Artificial intelligence or machine learning model transfer for sidelink positioning

Abstract

Aspects presented herein may enable the first user equipment (UE) and a second UE to transfer artificial intelligence (AI) or machine learning (ML) (AI / ML) models to each other based on specified conditions to promote a consistency in AI / ML models used by the UEs in the same area or from the same vendor. In one aspect, a first UE receives, from a second UE, capability information of the second UE related to AI / ML for sidelink (SL) positioning. The first UE communicates, based on the capability information, a request for a transferring of a set of AI / ML models. The first UE performs, with the second UE based on the request, the transferring of the set of AI / ML models for the SL positioning.

Description

Qualcomm Ref. No. 2404738WO 1ARTIFICIAL INTELLIGENCE OR MACHINE LEARNING MODEL TRANSFER FOR SIDELINK POSITIONINGCROSS-REFERENCE TO RELATED APPLICATION(S)

[0001] This application claims the benefit of Greek Patent Application Serial No. 20240100638, entitled “ARTIFICIAL INTELLIGENCE OR MACHINE LEARNING MODEL TRANSFER FOR SIDELINK POSITIONING” and filed on September 19, 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.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, scalability (e.g., with Internet of Things (IoT)), and other requirements. 5G NR129025-2405W001Qualcomm Ref. No. 2404738WO 2 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 receives, from a second user equipment (UE), capability information of the second UE related to artificial intelligence (Al) or machine learning (ML) (AI / ML) for sidelink (SL) positioning. The apparatus communicates, based on the capability information, a request for a transferring of a set of AI / ML models. The apparatus performs, with the second UE based on the request, the transferring of the set of AI / ML models for the SL positioning.

[0008] In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus receives, from a first UE, capability information of a second UE related to AI / ML for SL positioning. The apparatus transmits, based on the capability information, an indication of a list of AI / ML models that can be transferred to the second UE or at least one AI / ML model in list the of AI / ML models.129025-2405W001Qualcomm Ref. No. 2404738WO 3

[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.

[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.129025-2405W001Qualcomm Ref. No. 2404738WO 4

[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 diagram illustrating an example data monitoring related to AI / ML air interface and AI / ML positioning in accordance with various aspects of the present disclosure.

[0026] FIG. 11 is a communication flow illustrating an example of an AI / ML model transfer procedure for UEs in accordance with various aspects of the present disclosure.

[0027] FIG. 12 is a communication flow illustrating an example of configuring one or more default AI / ML models at a UE in accordance with various aspects of the present disclosure.

[0028] FIG. 13 is a communication flow illustrating an example of a network entity configuring one or more default AI / ML models at a UE in accordance with various aspects of the present disclosure.

[0029] FIG. 14 is a flowchart of a method of wireless communication.

[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 apparatus and / or network entity.

[0032] FIG. 17 is a flowchart of a method of wireless communication.

[0033] FIG. 18 is a diagram illustrating an example of a hardware implementation for an example network entity.129025-2405W001Qualcomm Ref. No. 2404738WO 5DETAILED DESCRIPTION

[0034] Various aspects relate generally to rules and configurations related to artificial intelligence (Al) or machine learning (ML) (AI / ML) model transferring for user equipments (UEs). Aspects presented herein may also promote UEs that are in proximity to each other or in a specified area to use the same or similar AI / ML model(s) for positioning, thereby enabling the AI / ML training and inferencingto be more consistent. For example, in one aspect of the present disclosure, a first UE or a network entity may be configured to transfer a set of AI / ML positioning models to at least a second UE based on a set of criteria, such as the capability of the second UE related to AI / ML processing, the range between the first UE and the second UE, the priority associated with the set of AI / ML positioning models and / or the second UE, and / or the power class of the second UE, etc.

