Antenna consistency for artificial intelligence or machine learning positioning data collection, training, and inference

Abstract

Aspects presented herein may enable a wireless device to indicate its antenna processing capabilities related to artificial intelligence (AI) or machine learning (ML) (AI / ML) positioning operations (e.g., data collection, model training / retraining, inferencing, etc.), thereby promoting or ensuring a consistency in antenna processing between different AI / ML operations. In one aspect, a wireless device transmits, to a network entity, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one AI / ML-related operation. The wireless device receives, from the network entity in response to transmission of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML-related operation.

Description

Qualcomm Ref. No. 2405604WO 1ANTENNA CONSISTENCY FOR ARTIFICIAL INTELLIGENCE OR MACHINE LEARNING POSITIONING DATA COLLECTION, TRAINING, AND INFERENCECROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of Greece Patent Application No. 20240100762, entitled “ANTENNA CONSISTENCY FOR ARTIFICIAL INTELLIGENCE OR MACHINE LEARNING POSITIONING DATA COLLECTION, TRAINING, AND INFERENCE” and filed on October 29, 2024, which is expressly incorporated by reference herein in its entirety.TECHNICAL FIELD

[0002] The present disclosure relates generally to communication systems, and more particularly, to wireless communication involving artificial intelligence (Al) or machine learning (ML) (AI / ML) positioning or sensing.INTRODUCTION

[0003] Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, and broadcasts. Typical wireless communication systems may employ multiple-access technologies capable of supporting communication with multiple users by sharing available system resources. Examples of such multiple-access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single-carrier frequency division multiple access (SC-FDMA) systems, and time division synchronous code division multiple access (TD-SCDMA) systems.

[0004] These multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different wireless devices to communicate on a municipal, national, regional, and even global level. An example telecommunication standard is 5G New Radio (NR). 5G NR is part of a continuous mobile broadband evolution promulgated by Third Generation Partnership Project (3 GPP) to meet new requirements associated with latency, reliability, security,129025-2422WO01Qualcomm Ref. No. 2405604WO 2 scalability (e.g., with Internet of Things (IoT)), and other requirements. 5G NR includes services associated with enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable low latency communications (URLLC). Some aspects of 5G NR may be based on the 4G Long Term Evolution (LTE) standard. There exists a need for further improvements in 5G NR technology. These improvements may also be applicable to other multi-access technologies and the telecommunication standards that employ these technologies.

[0005] Some telecommunication standards also provide positioningprotocols and techniques that enable mobile network operators to provide high-accuracy location services to their subscribers. For example, 5GNR include various standards for network-based positioning that use signals and features of the 5G network to perform or improve the positioning of a device. There also exists a need for further improvements in these positioning protocols and techniques.BRIEF SUMMARY

[0006] The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects. This summary neither identifies key or critical elements of all aspects nor delineates the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.

[0007] In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus transmits, to a network entity, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one artificial intelligence (Al) or machine learning (ML) (AI / ML)-related operation. The apparatus receives, from the network entity in response to transmission of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML-related operation.

[0008] In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus receives, from a wireless device, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one AI / ML-related operation. The apparatus transmits, to the wireless129025-2422WO01Qualcomm Ref. No. 2405604WO 3 device in response to reception of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML-related operation.

[0009] To the accomplishment of the foregoing and related ends, the one or more aspects may include the features hereinafter fully described and particularly pointed out in the claims. The following description and the drawings set forth in detail certain illustrative features of the one or more aspects. These features are indicative, however, of but a few of the various ways in which the principles of various aspects may be employed.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] FIG. l is a diagram illustrating an example of a wireless communications system and an access network.

[0011] FIG. 2A is a diagram illustrating an example of a first frame, in accordance with various aspects of the present disclosure.

[0012] FIG. 2B is a diagram illustrating an example of downlink (DL) channels within a subframe, in accordance with various aspects of the present disclosure.

[0013] FIG. 2C is a diagram illustrating an example of a second frame, in accordance with various aspects of the present disclosure.

[0014] FIG. 2D is a diagram illustrating an example of uplink (UL) channels within a subframe, in accordance with various aspects of the present disclosure.

[0015] FIG. 3 is a diagram illustrating an example of a base station and user equipment (UE) in an access network.

[0016] FIG. 4 is a diagram illustrating an example of a UE positioning based on reference signal measurements.

[0017] FIG. 5 A is a diagram illustrating an example of direct artificial intelligence (Al) / machine learning (ML) (AI / ML) positioning in accordance with various aspects of the present disclosure.

[0018] FIG. 5B is a diagram illustrating an example of AI / ML assisted positioning in accordance with various aspects of the present disclosure.

[0019] FIG. 6 is a diagram illustrating an example of different configurations for AI / ML assisted positioning in accordance with various aspects of the present disclosure.129025-2422WO01Qualcomm Ref. No. 2405604WO 4

[0020] FIG. 7 is a diagram illustrating an example of UE-based positioning with UE-side AI / ML model, direct AI / ML or AI / ML assisted positioning in accordance with various aspects of the present disclosure.

[0021] FIG. 8 A is a diagram illustrating an example of UE-assisted / location management function (LMF)-based positioning with UE-side AI / ML model, AI / ML assisted positioning in accordance with various aspects of the present disclosure.

[0022] FIG. 8B is a diagram illustrating an example of UE-assisted / LMF-based positioning with LMF-side model, direct AI / ML positioning in accordance with various aspects of the present disclosure.

[0023] FIG. 9A is a diagram illustrating an example of network node assisted positioning with gNB-side model, AI / ML assisted positioning in accordance with various aspects of the present disclosure.

[0024] FIG. 9B is a diagram illustrating an example of network node assisted positioning with LMF-side model, direct AI / ML positioning in accordance with various aspects of the present disclosure.

[0025] FIG. 10 is a communication flow illustrating an example of a UE communicating its antenna capabilities with a network entity to improve the overall performance of AI / ML-related operations in accordance with various aspects of the present disclosure.

[0026] FIG. 11 is a communication flow illustrating an example of abase station (BS) or a transmission reception point (TRP) communicating its antenna capabilities with a network entity to improve the overall performance of AI / ML-related operations in accordance with various aspects of the present disclosure.

[0027] FIG. 12 is a flowchart of a method of wireless communication.

[0028] FIG. 13 is a diagram illustrating an example of a hardware implementation for an example apparatus and / or network entity.

[0029] FIG. 14 is a diagram illustrating an example of a hardware implementation for an example network entity.

[0030] FIG. 15 is a flowchart of a method of wireless communication.

[0031] FIG. 16 is a diagram illustrating an example of a hardware implementation for an example network entity.129025-2422WO01Qualcomm Ref. No. 2405604WO 5DETAILED DESCRIPTION

[0032] Various aspects relate generally to improving the overall performance and efficiency of artificial intelligence (Al) or machine learning (ML) (AI / ML) positioning operations (e.g., data collection, AI / ML model training / retraining, inferencing, etc.) by promoting or ensuring a consistency in antenna processing between different AI / ML operations, such as between AI / ML training and inference, between AI / ML input and output, etc. Aspects presented herein provide signaling and solutions that helps reduce ambiguity / uncertainty about antenna selection and / or aggregation during AI / ML inferencewhen comparedto AI / ML data collection / training. In one aspect, for data collection related to AI / ML positioning, training data (e.g., obtained by a data collection entity) may be configured to include information on antenna processing considered during training data collection. For example, during data collection, a device may be provided with information related to antenna processing when obtaining measurements, and / or the device may have the capability to indicate information related to what / which antenna processing it did when obtaining measurements. In another aspect, for AI / ML model development related to AI / ML positioning, a model developer may leverage information to develop AI / ML model(s) and enhance their robustness to antenna processing inconsistencies. For example, a developer may develop multiple AI / ML models with different antenna processing assumptions, enable AI / ML model input indexing with information, or train AI / ML model(s) with measurements of different antenna processing, etc. In another aspect, for AI / ML model transfer related to AI / ML positioning, when an AI / ML model is transferred to an inference entity, the AI / ML model may be specified to be associated with information related on assumptions regarding antenna processing corresponding to measurements at AI / ML model input and / or output. In another aspect, for AI / ML inference related to AI / ML positioning, during an inference, a device may be provided with information related to antenna processing when obtaining measurements, and / or the device may have the capability to indicate information related to what / which antenna processing when obtaining measurements. Note information here may be an explicit indication of actual antenna processing or an implicit indication using an associated identification / identifier (ID) (e.g., a proxy ID). Also, a UE / base station may have the capability to recommend / request a network (NW) (e.g., a location server) to consider a certain antenna processing.129025-2422WO01Qualcomm Ref. No. 2405604WO 6

[0033] The detailed description set forth below in connection with the drawings describes various configurations and does not represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of various concepts. However, these concepts may be practiced without these specific details. In some instances, well known structures and components are shown in block diagram form in order to avoid obscuring such concepts.

[0034] Several aspects of telecommunication systems are presented with ref erenceto various apparatus and methods. These apparatus and methods are described in the following detailed description and illustrated in the accompanying drawings by various blocks, components, circuits, processes, algorithms, etc. (collectively referred to as “elements”). These elements may be implemented using electronic hardware, computer software, or any combination thereof. Whether such elements are implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system.

[0035] By way of example, an element, or any portion of an element, or any combination of elements may be implemented as a “processing system” that includes one or more processors. When multiple processors are implemented, the multiple processors may perform the functions individually or in combination. Examplesof processors include microprocessors, microcontrollers, graphics processing units (GPUs), central processing units (CPUs), application processors, digital signal processors (DSPs), reduced instruction set computing (RISC) processors, systems on a chip (SoC), baseband processors, field programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gated logic, discrete hardware circuits, and other suitable hardware configured to perform the various functionality described throughout this disclosure. One or more processors in the processing system may execute software. Software, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise, shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software components, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, or any combination thereof.129025-2422WO01Qualcomm Ref. No. 2405604WO 7

[0036] Accordingly, in one or more example aspects, implementations, and / or use cases, the functions described may be implemented in hardware, software, or any combination thereof. If implemented in software, the functions may be stored on or encoded as one or more instructions or code on a computer-readable medium. Computer-readable media includes computer storage media. Storage media may be any available media that can be accessed by a computer. By way of example, such computer-readable media can include a random-access memory (RAM), a read-only memory (ROM), an electrically erasable programmable ROM (EEPROM), optical disk storage, magnetic disk storage, other magnetic storage devices, combinations of the types of computer- readable media, or any other medium that can be used to store computer executable code in the form of instructions or data structures that can be accessed by a computer.

[0037] While aspects, implementations, and / or use cases are describedin this application by illustration to some examples, additional or different aspects, implementations and / or use cases may come about in many different arrangements and scenarios. Aspects, implementations, and / oruse cases described herein may be implemented across many differingplatform types, devices, systems, shapes, sizes, and packaging arrangements. For example, aspects, implementations, and / or use cases may come about via integrated chip implementations and other non-module-component based devices (e.g., end-user devices, vehicles, communication devices, computing devices, industrial equipment, retail / purchasing devices, medical devices, artificial intelligence (Al)-enabled devices, etc.). While some examples may or may not be specifically directed to use cases or applications, a wide assortment of applicability of described examples may occur. Aspects, implementations, and / oruse cases may range a spectrum from chip-level or modular components to non-modular, non-chip- level implementations and further to aggregate, distributed, or original equipment manufacturer (OEM) devices or systems incorporating one or more techniques herein. In some practical settings, devices incorporating described aspects and features may also include additional components and features for implementation and practice of claimed and described aspect. For example, transmission and reception of wireless signals necessarily includes a number of components for analog and digital purposes (e.g., hardware components including antenna, RF-chains, power amplifiers, modulators, buffer, processor(s), interleaver, adders / summers, etc.). Techniques described herein may be practiced in a wide variety of devices, chip-level129025-2422WO01Qualcomm Ref. No. 2405604WO 8 components, systems, distributed arrangements, aggregated or disaggregated components, end-user devices, etc. of varying sizes, shapes, and constitution.

[0038] Deployment of communication systems, such as 5GNR systems, may be arranged in multiple manners with various components or constituent parts. In a 5G NR system, or network, a network node, a network entity, a mobility element of a network, a radio access network (RAN) node, a core network node, a network element, or a network equipment, such as a base station (BS), or one or more units (or one or more components) performing base station functionality, may be implemented in an aggregated or disaggregated architecture. For example, a BS (such as a Node B (NB), evolved NB (eNB), NR BS, 5GNB, access point (AP), a transmission reception point (TRP), or a cell, etc.) may be implemented as an aggregated base station (also known as a standalone BS or a monolithic BS) or a disaggregated base station.

[0039] An aggregated base station may be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node. A disaggregated base station may be configured to utilize a protocol stack that is physically or logically distributed among two or more units (such as one or more central or centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs)). In some aspects, a CU may be implemented within a RAN node, and one or more DUs may be co-located with the CU, or alternatively, may be geographically or virtually distributed throughout one or multiple other RAN nodes. The DUs may be implemented to communicate with one or more RUs. Each of the CU, DU and RU can be implemented as virtual units, i.e., a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU).

[0040] Base station operation or network design may consider aggregation characteristics of base station functionality. For example, disaggregated base stations may be utilized in an integrated access backhaul (IAB) network, an open radio access network (O- RAN (such as the network configuration sponsored by the O-RAN Alliance)), or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN)). Disaggregation may include distributing functionality across two or more units at various physical locations, as well as distributing functionality for at least one unit virtually, which can enable flexibility in network design. The various units of the disaggregated base station, or disaggregated RAN architecture, can be configured for wired or wireless communication with at least one other unit.129025-2422WO01Qualcomm Ref. No. 2405604WO 9

[0041] FIG. 1 is a diagram 100 illustrating an example of a wireless communications system and an access network. The illustrated wireless communications system includes a disaggregated base station architecture. The disaggregated base station architecture may include one or more CUs 110 that can communicate directly with a core network 120 via a backhaul link, or indirectly with the core network 120 through one or more disaggregated base station units (such as a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC) 125 via an E2 link, or a Non-Real Time (Non-RT) RIC 115 associated with a Service Management and Orchestration (SMO) Framework 105, or both). A CU 110 may communicate with one or more DUs 130 via respective midhaul links, such as an Fl interface. The DUs 130 may communicate with one or more RUs 140 via respective fronthaul links. The RUs 140 may communicate with respective UEs 104 via one or more radio frequency (RF) access links. In some implementations, the UE 104 may be simultaneously served by multiple RUs 140.

[0042] Each of the units, i.e., the CUs 110, the DUs 130, the RUs 140, as well as the Near- RT RICs 125, the Non-RT RICs 115, and the SMO Framework 105, may include one or more interfaces or be coupled to one or more interfaces configured to receive or to transmit signals, data, or information (collectively, signals) via a wired or wireless transmission medium. Each of the units, or an associated processor or controller providing instructions to the communication interfaces of the units, can be configured to communicate with one or more of the other units via the transmission medium. For example, the units can include a wired interface configured to receive or to transmit signals over a wired transmission medium to one or more of the other units. Additionally, the units can include a wireless interface, which may include a receiver, a transmitter, or a transceiver (such as an RF transceiver), configured to receive or to transmit signals, or both, over a wireless transmission medium to one or more of the other units.

[0043] In some aspects, the CU 110 may host one or more higher layer control functions. Such control functions can include radio resource control (RRC), packet data convergence protocol (PDCP), service data adaptation protocol (SDAP), or the like. Each control function can be implemented with an interface configured to communicate signals with other control functions hosted by the CU 110. The CU 110 may be configured to handle user plane functionality (i.e., Central Unit - User Plane (CU-UP)), control plane functionality (i.e., Central Unit - Control Plane (CU-CP)),129025-2422WO01Qualcomm Ref. No. 2405604WO 10 or a combination thereof. In some implementations, the CU 110 can be logically split into one or more CU-UP units and one or more CU-CP units. The CU-UP unit can communicate bidirectionally with the CU-CP unit via an interface, such as an El interface when implemented in an O-RAN configuration. The CU 110 can be implemented to communicate with the DU 130, as necessary, for network control and signaling.

[0044] The DU 130 may correspond to a logical unit that includes one or more base station functions to control the operation of one or more RUs 140. In some aspects, the DU 130 may host one or more of a radio link control (RLC) layer, a medium access control (MAC) layer, and one or more high physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation, demodulation, or the like) depending, at least in part, on a functional split, such as those defined by 3 GPP. In some aspects, the DU 130 may further host one or more low PHY layers. Each layer (or module) can be implemented with an interface configured to communicate signals with other layers (and modules) hosted by the DU 130, or with the control functions hosted by the CU 110.

[0045] Lower-layer functionality can be implemented by one or more RUs 140. In some deployments, an RU 140, controlled by a DU 130, may correspond to a logical node that hosts RF processing functions, or low-PHY layer functions (such as performing fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, or the like), or both, based at least in part on the functional split, such as a lower layer functional split. In such an architecture, the RU(s) 140 can be implemented to handle over the air (OTA) communication with one or more UEs 104. In some implementations, real-time and non-real-time aspects of control and user plane communication with the RU(s) 140 can be controlled by the corresponding DU 130. In some scenarios, this configuration can enable the DU(s) 130 and the CU 110 to be implemented in a cloud-based RAN architecture, such as a vRAN architecture.

[0046] The SMO Framework 105 may be configured to support RAN deployment and provisioning of non-virtualizedandvirtualizednetwork elements. Fornon-virtualized network elements, the SMO Framework 105 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements that may be managed via an operations and maintenance interface (such as an 01 interface).129025-2422WO01Qualcomm Ref. No. 2405604WO 11For virtualized network elements, the SMO Framework 105 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) 190) to perform network element life cycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface (such as an 02 interface). Such virtualized network elements can include, but are not limited to, CUs 110, DUs 130, RUs 140 andNear-RTRICs 125. In some implementations, the SMO Framework 105 can communicate with a hardware aspect of a 4G RAN, such as an open eNB (O- eNB) 111, via an 01 interface. Additionally, in some implementations, the SMO Framework 105 can communicate directly with one or more RUs 140 via an 01 interface. The SMO Framework 105 also may include a Non-RT RIC 115 configured to support functionality of the SMO Framework 105.

[0047] The Non-RT RIC 115 may be configured to include a logical function that enables non-real-time control and optimization of RAN elements and resources, artificial intelligence (Al) / machine learning (ML) (AI / ML) workflows including model training and updates, or policy-based guidance of applications / features in the Near- RT RIC 125. The Non-RT RIC 115 may be coupled to or communicate with (such as via an Al interface) the Near-RT RIC 125. The Near-RT RIC 125 may be configured to include a logical function that enables near-real-time control and optimization of RAN elements and resources via dataset collection and actions over an interface (such as via an E2 interface) connecting one or more CUs 110, one or more DUs 130, or both, as well as an O-eNB, with the Near-RT RIC 125.

[0048] In some implementations, to generate AI / ML models to be deployed in the Near-RT RIC 125, the Non-RT RIC 115 may receive parameters or external enrichment information from external servers. Such information may be utilized by the Near-RT RIC 125 and may be received at the SMO Framework 105 or the Non-RT RIC 115 from non-network data sources or from network functions. In some examples, the Non-RT RIC 115 or the Near-RT RIC 125 may be configured to tune RANbehavior or performance. For example, the Non-RT RIC 115 may monitor long-term trends and patterns for performanceand employ AI / ML models to perform corrective actions through the SMO Framework 105 (such as reconfiguration via 01) or via creation of RAN management policies (such as Al policies).

[0049] At least one of the CU 110, the DU 130, and the RU 140 maybe referred to as a base station 102. Accordingly, a base station 102 may include one or more of the CU 110,129025-2422WO01Qualcomm Ref. No. 2405604WO 12 the DU 130, and the RU 140 (each component indicated with dotted lines to signify that each component may or may not be included in the base station 102). The base station 102 provides an access point to the core network 120 for a UE 104. The base station 102 may include macrocells (high power cellular base station) and / or small cells (low power cellular base station). The small cells include femtocells, picocells, and microcells. A network that includes both small cell and macrocells may be known as a heterogeneous network. A heterogeneous network may also include Home Evolved Node Bs (eNBs) (HeNBs), which may provide service to a restricted group known as a closed subscriber group (CSG). The communication links between the RUs 140 and the UEs 104 may include uplink (UL) (also referred to as reverse link) transmissions from a UE 104 to an RU 140 and / or downlink (DL) (also referred to as forward link) transmissions from an RU 140 to a UE 104. The communication links may use multiple-input and multiple-output (MIMO) antenna technology, including spatial multiplexing, beamforming, and / or transmit diversity. The communication links may be through one or more carriers. The base station 102 / UEs 104 may use spectrum up to EMHz (e.g., 5, 10, 15, 20, 100, 400, etc. MHz) bandwidth per carrier allocated in a carrier aggregation of up to a total of Ex MHz (x component carriers) used for transmission in each direction. The carriers may or may not be adjacent to each other. Allocation of carriers may be asymmetric with respecttoDL andUL (e.g., more or fewer carriers may be allocated for DL than for UL). The component carriers may include a primary component carrier and one or more secondary component carriers. A primary component carrier may be referred to as a primary cell (PCell) and a secondary component carrier may be referred to as a secondary cell (SCell).

