer, a laptop computer, a netbook, a smartbook, a personal computer, a smart device, a wearable device (e.g., a smart watch, smart clothing, smart glasses, virtual reality goggles, a smart wristband, smart jewelry (e.g., a smart ring, a smart bracelet) ) , a drone, a robot/robotic device, a vehicle, a vehicular device, a meter (e.g., parking meter, electric meter, gas meter, water meter) , a monitor, a gas pump, an appliance (e.g., kitchen appliance, washing machine, dryer) , a location tag, a medical/healthcare device, an implant, a sensor/actuator, a display, or any other suitable device configured to communicate via a wireless or wired medium. 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, or vehicles, meters, among other examples. In an aspect, techniques disclosed herein may be applicable to MTC or IoT UEs. MTC or IoT UEs may include MTC/enhanced MTC (eMTC, also referred to as CAT-M, Cat M1) UEs, NB-IoT (also referred to as CAT NB1) UEs, as well as other types of UEs. eMTC and NB-IoT may refer to future technologies that may evolve from or may be based on these technologies. For example, eMTC may include FeMTC (further eMTC) , eFeMTC (enhanced further eMTC) , and mMTC (massive MTC) , and NB-IoT may include eNB-IoT (enhanced NB-IoT) , and FeNB-IoT (further enhanced NB-IoT) .

The UEs 115 described herein may be able to communicate with various types of devices, such as other UEs 115 that may sometimes act 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 one or more communication links 125 (e.g., an access link) using resources associated with one or more carriers. The term “carrier” may refer to a set of RF spectrum resources having a defined physical layer structure for supporting the communication links 125. For example, a carrier used for a communication link 125 may include a portion of a RF spectrum band (e.g., a bandwidth part (BWP) ) that is operated according to one or more physical layer channels for a given radio access technology (e.g., LTE, LTE-A, LTE-A Pro, NR) . Each physical 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 communications 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 105) .

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.

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 communications systems 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 communications 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 communications 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 multiple UEs 115 and UE-specific search space sets for sending control information to a specific UE 115.

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) , or others) . 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 lower-powered network entity 105 (e.g., a lower-powered base station 140) , as compared with 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 multiple 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 110. In some examples, different coverage areas 110 associated with different technologies may overlap, but the different coverage areas 110 may be supported by the same network entity 105. In some other examples, the overlapping coverage areas 110 associated with different technologies may be supported by different network entities 105. The wireless communications system 100 may include, for example, a heterogeneous network in which different types of the network entities 105 provide coverage for various coverage areas 110 using the same or different radio access technologies.

The wireless communications system 100 may be configured to support ultra-reliable communications or low-latency communications, or various combinations thereof. For example, the wireless communications 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 115 via a device-to-device (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 each of the other 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.

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 communications 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 100 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 communications system 100 may utilize both licensed and unlicensed RF spectrum bands. For example, the wireless communications system 100 may employ License Assisted Access (LAA) , LTE-Unlicensed (LTE-U) radio access technology, 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.

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 transmitting device (e.g., a transmitting network entity 105, a transmitting UE 115) along a single beam direction (e.g., a direction associated with the receiving device, such as a receiving network entity 105 or a receiving 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 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 signal-to-noise ratio (SNR) , or otherwise acceptable signal quality based on listening according to multiple beam directions) .

The wireless communications 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., a communication link 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 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.

In some examples of wireless communications system 100, a UE 115 may receive from a network entity 105 a message indicating a correspondence between one or more AI models and one or more CSI reports. In some cases, such correspondence may be indicated in accordance with one or more techniques. In a first technique, the UE 115 may receive an indication regarding a linkage between one or more AI models and a given CSI report, based on signaling associated with the given CSI report (e.g., multiple AI models associated with a single CSI report) . In accordance with the first technique, the UE 115 may autonomously select an AI model from the one or more AI models and use the selected AI model to generate a set of predicted values to include in the single CSI report. In some examples, the UE 115 may receive the indication of the linkage between one or more AI models and the single CSI report via an RRC message, a MAC-CE message, or a DCI message. In a second technique, the UE 115 may receive an indication regarding a linkage between one or more CSI reports and one or more AI models (e.g., multiple CSI reports associated with a single AI model report) . For instance, a given geographic coverage area or set of cells of the network entity 105 may support the use of a given AI model, and as such, the network entity 105 may indicate which one or more CSI reports are applicable for the given AI model. In some examples, the UE 115 may receive the indication of the linkage between one or more CSI reports and the single AI model via an RRC message or a MAC-CE message.

FIG. 2 shows an example of a wireless communications system 200 that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure. The wireless communications system 200 may implement or may be implemented by aspects of the wireless communications system 100. For example, the wireless communications system 200 may include a UE 115-a, which may be an example of a UE 115 as described herein. The wireless communications system 200 may include a network entity 105-a, which may be an example of a network entity 105 as described herein. In some aspects, the wireless communications system 200 may support indications of a correspondence (e.g., a linkage) between one or more AI/ML models/functionalities (e.g., one or more model and/or functionalities IDs) and one or more CSI reports (e.g., one or more CSI report configuration IDs) . In some aspects, the features described herein may be used to indicate a correspondence between AI/ML models/functionalities and other reports signaled to/from the UE 115-a.

The UE 115-a may communicate with the network entity 105-a using a communication link 125-a. The communication link 125-a may be an example of an NR or LTE link between the UE 115-a and the network entity 105-a. The communication link 125-a may include a bi-directional link that enables both uplink and downlink communications. For example, the UE 115-a may transmit uplink signals 205 (e.g., uplink transmissions) , such as uplink control signals or uplink data signals, to the network entity 105-a using the communication link 125-a and the network entity 105-amay transmit downlink signals 210 (e.g., downlink transmissions) , such as downlink control signals or downlink data signals, to the UE 115-a using the communication link 125-a. In some examples, the network entity 105-a may perform beamforming procedures to transmit downlink signals 210 to the UE 115-a via one or more beams 215.

As described herein, the UE 115-a may perform beam prediction using an AI model to predict future measurements of the beams 215. For example, the network entity 105-a may transmit control signaling that schedules a set of beam measurement resources for a set of reference signals 245 (e.g., SSBs or CSI-RSs) . The network entity 105-a may transmit the set of reference signals 245 via a first set of beams 215 (e.g., set-B beams) . The UE 115-a may perform measurements on the set of reference signals 245 and may use an AI model to predict measurements for a second set of beams 215 (e.g., set-A beams) . The UE 115-a may transmit a report 250 indicating the predicted measurements for the second set of beams 215. In some examples, the report 250 may be a CSI message report and may also indicate the measurements of the set of reference signals 245.

As described herein, in some examples, the AI model used at the UE 115-amay be trained using a training procedure. During UE-side data collection for model training, the UE 115-a may identify orders of respective beams (e.g., set-A and set-B beams) , and the UE 115-a may map the orders of respective set-A and set-B beams to input and output features of the AI model. During AI model inference (e.g., during online beam prediction used to generate predicted measurement results that will be indicated in a report 250) , the UE 115-a and the network entity 105-a may agree on the orders of respective set-A and set-B beams mapped to input and output features of the AI model so that the UE 115-a knows to input a which set-B beam’s measured L1-RSRP into which AI model input feature and so that the UE 115-a can determine the predicted L1-RSRP for a given set-A beam according to a given AI model output feature. As the network entity 105-a has the flexibility to alter the mapping between beamforming codebooks and CMRs/IMRs, however, the CMR/IMR indexing framework may not be able to satisfy a consistent mapping between set-B beams to AI model input features and set-A beams to AI model output features.

To allow for flexibility of using multiple beamforming codebooks with reference to mapping set-B beams and set-A beams to the input/output features of an AI model, a set of trained AI models may be standardized or predefined. For example, a mapping or ordering may be defined for beam measurements (e.g., which SSB or CSI-RS index) to AI model inputs and/or AI outputs for some AI models. Thus, assuming either offline-or online-based parameter consistency across model training and inference, it may be advantageous for the UE 115-a to identify correspondence (e.g., a linkage) between applicable AI models from the set of trained AI models and one or more CSI reports that carry beam predication results (e.g., during model inference procedures) .

In accordance with the techniques described herein, the devices of wireless communications system 200 may communicate one or more messages regarding such a correspondence between AI models (e.g., for use at the UE 115-a) and the generation of various CSI reports for (e.g., transmission to the network entity 105-a) . For example, the UE 115-a may receive from the network entity 105-a a first message 220 indicating a correspondence between one or more AI models and one or more CSI reports. In some cases, the first message may communicate the correspondence between the AI models and the CSI reports in accordance with a first correspondence technique, a second correspondence technique, or both. In accordance with the first correspondence technique, the first message 220 may indicate to the UE 115-a a correspondence between one or more AI model IDs and a single CSI report, based on signaling associated with the involved CSI report. In accordance with the second correspondence technique, the first message 220 may indicate to the UE 115-a a correspondence between a single AI model ID and one or more CSI reports, based on signaling associated with the involved AI model associated with the single AI model ID.