[0035] Particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the following potential advantages. UEs in one area and from one vendor are most likely to share one or more common AI / ML positioning model(s). However, UEs may differ in their capabilities and complexity in apply AI / ML monitoring. For example, depending on battery status, some UEs may not be able to perform a heavy or complex monitoring process. In another example, depending on the processing capability and / or memory capability, some UEs may have limited buffer or processing capacities to perform frequent AI / ML monitoring For example, when a plurality of UEs are configured to perform positioning over SL- based on AI / ML, each of the plurality of UEs may be specified to run at least one AI / ML positioning model. However, due to different memory and processing capabilities, different class capabilities, different priorities, and / or different subscription plans, different UEs are likely to select / apply different AI / ML models for the SL positioning. Aspects presented herein may improve the overall performance and efficiency of AI / ML positioning by enabling one or more AI / ML models to be transferred to UEs for positioning based on the capabilities of the UEs. Aspects presented herein may also promote UEs that are in proximity to each other or in a specified area to use the same or similar AI / ML model(s) for positioning, thereby enabling the AI / ML training and inferencing to be more consistent.

[0036] The detailed description set forth below in connection with the drawings describes various configurations and does not represent the only configurations in which the129025-2405W001Qualcomm Ref. No. 2404738WO 6 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.

[0037] 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.

[0038] 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.

[0039] 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 one129025-2405W001Qualcomm Ref. No. 2404738WO 7 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.

[0040] 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-level components, systems, distributed arrangements, aggregated or disaggregated components, end-user devices, etc. of varying sizes, shapes, and constitution.129025-2405W001Qualcomm Ref. No. 2404738WO 8

[0041] 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.

[0042] 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).

[0043] 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.

[0044] FIG. 1 is a diagram 100 illustrating an example of a wireless communications system and an access network. The illustrated wireless communications system includes a129025-2405W001Qualcomm Ref. No. 2404738WO 9 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.

[0045] 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.

[0046] 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)), 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 can129025-2405W001Qualcomm Ref. No. 2404738WO 10 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.

[0047] 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.

[0048] 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.

[0049] 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). For 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) to129025-2405W001Qualcomm Ref. No. 2404738WO 11 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.

[0050] 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.

[0051] 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).

[0052] 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, 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 base129025-2405W001Qualcomm Ref. No. 2404738WO 12 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).

[0053] 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 on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard, LTE, or NR.129025-2405W001Qualcomm Ref. No. 2404738WO 13

[0054] 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.

[0055] 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.

[0056] 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 5 G NR op eration b ey ond 52.6 GHz . For example, three higher op erating b ands 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.

[0057] 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 herein 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.129025-2405W001Qualcomm Ref. No. 2404738WO 14

[0058] 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.

[0059] 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).

[0060] 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, user identification handling, access authorization, and subscription management. The one or more location servers 168 are illustrated as including a Gateway Mobile Location129025-2405W001Qualcomm Ref. No. 2404738WO 15Center (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.

[0061] 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 also 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, a129025-2405W001Qualcomm Ref. No. 2404738WO 16 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.

[0062] Referring again to FIG. 1 , in certain aspects, the UE 104 may have a model transfer component 198 that may be configured to receive, from a second UE, capability information of the second UE related to AI / ML for SL positioning; communicate, based on the capability information, a request for a transferring of a set of AI / ML models; and perform, with the second UE based on the request, the transferring of the set of AI / ML models for the SL positioning. In certain aspects, the one or more location servers 168 may have a model transfer configuration component 197 that may be configured to receive, from a first UE, capability information of a second UE related to AI / ML for SL positioning; and transmit, based on the capability information, an indication of a list of AI / ML models that can be transferred to the second UE or at least one AI / ML model in list the of AI / ML models. In certain aspects, the base station 102 may have a model transfer configuration component 199 that may be configured to receive, from a first UE, capability information of a second UE related to AI / ML for SL positioning; and transmit, based on the capability information, an indication of a list of AI / ML models that can be transferred to the second UE or at least one AI / ML model in list the of AI / ML models.

[0063] 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 280 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 and UL. In the examples provided by FIGs. 2A, 2C, the 5G NR frame structure is assumed to be TDD, with subframe 4 being129025-2405W001Qualcomm Ref. No. 2404738WO 17 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.

[0064] 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.129025-2405W001Qualcomm Ref. No. 2404738WO 18Table 1: Numerology, SCS, and CP

[0065] 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).

[0066] 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.