[0050] Certain UEs 104 may communicate with each other using device-to-device (D2D) communication link 158. The D2D communication link 158 may use the DL / UL wireless wide area network (WWAN) spectrum. The D2D communication link 158 may use one or more sidelink channels, such as a physical sidelink broadcast channel (PSBCH), a physical sidelink discovery channel (PSDCH), a physical sidelink shared channel (PSSCH), and a physical sidelink control channel (PSCCH). D2D communication may be through a variety of wireless D2D communications systems, such as for example, Bluetooth™ (Bluetooth is a trademark of the Bluetooth Special Interest Group (SIG)), Wi-Fi™ (Wi-Fi is a trademark of the Wi-Fi Alliance) based on129025-2422WO01Qualcomm Ref. No. 2405604WO 13 the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard, LTE, or NR.

[0051] The wireless communications system may further include a Wi-Fi AP 150 in communication with UEs 104 (also referred to as Wi-Fi stations (STAs)) via communication link 154, e.g., in a 5 GHz unlicensed frequency spectrum orthe like. When communicating in an unlicensed frequency spectrum, the UEs 104 / AP 150 may perform a clear channel assessment (CCA) prior to communicating in order to determine whether the channel is available.

[0052] The electromagnetic spectrum is often subdivided, based on frequency / wavelength, into various classes, bands, channels, etc. In 5GNR, two initial operating bands have been identified as frequency range designations FR1 (410 MHz - 7.125 GHz) and FR2 (24.25 GHz - 52.6 GHz). Although a portion of FR1 is greater than 6 GHz, FR1 is often referred to (interchangeably) as a “sub-6 GHz” band in various documents and articles. A similar nomenclature issue sometimes occurs with regard to FR2, which is often referred to (interchangeably) as a “millimeter wave” bandin documents and articles, despite being different from the extremely high frequency (EHF) band (30 GHz - 300 GHz) which is identified by the International Telecommunications Union (ITU) as a “millimeter wave” band.

[0053] The frequencies between FR1 andFR2 are often referred to as mid-band frequencies. Recent 5G NR studies have identified an operating band for these mid-band frequencies as frequency range designation FR3 (7.125 GHz - 24.25 GHz). Frequency bands falling within FR3 may inherit FR1 characteristics and / or FR2 characteristics, and thus may effectively extend features of FR1 and / or FR2 into midband frequencies. In addition, higher frequency bands are currently being explored to extend 5GNRoperationbeyond 52.6GHz. For example, three higher operatingbands have been identified as frequency range designations FR2-2 (52.6 GHz - 71 GHz), FR4 (71 GHz- 114.25 GHz), andFR5 (114.25 GHz- 300 GHz). Each of these hi^ier frequency bands falls within the EHF band.

[0054] With the above aspects in mind, unless specifically stated otherwise, the term “sub-6 GHz” or the like if used herein may broadly represent frequencies that may be less than 6 GHz, may be within FR1 , or may include mid-band frequencies. Further, unless specifically stated otherwise, the term “millimeter wave” or the like if used herein129025-2422WO01Qualcomm Ref. No. 2405604WO 14 may broadly represent frequencies that may include mid-band frequencies, may be within FR2, FR4, FR2-2, and / or FR5, or may be within the EHF band.

[0055] The base station 102 and the UE 104 may each include a plurality of antennas, such as antenna elements, antenna panels, and / or antenna arrays to facilitate beamforming The base station 102 may transmit a beamformed signal 182 to the UE 104 in one or more transmit directions. The UE 104 may receive the beamformed signal from the base station 102 in one or more receive directions. The UE 104 may also transmit a beamformed signal 184 to the base station 102 in one or more transmit directions. The base station 102 may receive the beamformed signal from the UE 104 in one or more receive directions. The base station 102 / UE 104 may perform beam training to determine the best receive and transmit directions for each of the base station 102 / UE 104. The transmit and receive directions for the base station 102 may or may not be the same. The transmit and receive directions for the UE 104 may or may not be the same.

[0056] The base station 102 may include and / or be referred to as a gNB, Node B, eNB, an access point, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), a TRP, network node, network entity, network equipment, or some other suitable terminology. The base station 102 can be implemented as an integrated access and backhaul (IAB) node, a relay node, a sidelink node, an aggregated (monolithic) base station with a baseband unit (BBU) (including a CU and a DU) and an RU, or as a disaggregated base station including one or more of a CU, a DU, and / or an RU. The set of base stations, which may include disaggregated base stations and / or aggregated base stations, may be referred to as next generation (NG) RAN (NG-RAN).

[0057] The core network 120 may include an Access and Mobility Management Function (AMF) 161, a Session Management Function (SMF) 162, a User Plane Function (UPF) 163, a Unified Data Management (UDM) 164, one or more location servers 168, and other functional entities. The AMF 161 is the control node that processes the signaling between the UEs 104 and the core network 120. The AMF 161 supports registration management, connection management, mobility management, and other functions. The SMF 162 supports session management and other functions. The UPF 163 supports packet routing, packet forwarding, and other functions. The UDM 164 supports the generation of authentication and key agreement (AKA) credentials, user129025-2422WO01Qualcomm Ref. No. 2405604WO 15 identification handling, access authorization, and subscription management. The one or more location servers 168 are illustrated as including a Gateway Mobile Location Center (GMLC) 165 and a Location Management Function (LMF) 166. However, generally, the one or more location servers 168 may include one or more location / positioning servers, which may include one or more of the GMLC 165, the LMF 166, a position determination entity (PDE), a serving mobile location center (SMLC), a mobile positioning center (MPC), or the like. The GMLC 165 and the LMF 166 support UE location services. The GMLC 165 provides an interface for clients / applications (e.g., emergency services) for accessing UE positioning information. The LMF 166 receives measurements and assistance information from the NG-RAN and the UE 104 via the AMF 161 to compute the position of the UE 104. The NG-RAN may utilize one or more positioning methods in order to determine the position of the UE 104. Positioningthe UE 104 may involve signal measurements, a position estimate, and an optional velocity computation based on the measurements. The signal measurements may be made by the UE 104 and / or the base station 102 serving the UE 104. The signals measured may be based on one or more of a satellite positioning system (SPS) 170 (e.g., one or more of a Global Navigation Satellite System (GNSS), global position system (GPS), non-terrestrial network (NTN), or other satellite position / location system), LTE signals, wireless local area network (WLAN) signals, Bluetooth signals, a terrestrial beacon system (TBS), sensor-based information (e.g., barometric pressure sensor, motion sensor), NR enhanced cell ID (NRE-CID) methods, NRsignals (e.g., multi-round trip time (Multi-RTT), DL angle- of-departure (DL-AoD), DL time difference of arrival (DL-TDOA), UL time difference of arrival (UL-TDOA), and UL angle-of-arrival (UL-AoA) positioning), and / or other systems / signals / sensors.

[0058] Examples of UEs 104 include a cellular phone, a smartphone, a session initiation protocol (SIP) phone, a laptop, a personal digital assistant (PDA), a satellite radio, a global positioning system, a multimedia device, a video device, a digital audio player (e.g., MP3 player), a camera, a game console, a tablet, a smart device, a wearable device, a vehicle, an electric meter, a gas pump, a large or small kitchen appliance, a healthcare device, an implant, a sensor / actuator, a display, or any other similar functioning device. Some of the UEs 104 may be referred to as loT devices (e.g, parking meter, gas pump, toaster, vehicles, heart monitor, etc.). TheUE 104 may also129025-2422WO01Qualcomm Ref. No. 2405604WO 16 be referred to as a station, a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless device, a wireless communications device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile client, a client, or some other suitable terminology. In some scenarios, the term UE may also apply to one or more companion devices such as in a device constellation arrangement. One or more of these devices may collectively access the network and / or individually access the network.

[0059] Referring again to FIG. 1, in certain aspects, the UE 104 may have an antenna capability indication component 198 that may be configured to transmit, to a network entity, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one artificial intelligence (Al) or machine learning (ML) (AI / ML)-related operation; and receive, from the network entity in response to transmission of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied forthe atleast one AI / ML-related operation. In certain aspects, the base station 102 may have an antenna capability indication component 199 that may be configured to transmit, to a network entity, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one AI / ML-related operation; and receive, from the network entity in response to transmission of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML-related operation. In certain aspects, the one or more location servers 168 may have an antenna capability configuration component 197 that may be configured to receive, from a wireless device, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one AI / ML-related operation; and transmit, to the wireless device in response to reception of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML-related operation.

[0060] FIG. 2 A is a diagram 200 illustrating an example of a first subframe within a 5GNR frame structure. FIG. 2B is a diagram 230 illustrating an example of DL channels within a 5G NR subframe. FIG. 2C is a diagram 250 illustrating an example of a second subframe within a 5G NR frame structure. FIG. 2D is a diagram 280129025-2422WO01Qualcomm Ref. No. 2405604WO 17 illustrating an example of UL channels within a 5 G NR subframe. The 5 G NR frame structure may be frequency division duplexed (FDD) in which for a particular set of subcarriers (carrier system bandwidth), subframes within the set of subcarriers are dedicated for either DL or UL, or may be time division duplexed (TDD) in which for a particular set of subcarriers (carrier system bandwidth), subframes within the set of subcarriers are dedicated for both DL andUL. In the examples provided by FIGs. 2A, 2C, the 5G NR frame structure is assumed to be TDD, with subframe 4 being configured with slot format 28 (with mostly DL), where D is DL, U is UL, and F is flexible for use between DL / UL, and subframe 3 being configured with slot format 1 (with all UL). While subframes 3, 4 are shown with slot formats 1, 28, respectively, any particular subframe may be configured with any of the various available slot formats 0-61 . Slot formats 0, 1 are all DL, UL, respectively. Other slot formats 2-61 include a mix of DL, UL, and flexible symbols. UEs are configured with the slot format (dynamically through DL control information (DCI), or semi- statically / statically through radio resource control (RRC) signaling) through a received slot format indicator (SFI). Note that the description infra applies also to a 5G NR frame structure that is TDD.

[0061] FIGs. 2 A-2D illustrate a frame structure, and the aspects of the present disclosure may be applicable to other wireless communication technologies, which may have a different frame structure and / or different channels. A frame (10 ms) may be divided into 10 equally sized subframes (1 ms). Each subframe may include one or more time slots. Subframes may also include mini-slots, which may include 7, 4, or 2 symbols. Each slot may include 14 or 12 symbols, depending on whether the cyclic prefix (CP) is normal or extended. For normal CP, each slot may include 14 symbols, and for extended CP, each slot may include 12 symbols. The symbols on DL may be CP orthogonal frequency division multiplexing (OFDM) (CP-OFDM) symbols. The symbols on UL may be CP-OFDM symbols (for high throughput scenarios) or discrete Fourier transform (DFT) spread OFDM (DFT-s-OFDM) symbols (for power limited scenarios; limited to a single stream transmission). The number of slots within a subframe is based on the CP and the numerology. The numerology defines the subcarrier spacing (SCS) (see Table 1). The symbol length / duration may scale with 1 / SCS.129025-2422WO01Qualcomm Ref. No. 2405604WO 18Table 1: Numerology, SCS, and CP

[0062] For normal CP (14 symbols / slot), different numerologies p 0 to 4 allow for 1, 2, 4, 8, and 16 slots, respectively, per subframe. For extended CP, the numerology 2 allows for 4 slots per subframe. Accordingly, for normal CP and numerology p, there are 14 symbols / slot and 2.Llsi ots / sub frame. The subcarrier spacing may be equal to 2^ * 15 kHz , where g is the numerology 0 to 4. As such, the numerology p=0 has a subcarrier spacing of 15 kHz and the numerology p=4 has a subcarrier spacing of 240 kHz. The symbol length / durationis inversely related to the subcarrier spacing. FIGs. 2A-2D provide an example of normal CP with 14 symbols per slot and numerology p=2 with 4 slots per subframe. The slot duration is 0.25 ms, the subcarrier spacing is 60 kHz, and the symbol duration is approximately 16.67 ps. Within a set of frames, there may be one or more different bandwidth parts (BWPs) (see FIG. 2B) that are frequency division multiplexed. Each BWP may have a particular numerology and CP (normal or extended).

[0063] A resource grid may be used to represent the frame structure. Each time slot includes a resource block (RB) (also referred to as physical RBs (PRBs)) that extends 12 consecutive subcarriers. The resource grid is divided into multiple resource elements (REs). The number of bits carried by each RE depends on the modulation scheme.

[0064] As illustrated in FIG. 2 A, some of the REs carry reference (pilot) signals (RS) for the UE. The RS may include demodulation RS (DM-RS) (indicated as Rfor one particular configuration, but other DM-RS configurations are possible) and channel state information reference signals (CSI-RS) for channel estimation attheUE. The RS may129025-2422WO01Qualcomm Ref. No. 2405604WO 19 also include beam measurement RS (BRS), beam refinement RS (BRRS), and phase tracking RS (PT-RS).

[0065] FIG. 2B illustrates an example of various DL channels within a subframe of a frame. The physical downlink control channel (PDCCH) carries DCI within one or more control channel elements (CCEs) (e.g., 1, 2, 4, 8, or 16 CCEs), each CCE including six RE groups (REGs), each REG including 12 consecutive REs in an OFDM symbol of an RB. A PDCCH within one BWP may be referred to as a control resource set (CORESET). A UE is configured to monitor PDCCH candidates in a PDCCH search space (e.g., common search space, UE-specific search space) during PDCCH monitoring occasions on the CORESET, where the PDCCH candidates have different DCI formats and different aggregation levels. Additional BWPs may be located at greater and / or lower frequencies across the channel bandwidth. A primary synchronization signal (PSS) may be within symbol 2 of particular subframes of a frame. The PSS is used by a UE 104 to determine subframe / symbol timing and a physical layer identity. A secondary synchronization signal (SSS) may be within symbol 4 of particular subframes of a frame. The SSS is used by a UE to determine a physical layer cell identity group number and radio frame timing. Based on the physical layer identity and the physical layer cell identity group number, the UE can determine a physical cell identifier (PCI). Based on the PCI, the UE can determine the locations of the DM-RS. The physical broadcast channel (PBCH), which carries a master information block (MIB), may be logically grouped with the PSS and SSS to form a synchronization signal (SS) / PBCH block (also referred to as SS block (SSB)). The MIB provides a number of RBs in the system bandwidth and a system frame number (SFN). The physical downlink shared channel (PDSCH) carries user data, broadcast system information not transmitted through the PBCH such as system information blocks (SIBs), and paging messages.

[0066] As illustrated in FIG. 2C, some of the REs carry DM-RS (indicated as R for one particular configuration, but other DM-RS configurations are possible) for channel estimation at the base station. The UE may transmit DM-RS for the physical uplink control channel (PUCCH) and DM-RS for the physical uplink shared channel (PUSCH). The PUSCH DM-RS may be transmitted in the first one or two symbols of the PUSCH. The PUCCH DM-RS may be transmitted in different configurations depending on whether short or long PUCCHs are transmitted and depending on the129025-2422WO01Qualcomm Ref. No. 2405604WO 20 particular PUCCH format used. The UE may transmit sounding reference signals (SRS). The SRS may be transmitted in the last symbol of a subframe. The SRS may have a comb structure, and a UE may transmit SRS on one of the combs. The SRS may be used by a base station for channel quality estimation to enable frequencydependent scheduling on the UL.

[0067] FIG. 2D illustrates an example of various UL channels within a subframe of a frame. The PUCCH may be located as indicated in one configuration. The PUCCH carries uplink control information (UCI), such as scheduling requests, a channel quality indicator (CQI), a precoding matrix indicator (PMI), a rank indicator (RI), and hybrid automatic repeat request (HARQ) acknowledgment (ACK) (HARQ-ACK) feedback (i.e., one or more HARQ ACK bits indicating one or more ACK and / or negative ACK (NACK)). The PUSCH carries data, and may additionally be used to carry a buffer status report (BSR), a power headroom report (PHR), and / or UCI.

[0068] FIG. 3 is a block diagram of a base station 310 in communication with a UE 350 in an access network. In the DL, Internet protocol (IP) packets may be provided to a controller / processor 375. The controller / processor 375 implements layer 3 and layer 2 functionality. Layer 3 includes a radio resource control (RRC) layer, and layer 2 includes a service data adaptation protocol (SDAP) layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, and a medium access control (MAC) layer. The controller / processor 375 provides RRC layer functionality associated with broadcasting of system information (e.g., MIB, SIBs), RRC connection control (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release), inter radio access technology (RAT) mobility, and measurement configuration for UE measurement reporting; PDCP layer functionality associated with header compression / decompression, security (ciphering, deciphering, integrity protection, integrity verification), and handover support functions; RLC layer functionality associated with the transfer of upper layer packet data units (PDUs), error correction through ARQ, concatenation, segmentation, and reassembly of RLC service data units (SDUs), re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto transport blocks (TBs),129025-2422WO01Qualcomm Ref. No. 2405604WO 21 demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through HARQ, priority handling, and logical channel prioritization.

[0069] The transmit (TX) processors 16 and the receive (RX) processor 370 implement layer 1 functionality associated with various signal processing functions. Layer 1, which includes a physical (PHY) layer, may include error detection on the transport channels, forward error correction (FEC) coding / decoding of the transport channels, interleaving, rate matching, mapping onto physical channels, modulation / demodulation of physical channels, andMIMO antenna processing The TX processor 316 handles mapping to signal constellations based on various modulation schemes (e.g., binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), M-phase-shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The coded and modulated symbols may then be split into parallel streams. Each stream may then be mapped to an OFDM subcarrier, multiplexed with a reference signal (e.g., pilot) in the time and / or frequency domain, and then combined together using an Inverse Fast Fourier Transform (IFFT) to produce a physical channel carryingatime domain OFDMsymbol stream. The OFDM stream is spatially precoded to produce multiple spatial streams. Channel estimates from a channel estimator 374 may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimate may be derived from a reference signal and / or channel condition feedback transmitted by the UE 350. Each spatial stream may then be provided to a different antenna 320 via a separate transmitter 318Tx. Each transmitter 318Tx may modulate a radio frequency (RF) carrier with a respective spatial stream for transmission.

[0070] At the UE 350, each receiver 354Rx receives a signal through its respective antenna 352. Each receiver 354Rx recovers information modulated onto an RF carrier and provides the information to the receive (RX) processor 356. The TX processor 368 and the RX processor 356 implement layer 1 functionality associated with various signal processing functions. The RX processor 356 may perform spatial processing on the information to recover any spatial streams destined for the UE 350. If multiple spatial streams are destined for the UE 350, they may be combined by the RX processor 356 into a single OFDM symbol stream. The RX processor 356 then converts the OFDM symbol stream from the time-domain to the frequency domain using a Fast Fourier Transform (FFT). The frequency domain signal includes a129025-2422WO01Qualcomm Ref. No. 2405604WO 22 separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols on each subcarrier, and the reference signal, are recovered and demodulated by determining the most likely signal constellation points transmitted by the base station 310. These soft decisions may b e based on channel estimates computed by the channel estimator 358. The soft decisions are then decoded and deinterleaved to recover the data and control signals that were originally transmitted by the base station 310 on the physical channel. The data and control signals are then provided to the controller / processor 359, which implements layer 3 and layer 2 functionality.

[0071] The controller / processor 359 can be associated with at least one memory 360 that stores program codes and data. The at least one memory 360 may be referred to as a computer-readable medium. In the UL, the controller / processor 359 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, and control signal processing to recover IP packets. The controller / processor 359 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0072] Similar to the functionality described in connection with the DL transmission by the base station 310, the controller / processor 359 provides RRC layer functionality associated with system information (e.g., MIB, SIBs) acquisition, RRC connections, and measurement reporting; PDCP layer functionality associated with header compression / decompression, and security (ciphering, deciphering, integrity protection, integrity verification); RLC layer functionality associated with the transfer of upper layer PDUs, error correction through ARQ, concatenation, segmentation, and reassembly of RLC SDUs, re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto TBs, demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through HARQ, priority handling, and logical channel prioritization.

[0073] Channel estimates derived by a channel estimator 358 from a reference signal or feedback transmitted by the base station 310 may be used by the TX processor 368 to select the appropriate coding and modulation schemes, and to facilitate spatial processing. The spatial streams generated by the TX processor 368 may be provided to different antenna 352 via separate transmitters 354Tx. Each transmitter 354 Tx may modulate an RF carrier with a respective spatial stream for transmission.129025-2422WO01Qualcomm Ref. No. 2405604WO 23

[0074] The UL transmission is processed at the base station 310 in a manner similar to that described in connection with the receiver function attheUE 350. Each receiver 318Rx receives a signal through its respective antenna 320. Each receiver 318Rx recovers information modulated onto an RF carrier and provides the information to a RX processor 370.

[0075] The controller / processor 375 can be associated with at least one memory 376 that stores program codes and data. The at least one memory 376 may be referred to as a computer-readable medium. In the UL, the controller / processor 375 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover IP packets. The controller / processor 375 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0076] At least one of the TX processor 368, the RX processor 356, and the controller / processor 359 may be configured to perform aspects in connection with the antenna capability indication component 198 of FIG. 1.

[0077] At least one of the TX processor 316, the RX processor 370, and the controller / processor 375 may be configured to perform aspects in connection with the antenna capability indication component 199 of FIG. 1.