According to the identified correspondences (e.g., linkage) , the UE 115-amay determine an appropriate AI model for UE-side inference with reference to a given CSI report, where the UE 115-a may determine of derive a report quantity (e.g., reportQuantity) associated with the given CSI report based on AI functionalities. In some examples, AI models/functionalities may be based on beam prediction results feedback. For instance, the UE 115-a may predict L1-RSRPs, L1-SINRs, or a set of resources (e.g., Top-K-Resources) in terms of L1-RSRP, L1-SINR, or both regarding one or more of a first quantity of SSB, CSI-RS, or virtual resources (e.g., set-A beams) based on measurements of a second quantity of SSB or CSI-RS resources (e.g., set-B beams) . Such prediction may be associated with temporal occasions before the slot carrying the CSI report where the first and second quantity of resources may include non-overlapping components (e.g., spatial prediction) . Additionally, or alternatively, such a prediction may be associated with temporal occasions after the slot carrying the CSI report (e.g., temporal, and spatial prediction) . The first and second quantity of resources may be indicated via signaling with reference to the CSI report. Additionally, or alternatively, AI models/functionalities may be based on CSI compression feedback. For instance, the UE 115-a may determine a compressed version of one or more pre-coding matrix indicators (PMIs) based on respective outputs of one or more AI models identified in the first message 220. Additionally, or alternatively, AI models/functionalities may be based on CSI prediction feedback. For instance, the UE 115-a may predict one or more PMIs associated with one or more temporal occasions after the slot carrying a given CSI report.

In some cases of the first correspondence technique, the network entity 105-amay transmit or output the first message 220 via one or more different types of control signals. For instance, the first message 220 may be an RRC message. In some examples of RRC messaging, the first message 220 may include a CSI report setting (e.g., CSI-ReportConfig) associated with a given CSI report that may configure (e.g., indicate) the one or more applicable AI model IDs for the given CSI report. The network entity 105-amay indicate the correspondence via the CSI report setting for any type of CSI report. In some examples of RRC messaging, the first message 220 may include a field (e.g., CSI-AssociatedReportConfigInfo) associated with a given CSI report, where the field may configure (e.g., indicate) the one or more applicable AI model IDs associated with the given CSI report. In some cases, the network entity 105-a may indicate the correspondence via the CSI-AssociatedReportConfigInfo for aperiodic (AP) CSI reports. For instance, an AP CSI report setting may be associated with multiple CSI-AssociatedReportConfigInfo fields, each associated with or indicating different measurement resources (e.g., set-B beams) , different prediction target resources (e.g., set-A beams) , or both. Based on each AI model ID being associated with respective set-A and set-B beams mapped to input and output features of the associated AI model, the UE 115-a may determine which AI models IDs correspond to the AP CSI report based on the resources indicated or associated with the multiple CSI-AssociatedReportConfigInfo fields.

Additionally, or alternatively, the first message 220 may be a MAC-CE message. In some examples of MAC-CE messaging, the first message 220 may activate a semi-persistent or semi-periodic CSI report and further indicate the applicable one or more AI model IDs. In some examples of MAC-CE messaging, the network entity 105-a may indicate a dedicated MAC-CE to change one or more applicable AI model IDs for a given CSI report. That is, the given CSI report may have a first correspondence with one or more first AI model IDs, and the MAC-CE may indicate for the UE 115-a to change to a second correspondence associated with one or more second AI model IDs. In such examples, the MAC-CE message may indicate both the one or more second AI model IDs and the given CSI report setting ID or indicate the CSI-AperiodicTriggerState and CSI-AssociatedReportConfigInfo IDs. In any of the cases of MAC-CE messaging used for the first message 220, the MAC-CE message may directly (e.g., explicitly) indicate each of the applicable AI model IDs in the MAC-CE message. Additionally, or alternatively, the MAC-CE message may indicate a down selected subset of one or more AI model IDs from a set of AI model IDs configured via one or more RRC configurations or messages.

In some examples, the first message 220 may be a DCI message. In some examples of DCI messaging, the first message 220 may include one or more DCI fields that indicate one or more applicable AI model IDs for a given CSI report. In some examples, each DCI field may correspond to a respective CSI report such that a first DCI field indicates one or more first applicable AI model IDs associated with a first CSI report and a second DCI field indicate one or more second applicable AI model IDs associated with a second CSI report. In some examples, the DCI message may directly (e.g., explicitly) indicate the one or more applicable AI model IDs in the DCI message. Additionally, or alternatively, the DCI message may indicate a down selected subset of one or more AI model IDs from a set of model IDs configured via one or more RRC configurations or messages. In some cases, the DCI message may be an example of an uplink grant DCI, where the UE 115-a may apply the AI model IDs indicated in the DCI message to one or more AP CSI reports triggered by the uplink grant DCI. In some cases, the DCI message may be an example of a downlink grant DCI, where the DCI message may include one or more associated CSI report setting IDs or include the associated CSI-AperiodicTriggerState and CSI-AssociatedReportConfigInfo IDs.

If the first message 220 identifies multiple AI model IDs for a single CSI report, the UE 115-a may determine which AI model ID of the multiple AI model IDs to use for the single CSI report. For instance, the UE 115-a may use an autonomous AI model selection procedure 235 to autonomously choose a most appropriate AI model ID from multiple AI model IDs. In some examples, the UE 115-a may determine appropriate AI model ID based on one or more environmental parameters, based on energy expenditure at the UE 115-a, based on signaling overhead of the wireless communications system 200, based on the signal quality associated with set of reference signals 245, or a combination thereof. Based on performing the autonomous AI model selection procedure 235, the UE 115-a may determine a single AI model to use for generating the single CSI report. As such, the UE 115-a may perform a predicted values generation procedure 240 using the single AI model. For example, the UE 115-a may generate a set of measured values associated with measuring the set of reference signals 245 (e.g., L1-RSRP, L1-SINR, or both) , where the UE 115-a may generate a set of predicted values using the measured values and the single AI model. The UE 115-a may include the predicted values in the report 250, where the report 250 may be an example of the single CSI report corresponding to the single AI model selected. In some examples, the report 250 further includes an indication of the single AI model ID selected for generating the set of predicted values.

Additionally, or alternatively, the UE 115-a may use multiple AI models to perform respective predicted value generation procedures 240. In some examples, a single report 250 may include the respective set of predicted values generated using each of the multiple AI models and further indicate each of the associated AI model IDs. In some examples, the UE 115-a may transmit multiple reports 250, each including a respective set predicted values and further indicating the AI model ID associated with generating the respective set of predicted values.

The UE 115-a may train various AI models for a particular type of beam prediction operation in accordance with different model complexities (e.g., semi-analytical methods or NN-based methods, among other examples) or different input features (e.g., time-series based inputs or instantaneous measurement-based inputs) . In some examples, the network entity 105-a may provide feedback associated with the performance of the predicted values generated by a given AI model. For instance, for an AI model using time-series based inputs, the predicted values from subsequent reports using the AI model may become more reliable over time, and the network entity 105-amay indicate to the UE 115-a aspects associated with the increase in reliability. As such, the UE 115-a may use feedback received from the network entity 105-a regarding the various AI models and may use the feedback when performing the autonomous AI model selection procedure 235.

In some examples of beam prediction (e.g., where the reportQuantity is for reporting beam prediction results) , the network entity 105-a may indicate mapping orders for a set of resources. For example, the network entity 105-a may transmit resource mapping indication 230, which may indicate mapping orders between set-Aand set-B beam resource IDs and AI model input and output feature IDs for each AI model of a set of AI models. For instance, a first mapping order may indicate the respective IDs of the first quantity of resources (e.g., set-A beams) to output feature IDs of an indicated AI model ID, and a second mapping order may indicate the respective IDs of the second quantity of resources (e.g., set-B beams) to input feature IDs of the indicated AI model ID. In some examples, the resource mapping indication 230 may allow for more flexibility by the network entity 105-a to arrange codebooks associated with SSBs, CSI-RSs, or both. For instance, different mapping orders may be signaled for different network vendors, may be based on different locations, or both.

The techniques of FIG. 2 describe the use of one or more AI models and AI model IDs. However, such techniques described herein may be applied to other types of UE 115-a functionality. For example, the UE 115-a may operate in accordance with one or more of the following: AI models and AI model IDs, AI functionalities and AI functionality IDs, machine learning (ML) models and ML model IDs, ML functionalities and ML functionality IDs, or a combination thereof. Additional terminologies may be used to describe AI/ML functionality/models and associated IDs. Such additional terminologies may include but are not limited to codebook IDs, configurations IDs, scenario IDs, inference dataset IDs, training dataset IDs. In some cases, the signaling described herein may use any of the additional terminologies without the involvement of AI/ML functionalities/models. In some examples, the signaling described herein may use any of the additional terminologies in association with AI/ML functionalities/models, utilizing any of the techniques described herein. Such additional terminologies may be used independently of one another, and the UE 115-a may identify the appropriate models to use based on implementation. As such, the first message 220 may indicate a correspondence between one or more CSI reports or other report types and any of the terminologies referenced herein.

In some cases of the second correspondence technique, the network entity 105-a may transmit or output the first message 220 via one or more different types of control signals. For example, the network entity 105-a may transmit or output an AI model ID indication 225, which may be one or more RRC configuration messages that indicate one or more AI model IDs supported for a given geographic coverage area, or for a given cell of the network entity 105-a. For instance, the UE 115-a my receive the AI model ID indication 225 via an RRC message with reference to a relatively large geographic area across one or more cells of the network entity 105-a. Additionally, or alternatively, the UE 115-a may receive the AI model ID indication 225 via an RRC message with reference to a particular cell of the network entity 105-a or to a particular cell group of the network entity 105-a. For each of the AI model IDs that are RRC configured, the first message 220 may indicate the applicable CSI reports associated with a given AI model ID.

In some examples of the second correspondence technique, the first message 220 may be an additional RRC message to the AI model ID indication 225. In such cases of RRC messaging, the first message 220 may indicate the one or more CSI report setting IDs or one or more pairs of CSI-AperiodicTriggerState and CSI-AssociatedReportConfigInfo IDs associated with a given AI model ID. In some examples, the UE 115-a may consider a given CSI report setting ID or a given pair CSI-AperiodicTriggerState and CSI-AssociatedReportConfigInfo IDs as applicable to a given AI model ID if the associated CSI report is active or if the associated AI model is activated by separate network entity signaling.

In some examples of the second correspondence technique, the first message 220 may be a MAC-CE message that is used in addition to the AI model ID indication 225. In such cases of MAC-CE messaging, the first message 220 may activate a given AI model ID and indicate the one or more applicable CSI reports associated with the activated AI model ID. In some examples, the MAC-CE message may indicate each applicable CSI report by indicating each associated CSI report setting IDs or by indicating each associated pair of CSI-AperiodicTriggerState and CSI-AssociatedReportConfigInfo IDs. In some examples, the UE 115-a may consider CSI report setting IDs or each pair of CSI-AperiodicTriggerState and CSI-AssociatedReportConfigInfo IDs as applicable to the given AI model ID if the associated CSI reports are active.

In some examples, the network entity 105-a and the UE 115-a may operate in accordance with a combination of the first and second correspondence techniques. For instance, multiple AI model IDs may correspond to a single CSI report setting or to a single pair of CSI-AperiodicTriggerState and CSI-AssociatedReportConfigInfo IDs. As such, for the single CSI report, the UE 115-a may down select from the multiple AI model IDs (e.g., the UE 115-a may select a subset of one or more AI model IDs from a set of AI model IDs) . In some examples, the UE 115-a may perform such a down selection based on signaling from the network entity 105-a. For instance, the network entity 105-a may transmit or output bit-maps or combinatorial indices that signal the down-selection of the multiple AI model IDs. Additionally, or alternatively, the network entity 105-a may signal the down-selection according to an order of candidate AI model IDs, where the AI model IDs are ordered based on the preference of which AI model ID should be used for the single CSI report. In such cases, the order of preference may be indicated in ascending order or descending order. In some examples of using both the first and second correspondence techniques, the RRC configurations used for the second correspondence technique may be applied to a relatively large area across various cells, and different cells of the various cells may use the first correspondence technique to down-select AI model IDs from the set of AI model IDs that are RRC configured via the second correspondence technique.

FIG. 3 shows an example of a process flow 300 that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure. In some examples, process flow 300 may implement aspects of wireless communications system 100 and wireless communications system 200. Process flow 300 includes a UE 115-b and a network entity 105-b, as described with reference to FIGs. 1 and 2. Alternative examples of the following may be implemented, where some steps are performed in a different order than described or are not performed at all. In some cases, steps may include additional features not mentioned below, or further steps may be added. In addition, it is understood that these processes may occur between any quantity of network devices and network device types. Additionally, the terminology AI model may encompass any of the terminologies described with reference to FIG. 2, including but not limited to AI functionality, ML model, ML functionality, codebooks, configurations, scenarios, inference datasets, training datasets, or a combination thereof. Further, any of the terminologies used may be associated with respective ID.

In some examples, at 305 the UE 115-b may receive a first RRC message that indicates one or more model IDs each associated with a respective AI model. In some examples, the indicated AI models may be supported for a geographic coverage area or one or more cells, or any combination thereof, associated with the network entity 105-b.

At 310, the UE 115-b may receive a first message indicating a correspondence between one or more AI models and one or more CSI reports.

In some examples, the first message indicating the correspondence may be an RRC message that includes a CSI report setting indicating that the one or more AI models are associated with a single CSI report. In some examples, the first message indicating the correspondence may be an RRC message that includes an AP CSI report setting associated with a set of CSI report configurations for a single CSI report, where each CSI report configuration of the set of CSI report configurations may indicate a respective set of resources associated with a respective AI model, and where each of the respective AI models are associated with the single CSI report.

In some examples, the first message indicating the correspondence may be a MAC-CE message for activation of a single SP CSI report and indicates that the one or more AI models are associated with the single SP CSI report. In some examples, the first message indicating the correspondence may be a MAC-CE message indicating a change from one or more first AI models, being associated with a single CSI report to one or more second AI models, being associated with the single CSI report.

In some examples, the first message indicating the correspondence may be a DCI message that includes one or more fields, where at least one field of the one or more fields indicates one or more respective AI models being associated with a respective CSI report.

In some examples, the first message indicates a respective model ID associated with each of the one or more AI models. Additionally, or alternatively, the first message indicates a subset of model IDs from a set of model IDs configured via one or more control messages, where each model ID of the subset of model IDs may be associated with a respective AI model, of the one or more AI models.

In examples where the UE 115-b receives the AI model ID indication (at 305) , the first message indicating the correspondence may be a second RRC message that indicates one or more respective CSI reports associated with each respective AI model (e.g., indicated in the AI model ID indication) .

In examples where the UE 115-b receives the AI model ID indication (at 305) , the first message indicating the correspondence may be a MAC-CE message that activates one or more of the respective AI models (e.g., indicated in the AI model ID indication) and further indicates one or more respective CSI reports associated with each of the activated AI models.

In examples where the UE 115-b receives the AI model ID indication (at 305) , the first message indicates a subset of model IDs (e.g., of the one or more model IDs indicated in the AI model ID indication) . In some examples, the subset of model IDs may be associated with a subset of AI models. In some examples, the subset of model IDs may be indicated via a set of indices each associated with a respective model ID of the one or more model IDs. For instance, a first value for an index of the set of indices may indicate that an associated model ID is included in the subset of model IDs. In some examples, the subset of model IDs may be indicated via a list of model IDs that includes the subset of model IDs, and where an ordering of the list of model IDs may be based on an order of AI models preferred for generating the set of predicted values.

In some examples, at 315 the UE 115-b may receive a second message indicating a first mapping of respective first identifiers of a first set of resources to respective output feature identifiers of an AI model, and a second mapping of respective second identifiers of a second set of resources to respective input feature identifiers of the AI model, for each AI model of the one or more AI models. In some examples, the first mapping and the second mapping may be based on a CSI report quantity associated with the CSI report message being associated with directional beam prediction.

At 320, the UE 115-b may perform an autonomous AI model selection procedure (e.g., the autonomous AI model selection procedure 235, with reference to FIG. 2) . For instance, the correspondence may indicate that a set of AI models are associated with a single CSI report. As such, the UE 115-b may determine a first AI model from the set of AI models based on the autonomous selection procedure, where the set of predicted values that are generated using the first AI model may be based on the autonomous selection procedure.

At 325, the UE 115-b may perform a predicted values generation procedure (e.g., the predicted values generation procedure 240, with reference to FIG. 2) . For instance, the UE 115-b may generate a set of measured values associated with a set of reference signals, where a set of predicted values may be generated using a first AI model in accordance with the correspondence and based on the set of measured values.

At 330, the UE 115-b may transmit a CSI report message that indicates the set of predicted values. In some examples, the UE 115-b may transmit as part of the CSI report an indication of a first model ID associated with the first AI model used to generate the predicted values.

FIG. 4 shows a block diagram 400 of a device 405 that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure. The device 405 may be an example of aspects of a UE 115 as described herein. The device 405 may include a receiver 410, a transmitter 415, and a communications manager 420. The device 405, or one or more components of the device 405 (e.g., the receiver 410, the transmitter 415, and the communications manager 420) , may include at least one processor, which may be coupled with (e.g., operatively, communicatively, functionally, electronically, or electrically) 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 410 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 correspondence between AI models and CSI reporting) . Information may be passed on to other components of the device 405. The receiver 410 may utilize a single antenna or a set of multiple antennas.

The transmitter 415 may provide a means for transmitting signals generated by other components of the device 405. For example, the transmitter 415 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 correspondence between AI models and CSI reporting) . In some examples, the transmitter 415 may be co-located with a receiver 410 in a transceiver module. The transmitter 415 may utilize a single antenna or a set of multiple antennas.

The communications manager 420, the receiver 410, the transmitter 415, or various combinations thereof or various components thereof may be examples of means for performing various aspects of correspondence between AI models and CSI reporting as described herein. For example, the communications manager 420, the receiver 410, the transmitter 415, 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 420, the receiver 410, the transmitter 415, 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) , graphics processing unit (GPU) , a neural processing unit (NPU) , 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 420, the receiver 410, the transmitter 415, or various combinations or components thereof may be implemented in code (e.g., as communications management software) executed by at least one processor. If implemented in code executed by at least one processor, the functions of the communications manager 420, the receiver 410, the transmitter 415, or various combinations or components thereof may be performed by a general-purpose processor, a DSP, a CPU, a GPU, an NPU, 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 420 may be configured to perform various operations (e.g., receiving, obtaining, monitoring, outputting, transmitting) using or otherwise in cooperation with the receiver 410, the transmitter 415, or both. For example, the communications manager 420 may receive information from the receiver 410, send information to the transmitter 415, or be integrated in combination with the receiver 410, the transmitter 415, or both to obtain information, output information, or perform various other operations as described herein.

The communications manager 420 may support wireless communications in accordance with examples as disclosed herein. For example, the communications manager 420 is capable of, configured to, or operable to support a means for receiving a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports. The communications manager 420 is capable of, configured to, or operable to support a means for generating a set of measured values associated with a set of reference signals, where a set of predicted values is generated using a first AI model or functionality, or both, in accordance with the correspondence and based on the set of measured values. The communications manager 420 is capable of, configured to, or operable to support a means for transmitting a CSI report message that indicates the set of predicted values.

By including or configuring the communications manager 420 in accordance with examples as described herein, the device 405 (e.g., at least one processor controlling or otherwise coupled with the receiver 410, the transmitter 415, the communications manager 420, or a combination thereof) may support techniques for reduced processing, reduced power consumption, and more efficient utilization of communication resources.

FIG. 5 shows a block diagram 500 of a device 505 that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure. The device 505 may be an example of aspects of a device 405 or a UE 115 as described herein. The device 505 may include a receiver 510, a transmitter 515, and a communications manager 520. The device 505, or one or more components of the device 505 (e.g., the receiver 510, the transmitter 515, and the communications manager 520) , 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 510 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 correspondence between AI models and CSI reporting) . Information may be passed on to other components of the device 505. The receiver 510 may utilize a single antenna or a set of multiple antennas.