[0078] FIG. 4 is a diagram 400 illustrating an example of a UE positioningbased on reference signal measurements (which may also be referred to as “network -based positioning”) in accordance with variousaspects ofthe present disclosure. The UE404 may transmit UL SRS 412 at time TSRS_TX and receive DL positioning reference signals (PRS) (DL PRS) 410 at time TPRS Rx- The TRP 406 may receive the UL SRS 412 at time TSRS_RX and transmit the DL PRS 410 at time TPRS_TX- The UE 404 may receive the DL PRS 410 before transmitting the UL SRS 412, or may transmit the UL SRS 412 before receiving the DL PRS 410. In both cases, a positioning server (e.g., location servers) 168) or the UE 404 may determine the RTT 414 based on ||TSRS RX - TPRS_TX| - |TsRs TX - TPRS_RX||. Accordingly, multi-RTT positioning may make use of the UE Rx-Tx time difference measurements (i.e., |TSRs TX - TPRS_RX|) and DL PRS reference signal received power (RSRP) (DL PRS-RSRP) of downlink signals received from multiple TRPs 402, 406 and measured by the UE 404, and the measured TRP Rx-Tx time difference measurements (i.e., |TSRs RX - TPRSTX|) and UL SRS-RSRP at multiple TRPs 402, 406 of uplink signals transmitted from UE 404. The UE 404129025-2422WO01Qualcomm Ref. No. 2405604WO 24 measures the UE Rx-Tx time difference measurements (and / or DL PRS-RSRP of the received signals) using assistance data received from the positioning server, and the TRPs 402, 406 measure the gNB Rx-Tx time difference measurements (and / or UL SRS-RSRP of the received signals) using assistance data received from the positioning server. The measurements may be used atthe positioning server or the UE 404 to determine the RTT, which is used to estimate the location of theUE 404. Other methods are possible for determining the RTT, such as for example using DL-TDOA and / or UL-TDOA measurements.

[0079] PRSs may be defined for network-based positioning (e.g., NR positioning) to enable UEs to detect and measure more neighbor transmission and reception points (TRPs), where multiple configurations are supported to enable a variety of deployments (e.g, indoor, outdoor, sub-6, mmW, etc.). To support PRS beam operation, beam sweeping may also be configured for PRS. The UL positioning reference signal may be based on sounding reference signals (SRSs) with enhancements / adjustments for positioning purposes. In some examples, UL-PRS may be referred to as “SRS for positioning” and a new Information Element (IE) may be configured for SRS for positioning in RRC signaling.

[0080] DL PRS-RSRP may be defined as the linear average over the power contributions (in [W]) of the resource elements of the antenna port(s) that carry DL PRS reference signals configured for RSRP measurements within the considered measurement frequency bandwidth. In some examples, for FR1, the referencepointfortheDL PRS- RSRP may be the antenna connector of the UE. For FR2, DL PRS-RSRP may be measured based on the combined signal from antenna elements corresponding to a given receiver branch. ForFRl and FR2, if receiver diversity is in use by the UE, the reported DL PRS-RSRP value may not be lower than the corresponding DL PRS- RSRP of any of the individual receiver branches. Similarly, UL SRS-RSRP may be defined as linear average of the power contributions (in [W]) of the resource elements carrying sounding reference signals (SRS). UL SRS-RSRP may be measured over the configured resource elements within the considered measurement frequency bandwidth in the configured measurement time occasions. In some examples, for FR1, the reference point for the UL SRS-RSRP may be the antenna connector of the base station (e.g., gNB). For FR2, UL SRS-RSRP may be measured based on the combined signal from antenna elements correspondingto a given receiver branch. For129025-2422WO01Qualcomm Ref. No. 2405604WO 25FR1 and FR2, if receiver diversity is in use by the base station, the reported UL SRS- RSRP value may not be lower than the corresponding UL SRS-RSRP of any of the individual receiver branches.

[0081] PRS-path RSRP (PRS-RSRPP) may be defined as the power of the linear average of the channel response at the i-th path delay of the resource elements that carry DL PRS signal configured for the measurement, where DL PRS-RSRPP for the 1 st path delay is the power contribution corresponding to the first detected path in time. In some examples, PRS path Phase measurement may refer to the phase associated with an i- th path of the channel derived using a PRS resource.

[0082] DL-AoD positioning may make use of the measured DL PRS-RSRP of downlink signals received from multiple TRPs 402, 406 at the UE 404. The UE 404 measures the DL PRS-RSRP of the received signals using assistance data received from the positioning server, and the resulting measurements are used along with the azimuth angle of departure (A-AoD), the zenith angle of departure (Z-AoD), and other configuration information to locate the UE 404 in relation to the neighboring TRPs 402, 406.

[0083] DL-TDOA positioning may make use of the DL reference signal time difference (RSTD) (and / or DL PRS-RSRP) of downlink signals received from multiple TRPs 402, 406 at the UE 404. The UE 404 measures the DL RSTD (and / or DL PRS-RSRP) of the received signals using assistance data received from the positioning server, and the resulting measurements are used along with other configuration information to locate the UE 404 in relation to the neighboring TRPs 402, 406.

[0084] UL-TDOA positioning may make use of the UL relative time of arrival (RTOA) (and / or UL SRS-RSRP) at multiple TRPs 402, 406 of uplink signals transmitted from UE 404. The TRPs 402, 406 measure the UL-RTOA (and / or UL SRS-RSRP) of the received signals using assistance data received from the positioning server, and the resulting measurements are used along with other configuration information to estimate the location of the UE 404.

[0085] UL-AoApositioningmay make use of the measured azimuth angle of arrival (A-AoA) and zenith angle of arrival (Z-AoA) at multiple TRPs 402, 406 of uplink signals transmitted from the UE 404. The TRPs 402, 406 measure the A-AoA and the Z-AoA of the received signals using assistance data received from the positioning server, and the resulting measurements are used along with other configuration information to129025-2422WO01Qualcomm Ref. No. 2405604WO 26 estimate the location of the UE 404. For purposes of the present disclosure, a positioning operation in which measurements are provided by a UE to a base station / positioning entity / serverto be used in the computation of the UE’s position may be described as “UE-assisted,” “UE-assisted positioning,” and / or “UE-assisted position calculation,” while a positioning operation in which a UE measures and computes its own position maybe described as“UE-based,” “UE-based positioning,” and / or “UE-based position calculation.”

[0086] Additional positioning methods may be used for estimating the location of the UE 404, such as for example, UE-side UL-AoD and / or DL-AoA. Note that data / measurements from various technologies may be combined in various ways to increase accuracy, to determine and / or to enhance certainty, to supplement / complement measurements, and / or to substitute / provide for missing information.

[0087] Note that the terms “positioning reference signal” and “PRS” generally refer to specific reference signals that are used for positioning in NR and LTE systems. However, as used herein, the terms “positioning reference signal” and “PRS” may also refer to any type of reference signal that can be used for positioning, such as but not limited to, PRS as defined in LTE and NR, TRS, PTRS, CRS, CSLRS, DMRS, PSS, SSS, SSB, SRS, UL-PRS, etc. In addition, the terms “positioning reference signal” and “PRS” may refer to downlink or uplink positioning reference signals, unless otherwise indicated by the context. To further distinguish the type of PRS, a downlink positioning reference signal may be referred to as a “DL PRS,” and an uplink positioning reference signal (e.g., an SRS-for-positioning, PTRS) may be referred to as an “UL-PRS.” In addition, for signals that may be transmitted in both the uplink and downlink (e.g., DMRS, PTRS), the signals may be prepended with “UL” or “DL” to distinguish the direction. For example, “UL-DMRS” may be differentiated from “DL-DMRS.” In addition, the term “location” and “position” may be used interchangeably throughout the specification, which may referto a particular geographical or a relative place.

[0088] For purposes of the present disclosure, “UE Rx - Tx time difference” may be defined as TUE.RX - TUE-TX, where: TUE.Rx is the UE received timing of downlink subframe #i from a Transmission Point (TP), defined by the first detected path in time. TUE-TX is the UE transmit timing of uplink subframe #j that is closest in time to the subframe #i129025-2422WO01Qualcomm Ref. No. 2405604WO 27 received from the TP. Multiple DL PRS or CSI-RS for tracking resources, as instructed by higher layers, can be used to determine the start of one subframe of the first arrival path of the TP. For frequency range 1, the reference point for TUE-RX measurement may be the Rx antenna connector of the UE and the reference point for TUE-TX measurement may be the Tx antenna connector of the UE. For frequency range 2, the reference point for TUE-RX measurement may be the Rx antenna of the UE and the reference point for TUE-TX measurement may be the Tx antenna of the UE.

[0089] “DL reference signal time difference (DLRSTD)” is the DL relative timing difference between the Transmission Point (TP) j and the reference TP z, defined as TsubframeRxj - TsubframeRxi, where: TsubframeRxj is the time when the UE receives the start of one subframe from TP j. TsubframeRxi is the time when the UE receives the corresponding start of one subframe from TP z that is closest in time to the subframe received from TP j. Multiple DL PRS resources can be used to determine the start of one subframe from a TP. For frequency range 1, the reference point for the DL RSTD may be the antenna connector of the UE. For frequency range 2, the reference point for the DL RSTD may be the antenna of the UE.

[0090] “DL PRS reference signal received power (DL PRS-RSRP),” is defined as the linear average over the power contributions (in [W]) of the resource elements that carry DL PRS reference signals configured for RSRP measurements within the considered measurement frequency bandwidth. For frequency range 1 , the reference point for the DL PRS-RSRP may be the antenna connector of the UE. For frequency range 2, DL PRS-RSRP may be measured based on the combined signal from antenna elements corresponding to a given receiver branch. For frequency range 1 and 2, if receiver diversity is in use by the UE, the reported DL PRS-RSRP value may notbe lower than the corresponding DL PRS-RSRP of any of the individual receiver branches.

[0091] “DL PRS reference signal received path power (DL PRS-RSRPP),” is defined as the power of the linear average of the channel response at the i-th path delay of the resource elements that carry DL PRS signal configured for the measurement, where DL PRS-RSRPP for the 1 st path delay is the power contribution corresponding to the first detected path in time. For frequency range 1 , the reference point for the DL PRS- RSRPP may be the antenna connector of the UE. For frequency range 2, DL PRS- RSRPP may be measured based on the combined signal from antenna elements corresponding to a given receiver branch. For frequency range 1 and 2, if receiver129025-2422WO01Qualcomm Ref. No. 2405604WO 28 diversity is in use by the UE for DL PRS-RSRPP measurements, the reported DL PRS-RSRPP value included in the higher layer parameter NR-DL-AoD-MeasElement for the first and additional measurements may be provided for the same receiver branch(es) as applied for DL PRS-RSRP measurements

[0092] “DL reference signal carrier phase (RSCP)” is defined as the phase of the channel response at the 1stpath delay derived from the resource elements carrying DL PRS configured for the measurement. DL RSCP is associated with the center frequency of the DL positioning frequency layer (PFL) configured for the measurement for RRC connected, RRC inactive, and RRC idle modes. For frequency range 1, the reference point for the DL RSCP may be the antenna connector of the UE. For frequency range 2, the reference point for the DL RSCP may be the antenna of the UE.

[0093] “DL reference signal carrier phase difference (RSCPD)” is defined as the difference of DL RSCPs measured from DL PRS transmitted in a DL PFL from the transmission point(TP) j and the reference TP i. If UE reports RSCPD measurements together with RSTD measurements in a measurement report element, the reference TP for RSCPD is the same as the reference TP reported for RSTD. For frequency range 1, the reference point for the DL RSCPD may be the antenna connector of the UE. For frequency range 2, the reference point for the DL RSCPD may be the antenna of the UE.

[0094] In some implementations, at least one artificial intelligence (Al) / machine learning (ML) (AI / ML) model may be configured / implemented at an entity / node (e.g., a UE, a network entity / node such as a base station, a location server, a location management function (LMF), etc.) for assisting the entity / node with the positioning of a target device. For example, an AI / ML model may be trained to determine the position of a UE based on DL-AoA, DL-TDOA, channel impulse response (CIR), radio frequency (RF) fingerprinting, etc. In most scenarios, using an AI / ML model may significantly improve UE positioning latency, accuracy / reliability, and / or efficiency. For purposes of the present disclosure, an AI / ML model that is implemented at a UE side may be referred to as a “UE-side model” and / or “UE-side AI / ML model.” On the other hand, an AI / ML model that is implemented at a network side may be referred to as a “network-side model,” “network-side AI / ML model,” and / or (network name)-side AI / ML model (e.g., base station-side AI / ML model, LMF-side AI / ML model, etc.).129025-2422WO01Qualcomm Ref. No. 2405604WO 29

[0095] In addition, positioning that is associated with a UE or a network entity / node using an AI / ML model to determine the position of the UE may be referred to as “direct AI / ML positioning,” whereas positioning that is associated with a UE or a network entity / node performingpositioningrelated measurements using an AI / ML model (and transmitting the positioning related measurements to another entity) to determine the position of the UE may be referred to as “AI / ML assisted positioning” and / or “assisted AI / ML positioning.” Also, UE-based positioning (e.g., UE determines its own position) using at least one UE-side AI / ML model may be referred to as “direct UE AI / ML positioning” and / or “UE direct AI / ML positioning,” whereas UE-assisted positioning (e.g., a UE provides positioning measurements and a network entity, such as anLMF, determines the position for the UEbased on the positioningmeasurements provided by the UE) using at least one UE-side AI / ML model may be referred to as “UE AI / ML assisted positioning,” “UE assisted AI / ML positioning” “AI / ML assisted UE positioning,” and / or “AI / ML UE assisted positioning,” etc. Similarly, networkbased positioning (e.g., a network entity, such as an LMF, determines the position for the UE) using at least one network / LMF-side AI / ML model may be referred to as “direct network / LMF AI / ML positioning” and / or “network / LMF direct AI / ML positioning.” Positioningthatinvolves using AI / ML, either attheUE or at the network entity, may collectively be referred to as “AI / ML positioning” or “AI / ML-based positioning.” Depending on the context, while the term “sensing” may refer to a process, mechanism, or ability for a device / system to detect and measure its own position or the position of object(s) relative to it, the term “sensing” may be used interchangeably with the term “positioning” in some examples.

[0096] For purposes of the present disclosure, at a high-level, an “AI / ML model” may refer to a program / algorithm that is capable of being trained on a set of data (which may be referred to as “training data”) to make certain decisions (without further human intervention), to recognize certain patterns, and / or predict certain outcomes, etc. In some examples and depending on the context, an “AI / ML model” may also refer to an actual physical model with given parameters and weights, and / or may refer to a logical model for which one or more models can be considered but all seen as one logical model from identification stand point. Similarly, depending on the context, an “AI / ML functionality” may refer to employing AI / ML to positioning without referringto an underlying model (physical and / or logical). The AI / ML functionality129025-2422WO01Qualcomm Ref. No. 2405604WO 30 may still be defined / identified based on measurements of information considered for its inputs and / or outputs. In some examples, the AI / ML functionality may refer to one or more AI / ML model for which model input may refer to a specific measurement type / or and quantities. The one or more model(s) may be logical or physical. The AI / ML functionality may also refer to one or more AI / ML model for which model outputmay referto a specific measurementtype / location information and / or quantity. The one or more model(s) can be logical or physical. Depending on the context, sometimes the term “AI / ML model” may be used interchangeably with the term “AI / ML functionality.”

[0097] FIG. 5 A is a diagram 500A illustrating an example of direct AI / ML positioning in accordance with various aspects of the present disclosure. For direct AI / ML positioning, an entity / node (e.g., a UE, a network entity / node such as a base station, a location server, etc.) may use at least one AI / ML model to determine the position of a UE or a target. For example, a UE may receive and measure PRSs transmitted from one or more base stations (or TRP(s) of one or more base stations), and the UE may determine its position using an AI / ML model based on the PRS measurements. In another example, a network entity (e.g., an LMF, a sensing management function, an AI / ML management function, etc.) may receive PRS measurements from a UE or SRS measurements from a base station, and the network entity may determine the position of the UE using an AI / ML model based on the PRS / SRS measurements.

[0098] FIG. 5B is a diagram 500B illustrating an example of AI / ML assisted positioning in accordance with various aspects of the present disclosure. For AI / ML assisted positioning, an entity / node (e.g., a UE, a network entity / node such as a base station, etc.) may use at least one AI / ML model to assistthe measurement of reference signals (e.g., positioningreference signals such as PRS, SRS, etc.). Then, the entity / node may transmit the reference signal measurements to a location server, such as an LMF, a sensing management function, an AI / ML management function, etc. In response, the location server may determine the position of the UE based on a non-AI / ML mechanism / algorithm or based on using an AI / ML model. For example, a UE may receive and measure PRSs transmitted from one or more base stations (or TRP(s) of one or more base stations), and the UE may transmit the PRS measurements to an LMF. The PRS measurements may include intermediate measurements, such as timing and / or angle of the PRSs, whether the PRSs are received based on a line-of-129025-2422WO01Qualcomm Ref. No. 2405604WO 31 sight (LOS) condition or a non-line-of-sight (NLOS) condition, etc. Then, the LMF may determine the position of the UE based on the PRS measurements (e.g., the intermediate measurements) with or withoutusing an AI / ML model. Similarly, a base station (or its TRP(s)) may receive and measure SRSs transmitted from a UE, and the base station may transmit the SRS measurements to an LMF. Then, the LMF may determine the position of the UE based on the SRS measurements (e.g., the intermediate measurements) with or without using an AI / ML model.

[0099] FIG. 6 is a diagram 600 illustrating an example of different configurations for AI / ML assisted positioning in accordance with various aspects of the present disclosure. In one example, as shown at 610, for AI / ML assisted positioning, a same AI / ML model may be used for multiple / diff erent TRPs, where one AI / ML model may be configured for each TRP (referring to as a “single-TRP” setting). For example, a UE 602 may receive a set of positioning reference signals from N TRPs (e.g., from a first TRP, a second TRP, . . ., and up to an NthTRP), and measure the channel impulse response (CIR) for the set of positioning reference signals from each TRP. Then, the UE 602 may input the measured CIR for each TRP to an AI / ML model (e.g., AI / ML Model A) configured for / associated with each TRP, where the AI / ML model may infer the time of arrival (ToA) of the positioning reference signal for the corresponding TRP based on the corresponding CIR. In other words, CIR of the first TRP is input to an AI / ML model A associated with the first TRP, CIR of the second TRP is input to an AI / ML model A associated with the second TRP, and CIR of the NthTRP is input to an AI / ML model A associated with the NthTRP, etc.

[0100] In another example, as shown at 612, multiple / different AI / ML models may be used for multiple / different TRPs, where one AI / ML model may be configured for each TRP (e.g., also the “single-TRP” setting but each TRP may use a different AI / ML model). For example, CIR of the first TRP may be input to a first AI / ML model (e.g, AI / ML Model Bi) for inferring the ToA of the first TRP, CIR of the second TRP may be input to a second AI / ML model (e.g., AI / ML Model B2that is different from AI / ML Model BQ for inferring the ToA of the second TRP, and CIR of the NthTRP may be inputto an NthAI / ML model (e.g., AI / ML Model BNthat is different from AI / ML Model Bi and AI / ML Model B2) for inferring the ToA of the AI / ML Model Bi TRP, etc.129025-2422WO01Qualcomm Ref. No. 2405604WO 32

[0101] In another example, as shown at 614, one AI / ML model may be used for multiple / different TRPs (referringto as a “multi-TRP” setting). For example, CIRs from a plurality of N TRPs may be input to one AI / ML model (e.g., AI / ML Model C), and the AI / ML model may infer the ToA for each of the N TRPs. For AI / ML assisted positioning, different model input realizations may have different implications on accuracy, generalization, robustness, as well as model complexity and life cycle management (LCM).

[0102] FIG. 7 is a diagram 700 illustrating an example of UE-based positioningwith UE-side AI / ML model, direct AI / ML or AI / ML assisted positioning in accordance with various aspects of the present disclosure. In one implementation, a UE 702 may be associated with at least one AI / ML model 708, and the UE 702 may use the at least one AI / ML model 708 to perform the direct AI / ML positioning and / or the assisted AI / ML positioning based on downlink (DL) reference signals, such as positioning reference signals (PRSs). For example, the UE 702 may receive and measure a set of PRSs transmitted from a base station 706, such as measuring the reference signal received power (RSRP), channel impulse response (CIR), DL-AoD, reference signal time difference (RSTD), time of arrival (ToA), and / or time of flight (ToF) of the set of PRSs, etc., which may be collectively be referred to as “PRS-based measurement(s)” or simply “PRS measurement(s).” Note while the example uses the base station 706 for illustration, the base station 706 may also be multiple base stations (e.g., a serving base station with one or more neighboring base stations), multiple TRPs of the base station 706, and / or multiple TRPs of multiple base stations, etc. In some examples, the UE 702 may use the at least one AI / ML model 708 for measuring the set of PRSs (e.g., for assisted AI / ML positioning). In some examples, based on the PRS measurement(s), the UE 702 may use the at least one AI / ML model 708 for determining its position (e.g., for direct AI / ML positioning). Note in this assisted AI / ML positioning example, the UE 702 may use the at least one AI / ML model 708 for performing PRS measurements, and the UE 702 may determine its position based on the PRS measurements without the assistance of an AI / ML model.

[0103] FIG. 8A is a diagram 800A illustrating an example of UE-assisted / LMF-based positioning with UE-side AI / ML model, AI / ML assisted positioning in accordance with various aspects of the present disclosure. In another implementation, a UE 702 may be associated with at least one AI / ML model 708, and the UE 702 may use the129025-2422WO01Qualcomm Ref. No. 2405604WO 33 at least one AI / ML model 708 to perform or assist measurement(s) of DL reference signals. For example, the UE 702 may receive and measure a set of PRSs transmitted from abase station 706 with the assistance of the at least one AI / ML model 708. Then, the UE 702 may transmit the PRS-based measurement(s) to a location server 704, such as an LMF, a sensing management function, an AI / ML management function, etc. In response, the location server 704 may determine the position of the UE 702 based on the PRS-based measurement(s) (with or without suing an AI / ML model).

[0104] FIG. 8B is a diagram 800B illustrating an example of UE-assisted / LMF-based positioning with LMF-side AI / ML model, direct AI / ML positioning in accordance with various aspects of the present disclosure. In another implementation, a UE 702 may not include a UE-side AI / ML model, and a location server 704 may use at least one AI / ML model 708 to determine the position of the UE 702. For example, the UE 702 may receive and measure a set of PRSs transmitted from a base station 706, and the UE 702 may transmit the PRS-based measurement(s) to the location server 704, such as an LMF, a sensing management function, an AI / ML management function, etc. In response, the location server 704 may use the at least one AI / ML model 708 to determine the position of the UE 702 based on the PRS-based measurement(s) from the UE 702.

[0105] FIG. 9A is a diagram 900A illustrating an example of network (e.g., NG-RAN) node assisted positioning with gNB-side AI / ML model, AI / ML assisted positioning in accordance with various aspects of the present disclosure. In another implementation, a network node, such as abase station 706, may be associated with at least one AI / ML model 708, and the base station 706 may use the at least one AI / ML model 708 to assist measurement(s) of uplink (UL) reference signals, such as sounding reference signals (SRSs). For example, the UE 702 may transmit a set of SRSsto the base station 706, and the base station 706 may receive and measure the set of SRSs (which may be referred to as “SRS-based measurement(s)” or simply “SRS measurements)”) with the assistance of the at least one AI / ML model 708. Then, the base station 706 may transmit the SRS-based measurement(s) to the location server 704, such as an LMF, a sensing management function, an AI / ML management function, etc. In response, the location server 704 may determine the position of the UE 702 based on the SRS-based measurement(s) from the base station 706 (with or without suing an AI / ML model).129025-2422WO01Qualcomm Ref. No. 2405604WO 34

[0106] FIG. 9B is a diagram 900B illustrating an example of network (e.g., NG-RAN) node assisted positioning with LMF-side AI / ML model, direct AI / ML positioning in accordance with various aspects of the present disclosure. In another implementation, a network node, such as a base station 706, may not include an AI / ML model, and a location server 704 may use at least one AI / ML model 708 to determine the position of a UE 702. For example, the UE 702 may transmit a set of SRSs to the base station 706, and the base station 706 may receive and measurethe set of SRSs. Then, the base station 706 may transmit the SRS-based measurement(s) to the location server 704, such as an LMF. Based on the SRS-based measurement(s) from the base station 706, the location server 704 may use the at least one AI / ML model 708 to determine the position of the UE 702. For purposes of the present disclosure, positioning described in connection with FIGs. 7, 8 A, and 8B may be referred to as AI / ML positioningbased on DL reference signals, and positioning described in connection with FIGs. 9 A and 9B may be referred to as AI / ML positioning based on UL reference signals.

[0107] Table 2 below provides an example list of positioning methods that may be supported by a UE and / or a network entity.129025-2422WO01Qualcomm Ref. No. 2405604WO 35

[0108] In some implementations, for direct AI / ML positioning as described in connection with FIGs. 8B and 9B, type(s) of measurement(s) that may be used as (suitable / potential) input for AI / ML model inference consideringperformance impact and associated signaling overhead may include channel impulse response (CIR), power delay profile (PDP), reference signal receive power (RSRP), reference signal received path power (RSRPP), and / or reference signal time difference (RSTD), etc. For AI / ML assisted positioning with UE-assisted and network node-assisted positioning described in connection with FIGs. 8Aand 9A, respectively, measurement report to carry AI / ML model (suitable / potential) output to a location server such as an LMF may include ToA, path phase, RSTD, line-of-sight (LOS) / non-line-of-sight129025-2422WO01Qualcomm Ref. No. 2405604WO 36(NLOS) indicator, RSRPP, and / or soft inf ormation / high resolution of RSTD, etc. In some examples, AI / ML model inference output that may provide performance benefits may include timing estimation (note the report to LMF may be derived based on and maybe different from the model inference output) and / or LOS / NLOS indicator.

[0109] For purposes of the present disclosure and for the simplicity of illustration, “data collection” may refer to a process of an entity / node collecting data that is to be used for one or more AI / ML positioning operations, such as for training and / or retraining / fine-tuning an AI / ML model. An entity / node that is configured to perform the data collection may collectively be referred to as a “data collection entity.” On the other hand, an entity that is configured to provide data to be collected by the data collection entity may be referredto as a “data source entity.” For example, if a location server (e.g., an LMF) is configured to collect training data (e.g., reference signal measurements) from a set of UEs and / or a set of TRPs fortraining / retraining one or more AI / ML models at the location server, the location server may be referred to as the data collection entity, and the set of UEs / TRPs may be referred to as the data source entities.

[0110] In some scenarios, for data collection related to the training for AI / ML based positioning, the collected data sample may be configured to include one or more following components: (1) channel measurement, (2) quality indicator of channel measurement, (3) time stamp of channel measurement, (4) ground truth label (or its approximation), (5) quality indicator of label, and / or (6) time stamp of label, etc. These components may be configured to be generated by different entities (e.g., different entities may be configured to perform different component) or by the same entity.

[0111] For purposes of the present disclosure, operations related to antenna such as antenna selection, antenna switching, antenna aggregation, and / or antenna beamforming, etc., may collectively be referredto as “antenna processing.” In addition, an antenna may refer to an antenna element, an antenna port, an antenna corresponding to a radio frequency (RF) chain, or an antenna panel. The antenna aggregation may also mean (power-based and / or phase-based) weighting across antennas. Apositioningreference unit (PRU) may refer to an entity / node at a known location that is capable of performing positioning measurements (e.g., RSTD, RSRP, UE Rx-Tx Time129025-2422WO01Qualcomm Ref. No. 2405604WO 37Difference measurements, DL-RSCPD, DL-RSCP, etc.) and report these measurements to a network entity (e.g., location server, a base station, etc.). The term PRU may be used interchangeably with the term UE depending on the context.

[0112] AI / ML positioning has shown to provide an excellent positioning accuracy in stringent non-line-of-sight (NLOS) conditions compared to non-AI / ML positioning A UE or a PRU may support different antenna processing with PRS reception and / or SRS transmission (this may also apply to other reference signals (RSs) such as SSB, CSI-RS, TRS, etc.). Similarly, a base station / TRP may also support different antenna processing with PRS transmission and / or SRS reception (this may also apply to other RSs such as SSB, CSI-RS, TRS, etc.). In some scenarios, an inconsistency between AI / ML training and AI / ML inference may occur when a device (e.g., a UE, a PRU, or a base station / TRP, etc.) considers / uses different antenna processing to obtain positioning measurements during the AI / ML inference when compared to the AI / ML training. This inconsistency may sometimes affect AI / ML model performance and worsen / degrade positioning accuracy if not properly informed and / or managed.

[0113] Aspects presented herein may improve the overall performance and efficiency of AI / ML positioning operations (e.g., data collection, AI / ML model training / retraining inferencing, etc.) by promoting or ensuring a consistency in antenna processing between different AI / ML operations, such as between AI / ML training and inference, between AI / ML input and output, etc. Aspects presented herein provide signaling and solutions that helps reduce ambiguity / uncertainty about antenna selection and / or aggregation during AI / ML inference when compared to AI / ML data collection / training. In one aspect, for data collection related to AI / ML positioning training data (e.g., obtained by a data collection entity) may be configured to include information on antenna processing considered during training data collection. For example, during data collection, a device may be provided with information related to antenna processing when obtaining measurements, and / or the device may have the capability to indicate information related to what / which antenna processing it did when obtaining measurements. Note the measurements here may refer / relate to measurements corresponding to AI / ML model input and / or AI / ML model output (as applicable). In another aspect, for AI / ML model development related to AI / ML positioning, a model developer may leverage information to develop AI / ML model(s) and enhance their robustness to antenna processing inconsistencies. For example, a129025-2422WO01Qualcomm Ref. No. 2405604WO 38 developer may develop multiple AI / ML models with different antenna processing assumptions, enable AI / ML model input indexing with information, or train AI / ML model(s) with measurements of different antenna processing, etc. In another aspect, for AI / ML model transfer related to AI / ML positioning, when an AI / ML model is transferred to an inference entity (e.g., referring to the entity that is configured to perform the AI / ML inference), the AI / ML model may be specified to be associated with information related on assumptions regarding antenna processing corresponding to measurements at AI / ML model input and / or output (as applicable). In another aspect, for AI / ML inference related to AI / ML positioning, during an inference, a device may be provided with information related to antenna processing when obtaining measurements, and / or the device may have the capability to indicate information related to what / which antenna processing when obtaining measurements. Note information here may be an explicit indication of actual antenna processing or an implicit indication using an associated ID (e.g., a proxy ID). Also, a UE / base station may have the capability to recommend / request a network (NW) (e.g., a location server) to consider a certain antenna processing.

[0114] FIG. 10 is a communication flow 1000 illustrating an example of a UE communicating its antenna capabilities with a network entity to improve the overall performance of AI / ML-related operations in accordance with various aspects of the present disclosure. The numberings associated with the communication flow 1000 do not specify a particular temporal order and are merely used as references for the communication flow 1000.

[0115] At 1020, a UE 1002 (which may include a positioning reference unit (PRU)) may transmit, to a network entity 1004, an indication 1008 that is indicative of (or related to) a set of antenna processing capabilities 1010 supported by the UE 1002 for AI / ML- related operation(s). Depending on implementations, the network entity 1004 may be a server, a location management function (LMF), a sensing management function, an AI / ML management function, an over-the-air (OTT) server, a training server, a network data analytics function (NWDAF), or an orchestration and management (0AM) system, etc.

[0116] The AI / ML-related operation(s) may include a data collection for at least one AI / ML model, an AI / ML model development, training a set of AI / ML models, performing inferencing using a set of AI / ML models, and / or checking consistency between129025-2422WO01Qualcomm Ref. No. 2405604WO 39 different AI / ML related operations (e.g., ensuring consistency between the data collection for the at least one AI / ML model, trained a set of AI / ML models, and / or the inference using the set of AI / ML models), etc. The UE 1002 may transmit the indication 1008 based on a request from the network entity 1004 (described below), or based on its initiation (e.g., without receiving a request). In other words, the signaling described herein may involve solicited and unsolicited actions by the UE 1002. In addition, the UE 1002 may transmit the indication 1008 via an LTE positioning protocol and / or NR positioning protocol A (NRPPa) (LTE / NRPPa) message / signaling (e.g., new LPP / NRPPa signaling may be configured for enabling antenna consistency during AI / ML-related operation(s) - same configuration(s) are configured to be applied during inference as has been done during training / data collection), and this message / signaling may occur during the AI / ML-related operation(s) (e.g., during a data collection session) while considering measurements for AI / ML positioning model input and / or output (as applicable). The UE 1002 may also transmit the indication 1008 during the AI / ML-related operation(s), such as during a data collection session. For purposes of the present disclosure and depending on the context, “measurement(s)” described herein may correspond to an AI / ML data collection corresponding to an AI / ML model input and / or an AI / ML model output.

[0117] In one aspect of the present disclosure, the set of antenna processing capabilities 1010 supported by the UE 1002 may be related to a set of transmission (Tx) antennas of the UE 1002, such as Tx antenna(s) that are used by the UE 1002 for transmitting reference signal(s). The reference signal(s) may or may not be related to positioning which may include sounding reference signals (SRS), synchronization signal blocks (SSB), channel state information reference signals (CSLRS), and / or tracking reference signals (TRS), etc.

[0118] In one example, the set of antenna processing capabilities 1010forTx antenna(s)may include one or more of the followings:(1) a (maximum) number of Tx antennas supported by the UE 1002 for the AI / ML- related operation(s) during transmission, e.g., a supported number of Tx antennas for the AI / ML data collection (corresponding to the AI / ML model input / output);(2) antenna co-phasing option(s) supported by the UE 1002 for the AI / ML-related operation(s) during transmission, e.g., antenna co-phasing option(s) supported for the AI / ML data collection, such as whether the antennas are coherent / non-129025-2422WO01Qualcomm Ref. No. 2405604WO 40 coherent, the max number of antennas that are coherent, co-phasing confidence [phase resolution], etc. (corresponding to the AI / ML model input / output);(3) a power splitting or weighting across the set of Tx antennas supported by the UE 1002 for the AI / ML-related op eration(s) during transmission, e.g., supported power splitting / weighting across antennas for AI / ML data collection, such as equal power, power weighting [supported weights], antenna switching / selection, etc. (corresponding to the AI / ML model input / output); and / or(4) an indication of a list of antenna processing functions which the UE 1002 is capable of performing, e.g., support for the UE 1002 to indicate, to the network entity 1004, whether the UE 1002 is able to performs antenna switching / aggregation, e.g., number of antennas, co-phasing option(s), beam codebook, aggregation / selection that UE 1002 selects (autonomously) during transmission of reference signals for AI / ML-related operation(s), such as data collection (corresponding to the AI / ML model input / output).

[0119] Depending on implementations, the indication 1008 may be the actual parameters related of the set of antenna processing capabilities 1010, or it may be a set of IDs associated with the set of antenna processing capabilities 1010. For example, if the UE 1002 is configured to indicate to the network entity 1004 that it supports using up to four antennas for AI / ML data collection, the UE 1002 may explicitly indicate this capability / information to the network entity 1004 (e.g., an indication of supporting up to four antennas for AI / ML data collection). However, such configuration may often specify larger signaling overhead, and may specify a vendor / manufacture to disclose propriety information (e.g., specific configuration(s) / parameter(s) of the UE 1002).

[0120] As such, in one aspect of the present disclosure, the UE 1002 may be configured to associated a list of IDs with the set of antenna processing capabilities 1010, and then the UE 1002 may indicate the set of antenna processing capabilities 1010 using their associated IDs. In other words, the associated ID maybe used as an implicit indicator of antenna processing capability details. For example, a first ID (e.g., 0001) may be associated with the UE 1002 supporting up to using two (2) Tx antenna for AI / ML data collection, a second ID (e.g., 0002) may be associated with the UE 1002 supporting up to using four (4) Tx antenna for AI / ML data collection, and a third ID (e.g., 0003) may be associated with the UE 1002 supporting up to using six (6) Tx antenna for AI / ML data collection, etc. Thus, to indicate that the UE 1002 supports129025-2422WO01Qualcomm Ref. No. 2405604WO 41 using up to four antennas for AI / ML data collection, the UE 1002 may just indicate the associated ID (e.g., 0004) to the network entity 1004. In other words, detailed properties related to the antenna processing capabilities of the UE 1002 are not necessarily exposed in signaling, and instead just abstracted into an “associated / association ID.”

[0121] Depending on implementations, a receiving entity (e.g., the network entity 1004) may or may not know the mapping between the associated IDs and the antenna processing capabilities. For example, if the antenna processing capabilities of the UE 1002 are not deemed to be proprietary information, the mapping between the associated IDs and the antenna processing capabilities (e.g., 0001 corresponds to two antennas and 0002 corresponds to four antennas, etc.) may be available to the receiving entity (e.g, provided by the UE 1002 or via other means). On the other hand, if the antenna processing capabilities of the UE 1002 are deemed to be proprietary information, the mapping between the associated IDs and the antenna processing capabilities may not be available to the receiving entity. In this case, the receiving entity may still use the associated IDs to ensure the antenna consistency for AI / ML-related operation(s). For example, to ensure that an AI / ML model is trained and performs the inferencingusing measurements that are collected with the same number of antennas (e.g., four antennas), the receiving entity may make sure that the ID associated with the measurements for the training and inferencing is the same (e.g., 0002). As such, the associated IDs may enable antenna consistency in AI / ML operation(s) (e.g., AI / ML positioning data collection, training, and inference) even if the mapping is not available to the receiving entity. In some examples, the mapping / assignment between the IDs and the antenna processing capabilities may also be coordinated between the UE 1002 and the network entity 1004.

[0122] While the examples here illustrate that a set of IDS may be associated with a set of antenna processing capabilities, such concept may also apply to other aspects of the disclosure described herein. For example, a set of IDs may be configured for or associated with Tx antenna(s) processing as discussed above, a set of IDs may be configured for or associated with reception (Rx) antenna(s) processing (discussed below), a set of IDs may be configured for or associated with joint Tx and Rx antenna(s) processing (e.g., a specific of processing / configuration for a Tx and Rx antenna combination), etc. In some examples, the associate ID may include of a129025-2422WO01Qualcomm Ref. No. 2405604WO 42 specified number of bits (e.g., N bits), where different bit allocations may be configured to correspond to various aspects related to antenna processing (e.g., three (3) bits indicate the beam codebook, two (2) bits indicate the co-phasing option(s), etc.). In another example, different bit allocations may be configured to correspond to various aspects related to Tx and Rx antenna(s) processing (e.g., N / 2 bits indicate the antenna processing at Tx side, and N / 2 bits indicate the antenna processing atRx side, etc.)

[0123] In another aspect of the present disclosure, the set of antenna processing capabilities 1010 supported by the UE 1002 may be related to a set of Rx antennas of the UE 1002, such as Rx antenna(s) that are used by the UE 1002 for receiving reference signal(s). The reference signal(s) may or may not be related to positioning, which may include PRS, SSB, CSI-RS, and / or TRS, etc.

[0124] In one example, the set of antenna processing capabilities 1010 forRx antenna(s)may include one or more of the followings:(1) a (maximum) number of Rx antennas supported by the UE 1002 for the AI / ML- related operation(s) during reception, e.g., a supported number of Rx antennas for the AI / ML data collection (corresponding to the AI / ML model input / output);(2) antenna co-phasing option(s) supported by the UE 1002 for the AI / ML-related operation(s) during reception, e.g., antenna co-phasing option(s) supported forthe AI / ML data collection, such as whether the antennas are coherent / non-coherent, the max number of antennas that are coherent, co-phasing confidence [phase resolution], etc. (corresponding to the AI / ML model input / output);(3) a (aggregation / selection) power splitting or weighting across the set of Rx antennas supported by the UE 1002 for the AI / ML-related operation(s) during reception, e.g., supported (aggregation / selection) power splitting / weighting across antennas for AI / ML data collection during reception, such as equal power, power weighting [supported weights], antenna switching / selection, etc. (corresponding to the AI / ML model input / output);(4) supported measurement type(s) with antenna processing for data collection during reception (e.g., reference signal received power (RSRP), reference signal received path power (RSRPP), reference signal time difference (RSTD), RSTD of additional paths, relative time of arrival (RTOA), RTOA of additional paths, time of arrival (ToA) / time of flight (ToF), reference signal carrier phase (RSCP),129025-2422WO01Qualcomm Ref. No. 2405604WO 43 reference signal carrier phase difference (RSCPD), sample-based channel impulse response (CIR) / power delay profile (PDP) / delay profile (DP) (CIR / PDP / DP), etc.); and / or(5) an indication of a list of antenna processing functions which the UE 1002 is capable of performing, e.g., support for the UE 1002 to indicate, to the network entity 1004, whether the UE 1002 is able to performs antenna processing, e.g., number of antennas, co-phasing option(s), beam codebook, antenna processing that UE 1002 selects (autonomously) during reception of reference signals for AI / ML-related operation(s), such as data collection (corresponding to the AI / ML model input / output).

[0125] Similarly, as discussed above, the indication 1008 for indicating the set of antenna processing capabilities 1010 related to Rx antennas of the UE 1002 may be a set of IDs that is mapped to the set of antenna processing capabilities 1010 related to Rx antennas. In other words, for Tx antenna and / or Rx antenna capabilities discussed above, the UE 1002 may indicate implicit capabilities via the associated ID(s), where the UE 1002 may select the associated ID or coordinate with the network entity 1004 on the ID assignment.

[0126] At 1022, based on the received indication 1008 (e.g., the antenna processing capabilities 1010 of the UE 1002), the network entity 1004 may determine a set of antenna configurations 1014 to be applied by the UE 1002 for the AI / ML-related operation(s). For example, the set of antenna configurations 1014 may be related to how the UE 1002 is to handle multi-antenna Tx / Rx processing during AI / ML-related operation(s) (e.g., during AI / ML data collection). Then, at 1024, the network entity 1004 may transmit, to the UE 1002, an indication 1012 of the set of antenna configurations 1014. Similarly, the indication 1012 may be actual / explicit antenna configurations (e.g., usingX antennas fortransmitting / receivingreferencesignals), or the indication 1012 may be a set of IDs that is mapped to a set of antenna configurations (e.g., applying a configuration ID Y which corresponds to using X antennas for transmitting / receiving reference signals). As discussed above, using IDs may reduce signaling overhead between the UE 1002 and the network entity 1004, and also enable proprietary information protection in some scenarios.