The transmitter 515 may provide a means for transmitting signals generated by other components of the device 505. For example, the transmitter 515 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 correspondence between AI models and CSI reporting) . In some examples, the transmitter 515 may be co-located with a receiver 510 in a transceiver module. The transmitter 515 may utilize a single antenna or a set of multiple antennas.

The device 505, or various components thereof, may be an example of means for performing various aspects of correspondence between AI models and CSI reporting as described herein. For example, the communications manager 520 may include a message monitoring component 525, a value predicting component 530, a CSI reporting component 535, or any combination thereof. The communications manager 520 may be an example of aspects of a communications manager 420 as described herein. In some examples, the communications manager 520, 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 510, the transmitter 515, or both. For example, the communications manager 520 may receive information from the receiver 510, send information to the transmitter 515, or be integrated in combination with the receiver 510, the transmitter 515, or both to obtain information, output information, or perform various other operations as described herein.

The communications manager 520 may support wireless communications in accordance with examples as disclosed herein. The message monitoring component 525 is capable of, configured to, or operable to support a means for receiving a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports. The value predicting component 530 is capable of, configured to, or operable to support a means for generating a set of measured values associated with a set of reference signals, where a set of predicted values is generated using a first AI model or functionality, or both, in accordance with the correspondence and based on the set of measured values. The CSI reporting component 535 is capable of, configured to, or operable to support a means for transmitting a CSI report message that indicates the set of predicted values.

FIG. 6 shows a block diagram 600 of a communications manager 620 that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure. The communications manager 620 may be an example of aspects of a communications manager 420, a communications manager 520, or both, as described herein. The communications manager 620, or various components thereof, may be an example of means for performing various aspects of correspondence between AI models and CSI reporting as described herein. For example, the communications manager 620 may include a message monitoring component 625, a value predicting component 630, a CSI reporting component 635, a model determination component 640, 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 620 may support wireless communications in accordance with examples as disclosed herein. The message monitoring component 625 is capable of, configured to, or operable to support a means for receiving a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports. The value predicting component 630 is capable of, configured to, or operable to support a means for generating a set of measured values associated with a set of reference signals, where a set of predicted values is generated using a first AI model or functionality, or both, in accordance with the correspondence and based on the set of measured values. The CSI reporting component 635 is capable of, configured to, or operable to support a means for transmitting a CSI report message that indicates the set of predicted values.

In some examples, the correspondence indicates that a set of AI models or functionalities, and the model determination component 640 is capable of, configured to, or operable to support a means for determining the first AI model or functionality, or both, from the set of AI models or functionalities, or both based on an autonomous selection procedure, where the set of predicted values that are generated using the first AI model or functionality, or both, based on the autonomous selection procedure.

In some examples, to support transmitting the CSI report message, the CSI reporting component 635 is capable of, configured to, or operable to support a means for transmitting, as part of the CSI report message, an indication of a first model or functionality identifier associated with the first AI model or functionality, or both.

In some examples, the message monitoring component 625 is capable of, configured to, or operable to support a means for receiving a second message indicating a first mapping of respective first identifiers of a first set of resources to respective output feature identifiers of an AI model or functionality, or both, and a second mapping of respective second identifiers of a second set of resources to respective input feature identifiers of the AI model or functionality, or both, for each AI model or functionality, or both, of the one or more AI models or functionalities, or both.

In some examples, the first mapping and the second mapping are based on a CSI report quantity associated with the CSI report message being associated with directional beam prediction.

In some examples, the first message indicating the correspondence is a RRC message including a CSI report setting indicating that the one or more AI models or functionalities, or both, are associated with a single CSI report.

In some examples, the first message indicating the correspondence is a RRC message including an aperiodic CSI report setting associated with a set of CSI report configurations for a single CSI report. In some examples, each CSI report configuration of the set of CSI report configurations indicates a respective set of resources associated with a respective AI model or functionality, or both. In some examples, each of the respective AI models or functionalities, or both, are associated with the single CSI report.

In some examples, the first message indicating the correspondence is a MAC-CE message for activation of a single semi-persistent CSI report and indicates that the one or more AI models or functionalities, or both, are associated with the single semi-persistent CSI report.

In some examples, the first message indicating the correspondence is a MAC-CE message indicating a change from one or more first AI models or functionalities, or both, being associated with a single CSI report to one or more second AI models or functionalities, or both, being associated with the single CSI report.

In some examples, the first message indicating the correspondence is a DCI message including one or more fields. In some examples, at least one field of the one or more fields indicates one or more respective AI models or functionalities, or both, being associated with a respective CSI report.

In some examples, the first message indicates a respective model or functionality identifier associated with each of the one or more AI models or functionalities, or both.

In some examples, the first message indicates a subset of model or functionality identifiers from a set of model or functionality identifiers configured via one or more control messages. In some examples, each model or functionality identifier of the subset of model or functionality identifiers is associated with a respective AI model or functionality, or both, of the one or more AI models or functionalities, or both.

In some examples, the message monitoring component 625 is capable of, configured to, or operable to support a means for receiving a first RRC message that indicates one or more model or functionality identifiers each associated with a respective AI model or functionality, or both, supported for a geographic coverage area or one or more cells, or any combination thereof, associated with a network entity.

In some examples, the first message indicating the correspondence is a second RRC message that indicates one or more respective CSI reports associated with each respective AI model or functionality, or both, indicated in the first RRC message.

In some examples, the first message indicating the correspondence is a MAC-CE message that activates one or more of the respective AI models or functionalities, or both, indicated in the first RRC message and further indicates one or more respective CSI reports associated with each of the activated AI models or functionalities, or both.

In some examples, the first message indicates a subset of model or functionality identifiers of the one or more model or functionality identifiers indicated in the first RRC message, the subset of model or functionality identifiers associated with a subset of AI models or functionalities, or both.

In some examples, the subset of model or functionality identifiers are indicated via a set of indices each associated with a respective model or functionality identifier of the one or more model or functionality identifiers. In some examples, a first value for an index of the set of indices indicates that an associated model or functionality identifier is included in the subset of model or functionality identifiers.

In some examples, the subset of model or functionality identifiers is indicated via a list of model or functionality identifiers that includes the subset of model or functionality identifiers. In some examples, an ordering of the list of model or functionality identifiers is based on an order of AI models or functionalities, or both, for generating the set of predicted values.

FIG. 7 shows a diagram of a system 700 including a device 705 that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure. The device 705 may be an example of or include the components of a device 405, a device 505, or a UE 115 as described herein. The device 705 may communicate (e.g., wirelessly) with one or more network entities 105, one or more UEs 115, or any combination thereof. The device 705 may include components for bi-directional voice and data communications including components for transmitting and receiving communications, such as a communications manager 720, an input/output (I/O) controller 710, a transceiver 715, an antenna 725, at least one memory 730, code 735, and at least one processor 740. 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 745) .

The I/O controller 710 may manage input and output signals for the device 705. The I/O controller 710 may also manage peripherals not integrated into the device 705. In some cases, the I/O controller 710 may represent a physical connection or port to an external peripheral. In some cases, the I/O controller 710 may utilize an operating system such as or another known operating system. Additionally, or alternatively, the I/O controller 710 may represent or interact with a modem, a keyboard, a mouse, a touchscreen, or a similar device. In some cases, the I/O controller 710 may be implemented as part of one or more processors, such as the at least one processor 740. In some cases, a user may interact with the device 705 via the I/O controller 710 or via hardware components controlled by the I/O controller 710.

In some cases, the device 705 may include a single antenna 725. However, in some other cases, the device 705 may have more than one antenna 725, which may be capable of concurrently transmitting or receiving multiple wireless transmissions. The transceiver 715 may communicate bi-directionally, via the one or more antennas 725, wired, or wireless links as described herein. For example, the transceiver 715 may represent a wireless transceiver and may communicate bi-directionally with another wireless transceiver. The transceiver 715 may also include a modem to modulate the packets, to provide the modulated packets to one or more antennas 725 for transmission, and to demodulate packets received from the one or more antennas 725. The transceiver 715, or the transceiver 715 and one or more antennas 725, may be an example of a transmitter 415, a transmitter 515, a receiver 410, a receiver 510, or any combination thereof or component thereof, as described herein.

The at least one memory 730 may include random access memory (RAM) and read-only memory (ROM) . The at least one memory 730 may store computer-readable, computer-executable code 735 including instructions that, when executed by the at least one processor 740, cause the device 705 to perform various functions described herein. The code 735 may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some cases, the code 735 may not be directly executable by the at least one processor 740 but may cause a computer (e.g., when compiled and executed) to perform functions described herein. In some cases, the at least one memory 730 may contain, 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 740 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, a CPU, a GPU, a NPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof) . In some cases, the at least one processor 740 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 740. The at least one processor 740 may be configured to execute computer-readable instructions stored in a memory (e.g., the at least one memory 730) to cause the device 705 to perform various functions (e.g., functions or tasks supporting correspondence between AI models and CSI reporting) . For example, the device 705 or a component of the device 705 may include at least one processor 740 and at least one memory 730 coupled with or to the at least one processor 740, the at least one processor 740 and at least one memory 730 configured to perform various functions described herein. In some examples, the at least one processor 740 may include multiple processors and the at least one memory 730 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 herein. In some examples, the at least one processor 740 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 740) and memory circuitry (which may include the at least one memory 730) ) , 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 740 or a processing system including the at least one processor 740 may be configured to, configurable to, or operable to cause the device 705 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 stored in the at least one memory 730 or otherwise, to perform one or more of the functions described herein.