[0127] In one example, the set of antenna configurations 1014 may include configurations related to Tx antenna(s) of the UE 1002 (e.g., antenna that can be used by the UE129025-2422WO01Qualcomm Ref. No. 2405604WO 441002 for transmitting reference signals (e.g., SRS, SSB, CSI-RS, TRS, etc.). For example, the set of antenna configurations 1014 may include one or more of the followings:(1) a number of Tx antenna (e.g., M Tx antennas) to be considered / usedby the UE 1002 for the AI / ML-related operation(s) (e.g., data collection) during transmission;(2) antenna co-phasing option(s) to be considered / used by the UE 1002 for the AI / ML-related operation(s) (e.g., data collection) during transmission (e.g., coherent / non-coherent, using a specified number of antennas that are coherent, co-phasing confidence [phase resolution], etc.);(3) a power splitting / weighting across Tx antennas to be considered / usedby the UE 1002 for the AI / ML-related operation(s) (e.g., data collection) duringtransmission (e.g., equal power, power weighting [supported weights], antenna switching / selection, etc.);(4) specifying the UE 1002 to consider indicating, to the network entity 1004, the number of antennas, co-phasing option(s), the beam codebook, and / or aggregation / selection that the UE 1002 selects (autonomously) during transmission of reference signals for the AI / ML-related operation(s), such as for data collection; and / or(5) specifying the UE 1002 to apply antenna processing (e.g., for transmitting reference signals) based on what UE 1002 indicated using associated ID(s) (as discussed above).

[0128] In another example, the set of antenna configurations 1014 may include configurations related to Rx antenna(s) of theUE 1002 (e.g., antenna that can be used by the UE 1002 for receiving reference signals (e.g., PRS, SSB, CSLRS, TRS, etc.). For example, the set of antenna configurations 1014 may include one or more of the followings:(1) a number of Rx antenna (e.g., M Rx antennas) to be considered / usedby the UE 1002 for the AI / ML-related operation(s) (e.g., data collection) during reception;(2) antenna co-phasing option(s) to be considered / used by the UE 1002 for the AI / ML-related operation(s) (e.g., data collection) during reception (e.g., coherent / non-coherent, using a specified number of antennas that are coherent, co-phasing confidence [phase resolution], etc.);129025-2422WO01Qualcomm Ref. No. 2405604WO 45(3) (aggregation / selection) power splitting / weighting across Rx antennas to be considered / used by the UE 1002 for the AI / ML-related operation(s) (e.g., data collection) during reception (e.g., equal power, power weighting [supported weights], antenna switching / selection, etc.);(4) measurement type(s) with antenna aggregation / selection to be considered / used by the UE 1002 for the AI / ML-related operation(s) (e.g., data collection) during reception (e.g., RSRP, RSRPP, RSTD, RSTD of additional paths, RTOA, RTOA of additional paths, ToA / ToF, RSCP, RSCPD, sample-based CIR / PDP / DP, etc.);(5) specifying the UE 1002 to consider indicating, to the network entity 1004, the number of Rx antennas, Rx co-phasing option(s), the Rx beam codebook, and / or the Rx aggregation / selection on measurement type level that the UE 1002 applies (autonomously) for the AI / ML-related operation(s) (e.g., data collection) during reception; and / or(6) specifying the UE 1002 to apply antenna processing (e.g., for receiving reference signals) based on what UE 1002 indicated using associated ID(s) (as discussed above).

[0129] At 1026, after receiving the indication 1012 related to set of antenna configurations 1014, the UE 1002 may apply the set of antenna configurations 1014 for the AI / MU related operation(s), such as data collection. For example, as shown at 1028, the UE 1002 may transmit a set of reference signals (e.g., a set of SRSs) to a base station (BS) or a TRP (BS / TRP) 1006 based on the set of antenna configurations 1014 for the AI / ML-related operation(s), and / or receive a set of reference signals (e.g., a set of PRSs) from the BS / TRP 1006 based on the set of antenna configurations 1014 for the AI / ML-related operation(s).

[0130] In some scenarios, the UE 1002 may also determine not to apply the set of antenna configurations 1014. For example, the (internal) status / configuration(s) of the UE 1002 may have changed after indicating the set of antenna processing capabilities 1010 to the network entity 1004 (e.g., at 1020), such that the UE 1002 is unable to apply the set of configurations 1014. For example, at 1020, the UE 1002 may indicate to the network entity 1004 that it is able to use up to four antennas for transmitting or receiving reference signals for the AI / ML-related operation(s). Then, at 1024, the network entity 1004 may configure the UE 1002 to use four antennas for transmitting or receiving reference signals for the AI / ML-related operation(s). However, the UE129025-2422WO01Qualcomm Ref. No. 2405604WO 461002 may encounter a low power scenario or is configured to process other tasks with higher priorities, such that the UE 1002 is now just able to use at most two antennas for transmitting or receiving reference signals for the AI / ML-related operation(s). Thus, at 1026, the UE 1002 may determine to apply other / alternative configurations (e.g., using two antennas instead of four antennas for transmitting or receiving reference signals for the AI / ML-related operation(s)). In some implementations, as shown at 1030, the UE 1002 may indicate, to the network entity 1004, the other / alternative configurations it applied if the UE 1002 does not apply the set of antenna configurations 1014 from the network entity 1004. In some examples, the UE 1002 may also indicate, while reporting AI / ML-related measurements such as collected data, the configurations / operationsit applied / used for the measurements. In some examples, the UE 1002 may also indicate the set of antenna configurations 1014 to other entities, such as to the BS / TRP 1006.

[0131] In some implementations, as shown at 1032, the UE 1002 and the network entity 1004 may communicate with each other regarding desired / recommend antenna processing to be used during AI / ML-related operation(s). For example, during data collection, the UE 1002 may indicate desired / recommend antenna processing to the network entity 1004. In response, the network entity 1004 may configure the UE 1002 with antenna processing during data collection as desired / recommended by the UE 1002.

[0132] In some scenarios, there may be no capability or configuration exchange between the UE 1002 and the network entity 1004 (e.g., the UE 1002 does notindicate the set of processing capabilities to the network entity 1004 at 1020 and / or receive the set of antenna configurations 1014 from the network entity at 1024). In such scenarios, as shown at 1034, the UE 1002 may be configured to determine / select (autonomously) on what antenna processing it is going to use during AI / ML-related operation(s) (e.g, during data collection). Then, the UE 1002 may indicate to the network entity 1004 what the antenna processing it used explicitly or implicitly (e.g., using associated ID (if available / configured)). In addition, the UE 1002 may indicate the antenna processing it used based on a request from the network entity 1004 or without receiving a request.

[0133] In another example, as shown at 1036, the network entity 1004 may transmit, to the UE 1002, a request to participate in the AI / ML-related operation(s), such as data collection. Then, based on this request, theUE 1002 may transmit the indication 1008129025-2422WO01Qualcomm Ref. No. 2405604WO 47 related to the set of antenna processing capabilities 1010 supported by the UE 1002 for AI / ML-related operation(s) at 1020.

[0134] FIG. 11 is a communication flow 1100 illustrating an example of a BS / TRP communicating its antenna capabilities with a network entity to improve the overall performance of AI / ML-related operations in accordance with various aspects of the present disclosure. The numberings associated with the communication flow 1100 do not specify a particular temporal order and are merely used as references for the communication flow 1100.

[0135] At 1120, abase station or TRP (BS / TRP) 1106 may transmit, to a network entity 1104, an indication 1108 that is indicative of (or related to) a set of antenna processing capabilities 1110 supported by the BS / TRP 1106 for AI / ML-related operation(s). Depending on implementations, the network entity 1104 may be a server, an LMF, a sensing management function, an AI / ML management function, an OTT server, a training server, an NWDAF, or an 0AM system, etc.

[0136] The AI / ML-related operation(s) may include a data collection for at least one AI / ML model, an AI / ML model development, training a set of AI / ML models, and / or performing inf erencing using a set of AI / ML models, etc. The BS / TRP 1106 may transmit the indication 1108 based on a request from the network entity 1104 (described below), orbasedon its initiation (e.g., without receiving a request). In other words, the signaling described herein may involve solicited and unsolicited actions by the BS / TRP 1106. In addition, the BS / TRP 1106 may transmit the indication 1108 via an LTE / NRPPa message / signaling (e.g., new LPP / NRPPa signaling may be configured for enabling antenna consistency during AI / ML-related operation(s)), and this message / signaling may occur during the AI / ML-related operation(s) (e.g., during a data collection session) while considering measurements for AI / ML positioning model input and / or output (as applicable). The BS / TRP 1106 may also transmit the indication 1108 during the AI / ML-related operation(s), such as during a data collection session.

[0137] In one aspect of the present disclosure, the set of antenna processing capabilities 1110 supported by the BS / TRP 1106 may be related to a set of Tx antennas of the BS / TRP 1106, such as Tx antenna(s) that are used by the BS / TRP 1106 for transmitting reference signal(s). The reference signal(s) may or may not be related to positioning which may include PRS, SSB, CSI-RS, and / or TRS, etc.129025-2422WO01Qualcomm Ref. No. 2405604WO 48

[0138] In one example, the setof antenna processing capabilities 1110forTx antenna(s)may include one or more of the followings:(1) a (maximum) number of Tx antennas supported by the BS / TRP 1106 for the AI / ML-related operation(s) during transmission, e.g., a supported number of Tx antennas for the AI / ML data collection (corresponding to the AI / ML model input / output);(2) antenna co-phasing option(s) supported by the BS / TRP 1106 for the AI / ML- related operation(s) during transmission, e.g., antenna co-phasing option(s) supported for the AI / ML data collection, such as whether the antennas are coherent / non-coherent, the max number of antennas that are coherent, co-phasing confidence [phase resolution], etc. (corresponding to the AI / ML model input / output);(3) a power splitting or weighting across the set of Tx antennas supported by the BS / TRP 1106 for the AI / ML-related operation(s) during transmission, e.g., supported power splitting / weighting across antennas for AI / ML data collection, such as equal power, power weighting [supported weights], antenna switching / selection, etc. (correspondingto the AI / ML model input / output); and / or(4) an indication of a list of antenna processing functions which the BS / TRP 1106 is capable of performing, e.g., support for the BS / TRP 1106 to indicate, to the network entity 1104, whether the BS / TRP 1106 is able to performs antenna switching / aggregation, e.g., number of antennas, co-phasing option(s), beam codebook, aggregation / selectionthatBS / TRP 1106 selects (autonomously) during transmission of reference signals for AI / ML-related operation(s), such as data collection (corresponding to the AI / ML model input / output).

[0139] Depending on implementations, the indication 1108 may be the actual parameters related of the set of antenna processing capabilities 1110, or it may be a set of IDs associated with the set of antenna processing capabilities 1110. For example, if the BS / TRP 1106 is configured to indicate to the network entity 1104 that it supports using up to four antennas for AI / ML data collection, the BS / TRP 1106 may explicitly indicate this capability / information to the network entity 1104 (e.g., an indication of supporting up to four antennas for AI / ML data collection). In another example, as discussed in connection with FIG. 10, the BS / TRP 1106 may also be configured to associated a list of IDs with the set of antenna processing capabilities 1110, and then129025-2422WO01Qualcomm Ref. No. 2405604WO 49 the BS / TRP 1106 may indicate the set of antenna processing capabilities 1110 using their associated IDs. In other words, the associated ID may be used as an implicit indicator of antenna processing capability details. Similarly, this concept may also apply to other aspects of the disclosure described herein.

[0140] In another aspect of the present disclosure, the set of antenna processing capabilities1110 supported by the BS / TRP 1106 may be related to a set of Rx antennas of the BS / TRP 1106, such as Rx antenna(s) that are used by the BS / TRP 1106 for receiving reference signal(s). The reference signal(s) may or may not be related to positioning which may include SRS, SSB, CSI-RS, and / or TRS, etc.

[0141] In one example, the set of antenna processing capabilities 1110 for Rx antenna(s) may include one or more of the followings:(1) a (maximum) number of Rx antennas supported by the BS / TRP 1106 for the AI / ML-related operation(s) during reception, e.g., a supported number of Rx antennas for the AI / ML data collection (corresponding to the AI / ML model input / output);(2) antenna co-phasing option(s) supported by the BS / TRP 1106 for the AI / ML- related operation(s) during reception, e.g., antenna co-phasing option(s) supported for the AI / ML data collection, such as whether the antennas are coherent / non- coherent, the max number of antennas that are coherent, co-phasing confidence [phase resolution], etc. (corresponding to the AI / ML model input / output);(3) a (aggregation / selection) power splitting or weighting across the set of Rx antennas supported by the BS / TRP 1106 for the AI / ML-related operation(s) during reception, e.g., supported (aggregation / selection) power splitting / weighting across antennas for AI / ML data collection during reception, such as equal power, power weighting [supported weights], antenna switching / selection, etc. (corresponding to the AI / ML model input / output);(4) supported measurement type(s) with antenna processing for data collection during reception (e.g., RSRP, RSRPP, RSTD, RSTD of additional paths, RTOA, RTOA of additional paths, ToA / ToF, RSCP, RSCPD, sample-based CIR / PDP / DP, etc.); and / or(5) an indication of a list of antenna processing functions which the BS / TRP 1106 is capable of performing, e.g., support for the BS / TRP 1106 to indicate, to the network entity 1104, whether the BS / TRP 1106 is able to performs antenna129025-2422WO01Qualcomm Ref. No. 2405604WO 50 processing, e.g., number of antennas, co-phasing option(s), beam codebook, antenna processing that BS / TRP 1106 selects (autonomously) during reception of reference signals for AI / ML-related operation(s), such as data collection (corresponding to the AI / ML model input / output).

[0142] Similarly, as discussed above, the indication 1108 for indicating the set of antenna processing capabilities 1110 related to Rx antennas of the BS / TRP 1106 may be a set of IDs that is mapped to the set of antenna processing capabilities 1110 related to Rx antennas. In other words, for Tx antenna and / or Rx antenna capabilities discussed above, the BS / TRP 1106 may indicate implicit capabilities via the associated ID(s), where the BS / TRP 1106 may select the associated ID or coordinate with the network entity 1104 on the ID assignment.

[0143] At 1122, based on the received indication 1108 (e.g., the antenna processing capabilities 1110 of the BS / TRP 1106), the network entity 1104 may determine a set of antenna configurations 1114 to be applied by the BS / TRP 1106 for the AI / ML- related operation(s). For example, the set of antenna configurations 1114 may be related to how the BS / TRP 1106 is to handle multi-antenna Tx / Rx processing during AI / ML-related operation(s) (e.g., during AI / ML data collection). Then, at 1124, the network entity 1104 may transmit, to the BS / TRP 1106, an indication 1112 of the set of antenna configurations 1114. Similarly, the indication 1112 may be actual / explicit antenna configurations (e.g., using X antennas for transmitting / receiving reference signals), or the indication 1112 may be a set of IDs that is mapped to a set of antenna configurations (e.g., applying a configuration ID Y which corresponds to using X antennas for transmitting / receiving reference signals). As discussed above, using IDs may reduce signaling overhead between the BS / TRP 1106 and the network entity 1104, and also enable proprietary information protection in some scenarios.

[0144] In one example, the set of antenna configurations 1114 may include configurations related to Tx antenna(s) of the BS / TRP 1106 (e.g., antenna that can be used by the BS / TRP 1106 for transmitting reference signals (e.g., PRS, SSB, CSLRS, TRS, etc.). For example, the set of antenna configurations 1114 may include one or more of the followings:(1) a number of Tx antenna (e.g., M Tx antennas) to be considered / used by the BS / TRP 1106 for the AI / ML-related operation(s) (e.g., data collection) during transmission;129025-2422WO01Qualcomm Ref. No. 2405604WO 51(2) antenna co-phasing option(s) to be considered / usedby the BS / TRP 1106 for the AI / ML-related operation(s) (e.g., data collection) during transmission (e.g., coherent / non-coherent, using a specified number of antennas that are coherent, co-phasing confidence [phase resolution], etc.);(3) a power splitting / weighting across Tx antennas to be considered / used by the BS / TRP 1106 for the AI / ML-related operation(s) (e.g., data collection) during transmission (e.g., equal power, power weighting [supported weights], antenna switching / selection, etc.);(4) specifyingthe BS / TRP 1106 to consider indicating, to the network entity 1104, the number of antennas, co-phasing option(s), the beam codebook, and / or aggregation / selection that the BS / TRP 1106 selects (autonomously) during transmission of reference signals for the AI / ML-related operation(s), such as for data collection; and / or(5) specifyingthe BS / TRP 1106 to apply antenna processing (e.g., for transmitting reference signals) based on what BS / TRP 1106 indicated using associated ID(s) (as discussed above).

[0145] In another example, the set of antenna configurations 1114 may include configurations related to Rx antenna(s) of the BS / TRP 1106 (e.g., antenna that can be used by the BS / TRP 1106 for receiving reference signals (e.g., SRS, SSB, CSLRS, TRS, etc.). For example, the set of antenna configurations 1114 may include one or more of the followings:(1) a number of Rx antenna (e.g., M Rx antennas) to be considered / used by the BS / TRP 1106 for the AI / ML-related operation(s) (e.g., data collection) during reception;(2) antenna co-phasing option(s) to be considered / usedby the BS / TRP 1106 for the AI / ML-related operation(s) (e.g., data collection) during reception (e.g., coherent / non-coherent, using a specified number of antennas that are coherent, co-phasing confidence [phase resolution], etc.);(3) (aggregation / selection) power splitting / weighting across Rx antennas to be considered / used by the BS / TRP 1106 for the AI / ML-related operation(s) (e.g, data collection) during reception (e.g., equal power, power weighting [supported weights], antenna switching / selection, etc.);129025-2422WO01Qualcomm Ref. No. 2405604WO 52(4) measurement type(s) with antenna aggregation / selection to be considered / used by the BS / TRP 1106 for the AI / ML-related operation(s) (e.g., data collection) during reception (e g., RSRP, RSRPP, RSTD, RSTD of additional paths, RTOA, RTOA of additional paths, ToA / ToF, RSCP, RSCPD, sample-based CIR / PDP / DP, etc.);(5) specifyingthe BS / TRP 1106 to consider indicating, to the network entity 1104, the number of Rx antennas, Rx co-phasing option(s), the Rx beam codebook, and / or the Rx aggregation / selection on measurement type level that the BS / TRP 1106 applies (autonomously) for the AI / ML-related operation(s) (e.g., data collection) during reception; and / or(6) specifying the BS / TRP 1106 to apply antenna processing (e.g., for receiving reference signals) based on what BS / TRP 1106 indicated using associated ID(s) (as discussed above).

[0146] At 1126, after receiving the indication 1112 related to set of antenna configurations1114, the BS / TRP 1106 may apply the set of antenna configurations 1114 for the AI / ML-related operation(s), such as data collection. For example, as shown at 1128, the BS / TRP 1106 may transmit a set of reference signals (e.g., a set of PRSs) to a UE 1102 based on the set of antenna configurations 1114 for the AI / ML-related operation(s), and / or receive a set of reference signals (e.g., a set of SRSs) from the UE 1102 based on the set of antenna configurations 1114 for the AI / ML-related operation(s).

[0147] In some scenarios, the BS / TRP 1106 may also determine not to apply the set of antenna configurations 1114. For example, the (internal) status / configuration(s) of the UE 1102 may have changed after indicating the set of antenna processing capabilities 1110 to the network entity 1104 (e.g., at 1120), such that the BS / TRP 1106 is unable to apply the set of configurations 1114. For example, at 1120, the BS / TRP 1106 may indicate to the network entity 1104 that it is able to use up to four antennas for transmitting or receiving reference signals for the AI / ML-related operation(s). Then, at 1124, the network entity 1104may configure the BS / TRP 1106 to use four antennas for transmitting or receiving reference signals for the AI / ML-related operation(s). However, the BS / TRP 1106 may encounter a low power scenario or is configured to process other tasks with higher priorities, such that the BS / TRP 1106 is now just able to use at most two antennas for transmitting or receiving reference signals for the AI / ML-related operation(s). Thus, at 1126, the BS / TRP 1106 may determine to apply129025-2422WO01Qualcomm Ref. No. 2405604WO 53 other / alternative configurations (e.g., using two antennas instead of four antennas for transmitting or receiving reference signals for the AI / ML-related operation(s)). In some implementations, as shown at 1130, the BS / TRP 1106 may indicate, to the network entity 1104, the other / alternative configurations it applied if the BS / TRP 1106 does not apply the set of antenna configurations 1114 from the network entity 1104. In some examples, the BS / TRP 1106 may also indicate, while reporting AI / ML- related measurements such as collected data, the configurations / operations it applied / used for the measurements. In some examples, the BS / TRP 1106 may also indicate the set of antenna configurations 1114 to other entities, such as to the UE 1102.

[0148] In some implementations, as shown at 1132, the BS / TRP 1106 and the network entity 1104 may communicate with each other regarding desired / recommend antenna processing to be used during AI / ML-related operation(s). For example, during data collection, the BS / TRP 1106 may indicate desired / recommend antenna processing to the network entity 1104. In response, the network entity 1104 may configure the BS / TRP 1106 with antenna processing during data collection as desired / recommended by the BS / TRP 1106.

[0149] In some scenarios, there may be no capability or configuration exchange between the BS / TRP 1106 and the network entity 1104 (e.g., the BS / TRP 1106 does notindicate the set of processing capabilities to the network entity 1104 at 1120 and / or receive the set of antenna configurations 1114 from the network entity at 1124). In such scenarios, as shown at 1134, the BS / TRP 1106 may be configured to determine / select (autonomously) on what antenna processing it is going to use during AI / ML-related operation(s) (e.g., during data collection). Then, the BS / TRP 1106 may indicate to the network entity 1104 what the antenna processing it used explicitly or implicitly (e.g, using associated ID (if available / configured)). In addition, the BS / TRP 1106 may indicate the antenna processing it used based on a request from the network entity 1104 or without receiving a request.

[0150] In another example, as shown at 1136, the network entity 1104 may transmit, to the BS / TRP 1106, a request to participate in the AI / ML-related operation(s), such as data collection. Then, based on this request, the BS / TRP 1106 may transmit the indication 1108 related to the set of antenna processing capabilities 1110 supported by the BS / TRP 1106 for AI / ML-related operation(s) at 1120.129025-2422WO01Qualcomm Ref. No. 2405604WO 54

[0151] Aspects discussed in connection with FIGs. 10 and 11 may improve AI / ML model development and training by ensuring there is an antenna processing consistency for AI / ML positioning model input and / or output. For example, for developing / training multiple AI / ML positioning models, aspects presented herein may enable the UE 1002 and / or the BS / TRP 1106 (collectively as a “wireless device”) to apply AI / ML model selection / switching during inference to ensure antenna processing consistency consistency). For example, for AI / ML model output, multiple / diff erent AI / ML models may be configured for (or associated with) outputting measurements corresponding to a specific antenna selection / aggregation (e.g., a first AI / ML model may be configured to provide output measurement corresponding to antenna(s) with strongestmeasurementvalue(s), a second AI / ML model may be configuredto provide output measurements corresponding to aggregation of antennas, etc.). Similarly, for AI / ML model input, multiple / different AI / ML models may be configured for inputting measurements with specific antenna processing (e.g., a first AI / ML model may be configured to input measurement(s) corresponding to antenna(s) with strongest measurement value(s), a second AI / ML model may be configured to input measurements corresponding to aggregation of antennas, etc.). In other words, different AI / ML models may be configured for or associated with different antenna settings / processing.

[0152] In another example, aspects presented herein may enable / improve AI / ML model input indexing, where additional information (e.g., an index at input) may be provided to AI / ML model(s) during inference to ensure antenna processing consistency. For example, for AI / ML model output, a single AI / ML model that has the additional input information may decide whether the model output is to be specific to an antenna processing. Similarly, for AI / ML model input, a single AI / ML model whose input measurements can be any antenna processing option but the mode may have an additional inputinformationto inform the model aboutthe type of antenna processing of the associated model input measurements.

[0153] In another example, aspects presented herein may enable performing AI / ML-related operation(s) with mixed dataset. Forexample, for AI / ML model input, a single AI / ML model may be trained on a mixture of antenna processing options.

[0154] Aspects discussed in connection with FIGs. 10 and 11 may also improve AI / ML model transfer. For example, AI / ML model(s) may be trained and developed based129025-2422WO01Qualcomm Ref. No. 2405604WO 55 on aspects described in connection with FIG. 10, and then transferred to corresponding inference entity (e.g., a UE, a BS / TRP, an LMF, etc.). The transferred AI / ML model(s) may be associated with additional information related to antenna processing, in which this additional information may list corresponding antenna processing for AI / ML model input and / or output (or associated ID with antenna processing), etc.

[0155] In addition, aspects discussed in connection with FIGs. 10 and 11 may also improveAI / ML model inference / inferencing. For example, during AI / ML model inference / inferencing, a wireless device (e.g., the UE 1002, the BS / TRP 1106, etc.) may indicate the supported antenna processing for measurements corresponding to AI / ML model input or output (as applicable). A network entity (e.g., an LMF, the network entity 1004, 1104, etc.) may request the wireless device to consider / use specific antenna processing for measurements corresponding to AI / ML model input or output (as applicable). As discussed above, in some scenarios, the wireless device (e.g., during inferencing) may or may not apply the requested / configured antenna processing for measurements corresponding to AI / ML model input or output (as applicable). In such scenarios, the wireless device may indicate the antenna processing for measurements corresponding to model input or output (as applicable).

[0156] Aspects presented herein may improve the overall performance of AI / ML positioning by enabling antenna consistency in AI / ML data collection, training, and inference. UEs and / or PRUs may support different antenna processing (i.e., antenna selection, switching, aggregation, beamforming, etc.) with PRS reception and / or SRS transmission (this may also apply to other RSs, e.g., SSB, CSI-RS, TRS). A gNB / TRP may also support different antenna processing with PRS transmission and / or SRS reception (this may also apply to other RSs, e.g., SSB, CSLRS, TRS). However, an inconsistency between training and inference may occur when the device (UE, PRU, gNB / TRP) considers different antennas or performs different antenna processing to obtain positioning measurements during inference when compared to training. This inconsistency may affect model performance and worsen positioning accuracy if not properly informed and / or managed. Aspects presented herein may provide signaling and solutions to reduce ambiguity / uncertainty about antenna selection and aggregation during inference as compared to data collection / training. For data collection, training data may have information on antenna processing considered129025-2422WO01Qualcomm Ref. No. 2405604WO 56 during training data collection. During data collection, devices maybe provided with information related to antenna processing when obtaining measurements. Devices may also indicate information related to what antenna processing when obtaining measurements. For model development, model developers may leverage information to develop the model and enhance its robustness to antenna processing inconsistencies, e.g., develop multiple models with different antenna processing assumptions; model input indexing with information; or train the model with measurements of different antenna processing. For model transfer, when the model is transferred to an inference entity, the model may be associated with information related to assumptions regarding antenna selection / aggregation corresponding to measurements at model inputs and / or outputs (as applicable). During inference, devices may be provided with information related to antenna processing when obtaining measurements. Devices may also indicate information related to antenna processing when obtaining measurements.

[0157] FIG. 12 is a flowchart 1200 of wireless communication. The method may be performedby a wireless device (e.g., the UE 104, 404, 602, 702, 1002; the base station 102, 706; the TRP 402, 406; the BS / TRP 1106; the apparatus 1304). The method may enable the wireless device to indicate its antenna processing capabilities related to AI / ML positioning operations (e.g., data collection, model training / retraining, inferencing, etc.), thereby promoting or ensuring a consistency in antenna processing between different AI / ML operations.

[0158] At 1202, the wireless device may transmit, to a network entity, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one AI / ML-related operation, such as described in connection with FIGs. 10 and 11. For example, at 1020 of FIG. 10, a UE 1002 (which may include a positioning reference unit (PRU)) may transmit, to a network entity 1004, an indication 1008 that is indicative of (or related to) a set ofantenna processing capabilities 1010 supported by the UE 1002 for AI / ML-related operation(s). Similarly, at 1120 of FIG. 11, a base station or TRP (BS / TRP) 1106 may transmit, to a network entity 1104, an indication 1108 that is indicative of (or related to) a set of antenna processing capabilities 1110 supported by the BS / TRP 1106 for AI / ML-related operation(s). The transmission of the first indication may be performed by, e.g., the antenna capability indication component 198, the transceiver(s) 1322, the cellular baseband processor(s) 1324,129025-2422WO01Qualcomm Ref. No. 2405604WO 57 and / or the application processor(s) 1306 of the apparatus 1304 in FIG. 13, and / or by the antenna capability indication component 199, the transceiver(s) 1446, the RU processor(s) 1442, the DU processor(s) 1432, and / or the CU processor(s) 1412, of the network entity 1402 in FIG. 14.

[0159] At 1204, the wireless device may receive, from the network entity in response to transmission of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML-related operation, such as described in connection with FIGs. 10 and 11 . For example, at 1022 of FIG. 10, based on the received indication 1008 (e.g., the antenna processing capabilities 1010 of the UE 1002), the network entity 1004 may determine a set of antenna configurations 1014 to be applied by the UE 1002 for the AI / ML- related operation(s). For example, the set of antenna configurations 1014 may be related to how the UE 1002 is to handle multi-antenna Tx / Rx processing during AI / ML-related operation(s) (e.g., during AI / ML data collection). Then, at 1024, the network entity 1004 may transmit, to the UE 1002, an indication 1012 of the set of antenna configurations 1014. Similarly, at 1122 of FIG. 11, based on the received indication 1108 (e.g., the antenna processing capabilities 1110 ofthe BS / TRP 1106), the network entity 1104 may determine a set of antenna configurations 1114 to be applied by the BS / TRP 1106 for the AI / ML-related operation(s). For example, the set of antenna configurations 1114 may be related to how the BS / TRP 1106 is to handle multi-antenna Tx / Rx processing during AI / ML-related operation(s) (e.g., during AI / ML data collection). Then, at 1124, the network entity 1104 may transmit, to the BS / TRP 1106, an indication 1112 of the set of antenna configurations 1114. The reception of the second indication may be performed by, e.g., the antenna capability indication component 198, thetransceiver(s) 1322, the cellular baseband processors) 1324, and / or the application processor(s) 1306 of the apparatus 1304 in FIG. 13, and / or by the antenna capability indication component 199, the transceiver(s) 1446, the RU processor(s) 1442, the DU processor(s) 1432, and / or the CU processor(s) 1412, of the network entity 1402 in FIG. 14.

[0160] In one example, the wireless device may apply the set of antenna configurations for the at least one AI / ML-related operation. In some implementations, to apply the set of antenna configurations for the at least one AI / ML-related operation, the wireless device may be configured to transmit a first set of reference signals based on the set129025-2422WO01Qualcomm Ref. No. 2405604WO 58 of antenna configurations for the at least one AI / ML-related operation, or receive a second set of reference signals based on the set of antenna configurations for the at least one AI / ML-related operation.

[0161] In another example, the wireless device may receive a request to participate in the at least one AI / ML-related operation, where the transmission of the first indication is based on the request.

[0162] In another example, the set of antenna processing capabilities is related to a set of Tx antennas of the wireless device for a transmission of one or more reference signals, and where the set of antenna processing capabilities includes at least one of: (1) a number of Tx antennas supported for the at least one AI / ML-related operation, (2) a set of antenna co-phasing options supported for the at least one AI / ML-related operation, (3) a power splitting or weighting across the set of Tx antennas supported for the at least one AI / ML-related operation, or (4) a third indication of a list of antenna processing functions which the wireless is capable of performing. In some implementations, the one or more reference signals include at least one of: one or more PRS, one or more SRS, one or more SSB, one or more CS RS, or one or more TRS. In some implementations, the set of antenna configurations includes at least one of: a second number of Tx antennas to be used for the at least one AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, or the power splitting or weighting across the set of Tx antennas to be used for the at least one AI / ML- related operation.

[0163] In another example, the set of antenna processing capabilities is related to a set of Rx antennas of the wireless device for a reception of one or more reference signals, and where the set of antenna processing capabilities includes at least one of: (1) a number of Rx antennas supported for the at least one AI / ML-related operation, (2) a set of antenna co-phasing options supported for the at least one AI / ML-related operation, (3) a power splitting or weighting across the set of Rx antennas supported for the at least one AI / ML-related operation, (4) a set of measurement types supported for the at least one AI / ML-related operation, or (5) a third indication of a list of antenna processing functions which the wireless is capable of performing. In some implementations, the one or more reference signals include at least one of: one or more PRS, one or more SRS, one or more SSB, one or more CSLRS, or one or more129025-2422WO01Qualcomm Ref. No. 2405604WO 59TRS. In some implementations, the set of antenna configurations includes at least one of: a second number of Rx antennas to be used for the at least one AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, the power splitting or weighting across the set of Rx antennas to be used for the at least one AI / ML-related operation, or at least one measurement type in the set of measurementtypes to be used for the at least one AI / ML-related operation.

[0164] In another example, the first indication corresponds to a set of identifications associated with the set of antenna processing capabilities. In some implementations, the wireless device may associate the set of identifications with the set of antenna processing capabilities, and transmit, to the network entity, information related to the association of the set of identifications with the set of antenna processing capabilities.

[0165] In another example, the wireless device may transmit, to the network entity, a third indication of a setof recommended antenna configurations for the at least one AI / ML- related operation, where the set of antenna configurations is based on the set of recommended antenna configurations.

[0166] In another example, the at least one AI / ML-related operation corresponds to at least one: a data collection for at least one AI / ML model, an AI / ML model development, training a first set of AI / ML models, performing inferencing using a second set of AI / ML models, or checking consistency between different AI / ML related operations.

[0167] In another example, the at least one AI / ML-related operation corresponds to at least one AI / ML-based positioning operation.

[0168] In another example, the wireless device corresponds to one of a UE, a base station, a TRP, or a PRU, and the network entity corresponds to one of a server, an LMF, a sensing management function, an AI / ML management function, an OTT server, a training server, an NWDAF, or an 0AM system.

[0169] In another example, the wireless device may transmit a third indication of the set of antenna configurations.

[0170] FIG. 13 is a diagram 1300 illustrating an example of a hardware implementation for an apparatus 1304. The apparatus 1304 may be a UE, a component of a UE, or may implement UE functionality. In some aspects, the apparatus 1304 may include at least one cellular baseband processor 1324 (also referred to as a modem) coupled to one or more transceivers 1322 (e.g., cellular RF transceiver). The cellular baseband129025-2422WO01Qualcomm Ref. No. 2405604WO 60 processor(s) 1324 may include at least one on-chip memory 1324'. In some aspects, the apparatus 1304 may further include one or more subscriber identity modules (SIM) cards 1320 and at least one application processor 1306 coupled to a secure digital (SD) card 1308 and a screen 1310. The application processor(s) 1306 may include on-chip memory 1306'. In some aspects, the apparatus 1304 may further include a Bluetooth module 1312, a WLAN module 1314, an ultrawide band (UWB) module 1338 (e.g., a UWB transceiver), an SPS module 1316 (e.g., GNSS module), one or more sensors 1318 (e.g., barometric pressure sensor / altimeter; motion sensor such as inertial measurement unit (IMU), gyroscope, and / or accelerometer(s); light detection and ranging (LIDAR), radio assisted detection and ranging (RADAR), sound navigation and ranging (SONAR), magnetometer, audio and / or other technologies used for positioning), additional memory modules 1326, a power supply 1330, and / oracamera 1332. The Bluetooth module 1312, the UWB module 1338, the WLAN module 1314, and the SPS module 1316 may include an on-chip transceiver (TRX) (or in some cases, just a receiver (RX)). The Bluetooth module 1312, the WLAN module 1314, and the SPS module 1316 may include their own dedicated antennas and / or utilize the antennas 1380 for communication. The cellular baseband processor(s) 1324 communicates through the transceiver(s) 1322 via one or more antennas 1380 with the UE 104 and / or with an RU associated with a network entity 1302. The cellular baseband processor(s) 1324 and the application processor(s) 1306 may each include a computer-readable medium / memory 1324', 1306', respectively. The additional memory modules 1326 may also be considered a computer-readable medium / memory. Each computer-readable medium / memory 1324', 1306', 1326 may be non-transitory. The cellular baseband processor(s) 1324 and the application processor(s) 1306 are each responsible for general processing, includingthe execution of software stored on the computer-readable medium / memory. The software, when executed by the cellular baseband processor(s) 1324 / application processor(s) 1306, causes the cellular baseband processor(s) 1324 / application processor(s) 1306 to perform the various functions described supra. The cellular baseband processors) 1324 and the application processor(s) 1306 are configured to perform the various functions described supra based at least in part of the information stored in the memory. That is, the cellular baseband processor(s) 1324 and the application processor(s) 1306 may be configuredto perform a first sub set of the various functions129025-2422WO01Qualcomm Ref. No. 2405604WO 61 described supra with out information storedin the memory and may be configured to perform a second subset of the various functions described supra based on the information stored in the memory. The computer-readable medium / memory may also be used for storing data that is manipulated by the cellular baseband processors) 1324 / application processor(s) 1306 when executing software. The cellular baseband processor(s) 1324 / application processor(s) 1306 may be a component of the UE 350 and may include the at least one memory 360 and / or at least one of the TX processor 368, the RX processor 356, and the controller / processor 359. In one configuration, the apparatus 1304 may be at least one processor chip (modem and / or application) and include just the cellular baseband processor(s) 1324 and / or the application processor(s) 1306, and in another configuration, the apparatus 1304 may be the entire UE (e.g., see UE 350 of FIG. 3) and include the additional modules of the apparatus 1304.

[0171] As discussed supra, the antenna capability indication component 198 may be configured to transmit, to a network entity, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one AI / ML- related operation. The antenna capability indication component 198 may also be configured to receive, from the network entity in response to transmission of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML-related operation. The antenna capability indication component 198 may be within the cellular baseband processor(s) 1324, the application processor(s) 1306, or both the cellular baseband processor(s) 1324 and the application processor(s) 1306. The antenna capability indication component 198 may be one or more hardware components specifically configured to carry out the stated processes / algorithm, implemented by one or more processors configured to perform the stated processes / algorithm, stored within a computer-readable medium for implementation by one or more processors, or some combination thereof. When multiple processors are implemented, the multiple processors may perform the stated processes / algorithm individually or in combination. As shown, the apparatus 1304 may include a variety of components configured for various functions. In one configuration, the apparatus 1304, and in particular the cellular baseband processor(s) 1324 and / or the application processors) 1306, may include means for transmitting, to a network entity, a first indication of a129025-2422WO01Qualcomm Ref. No. 2405604WO 62 set of antenna processing capabilities supported by the wireless device for at least one AI / ML-related operation. The apparatus 1304 may further include means for receiving, from the network entity in response to transmission of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML-related operation.

[0172] In one configuration, the apparatus 1304 may further include means for applying the set of antenna configurations for the at least one AI / ML-related operation. In some implementations, the means for applying the set of antenna configurations for the at least one AI / ML-related operation may include configuring the apparatus 1304 to transmit a first set of reference signals based on the set of antenna configurations for the at least one AI / ML-related operation, or receive a second set of reference signals based on the set of antenna configurations for the at least one AI / ML-related operation.

[0173] In another configuration, the apparatus 1304 may further include means for receiving a request to participate in the at least one AI / ML-related operation, where the transmission of the first indication is based on the request.

[0174] In another configuration, the set of antenna processing capabilities is related to a set of Tx antennas of the wireless device for a transmission of one or more reference signals, and where the set of antenna processing capabilities includes at least one of(1) a number of Tx antennas supported for the at least one AI / ML-related operation,(2) a set of antenna co-phasing options supported for the at least one AI / ML-related operation, (3) a power splitting or weighting across the set of Tx antennas supported for the at least one AI / ML-related operation, or (4) a third indication of a list of antenna processing functions which the wireless is capable of performing. In some implementations, the one or more reference signals include at least one of: one or more PRS, one or more SRS, one or more SSB, one or more CS RS, or one or more TRS. In some implementations, the set of antenna configurations includes at least one of: a second number of Tx antennas to be used for the at least one AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, or the power splitting or weighting across the set of Tx antennas to be used for the at least one AI / ML- related operation.129025-2422WO01Qualcomm Ref. No. 2405604WO 63

[0175] In another configuration, the set of antenna processing capabilities is related to a set of Rx antennas of the wireless device for a reception of one or more reference signals, and where the set of antenna processing capabilities includes at least one of: (1) a number of Rx antennas supported for the at least one AI / ML-related operation, (2) a set of antenna co-phasing options supported for the at least one AI / ML-related operation, (3) a power splitting or weighting across the set of Rx antennas supported forthe at least one AI / ML-related operation, (4) a set of measurementtypes supported for the at least one AI / ML-related operation, or (5) a third indication of a list of antenna processing functions which the wireless is capable of performing. In some implementations, the one or more reference signals include at least one of: one or more PRS, one or more SRS, one or more SSB, one or more CS RS, or one or more TRS. In some implementations, the set of antenna configurations includes at least one of: a second number of Rx antennas to be used for the at least one AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, the power splitting or weighting across the set of Rx antennas to be used forthe at least one AI / ML-related operation, or at least one measurement type in the set of measurementtypes to be used for the at least one AI / ML-related operation.

[0176] In another configuration, the first indication corresponds to a set of identifications associated with the set of antenna processing capabilities. In some implementations, the apparatus 1304 may further include means for associatingthe set of identifications with the set of antenna processing capabilities, and means for transmitting, to the network entity, information related to the association of the set of identifications with the set of antenna processing capabilities.

[0177] In another configuration, the apparatus 1304 may further include means for transmitting, to the network entity, a third indication of a setof recommended antenna configurations forthe at least one AI / ML-related operation, where the set of antenna configurations is based on the set of recommended antenna configurations.

[0178] In another configuration, the at least one AI / ML-related operation corresponds to at least one: a data collection for at least one AI / ML model, an AI / ML model development, training a first set of AI / ML models, performing inferencing using a second set of AI / ML models, or checking consistency between different AI / ML related operations.129025-2422WO01Qualcomm Ref. No. 2405604WO 64

[0179] In another configuration, the at least one AI / ML-related operation corresponds to at least one AI / ML-based positioning operation.

[0180] In another configuration, the wireless device corresponds to one of a UE, a base station, a TRP, or a PRU, and the network entity corresponds to one of a server, an LMF, a sensing management function, an AI / ML management function, an OTT server, a training server, an NWDAF, or an 0AM system.