The communications manager 720 may support wireless communications in accordance with examples as disclosed herein. For example, the communications manager 720 is capable of, configured to, or operable to support a means for receiving a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports. The communications manager 720 is capable of, configured to, or operable to support a means for generating a set of measured values associated with a set of reference signals, where a set of predicted values is generated using a first AI model or functionality, or both, in accordance with the correspondence and based on the set of measured values. The communications manager 720 is capable of, configured to, or operable to support a means for transmitting a CSI report message that indicates the set of predicted values.

By including or configuring the communications manager 720 in accordance with examples as described herein, the device 705 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, and improved utilization of processing capability.

In some examples, the communications manager 720 may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using or otherwise in cooperation with the transceiver 715, the one or more antennas 725, or any combination thereof. Although the communications manager 720 is illustrated as a separate component, in some examples, one or more functions described with reference to the communications manager 720 may be supported by or performed by the at least one processor 740, the at least one memory 730, the code 735, or any combination thereof. For example, the code 735 may include instructions executable by the at least one processor 740 to cause the device 705 to perform various aspects of correspondence between AI models and CSI reporting as described herein, or the at least one processor 740 and the at least one memory 730 may be otherwise configured to, individually or collectively, perform or support such operations.

FIG. 8 shows a block diagram 800 of a device 805 that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure. The device 805 may be an example of aspects of a network entity 105 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, and 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 obtaining (e.g., receiving, determining, identifying) information such as user data, control information, or any combination thereof (e.g., I/Q samples, symbols, packets, protocol data units, service data units) associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack) . Information may be passed on to other components of the device 805. In some examples, the receiver 810 may support obtaining information by receiving signals via one or more antennas. Additionally, or alternatively, the receiver 810 may support obtaining information by receiving signals via one or more wired (e.g., electrical, fiber optic) interfaces, wireless interfaces, or any combination thereof.

The transmitter 815 may provide a means for outputting (e.g., transmitting, providing, conveying, sending) information generated by other components of the device 805. For example, the transmitter 815 may output information such as user data, control information, or any combination thereof (e.g., I/Q samples, symbols, packets, protocol data units, service data units) associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack) . In some examples, the transmitter 815 may support outputting information by transmitting signals via one or more antennas. Additionally, or alternatively, the transmitter 815 may support outputting information by transmitting signals via one or more wired (e.g., electrical, fiber optic) interfaces, wireless interfaces, or any combination thereof. In some examples, the transmitter 815 and the receiver 810 may be co-located in a transceiver, which may include or be coupled with a modem.

The communications manager 820, the receiver 810, the transmitter 815, or various combinations thereof or various components thereof may be examples of means for performing various aspects of correspondence between AI models and CSI reporting 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 DSP, a CPU, a GPU, a NPU, an ASIC, an 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) executed by at least one processor. 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, a GPU, a NPU, 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 outputting a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports. The communications manager 820 is capable of, configured to, or operable to support a means for outputting a set of reference signals. The communications manager 820 is capable of, configured to, or operable to support a means for obtaining a CSI report message that indicates a set of predicted values that are based on a set of measured values associated with the set of reference signals, where the set of predicted values of the CSI report message is associated with a first AI model or functionality, or both, in accordance with the correspondence.

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, and more efficient utilization of communication resources.

FIG. 9 shows a block diagram 900 of a device 905 that supports correspondence between AI models and CSI reporting 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 network entity 105 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, and 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 obtaining (e.g., receiving, determining, identifying) information such as user data, control information, or any combination thereof (e.g., I/Q samples, symbols, packets, protocol data units, service data units) associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack) . Information may be passed on to other components of the device 905. In some examples, the receiver 910 may support obtaining information by receiving signals via one or more antennas. Additionally, or alternatively, the receiver 910 may support obtaining information by receiving signals via one or more wired (e.g., electrical, fiber optic) interfaces, wireless interfaces, or any combination thereof.

The transmitter 915 may provide a means for outputting (e.g., transmitting, providing, conveying, sending) information generated by other components of the device 905. For example, the transmitter 915 may output information such as user data, control information, or any combination thereof (e.g., I/Q samples, symbols, packets, protocol data units, service data units) associated with various channels (e.g., control channels, data channels, information channels, channels associated with a protocol stack) . In some examples, the transmitter 915 may support outputting information by transmitting signals via one or more antennas. Additionally, or alternatively, the transmitter 915 may support outputting information by transmitting signals via one or more wired (e.g., electrical, fiber optic) interfaces, wireless interfaces, or any combination thereof. In some examples, the transmitter 915 and the receiver 910 may be co-located in a transceiver, which may include or be coupled with a modem.

The device 905, or various components thereof, may be an example of means for performing various aspects of correspondence between AI models and CSI reporting as described herein. For example, the communications manager 920 may include a message outputting component 925, a reference signaling component 930, a CSI report monitoring component 935, 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 message outputting component 925 is capable of, configured to, or operable to support a means for outputting a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports. The reference signaling component 930 is capable of, configured to, or operable to support a means for outputting a set of reference signals. The CSI report monitoring component 935 is capable of, configured to, or operable to support a means for obtaining a CSI report message that indicates a set of predicted values that are based on a set of measured values associated with the set of reference signals, where the set of predicted values of the CSI report message is associated with a first AI model or functionality, or both, in accordance with the correspondence.

FIG. 10 shows a block diagram 1000 of a communications manager 1020 that supports correspondence between AI models and CSI reporting 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 correspondence between AI models and CSI reporting as described herein. For example, the communications manager 1020 may include a message outputting component 1025, a reference signaling component 1030, a CSI report monitoring component 1035, 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) which may include communications within a protocol layer of a protocol stack, communications associated with a logical channel of a protocol stack (e.g., between protocol layers of a protocol stack, within a device, component, or virtualized component associated with a network entity 105, between devices, components, or virtualized components associated with a network entity 105) , or any combination thereof.

The communications manager 1020 may support wireless communications in accordance with examples as disclosed herein. The message outputting component 1025 is capable of, configured to, or operable to support a means for outputting a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports. The reference signaling component 1030 is capable of, configured to, or operable to support a means for outputting a set of reference signals. The CSI report monitoring component 1035 is capable of, configured to, or operable to support a means for obtaining a CSI report message that indicates a set of predicted values that are based on a set of measured values associated with the set of reference signals, where the set of predicted values of the CSI report message is associated with a first AI model or functionality, or both, in accordance with the correspondence.

In some examples, to support obtaining the CSI report message, the CSI report monitoring component 1035 is capable of, configured to, or operable to support a means for obtaining, as part of the CSI report message, an indication of a first model or functionality identifier associated with the first AI model or functionality, or both.

In some examples, the message outputting component 1025 is capable of, configured to, or operable to support a means for outputting a second message indicating a first mapping of respective first identifiers of a first set of resources to respective output feature identifiers of an AI model or functionality, or both, and a second mapping of respective second identifiers of a second set of resources to respective input feature identifiers of the AI model or functionality, or both, for each AI model or functionality, or both, of the one or more AI models or functionalities, or both.

In some examples, the first message indicates a subset of model or functionality identifiers from a set of model or functionality identifiers configured via one or more control messages, where each model or functionality identifier of the subset of model or functionality identifiers is associated with a respective AI model or functionality, or both, of the one or more AI models or functionalities, or both.

In some examples, the message outputting component 1025 is capable of, configured to, or operable to support a means for outputting a first RRC message that indicates one or more model or functionality identifiers each associated with a respective AI model or functionality, or both, supported for a geographic coverage area or one or more cells, or any combination thereof, associated with the network entity.

In some examples, the subset of model or functionality identifiers is indicated via a set of indices each associated with a respective model or functionality identifier of the one or more model or functionality identifiers. In some examples, a first value for an index of the set of indices indicates that an associated model or functionality identifier is included in the subset of model or functionality identifiers.

FIG. 11 shows a diagram of a system 1100 including a device 1105 that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure. The device 1105 may be an example of or include the components of a device 805, a device 905, or a network entity 105 as described herein. The device 1105 may communicate with one or more network entities 105, one or more UEs 115, or any combination thereof, which may include communications over one or more wired interfaces, over one or more wireless interfaces, or any combination thereof. The device 1105 may include components that support outputting and obtaining communications, such as a communications manager 1120, a transceiver 1110, an antenna 1115, at least one memory 1125, code 1130, and at least one processor 1135. 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 1140) .

The transceiver 1110 may support bi-directional communications via wired links, wireless links, or both as described herein. In some examples, the transceiver

1110 may include a wired transceiver and may communicate bi-directionally with another wired transceiver. Additionally, or alternatively, in some examples, the transceiver 1110 may include a wireless transceiver and may communicate bi-directionally with another wireless transceiver. In some examples, the device 1105 may include one or more antennas 1115, which may be capable of transmitting or receiving wireless transmissions (e.g., concurrently) . The transceiver 1110 may also include a modem to modulate signals, to provide the modulated signals for transmission (e.g., by one or more antennas 1115, by a wired transmitter) , to receive modulated signals (e.g., from one or more antennas 1115, from a wired receiver) , and to demodulate signals. In some implementations, the transceiver 1110 may include one or more interfaces, such as one or more interfaces coupled with the one or more antennas 1115 that are configured to support various receiving or obtaining operations, or one or more interfaces coupled with the one or more antennas 1115 that are configured to support various transmitting or outputting operations, or a combination thereof. In some implementations, the transceiver 1110 may include or be configured for coupling with one or more processors or one or more memory components that are operable to perform or support operations based on received or obtained information or signals, or to generate information or other signals for transmission or other outputting, or any combination thereof. In some implementations, the transceiver 1110, or the transceiver 1110 and the one or more antennas 1115, or the transceiver 1110 and the one or more antennas 1115 and one or more processors or one or more memory components (e.g., the at least one processor 1135, the at least one memory 1125, or both) , may be included in a chip or chip assembly that is installed in the device 1105. In some examples, the transceiver 1110 may be operable to support communications via one or more communications links (e.g., a communication link 125, a backhaul communication link 120, a midhaul communication link 162, a fronthaul communication link 168) .