[0181] In another configuration, the apparatus 1304 may further include means for transmitting a third indication of the set of antenna configurations.

[0182] The means may be the antenna capability indication component 198 of the apparatus 1304 configured to perform the functions recited by the means. As described supra, the apparatus 1304 may include the TX processor 368, the RX processor 356, and the controller / processor 359. As such, in one configuration, the means may be the TX processor 368, the RX processor 356, and / or the controller / processor 359 configured to perform the functions recited by the means.

[0183] FIG. 14 is a diagram 1400 illustrating an example of a hardware implementation for a network entity 1402. The network entity 1402 may be a BS, a component of a BS, or may implement BS functionality. The network entity 1402 may include at least one of a CU 1410, a DU 1430, or an RU 1440. For example, depending on the layer functionality handled by the antenna capability indication component 199, the network entity 1402 may include the CU 1410; both the CU 1410 and the DU 1430; each ofthe CU 1410, the DU 1430, and theRU 1440; theDU 1430;boththeDU 1430 and the RU 1440; or the RU 1440. The CU 1410 may include at least one CU processor 1412. The CU processor(s) 1412 may include on-chip memory 1412'. In some aspects, the CU 1410 may further include additional memory modules 1414 and a communications interface 1418. The CU 1410 communicates with the DU 1430 through a midhaul link, such as an Fl interface. The DU 1430 may include at least one DU processor 1432. The DU processor(s) 1432 may include on-chip memory 1432'. In some aspects, theDU 1430 may further include additional memory modules 1434 and a communications interface 1438. TheDU 1430 communicates with theRU 1440 through a fronthaul link. The RU 1440 may include at least one RU processor 1442. The RU processor(s) 1442 may include on-chip memory 1442'. In some aspects, the RU 1440 may further include additional memory modules 1444, one or more transceivers 1446, antennas 1480, and a communications interface 1448. The RU129025-2422WO01Qualcomm Ref. No. 2405604WO 651440 communicates with the UE 104. The on-chip memory 1412', 1432', 1442' and the additional memory modules 1414, 1434, 1444 may each be considered a computer-readable medium / memory. Each computer-readable medium / memory may be non-transitory. Each of the processors 1412, 1432, 1442 is responsible for general processing, including the execution of software stored on the computer- readable medium / memory. The software, when executed by the corresponding processor(s) causes the processor(s) to perform the various functions described supra. The computer-readable medium / memory may also be used for storing data that is manipulated by the processor(s) when executing software.

[0184] As discussed supra, the antenna capability indication component 199 may be configured to transmit, to a network entity, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one AI / ML- related operation. The antenna capability indication component 199 may also be configured to receive, from the network entity in response to transmission of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML-related operation. The antenna capability indication component 199 may be within one or more processors of one or more of the CU 1410, DU 1430, and the RU 1440. The antenna capability indication component 199 may be one or more hardware components specifically configured to carry out the stated processes / algorithm, implemented by one or more processors configured to perform the stated processes / algorithm, stored within a computer-readable medium for implementation by one or more processors, or some combination thereof. When multiple processors are implemented, the multiple processors may perform the stated processes / algorithm individually or in combination. The network entity 1402 may include a variety of components configured for various functions. In one configuration, the network entity 1402 may include means for transmitting, to a network entity, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one AI / ML-related operation. The network entity 1402 may further include means for receiving, from the network entity in response to transmission of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML-related operation.129025-2422WO01Qualcomm Ref. No. 2405604WO 66

[0185] In one configuration, the network entity 1402 may further include means for applying the set of antenna configurations for the at least one AI / ML-related operation. In some implementations, the means for applying the set of antenna configurations for the at least one AI / ML-related operation may include configuring the network entity 1402 to transmit a first set of reference signals based on the set of antenna configurations for the at least one AI / ML-related operation, or receive a second set of reference signals based on the set of antenna configurations for the at least one AI / ML-related operation.

[0186] In another configuration, the network entity 1402 may further include means for receiving a request to participate in the at least one AI / ML-related operation, where the transmission of the first indication is based on the request.

[0187] In another configuration, the set of antenna processing capabilities is related to a set of Tx antennas of the wireless device for a transmission of one or more reference signals, and where the set of antenna processing capabilities includes at least one of(1) a number of Tx antennas supported for the at least one AI / ML-related operation,(2) a set of antenna co-phasing options supported for the at least one AI / ML-related operation, (3) a power splitting or weighting across the set of Tx antennas supported for the at least one AI / ML-related operation, or (4) a third indication of a list of antenna processing functions which the wireless is capable of performing. In some implementations, the one or more reference signals include at least one of: one or more PRS, one or more SRS, one or more SSB, one or more CS RS, or one or more TRS. In some implementations, the set of antenna configurations includes at least one of: a second number of Tx antennas to be used for the at least one AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, or the power splitting or weighting across the set of Tx antennas to be used for the at least one AI / ML- related operation.

[0188] In another configuration, the set of antenna processing capabilities is related to a set of Rx antennas of the wireless device for a reception of one or more reference signals, and where the set of antenna processing capabilities includes at least one of: (1) a number of Rx antennas supported for the at least one AI / ML-related operation, (2) a set of antenna co-phasing options supported for the at least one AI / ML-related operation, (3) a power splitting or weighting across the set of Rx antennas supported129025-2422WO01Qualcomm Ref. No. 2405604WO 67 forthe at least one AI / ML-related operation, (4) a set of measurementtypes supported for the at least one AI / ML-related operation, or (5) a third indication of a list of antenna processing functions which the wireless is capable of performing. In some implementations, the one or more reference signals include at least one of: one or more PRS, one or more SRS, one or more SSB, one or more CSLRS, or one or more TRS. In some implementations, the set of antenna configurations includes at least one of: a second number of Rx antennas to be used for the at least one AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, the power splitting or weighting across the set of Rx antennas to be used forthe at least one AI / ML-related operation, or atleast one measurement type in the setof measurementtypes to be used for the at least one AI / ML-related operation.

[0189] In another configuration, the first indication corresponds to a set of identifications associated with the set of antenna processing capabilities. In some implementations, the network entity 1402 may further include means for associating the set of identifications with the set of antenna processing capabilities, and means for transmitting, to the network entity, information related to the association of the set of identifications with the set of antenna processing capabilities.

[0190] In another configuration, the network entity 1402 may further include means for transmitting, to the network entity, a third indication of a setof recommended antenna configurations forthe at least one AI / ML-related operation, where the set of antenna configurations is based on the set of recommended antenna configurations.

[0191] In another configuration, the at least one AI / ML-related operation corresponds to at least one: a data collection for at least one AI / ML model, an AI / ML model development, training a first set of AI / ML models, performing inferencing using a second set of AI / ML models, checking consistency between different AI / ML related operations.

[0192] In another configuration, the at least one AI / ML-related operation corresponds to at least one AI / ML-based positioning operation.

[0193] In another configuration, the wireless device corresponds to one of a UE, a base station, a TRP, or a PRU, and the network entity corresponds to one of a server, an LMF, a sensing management function, an AI / ML management function, an OTT server, a training server, an NWDAF, or an 0AM system.129025-2422WO01Qualcomm Ref. No. 2405604WO 68

[0194] In another configuration, the network entity 1402 may further include means for transmitting a third indication of the set of antenna configurations.

[0195] The means may be the antenna capability indication component 199 of the network entity 1402 configured to perform the functions recited by the means. As described supra, the network entity 1402 may include the TX processor 316, the RX processor 370, and the controller / processor 375. As such, in one configuration, the means may be the TX processor 316, the RX processor 370, and / or the controller / processor 375 configured to perform the functions recited by the means.

[0196] FIG. 15 is a flowchart 1500 of a method of wireless communication. The methodmay be performed by a network entity (e.g., the one or more location servers 168; the location server 704; the network entity 1004, 1104, 1660). The method may enable the network entity to determine and provide a set of antenna configurations to a wireless device based on the antenna processing capabilities of the wireless device related to AI / ML positioning operations, thereby promotingor ensuring a consistency in antenna processing between different AI / ML operations (e.g., data collection, model training / retraining, inferencing, etc.).

[0197] At 1502, the network entity may receive, from a wireless device, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one AI / ML-related operation, such as described in connection with FIGs. 10 and 11. For example, at 1020 of FIG. 10, a network entity 1004 may receive, from aUE 1002, an indication 1008 that is indicative of (or related to) a set of antenna processing capabilities 1010 supported by the UE 1002 for AI / ML-related operation(s). Similarly, at 1120 of FIG. 11, a network entity 1004 may receive, from a base station or TRP (BS / TRP) 1106, an indication 1108 that is indicative of (or related to) a set of antenna processing capabilities 1110 supported by the BS / TRP 1106 for AI / ML- related operation(s). The reception of the first indication may be performed by, e.g, the antenna capability configuration component 197, the network processor(s) 1612, and / or the network interface 1680 of the network entity 1660 in FIG. 16.

[0198] At 1504, the network entity may transmit, to the wireless device in response to reception of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML- related operation, such as described in connection with FIGs. 10 and 11. For example, at 1022 of FIG. 10, based on the received indication 1008 (e.g., the antennaprocessing129025-2422WO01Qualcomm Ref. No. 2405604WO 69 capabilities 1010 of the UE 1002), the network entity 1004 may determine a set of antenna configurations 1014 to be applied by the UE 1002 for the AI / ML-related operation(s). For example, the set of antenna configurations 1014 may be related to how the UE 1002 is to handle multi-antenna Tx / Rx processing during AI / ML-related operation(s) (e.g., during AI / ML data collection). Then, at 1024, the network entity 1004 may transmit, to the UE 1002, an indication 1012 of the set of antenna configurations 1014. Similarly, at 1122 of FIG. 11, based on the received indication 1108 (e.g., the antennaprocessingcapabilities 11 lOoftheBS / TRP 1106), the network entity 1104 may determine a set of antenna configurations 1114 to be applied by the BS / TRP 1106 for the AI / ML-related operation(s). For example, the set of antenna configurations 1114 may be related to how the BS / TRP 1106 is to handle multiantenna Tx / Rx processing during AI / ML-related operation(s) (e.g., during AI / ML data collection). Then, at 1124, the network entity 1104 may transmit, to the BS / TRP 1106, an indication 1112 of the set of antenna configurations 1114. The transmission of the second indication may be performed by, e.g., the antenna capability configuration component 197, the network processor(s) 1612, and / or the network interface 1680 of the network entity 1660 in FIG. 16.

[0199] In one example, the network entity may transmit, to the wireless device, a request to participate in the at least one AI / ML-related operation, where the reception of the first indication is based on the request.

[0200] In another example, the set of antenna processing capabilities is related to a set of Tx antennas of the wireless device for a transmission of one or more reference signals, and where the set of antenna processing capabilities includes at least one of: (1) a number of Tx antennas supported for the at least one AI / ML-related operation, (2) a set of antenna co-phasing options supported for the at least one AI / ML-related operation, (3) a power splitting or weighting across the set of Tx antennas supported for the at least one AI / ML-related operation, or (4) a third indication of a list of antenna processing functions which the wireless is capable of performing. In some implementation, the one or more reference signals include at least one of : one or more PRS, one or more SRS, one or more SSB, one or more CSLRS, or one or more TRS. In some implementation, the set of antenna configurations includes at least one of a second number of Tx antennas to be used forthe atleast one AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be129025-2422WO01Qualcomm Ref. No. 2405604WO 70 used for the at least one AI / ML-related operation, or the power splitting or weighting across the set of Tx antennas to be used for the at least one AI / ML-related operation.

[0201] In another example, the set of antenna processing capabilities is related to a set of Rx antennas of the wireless device for a reception of one or more reference signals, and where the set of antenna processing capabilities includes at least one of: (1) a number of Rx antennas supported for the at least one AI / ML-related operation, (2) a set of antenna co-phasing options supported for the at least one AI / ML-related operation, (3) a power splitting or weighting across the set of Rx antennas supported for the at least one AI / ML-related operation, (4) a set of measurement types supported for the at least one AI / ML-related operation, or (5) a third indication of a list of antenna processing functions which the wireless is capable of performing. In some implementation, the one or more reference signals include atleast one of : one or more PRS, one or more SRS, one or more SSB, one or more CS RS, or one or more TRS. In some implementation, the set of antenna configurations includes at least one of: a second number of Rx antennas to be used for the atleast one AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, the power splitting or weighting across the set of Rx antennas to be used for the atleast one AI / ML-related operation, or at least one measurement type in the set of measurement types to be used for the at least one AI / ML-related operation.

[0202] In another example, the first indication corresponds to a set of identifications associated with the set of antenna processing capabilities. In some implementation, the network entity may receive, from the wireless device, information related to an association of the set of identifications with the set of antenna processing capabilities.

[0203] In another example, the network entity may receive, from the wireless device, a third indication of a set of recommended antenna configurations for the at least one AI / ML- related operation, where the set of antenna configurations is based on the set of recommended antenna configurations.

[0204] In another example, the at least one AI / ML-related operation corresponds to at least one: a data collection for at least one AI / ML model, an AI / ML model development, training a first set of AI / ML models, performing inferencing using a second set of AI / ML models, or checking consistency between different AI / ML related operations.129025-2422WO01Qualcomm Ref. No. 2405604WO 71

[0205] In another example, the at least one AI / ML-related operation corresponds to at least one AI / ML-based positioning operation.

[0206] In another example, the network entity corresponds to one of a server, an LMF, a sensing management function, an AI / ML management function, an OTT server, a training server, an NWDAF, or an 0AM system, and the wireless device corresponds to one of a UE, a base station, a TRP, or a PRU.

[0207] FIG. 16 is a diagram 1600 illustrating an example of a hardware implementation for a network entity 1660. In one example, the network entity 1660 may be within the core network 120. The network entity 1660 may include at least one network processor 1612. The network processor(s) 1612 may include on-chip memory 1612'. In some aspects, the network entity 1660 may further include additional memory modules 1614. The network entity 1660communicatesviathenetworkinterface l680 directly (e.g., backhaul link) or indirectly (e.g., through a RIC) with the CU 1602. The on-chip memory 1612' and the additional memory modules 1614 may each be considered a computer-readable medium / memory. Each computer-readable medium / memory may be non-transitory. The network processor(s) 1612 is responsible for general processing, including the execution of software stored on the computer- readable medium / memory. The software, when executed by the corresponding processor(s) causes the processor(s) to perform the various functions described supra. The computer-readable medium / memory may also be used for storing data that is manipulated by the processor(s) when executing software.

[0208] As discussed supra, the antenna capability configuration component 197 may be configured to receive, from a wireless device, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one AI / ML- related operation. The antenna capability configuration component 197 may also be configured to transmit, to the wireless device in response to reception of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the atleastone AI / ML-related operation. The antenna capability configuration component 197 may be within the network processor(s) 1612. The antenna capability configuration component 197 may be one or more hardware components specifically configured to carry out the stated processes / algorithm, implemented by one or more processors configured to perform the stated processes / algorithm, stored within a computer-readable medium for129025-2422WO01Qualcomm Ref. No. 2405604WO 72 implementation by one or more processors, or some combination thereof. When multiple processors are implemented, the multiple processors may perform the stated processes / algorithm individually or in combination. The network entity 1660 may include a variety of components configured for various functions. In one configuration, the network entity 1660 may include means for receiving, from a wireless device, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one AI / ML-related operation. The network entity 1660 may further include means for transmitting, to the wireless device in response to reception of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML-related operation.

[0209] In one configuration, the network entity 1660 may further include means for transmitting, to the wireless device, a request to participate in the at least one AI / ML- related operation, where the reception of the first indication is based on the request.

[0210] In another configuration, the set of antenna processing capabilities is related to a set of Tx antennas of the wireless device for a transmission of one or more reference signals, and where the set of antenna processing capabilities includes at least one of(1) a number of Tx antennas supported for the at least one AI / ML-related operation,(2) a set of antenna co-phasing options supported for the at least one AI / ML-related operation, (3) a power splitting or weighting across the set of Tx antennas supported for the at least one AI / ML-related operation, or (4) a third indication of a list of antenna processing functions which the wireless is capable of performing. In some implementation, the one or more reference signals include at least one of : one or more PRS, one or more SRS, one or more SSB, one or more CS RS, or one or more TRS. In some implementation, the set of antenna configurations includes at least one of a second number of Tx antennas to be used forthe atleast one AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, or the power splitting or weighting across the set of Tx antennas to be used for the at least one AI / ML-related operation.

[0211] In another configuration, the set of antenna processing capabilities is related to a set of Rx antennas of the wireless device for a reception of one or more reference signals, and where the set of antenna processing capabilities includes at least one of: (1) a number of Rx antennas supported for the at least one AI / ML-related operation, (2) a129025-2422WO01Qualcomm Ref. No. 2405604WO 73 set of antenna co-phasing options supported for the at least one AI / ML-related operation, (3) a power splitting or weighting across the set of Rx antennas supported forthe at least one AI / ML-related operation, (4) a set of measurementtypes supported for the at least one AI / ML-related operation, or (5) a third indication of a list of antenna processing functions which the wireless is capable of performing. In some implementation, the one or more reference signals include at least one of : one or more PRS, one or more SRS, one or more SSB, one or more CS RS, or one or more TRS. In some implementation, the set of antenna configurations includes at least one of: a second number of Rx antennas to be used forthe at least one AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, the power splitting or weighting across the set of Rx antennas to be used forthe at least one AI / ML-related operation, or at least one measurement type in the set of measurement types to be used for the at least one AI / ML-related operation.

[0212] In another configuration, the first indication corresponds to a set of identifications associated with the set of antenna processing capabilities. In some implementation, the network entity 1660 may further include means for receiving, from the wireless device, information related to an association of the set of identifications with the set of antenna processing capabilities.

[0213] In another configuration, the network entity 1660 may further include means for receiving, from the wireless device, a third indication of a set of recommended antenna configurations for the at least one AI / ML-related operation, where the set of antenna configurations is based on the set of recommended antenna configurations.

[0214] In another configuration, the at least one AI / ML-related operation corresponds to at least one: a data collection for at least one AI / ML model, an AI / ML model development, training a first set of AI / ML models, performing inferencing using a second set of AI / ML models, or checking consistency between different AI / ML related operations.

[0215] In another configuration, the at least one AI / ML-related operation corresponds to at least one AI / ML-based positioning operation.

[0216] In another configuration, the network entity corresponds to one of a server, an LMF, a sensing management function, an AI / ML management function, an OTT server, a129025-2422WO01Qualcomm Ref. No. 2405604WO 74 training server, an NWDAF, or an OAM system, and the wireless device corresponds to one of a UE, a base station, a TRP, or a PRU.

[0217] The means may be the antenna capability configuration component 197 of the network entity 1660 configured to perform the functions recited by the means.

[0218] It is understood that the specific order or hierarchy of blocks in the processes / flowcharts disclosed is an illustration of example approaches. Based upon design preferences, it is understood that the specific order or hierarchy of blocks in the processes / flowcharts maybe rearranged. Further, some blocks may be combined or omitted. The accompanying method claims present elements of the various blocks in a sample order, and are not limited to the specific order or hierarchy presented.

[0219] The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not limited to the aspects described herein, but are to be accorded the full scope consistent with the language claims. Reference to an element in the singular does not mean “one and only one” unless specifically so stated, but rather “one or more.” Terms such as “if,” “when,” and “while” do not imply an immediate temporal relationship or reaction. That is, these phrases, e.g., “when,” do notimply an immediate action in response to or during the occurrence of an action, but simply imply that if a condition is met then an action will occur, butwithoutrequiringa specific or immediate time constraint for the action to occur. The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects. Unless specifically stated otherwise, the term “some” refers to one or more. Combinations such as “at least one of A, B, or C,” “one or more of A, B, or C,” “at least one of A, B, and C,” “one or more of A, B, and C,” and “A, B, C, or any combination thereof’ include any combination of A, B, and / or C, and may include multiples of A, multiples of B, or multiples of C. Specifically, combinations such as “at least one of A, B, or C,” “one or more of A, B, or C,” “at least one of A, B, and C,” “one or more of A, B, and C,” and “A, B, C, or any combination thereof’ may be A only, B only, C only, A and B, A and C, B and C, or A and B and C, where any such combinations may contain one or more member or members of A, B, orC. Sets should be interpreted129025-2422WO01Qualcomm Ref. No. 2405604WO 75 as a set of elements where the elements number one or more. Accordingly, for a set of X, X would include one or more elements. When at least one processor is configured to perform a set of functions, the at least one processor, individually or in any combination, is configured to perform the set of functions. Accordingly, each processor of the at least one processor may be configured to perform a particular subset of the set of functions, where the subset is the full set, a proper subset of the set, or an empty subset of the set. A processor may be referred to as processor circuitry. A memory / memory module may be referred to as memory circuitry. If a first apparatus receives data from or transmits data to a second apparatus, the data may be received / transmitted directly between the first and second apparatuses, or indirectly between the first and second apparatuses through a set of apparatuses. A device configured to “output” data or “provide” data, such as a transmission, signal, or message, may transmit the data, for example with a transceiver, or may send the data to a device that transmits the data. A device configured to “obtain” data, such as a transmission, signal, or message, may receive, for example with a transceiver, or may obtain the data from a device that receives the data. Information stored in a memory includes instructions and / or data. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are encompassed by the claims. Moreover, nothing disclosed herein is dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. The words “module,” “mechanism,” “element,” “device,” and the like may not be a substitute for the word “means.” As such, no claim element is to be construed as a means plus function unless the element is expressly recited using the phrase “means for.”

[0220] As used herein, the phrase “based on” shall not be construed as a reference to a closed set of information, one or more conditions, one or more factors, or the like. In other words, the phrase “based on A” (where “A” may be information, a condition, a factor, or the like) shall be construed as “based at least on A” unless specifically recited differently.

[0221] The following aspects are illustrative only and may be combined with other aspects or teachings described herein, without limitation.129025-2422WO01Qualcomm Ref. No. 2405604WO 76

[0222] Aspect 1 is a method of wireless communication at a wireless device, comprising: transmitting, to a network entity, a first indication of a set of antenna processing capabilities supported by the wireless deviceforatleast one artificial intelligence (Al) or machine learning (ML) (AI / ML)-related operation; and receiving, from the network entity in response to transmission of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML-related operation.

[0223] Aspect 2 is the method of aspect 1, further comprising: applying the set of antenna configurations for the at least one AI / ML-related operation.

[0224] Aspect s is the method of aspect 1 or aspect 2, wherein applying the set of antenna configurations for the at least one AI / ML-related operation comprises: transmitting a first set of reference signals based on the set of antenna configurations for the at least one AI / ML-related operation; or receiving a second set of reference signals based on the set of antenna configurations for the at least one AI / ML-related operation.

[0225] Aspect 4 is the method of any of aspects 1 to 3, further comprising: receiving a request to participate in the at least one AI / ML-related operation, wherein the transmission of the first indication is based on the request.

[0226] Aspect 5 is the method of any of aspects 1 to 4, wherein the set of antenna processing capabilities is related to a set of transmission (Tx) antennas of the wireless device for a transmission of one or more reference signals, and wherein the set of antenna processing capabilities includes atleastone of : (1) a number of Tx antennas supported for the at least one AI / ML-related operation, (2) a set of antenna co-phasing options supported for the at least one AI / ML-related operation, (3) a power splitting or weighting across the set of Tx antennas supported for the atleast one AI / ML-related operation, or (4) a third indication of a list of antenna processing functions which the wireless is capable of performing.

[0227] Aspect 6 is the method of any of aspects 1 to 5, wherein the one or more reference signals include at least one of: one or more positioning reference signals (PRS), one or more sounding reference signals (SRS), one or more synchronization signal blocks (SSB), one or more channel state information (CSI) reference signals (CS RS), or one or more tracking reference signals (TRS).

[0228] Aspect 7 is the method of any of aspects 1 to 6, wherein the set of antenna configurations includes at least one of: a second number of Tx antennasto be used for129025-2422WO01Qualcomm Ref. No. 2405604WO 77 the at least one AI / ML-related operation, atleastone antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, or the power splitting or weighting across the set of Tx antennas to be used for the at least one AI / ML-related operation.

[0229] Aspect 8 is the method of any of aspects 1 to 7, wherein the set of antenna processing capabilities is related to a set of reception (Rx) antennas of the wireless device for a reception of one or more reference signals, and wherein the set of antenna processing capabilities includes at least one of: (1) a number of Rx antennas supported for the at least one AI / ML-related operation, (2) a set of antenna co-phasing options supported for the at least one AI / ML-related operation, (3) a power splitting or weighting across the set of Rx antennas supported for the at least one AI / ML-related operation, (4) a set of measurement types supported for the at least one AI / ML-related operation, or (5) a third indication of a list of antenna processing functions which the wireless is capable of performing.

[0230] Aspect 9 is the method of any of aspects 1 to 8, wherein the one or more reference signals include at least one of: one or more positioning reference signals (PRS), one or more sounding reference signals (SRS), one or more synchronization signal blocks (SSB), one or more channel state information (CSI) reference signals (CS RS), or one or more tracking reference signals (TRS).

[0231] Aspect 10 is the method of any of aspects 1 to 9, wherein the set of antenna configurations includes at least one of: a second number of Rx antennas to be used for the at least one AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, the power splitting or weighting across the set of Rx antennas to be used for the at least one AI / ML-related operation, or at least one measurement type in the set of measurement types to be used for the at least one AI / ML-related operation.

[0232] Aspect 11 is the method of any of aspects 1 to 10, wherein the first indication corresponds to a set of identifications associated with the set of antenna processing capabilities.

[0233] Aspect 12 is the method of any of aspects 1 to 11, further comprising: associating the set of identifications with the set of antenna processing capabilities; and transmitting to the network entity, information relatedto the association of the set of identifications with the set of antenna processing capabilities.129025-2422WO01Qualcomm Ref. No. 2405604WO 78

[0234] Aspect 13 is the method of any of aspects 1 to 12, further comprising: transmitting to the network entity, a third indication of a set of recommended antenna configurations for the at least one AI / ML-related operation, wherein the set of antenna configurations is based on the set of recommended antenna configurations.

[0235] Aspect 14 is the method of any of aspects 1 to 13, wherein the at least one AI / ML- related operation corresponds to at least one: a data collection for at least one AI / ML model, an AI / ML model development, training a first set of AI / ML models, performing inferencing using a second set of AI / ML models, checking consistency between different AI / ML related operations.

[0236] Aspect 15 is the method of any of aspects 1 to 14, wherein the at least one AI / ML- related operation corresponds to at least one AI / ML-based positioning operation.

[0237] Aspect 16 is the method of any of aspects 1 to 15, wherein the wireless device corresponds to one of a user equipment (UE), a base station, a transmission reception point (TRP), or a positioning reference unit (PRU), and wherein the network entity corresponds to one of a server, a location management function (LMF), a sensing management function, an AI / ML management function, an over-the-air (OTT) server, a training server, a network data analytics function (NWDAF), or an orchestration and management (0AM) system.

[0238] Aspect 17 is the method of any of aspects 1 to 16, further comprising: transmitting a third indication of the set of antenna configurations.

[0239] Aspect 18 is an apparatus for wireless communication at a wireless device, including: at least one memory; and at least one processor coupled to the at least one memory and, based at least in part on stored information that is stored in the at least one memory, the at least one processor, individually or in any combination, is configured to implement any of aspects 1 to 17.

[0240] Aspect 19 is the apparatus of aspect 18, further including at least one transceiver coupled to the at least one processor.

[0241] Aspect 20 is an apparatus for wireless communication at a wireless device including means for implementing any of aspects 1 to 17.

[0242] Aspect 21 is a computer-readable medium (e.g., a non-transitory computer-readable medium) storing computer executable code, where the code when executed by a processor causes the processor to implement any of aspects 1 to 17.129025-2422WO01Qualcomm Ref. No. 2405604WO 79

[0243] Aspect 22 is a method of wireless communication at a network entity, comprising: receiving, from a wireless device, a first indication of a set of antenna processing capabilities supported by the wireless deviceforatleast one artificial intelligence (Al) or machine learning (ML) (AI / ML)-related operation; and transmitting, to the wireless device in response to reception of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML-related operation.

[0244] Aspect 23 is the method of aspect 22, further comprising: receiving, transmitting to the wireless device, a request to participate in the at least one AI / ML-related operation, wherein the reception of the first indication is based on the request.

[0245] Aspect 24 is the method of aspect 22 or aspect 23, wherein the set of antenna processing capabilities is related to a set of transmission (Tx) antennas of the wireless device for a transmission of one or more reference signals, and wherein the set of antenna processing capabilities includes at least one of: (1) a number of Tx antennas supported for the atleastone AI / ML-related operation, (2) a set of antenna co-phasing options supported for the at least one AI / ML-related operation, (3) a power splitting or weighting across the setof Tx antennas supportedforthe atleastone AI / ML-related operation, or (4) a third indication of a list of antenna processing functions which the wireless is capable of performing.

[0246] Aspect 25 is the method of any of aspects 22 to 24, wherein the one or more reference signals include at least one of: one or more positioning reference signals (PRS), one or more sounding reference signals (SRS), one or more synchronization signal blocks (SSB), one or more channel state information (CSI) reference signals (CS RS), or one or more tracking reference signals (TRS).

[0247] Aspect 26 is the method of any of aspects 22 to 25, wherein the set of antenna configurations includes at least one of: a second number of Tx antennasto be used for the atleastone AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, orthe power splitting or weighting across the set of Tx antennas to beused for the at least one AI / ML-related operation.

[0248] Aspect 27 is the method of any of aspects 22 to 26, wherein the set of antenna processing capabilities is related to a set of reception (Rx) antennas of the wireless device for a reception of one or more reference signals, and wherein the setof antenna129025-2422WO01Qualcomm Ref. No. 2405604WO 80 processing capabilities includesatleast one of : (1) a number of Rx antennas supported for the at least one AI / ML-related operation, (2) a set of antenna co-phasing options supported for the at least one AI / ML-related operation, (3) a power splitting or weighting across the set of Rx antennas supported for the at least one AI / ML-related operation, (4) a set of measurement types supported for the atleast one AI / ML-related operation, or (5) a third indication of a list of antenna processing functions which the wireless is capable of performing.

[0249] Aspect 28 is the method of any of aspects 22 to 27, wherein the one or more reference signals include atleast one of: one or more positioning reference signals (PRS), one or more sounding reference signals (SRS), one or more synchronization signal blocks (SSB), one or more channel state information (CSI) reference signals (CS RS), or one or more tracking reference signals (TRS).

[0250] Aspect 29 is the method of any of aspects 22 to 28, wherein the set of antenna configurations includes at least one of: a second number of Rx antennas to be used for the at least one AI / ML-related operation, atleast one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, the power splitting or weighting across the set of Rx antennas to be used for the at least one AI / ML-related operation, or atleast one measurement type in the set of measurement types to be used for the at least one AI / ML-related operation.

[0251] Aspect 30 is the method of any of aspects 22 to 29, wherein the first indication corresponds to a set of identifications associated with the set of antenna processing capabilities.

[0252] Aspect 31 is the method of any of aspects 22 to 30, further comprising: receiving from the wireless device, information related to an association of the set of identifications with the set of antenna processing capabilities.

[0253] Aspect 32 is the method of any of aspects 22 to 31, further comprising: receiving from the wireless device, a third indication of a set of recommended antenna configurations forthe atleast one AI / ML-related operation, wherein the set of antenna configurations is based on the set of recommended antenna configurations.

[0254] Aspect 33 is the method of any of aspects 22 to 32, wherein the at least one AI / ML- related operation corresponds to at least one: a data collection for at least one AI / ML model, an AI / ML model development, training a first set of AI / ML models,129025-2422WO01Qualcomm Ref. No. 2405604WO 81 performinginferencing using a second set of AI / ML models, or checking consistency between different AI / ML related operations.

[0255] Aspect 34 is the method of any of aspects 22 to 33, wherein the at least one AI / ML- related operation corresponds to at least one AI / ML-based positioning operation.

[0256] Aspect 35 is the method of any of aspects 22 to 34, wherein the network entity corresponds to one of a server, a location management function (LMF), a sensing management function, an AI / ML management function, an over-the-air (OTT) server, a training server, a network data analytics function (NWDAF), or an orchestration and management (0AM) system, and wherein the wireless device corresponds to one of a user equipment (UE), a base station, a transmission reception point (TRP), or a positioning reference unit (PRU).

[0257] Aspect 36 is an apparatus for wireless communication at a network entity, including: at least one memory; and at least one processor coupled to the at least one memory and, based at least in part on stored information that is stored in the at least one memory, the at least one processor, individually or in any combination, is configured to implement any of aspects 22 to 35.

[0258] Aspect 37 is the apparatus of aspect 36, further including at least one network interface coupled to the at least one processor.

[0259] Aspect 38 is an apparatus for wireless communication at a network entity including means for implementing any of aspects 22 to 35.

[0260] Aspect 39 is a computer-readable medium (e.g., a non-transitory computer-readable medium) storing computer executable code, where the code when executed by a processor causes the processor to implement any of aspects 22 to 35.129025-2422WO01

Claims

1. Qualcomm Ref. No. 2405604WO 82CLAIMSWHAT IS CLAIMED IS:1 . An apparatus for wireless communication at a wireless device, comprising: at least one memory; and at least one processor coupled to the at least one memory, the at least one processor, individually or in any combination, is configured to: transmit, to a network entity, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one artificial intelligence (Al) or machine learning (ML) (AI / ML)-related operation; and receive, from the network entity in response to transmission of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied forthe atleast one AI / ML-related operation.

2. The apparatus of claim 1, wherein the at least one processor, individually or in any combination, is further configured to: apply the set of antenna configurations for the at least one AI / ML-related operation.

3. The apparatus of claim 2, wherein to apply the set of antenna configurations for the at least one AI / ML-related operation, the at least one processor, individually or in any combination, is configured to: transmit a first set of reference signals based on the set of antenna configurations for the at least one AI / ML-related operation; or receive a second set of ref erence signals b ased on the set of antennaconfigurations for the at least one AI / ML-related operation.

4. The apparatus of claim 1, wherein the at least one processor, individually or in any combination, is further configured to: receive a request to participate in the at least one AI / ML-related operation, wherein the transmission of the first indication is based on the request.129025-2422WO01Qualcomm Ref. No. 2405604WO 835. The apparatus of claim 1, wherein the set of antenna processing capabilities is related to a set of transmission (Tx) antennas of the wireless device for a transmission of one or more reference signals, and wherein the set of antenna processing capabilities includes at least one of:(1) a number of Tx antennas supported for the at least one AI / ML-related operation,(2) a set of antenna co-phasing options supported for the at least one AI / ML- related operation,(3) a power splitting or weighting across the set of Tx antennas supported for the at least one AI / ML-related operation, or(4) a third indication of a list of antenna processing functions which the wireless is capable of performing.

6. The apparatus of claim 5, wherein the one or more reference signals include at least one of: one or more positioning reference signals (PRS), one or more sounding reference signals (SRS), one or more synchronization signal blocks (SSB), one or more channel state information (CSI) reference signals (CSI-RS), or one or more tracking reference signals (TRS).

7. The apparatus of claim 5, wherein the set of antenna configurations includes at least one of: a second number of Tx antennas to be used for the at least one AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, or the power splitting or weighting across the set of Tx antennas to be used for the at least one AI / ML-related operation.129025-2422WO01Qualcomm Ref. No. 2405604WO 848. The apparatus of claim 1, wherein the set of antenna processing capabilities is related to a set of reception (Rx) antennas of the wireless device for a reception of one or more reference signals, and wherein the set of antenna processing capabilities includes at least one of:(1) a number of Rx antennas supported for the at least one AI / ML-related operation,(2) a set of antenna co-phasing options supported for the at least one AI / ML- related operation,(3) a power splitting or weighting across the set of Rx antennas supported for the at least one AI / ML-related operation,(4) a set of measurement types supported for the at least one AI / ML-related operation, or(5) a third indication of a list of antenna processing functions which the wireless is capable of performing.

9. The apparatus of claim 8, wherein the one or more reference signals include at least one of: one or more positioning reference signals (PRS), one or more sounding reference signals (SRS), one or more synchronization signal blocks (SSB), one or more channel state information (CSI) reference signals (CSI-RS), or one or more tracking reference signals (TRS).

10. The apparatus of claim 8, wherein the set of antenna configurations includes at least one of: a second number of Rx antennas to be used for the at least one AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, the power splitting or weighting across the set of Rx antennas to be used for the at least one AI / ML-related operation, or129025-2422WO01Qualcomm Ref. No. 2405604WO 85 at least one measurement type in the set of measurement types to be used for the at least one AI / ML-related operation.

11. The apparatus of claim 1, wherein the first indication corresponds to a set of identifications associated with the set of antenna processing capabilities.

12. The apparatus of claim 11, wherein the at least one processor, individually or in any combination, is further configured to: associate the set of identifications with the set of antenna processing capabilities; and transmit, to the network entity, information related to the association of the set of identifications with the set of antenna processing capabilities.

13. The apparatus of claim 1, wherein the at least one processor, individually or in any combination, is further configured to: transmit, to the network entity, a third indication of a set of recommended antenna configurations for the at least one AI / ML-related operation, wherein the set of antenna configurations is based on the set of recommended antenna configurations.

14. The apparatus of claim 1, wherein the at least one AI / ML-related operation corresponds to at least one: a data collection for at least one AI / ML model, an AI / ML model development, training a first set of AI / ML models, performing inferencing using a second set of AI / ML models, or checking consistency between different AI / ML related operations.

15. The apparatus of claim 1, wherein the at least one AI / ML-related operation corresponds to at least one AI / ML-based positioning operation.129025-2422WO01Qualcomm Ref. No. 2405604WO 8616. The apparatus of claim 1 , wherein the wireless device corresponds to one of a user equipment (UE), a base station, a transmission reception point (TRP), or a positioning reference unit (PRU), and wherein the network entity corresponds to one of a server, a location management function (LMF), a sensing management function, an AI / ML management function, an over-the-air (OTT) server, a training server, a network data analytics function (NWDAF), or an orchestration and management (OAM) system.

17. The apparatus of claim 1, wherein the at least one processor, individually or in any combination, is further configured to: transmit a third indication of the set of antenna configurations.

18. A method of wireless communication at a wireless device, comprising: transmitting, to a network entity, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one artificial intelligence (Al) or machine learning (ML) (ALML)-related operation; and receiving, from the network entity in response to transmission of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML-related operation.

19. An apparatus for wireless communication at a network entity, comprising: at least one memory; and at least one processor coupled to the at least one memory, the at least one processor, individually or in any combination, is configured to: receive, from a wireless device, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one artificial intelligence (Al) or machine learning (ML) (AI / ML)-related operation; and transmit, to the wireless device in response to reception of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied forthe atleast one AI / ML-related operation.129025-2422WO01Qualcomm Ref. No. 2405604WO 8720. The apparatus of claim 19, wherein the at least one processor, individually or in any combination, is further configured to: transmit, to the wireless device, a request to participate in the at least one AI / ML- related operation, wherein the reception of the first indication is based on the request.21 . The apparatus of claim 19, wherein the set of antenna processing capabilities is related to a set of transmission (Tx) antennas of the wireless device for a transmission of one or more reference signals, and wherein the set of antenna processing capabilities includes at least one of:(1) a number of Tx antennas supported for the at least one AI / ML-related operation,(2) a set of antenna co-phasing options supported for the at least one AI / ML- related operation,(3) a power splitting or weighting across the set of Tx antennas supported for the at least one AI / ML-related operation, or(4) a third indication of a list of antenna processing functions which the wireless is capable of performing.

22. The apparatus of claim 21, wherein the set of antenna configurations includes at least one of: a second number of Tx antennas to be used for the at least one AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, or the power splitting or weighting across the set of Tx antennas to be used for the at least one AI / ML-related operation.

23. The apparatus of claim 19, wherein the set of antenna processing capabilities is related to a set of reception (Rx) antennas of the wireless device for a reception of one or more reference signals, and wherein the set of antenna processing capabilities includes at least one of:129025-2422WO01Qualcomm Ref. No. 2405604WO 88(1) a number of Rx antennas supported for the at least one AI / ML-related operation,(2) a set of antenna co-phasing options supported for the at least one AI / ML- related operation,(3) a power splitting or weighting across the set of Rx antennas supported for the at least one AI / ML-related operation,(4) a set of measurement types supported for the at least one AI / ML-related operation, or(5) a third indication of a list of antenna processing functions which the wireless is capable of performing.

24. The apparatus of claim 23, wherein the set of antenna configurations includes at least one of: a second number of Rx antennas to be used for the at least one AI / ML-related operation, at least one antenna co-phasing option in the set of antenna co-phasing options to be used for the at least one AI / ML-related operation, the power splitting or weighting across the set of Rx antennas to be used for the at least one AI / ML-related operation, or at least one measurement type in the set of measurement types to be used for the at least one AI / ML-related operation.

25. The apparatus of claim 19, wherein the first indication corresponds to a set of identifications associated with the set of antenna processing capabilities.

26. The apparatus of claim 25, wherein the at least one processor, individually or in any combination, is further configured to: receive, from the wireless device, information related to an association of the set of identifications with the set of antenna processing capabilities.129025-2422WO01Qualcomm Ref. No. 2405604WO 8927. The apparatus of claim 19, wherein the at least one processor, individually or in any combination, is further configured to: receive, from the wireless device, a third indication of a set of recommended antenna configurations for the at least one AI / ML-related operation, wherein the set of antenna configurations is based on the set of recommended antenna configurations.

28. The apparatus of claim 19, wherein the at least one AI / ML-related operation corresponds to at least one: a data collection for at least one AI / ML model, an AI / ML model development, training a first set of AI / ML models, performing inferencing using a second set of AI / ML models, or checking consistency between different AI / ML related operations.

29. The apparatus of claim 19, wherein the at least one AI / ML-related operation corresponds to at least one AI / ML-based positioning operation.

30. A method of wireless communication at a network entity, comprising: receiving, from a wireless device, a first indication of a set of antenna processing capabilities supported by the wireless device for at least one artificial intelligence (Al) or machine learning (ML) (AI / ML)-related operation; and transmitting, to the wireless device in response to reception of the first indication of the set of antenna processing capabilities, a second indication of a set of antenna configurations to be applied for the at least one AI / ML-related operation.129025-2422WO01

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