The at least one memory 1125 may include RAM, ROM, or any combination thereof. The at least one memory 1125 may store computer-readable, computer-executable code 1130 including instructions that, when executed by one or more of the at least one processor 1135, cause the device 1105 to perform various functions described herein. The code 1130 may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. In some cases, the code 1130 may not be directly executable by a processor of the at least one processor 1135 but may cause a computer (e.g., when compiled and executed) to perform functions described herein. In some cases, the at least one memory 1125 may contain, among other things, a BIOS which may control basic hardware or software operation such as the interaction with peripheral components or devices. In some examples, the at least one processor 1135 may include multiple processors and the at least one memory 1125 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 herein (for example, as part of a processing system) .

The at least one processor 1135 may include an intelligent hardware device (e.g., a general-purpose processor, a DSP, an ASIC, a CPU, a GPU, a NPU, an FPGA, a microcontroller, a programmable logic device, discrete gate or transistor logic, a discrete hardware component, or any combination thereof) . In some cases, the at least one processor 1135 may be configured to operate a memory array using a memory controller. In some other cases, a memory controller may be integrated into one or more of the at least one processor 1135. The at least one processor 1135 may be configured to execute computer-readable instructions stored in a memory (e.g., one or more of the at least one memory 1125) to cause the device 1105 to perform various functions (e.g., functions or tasks supporting correspondence between AI models and CSI reporting) . For example, the device 1105 or a component of the device 1105 may include at least one processor 1135 and at least one memory 1125 coupled with one or more of the at least one processor 1135, the at least one processor 1135 and the at least one memory 1125 configured to perform various functions described herein. The at least one processor 1135 may be an example of a cloud-computing platform (e.g., one or more physical nodes and supporting software such as operating systems, virtual machines, or container instances) that may host the functions (e.g., by executing code 1130) to perform the functions of the device 1105. The at least one processor 1135 may be any one or more suitable processors capable of executing scripts or instructions of one or more software programs stored in the device 1105 (such as within one or more of the at least one memory 1125) . In some examples, the at least one processor 1135 may include multiple processors and the at least one memory 1125 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 herein. In some examples, the at least one processor 1135 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 1135) and memory circuitry (which may include the at least one memory 1125) ) , 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 1135 or a processing system including the at least one processor 1135 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 stored in the at least one memory 1125 or otherwise, to perform one or more of the functions described herein.

In some examples, a bus 1140 may support communications of (e.g., within) a protocol layer of a protocol stack. In some examples, a bus 1140 may support communications associated with a logical channel of a protocol stack (e.g., between protocol layers of a protocol stack) , which may include communications performed within a component of the device 1105, or between different components of the device 1105 that may be co-located or located in different locations (e.g., where the device 1105 may refer to a system in which one or more of the communications manager 1120, the transceiver 1110, the at least one memory 1125, the code 1130, and the at least one processor 1135 may be located in one of the different components or divided between different components) .

In some examples, the communications manager 1120 may manage aspects of communications with a core network 130 (e.g., via one or more wired or wireless backhaul links) . For example, the communications manager 1120 may manage the transfer of data communications for client devices, such as one or more UEs 115. In some examples, the communications manager 1120 may manage communications with other network entities 105, and may include a controller or scheduler for controlling communications with UEs 115 in cooperation with other network entities 105. In some examples, the communications manager 1120 may support an X2 interface within an LTE/LTE-A wireless communications network technology to provide communication between network entities 105.

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 outputting a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports. The communications manager 1120 is capable of, configured to, or operable to support a means for outputting a set of reference signals. The communications manager 1120 is capable of, configured to, or operable to support a means for obtaining a CSI report message that indicates a set of predicted values that are based on a set of measured values associated with the set of reference signals, where the set of predicted values of the CSI report message is associated with a first AI model or functionality, or both, in accordance with the correspondence.

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, and improved utilization of processing capability.

In some examples, the communications manager 1120 may be configured to perform various operations (e.g., receiving, obtaining, monitoring, outputting, transmitting) using or otherwise in cooperation with the transceiver 1110, the one or more antennas 1115 (e.g., where applicable) , 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 transceiver 1110, one or more of the at least one processor 1135, one or more of the at least one memory 1125, the code 1130, or any combination thereof (for example, by a processing system including at least a portion of the at least one processor 1135, the at least one memory 1125, the code 1130, or any combination thereof) . For example, the code 1130 may include instructions executable by one or more of the at least one processor 1135 to cause the device 1105 to perform various aspects of correspondence between AI models and CSI reporting as described herein, or the at least one processor 1135 and the at least one memory 1125 may be otherwise configured to, individually or collectively, perform or support such operations.

FIG. 12 shows a flowchart illustrating a method 1200 that supports correspondence between AI models and CSI reporting 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 7. 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 a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports. 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 message monitoring component 625 as described with reference to FIG. 6.

At 1210, the method may include generating a set of measured values associated with a set of reference signals, wherein a set of predicted values is generated using a first AI model or functionality, or both, in accordance with the correspondence and based at least in part on the set of measured values. 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 value predicting component 630 as described with reference to FIG. 6.

At 1215, the method may include transmitting a CSI report message that indicates the set of predicted values. 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 CSI reporting component 635 as described with reference to FIG. 6.

FIG. 13 shows a flowchart illustrating a method 1300 that supports correspondence between AI models and CSI reporting 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 7. 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 a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports. 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 message monitoring component 625 as described with reference to FIG. 6.

At 1310, the method may include receiving a second message indicating a first mapping of respective first identifiers of a first set of resources to respective output feature identifiers of an AI model or functionality, or both, and a second mapping of respective second identifiers of a second set of resources to respective input feature identifiers of the AI model or functionality, or both, for each AI model or functionality, or both, of the one or more AI models or functionalities, or both. 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 message monitoring component 625 as described with reference to FIG. 6.

At 1315, the method may include generating a set of measured values associated with a set of reference signals, wherein a set of predicted values is generated using a first AI model or functionality, or both, in accordance with the correspondence and based at least in part on the set of measured values. 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 value predicting component 630 as described with reference to FIG. 6.

At 1320, the method may include transmitting a CSI report message that indicates the set of predicted values. 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 a CSI reporting component 635 as described with reference to FIG. 6.

FIG. 14 shows a flowchart illustrating a method 1400 that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure. The operations of the method 1400 may be implemented by a network entity or its components as described herein. For example, the operations of the method 1400 may be performed by a network entity as described with reference to FIGs. 1 through 3 and 8 through 11. 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 1405, the method may include outputting a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports. The operations of 1405 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1405 may be performed by a message outputting component 1025 as described with reference to FIG. 10.

At 1410, the method may include outputting a set of reference signals. The operations of 1410 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1410 may be performed by a reference signaling component 1030 as described with reference to FIG. 10.

At 1415, the method may include obtaining a CSI report message that indicates a set of predicted values that are based at least in part on a set of measured values associated with the set of reference signals, wherein the set of predicted values of the CSI report message is associated with a first AI model or functionality, or both, in accordance with the correspondence. The operations of 1415 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1415 may be performed by a CSI report monitoring component 1035 as described with reference to FIG. 10.

FIG. 15 shows a flowchart illustrating a method 1500 that supports correspondence between AI models and CSI reporting in accordance with one or more aspects of the present disclosure. The operations of the method 1500 may be implemented by a network entity or its components as described herein. For example, the operations of the method 1500 may be performed by a network entity as described with reference to FIGs. 1 through 3 and 8 through 11. 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 1505, the method may include outputting a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports. The operations of 1505 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1505 may be performed by a message outputting component 1025 as described with reference to FIG. 10.

At 1510, the method may include outputting a second message indicating a first mapping of respective first identifiers of a first set of resources to respective output feature identifiers of an AI model or functionality, or both, and a second mapping of respective second identifiers of a second set of resources to respective input feature identifiers of the AI model or functionality, or both, for each AI model or functionality, or both, of the one or more AI models or functionalities, or both. The operations of 1510 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1510 may be performed by a message outputting component 1025 as described with reference to FIG. 10.

At 1515, the method may include outputting a set of reference signals. The operations of 1515 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1515 may be performed by a reference signaling component 1030 as described with reference to FIG. 10.

At 1520, the method may include obtaining a CSI report message that indicates a set of predicted values that are based at least in part on a set of measured values associated with the set of reference signals, wherein the set of predicted values of the CSI report message is associated with a first AI model or functionality, or both, in accordance with the correspondence. The operations of 1520 may be performed in accordance with examples as disclosed herein. In some examples, aspects of the operations of 1520 may be performed by a CSI report monitoring component 1035 as described with reference to FIG. 10.

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

Aspect 1: A method for wireless communications at a UE, comprising: receiving a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports; generating a set of measured values associated with a set of reference signals, wherein a set of predicted values is generated using a first AI model or functionality, or both, in accordance with the correspondence and based at least in part on the set of measured values; and transmitting a CSI report message that indicates the set of predicted values.

Aspect 2: The method of aspect 1, wherein the correspondence indicates that a set of AI models or functionalities, or both, are associated with a single CSI report, the method further comprising: determining the first AI model or functionality, or both, from the set of AI models or functionalities, or both based at least in part on an autonomous selection procedure, wherein the set of predicted values that are generated using the first AI model or functionality, or both, based at least in part on the autonomous selection procedure.

Aspect 3: The method of aspect 2, wherein transmitting the CSI report message comprises: transmitting, as part of the CSI report message, an indication of a first model or functionality identifier associated with the first AI model or functionality, or both.

Aspect 4: The method of any of aspects 1 through 3, further comprising: receiving a second message indicating a first mapping of respective first identifiers of a first set of resources to respective output feature identifiers of an AI model or functionality, or both, and a second mapping of respective second identifiers of a second set of resources to respective input feature identifiers of the AI model or functionality, or both, for each AI model or functionality, or both, of the one or more AI models or functionalities, or both.

Aspect 5: The method of aspect 4, wherein the first mapping and the second mapping are based at least in part on a CSI report quantity associated with the CSI report message being associated with directional beam prediction.

Aspect 6: The method of any of aspects 1 through 5, wherein the first message indicating the correspondence is a RRC message comprising a CSI report setting indicating that the one or more AI models or functionalities, or both, are associated with a single CSI report.

Aspect 7: The method of any of aspects 1 through 5, wherein the first message indicating the correspondence is a RRC message comprising an aperiodic CSI report setting associated with a set of CSI report configurations for a single CSI report, each CSI report configuration of the set of CSI report configurations indicates a respective set of resources associated with a respective AI model or functionality, or both, and each of the respective AI models or functionalities, or both, are associated with the single CSI report.

Aspect 8: The method of any of aspects 1 through 5, wherein the first message indicating the correspondence is a MAC-CE message for activation of a single semi-persistent CSI report and indicates that the one or more AI models or functionalities, or both, are associated with the single semi-persistent CSI report.

Aspect 9: The method of any of aspects 1 through 5, wherein the first message indicating the correspondence is a MAC-CE message indicating a change from one or more first AI models or functionalities, or both, being associated with a single CSI report to one or more second AI models or functionalities, or both, being associated with the single CSI report.

Aspect 10: The method of any of aspects 1 through 5, wherein the first message indicating the correspondence is a DCI message comprising one or more fields, and at least one field of the one or more fields indicates one or more respective AI models or functionalities, or both, being associated with a respective CSI report.

Aspect 11: The method of any of aspects 1 through 10, wherein the first message indicates a respective model or functionality identifier associated with each of the one or more AI models or functionalities, or both.

Aspect 12: The method of any of aspects 1 through 11, wherein the first message indicates a subset of model or functionality identifiers from a set of model or functionality identifiers configured via one or more control messages, each model or functionality identifier of the subset of model or functionality identifiers is associated with a respective AI model or functionality, or both, of the one or more AI models or functionalities, or both.

Aspect 13: The method of any of aspects 1 through 12, further comprising: receiving a first RRC message that indicates one or more model or functionality identifiers each associated with a respective AI model or functionality, or both, supported for a geographic coverage area or one or more cells, or any combination thereof, associated with a network entity.

Aspect 14: The method of aspect 13, wherein the first message indicating the correspondence is a second RRC message that indicates one or more respective CSI reports associated with each respective AI model or functionality, or both, indicated in the first RRC message.

Aspect 15: The method of any of aspects 13 through 14, wherein the first message indicating the correspondence is a MAC-CE message that activates one or more of the respective AI models or functionalities, or both, indicated in the first RRC message and further indicates one or more respective CSI reports associated with each of the activated AI models or functionalities, or both.

Aspect 16: The method of any of aspects 13 through 15, wherein the first message indicates a subset of model or functionality identifiers of the one or more model or functionality identifiers indicated in the first RRC message, the subset of model or functionality identifiers associated with a subset of AI models or functionalities, or both.

Aspect 17: The method of aspect 16, wherein the subset of model or functionality identifiers are indicated via a set of indices each associated with a respective model or functionality identifier of the one or more model or functionality identifiers, and a first value for an index of the set of indices indicates that an associated model or functionality identifier is included in the subset of model or functionality identifiers.

Aspect 18: The method of any of aspects 16 through 17, wherein the subset of model or functionality identifiers is indicated via a list of model or functionality identifiers that comprises the subset of model or functionality identifiers, and an ordering of the list of model or functionality identifiers is based at least in part on an order of AI models or functionalities, or both, for generating the set of predicted values.

Aspect 19: A method for wireless communications at a network entity, comprising: outputting a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports; outputting a set of reference signals; and obtaining a CSI report message that indicates a set of predicted values that are based at least in part on a set of measured values associated with the set of reference signals, wherein the set of predicted values of the CSI report message is associated with a first AI model or functionality, or both, in accordance with the correspondence.

Aspect 20: The method of aspect 19, wherein the correspondence indicates that a set of AI models or functionalities, or both, are associated with a single CSI report, wherein obtaining the CSI report message comprises: obtaining, as part of the CSI report message, an indication of a first model or functionality identifier associated with the first AI model or functionality, or both.

Aspect 21: The method of any of aspects 19 through 20, further comprising: outputting a second message indicating a first mapping of respective first identifiers of a first set of resources to respective output feature identifiers of an AI model or functionality, or both, and a second mapping of respective second identifiers of a second set of resources to respective input feature identifiers of the AI model or functionality, or both, for each AI model or functionality, or both, of the one or more AI models or functionalities, or both.

Aspect 22: The method of aspect 21, wherein the first mapping and the second mapping are based at least in part on a CSI report quantity associated with the CSI report message being associated with directional beam prediction.

Aspect 23: The method of any of aspects 19 through 22, wherein the first message indicating the correspondence is a RRC message comprising a CSI report setting indicating that the one or more AI models or functionalities, or both, are associated with a single CSI report.

Aspect 24: The method of any of aspects 19 through 22, wherein the first message indicating the correspondence is a RRC message comprising an aperiodic CSI report setting associated with a set of CSI report configurations for a single CSI report, each CSI report configuration of the set of CSI report configurations indicates a respective set of resources associated with a respective AI model or functionality, or both, and each of the respective AI models or functionalities, or both, are associated with the single CSI report.

Aspect 25: The method of any of aspects 19 through 22, wherein the first message indicating the correspondence is a MAC-CE message for activation of a single semi-persistent CSI report and indicates that the one or more AI models or functionalities, or both, are associated with the single semi-persistent CSI report.

Aspect 26: The method of any of aspects 19 through 22, wherein the first message indicating the correspondence is a MAC-CE message indicating a change from one or more first AI models or functionalities, or both, being associated with a single CSI report to one or more second AI models or functionalities, or both, being associated with the single CSI report.

Aspect 27: The method of any of aspects 19 through 22, wherein the first message indicating the correspondence is a DCI message comprising one or more fields, and at least one field of the one or more fields indicates one or more respective AI models or functionalities, or both, being associated with a respective CSI report.

Aspect 28: The method of any of aspects 19 through 27, wherein the first message indicates a respective model or functionality identifier associated with each of the one or more AI models or functionalities, or both.

Aspect 29: The method of any of aspects 19 through 28, wherein the first message indicates a subset of model or functionality identifiers from a set of model or functionality identifiers configured via one or more control messages, where each model or functionality identifier of the subset of model or functionality identifiers is associated with a respective AI model or functionality, or both, of the one or more AI models or functionalities, or both.

Aspect 30: The method of any of aspects 19 through 29, further comprising: outputting a first RRC message that indicates one or more model or functionality identifiers each associated with a respective AI model or functionality, or both, supported for a geographic coverage area or one or more cells, or any combination thereof, associated with the network entity.

Aspect 31: The method of aspect 30, wherein the first message indicating the correspondence is a second RRC message that indicates one or more respective CSI reports associated with each respective AI model or functionality, or both, indicated in the first RRC message.

Aspect 32: The method of any of aspects 30 through 31, wherein the first message indicating the correspondence is a MAC-CE message that activates one or more of the respective AI models or functionalities, or both, indicated in the first RRC message and further indicates one or more respective CSI reports associated with each of the activated AI models or functionalities, or both.

Aspect 33: The method of any of aspects 30 through 32, wherein the first message indicates a subset of model or functionality identifiers of the one or more model or functionality identifiers indicated in the first RRC message, the subset of model or functionality identifiers associated with a subset of AI models or functionalities, or both.

Aspect 34: The method of aspect 33, wherein the subset of model or functionality identifiers is indicated via a set of indices each associated with a respective model or functionality identifier of the one or more model or functionality identifiers, and a first value for an index of the set of indices indicates that an associated model or functionality identifier is included in the subset of model or functionality identifiers.

Aspect 35: The method of any of aspects 33 through 34, wherein the subset of model or functionality identifiers is indicated via a list of model or functionality identifiers that comprises the subset of model or functionality identifiers, and an ordering of the list of model or functionality identifiers is based at least in part on an order of AI models or functionalities, or both, for generating the set of predicted values.

Aspect 36: A UE for wireless communications, comprising one or more memories storing processor-executable code, and one or more processors coupled with (e.g., operatively, communicatively, functionally, electronically, or electrically) the one or more memories and individually or collectively operable to execute the code (e.g., directly, indirectly, after pre-processing, without pre-processing) to cause the UE to perform a method of any of aspects 1 through 18.

Aspect 37: A UE for wireless communications, comprising at least one means for performing a method of any of aspects 1 through 18.

Aspect 38: A non-transitory computer-readable medium storing code for wireless communications, the code comprising instructions executable by one or more processors (e.g., directly, indirectly, after pre-processing, without pre-processing) to perform a method of any of aspects 1 through 18.

Aspect 39: A network entity for wireless communications, comprising one or more memories storing processor-executable code, and one or more processors coupled with (e.g., operatively, communicatively, functionally, electronically, or electrically) the one or more memories and individually or collectively operable to execute the code (e.g., directly, indirectly, after pre-processing, without pre-processing) to cause the network entity to perform a method of any of aspects 19 through 35.

Aspect 40: A network entity for wireless communications, comprising at least one means for performing a method of any of aspects 19 through 35.

Aspect 41: A non-transitory computer-readable medium storing code for wireless communications, the code comprising instructions executable by one or more processors (e.g., directly, indirectly, after pre-processing, without pre-processing) to perform a method of any of aspects 19 through 35.

It should be noted that the methods described herein describe possible implementations, and that the operations and the steps may be rearranged or otherwise modified and that 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 communications 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, including future systems and radio technologies, not explicitly mentioned herein. Components within a wireless communication system may be coupled (for example, operatively, communicatively, functionally, electronically, and/or electrically) to each other.

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 GPU, a 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 (e.g., executed by a processor) , or any combination thereof. Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, or functions, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. 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, 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, phase change 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 (e.g., 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, the term “and/or, ” when used in a list of two or more items, means that any one of the listed items can be employed by itself, or any combination of two or more of the listed items can be employed. For example, if a composition is described as containing components A, B, and/or C, the composition can contain A alone; B alone; C alone; A and B in combination; A and C in combination; B and C in combination; or A, B, and C in combination.

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, ” “at least one of one or more” may be interchangeable. For example, if a claim recites “a component” 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” or “identify” or “identifying” encompasses a variety of actions and, therefore, “determining” or “identifying” can include calculating, computing, processing, deriving, investigating, looking up (such as via looking up in a table, a database or another data structure) , ascertaining, among other examples. Also, “determining” or “identifying” can include receiving (such as receiving information or signaling, e.g., receiving information or signaling for determining, receiving information or signaling for identifying) , accessing (such as accessing data in a memory, or accessing information) , among other examples. Also, “determining” or “identifying” 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 instances, 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

A user equipment (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:

receive a first message indicating a correspondence between one or more artificial intelligence (AI) models or functionalities, or both, and one or more channel state information (CSI) reports;

generate a set of measured values associated with a set of reference signals, wherein a set of predicted values is generated using a first AI model or functionality, or both, in accordance with the correspondence and based at least in part on the set of measured values; and

transmit a CSI report message that indicates the set of predicted values.
The UE of claim 1, wherein the correspondence indicates that a set of AI models or functionalities, and the one or more processors are individually or collectively further operable to execute the code to cause the UE to:

determine the first AI model or functionality, or both, from the set of AI models or functionalities, or both based at least in part on an autonomous selection procedure, wherein the set of predicted values that are generated using the first AI model or functionality, or both, based at least in part on the autonomous selection procedure.
The UE of claim 2, wherein, to transmit the CSI report message, the one or more processors are individually or collectively operable to execute the code to cause the UE to:

transmit, as part of the CSI report message, an indication of a first model or functionality identifier associated with the first AI model or functionality, or both.
The UE of claim 1, wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to:

receive a second message indicating a first mapping of respective first identifiers of a first set of resources to respective output feature identifiers of an AI model or functionality, or both, and a second mapping of respective second identifiers of a second set of resources to respective input feature identifiers of the AI model or functionality, or both, for each AI model or functionality, or both, of the one or more AI models or functionalities, or both.
The UE of claim 4, wherein the first mapping and the second mapping are based at least in part on a CSI report quantity associated with the CSI report message being associated with directional beam prediction.
The UE of claim 1, wherein the first message indicating the correspondence is a radio resource control message comprising a CSI report setting indicating that the one or more AI models or functionalities, or both, are associated with a single CSI report.
The UE of claim 1, wherein the first message indicating the correspondence is a radio resource control message comprising an aperiodic CSI report setting associated with a set of CSI report configurations for a single CSI report, wherein each CSI report configuration of the set of CSI report configurations indicates a respective set of resources associated with a respective AI model or functionality, or both, and wherein each of the respective AI models or functionalities, or both, are associated with the single CSI report.
The UE of claim 1, wherein the first message indicating the correspondence is a medium access control-control element message for activation of a single semi-persistent CSI report and indicates that the one or more AI models or functionalities, or both, are associated with the single semi-persistent CSI report.
The UE of claim 1, wherein the first message indicating the correspondence is a medium access control-control element message indicating a change from one or more first AI models or functionalities, or both, being associated with a single CSI report to one or more second AI models or functionalities, or both, being associated with the single CSI report.
The UE of claim 1, wherein the first message indicating the correspondence is a downlink control information message comprising one or more fields, and wherein at least one field of the one or more fields indicates one or more respective AI models or functionalities, or both, being associated with a respective CSI report.
The UE of claim 1, wherein the first message indicates a respective model or functionality identifier associated with each of the one or more AI models or functionalities, or both.
The UE of claim 1, wherein the first message indicates a subset of model or functionality identifiers from a set of model or functionality identifiers configured via one or more control messages, and wherein each model or functionality identifier of the subset of model or functionality identifiers is associated with a respective AI model or functionality, or both, of the one or more AI models or functionalities, or both.
The UE of claim 1, wherein the one or more processors are individually or collectively further operable to execute the code to cause the UE to:

receive a first radio resource control message that indicates one or more model or functionality identifiers each associated with a respective AI model or functionality, or both, supported for a geographic coverage area or one or more cells, or any combination thereof, associated with a network entity.
The UE of claim 13, wherein the first message indicating the correspondence is a second radio resource control message that indicates one or more respective CSI reports associated with each respective AI model or functionality, or both, indicated in the first radio resource control message.
The UE of claim 13, wherein the first message indicating the correspondence is a medium access control-control element message that activates one or more of the respective AI models or functionalities, or both, indicated in the first radio resource control message and further indicates one or more respective CSI reports associated with each of the activated AI models or functionalities, or both.
The UE of claim 13, wherein the first message indicates a subset of model or functionality identifiers of the one or more model or functionality identifiers indicated in the first radio resource control message, the subset of model or functionality identifiers associated with a subset of AI models or functionalities, or both.
The UE of claim 16, wherein the subset of model or functionality identifiers are indicated via a set of indices each associated with a respective model or functionality identifier of the one or more model or functionality identifiers, and wherein a first value for an index of the set of indices indicates that an associated model or functionality identifier is included in the subset of model or functionality identifiers.
The UE of claim 16, wherein the subset of model or functionality identifiers is indicated via a list of model or functionality identifiers that comprises the subset of model or functionality identifiers, and wherein an ordering of the list of model or functionality identifiers is based at least in part on an order of AI models or functionalities, or both, for generating the set of predicted values.
A network entity, 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 network entity to:

output a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports;

output a set of reference signals; and

obtain a CSI report message that indicates a set of predicted values that are based at least in part on a set of measured values associated with the set of reference signals, wherein the set of predicted values of the CSI report message is associated with a first AI model or functionality, or both, in accordance with the correspondence.
The network entity of claim 19, wherein the correspondence indicates that a set of AI models or functionalities, or both, are associated with a single CSI report, and to obtain the CSI report message, the one or more processors are individually or collectively operable to execute the code to cause the network entity to:

obtain, as part of the CSI report message, an indication of a first model or functionality identifier associated with the first AI model or functionality, or both.
The network entity of claim 19, wherein the one or more processors are individually or collectively further operable to execute the code to cause the network entity to:

output a second message indicating a first mapping of respective first identifiers of a first set of resources to respective output feature identifiers of an AI model or functionality, or both, and a second mapping of respective second identifiers of a second set of resources to respective input feature identifiers of the AI model or functionality, or both, for each AI model or functionality, or both, of the one or more AI models or functionalities, or both.
The network entity of claim 21, wherein the first mapping and the second mapping are based at least in part on a CSI report quantity associated with the CSI report message being associated with directional beam prediction.
The network entity of claim 19, wherein the first message indicating the correspondence is a radio resource control message comprising a CSI report setting indicating that the one or more AI models or functionalities, or both, are associated with a single CSI report.
The network entity of claim 19, wherein the first message indicating the correspondence is a radio resource control message comprising an aperiodic CSI report setting associated with a set of CSI report configurations for a single CSI report, wherein each CSI report configuration of the set of CSI report configurations indicates a respective set of resources associated with a respective AI model or functionality, or both, and wherein each of the respective AI models or functionalities, or both, are associated with the single CSI report.
The network entity of claim 19, wherein the first message indicating the correspondence is a medium access control-control element message for activation of a single semi-persistent CSI report and indicates that the one or more AI models or functionalities, or both, are associated with the single semi-persistent CSI report.
The network entity of claim 19, wherein the first message indicating the correspondence is a medium access control-control element message indicating a change from one or more first AI models or functionalities, or both, being associated with a single CSI report to one or more second AI models or functionalities, or both, being associated with the single CSI report.
The network entity of claim 19, wherein the first message indicating the correspondence is a downlink control information message comprising one or more fields, and wherein at least one field of the one or more fields indicates one or more respective AI models or functionalities, or both, being associated with a respective CSI report.
The network entity of claim 19, wherein the first message indicates a respective model or functionality identifier associated with each of the one or more AI models or functionalities, or both.
A method for wireless communications at a user equipment (UE) , comprising:

receiving a first message indicating a correspondence between one or more artificial intelligence (AI) models or functionalities, or both, and one or more channel state information (CSI) reports;

generating a set of measured values associated with a set of reference signals, wherein a set of predicted values is generated using a first AI model or functionality, or both, in accordance with the correspondence and based at least in part on the set of measured values; and

transmitting a CSI report message that indicates the set of predicted values.
A method for wireless communications at a network entity, comprising:

outputting a first message indicating a correspondence between one or more AI models or functionalities, or both, and one or more CSI reports;

outputting a set of reference signals; and

obtaining a CSI report message that indicates a set of predicted values that are based at least in part on a set of measured values associated with the set of reference signals, wherein the set of predicted values of the CSI report message is associated with a first AI model or functionality, or both, in accordance with the correspondence.