[0067] 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 may129025-2405W001Qualcomm Ref. No. 2404738WO 19 also include beam measurement RS (BRS), beam refinement RS (BRRS), and phase tracking RS (PT-RS).

[0068] 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.

[0069] 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 the129025-2405W001Qualcomm Ref. No. 2404738WO 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.

[0070] 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.

[0071] 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),129025-2405W001Qualcomm Ref. No. 2404738WO 21 demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through HARQ, priority handling, and logical channel prioritization.

[0072] 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.

[0073] 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 a129025-2405W001Qualcomm Ref. No. 2404738WO 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.

[0074] 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.

[0075] 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.

[0076] 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.129025-2405W001Qualcomm Ref. No. 2404738WO 23

[0077] 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.

[0078] 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.

[0079] 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 model transfer component 198 of FIG. 1.

[0080] 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 model transfer configuration component 199 of FIG. 1.

[0081] 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 various aspects of the present disclosure . The UE 404 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 404129025-2405W001Qualcomm Ref. No. 2404738WO 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.

[0082] 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.

[0083] 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. For129025-2405W001Qualcomm Ref. No. 2404738WO 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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 to129025-2405W001Qualcomm Ref. No. 2404738WO 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.”

[0089] 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.

[0090] 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.

[0091] 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 #i129025-2405W001Qualcomm Ref. No. 2404738WO 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.

[0092] “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.

[0093] “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.

[0094] “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 receiver129025-2405W001Qualcomm Ref. No. 2404738WO 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

[0095] “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.

[0096] 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.

[0097] 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 UE. 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.).129025-2405W001Qualcomm Ref. No. 2404738WO 29

[0098] 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.”

[0099] 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, and the UE may determine its position using an AI / ML model based on the PRS measurements. In another example, an LMF may receive PRS measurements from a UE or SRS measurements from a baes station, and the LMF may determine the position of the UE using an AI / ML model based on the PRS / SRS measurements.

[0100] 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 may129025-2405W001Qualcomm Ref. No. 2404738WO 30 transmit the reference signal measurements to a location server, such as an LMF. 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 to determine the position of the UE. For example, a UE may receive and measure PRSs transmitted from 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- 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 may receive and measure SRSs transmitted from a UE, and the baes 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.

[0101] 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 TRPs, where one AI / ML model may be configured for each TRP (referringto 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.

[0102] In another example, as shown at 612, different AI / ML models may be used for multiple 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 / ML129025-2405W001Qualcomm Ref. No. 2404738WO 31Model BQ for inferring the To A of the first TRP, CIR of the second TRPmay 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 input to 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.

[0103] In another example, as shown at 614, one AI / ML model may be used for multiple TRPs (referring to as a “multi-TRP” setting). For example, CIRs from the N TRPs may be input to one AI / ML model (e.g., AI / ML Model C), andthe AI / ML modelmay infer the ToA for each TRP. 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).

[0104] FIG. 7 is a diagram 700 illustrating an example of UE-based positioning with UE-sideAI / 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 measurements)” and / or “PRS-based measurement(s).” 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.

[0105] 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 702129025-2405W001Qualcomm Ref. No. 2404738WO 32 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 or assist measurement(s) of DL reference signals. For example, the UE 702 may receive and measure a set of PRSs transmitted from a base station 706 with the assistance of the at least one AI / ML model 708, which may be referred to as “PRS-based measurement(s).” Then, the UE 702 may transmit the PRS-based measurement(s) to a location server 704, such as an LMF. 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).

[0106] 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. 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.

[0107] 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)”) 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. 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).

[0108] 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 in129025-2405W001Qualcomm Ref. No. 2404738WO 33 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.

[0109] Table 2 below provides an example list of positioningmethods thatmay be supported by a UE and / or a network entity.129025-2405W001Qualcomm Ref. No. 2404738WO 34Table 2 - Example of supported UE positioning methods

[0110] 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 positioningdescribed in connection with FIGs. 8 A and 9 A, 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-sight129025-2405W001Qualcomm Ref. No. 2404738WO 35(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.

[0111] FIG. 10 is a diagram 1000 illustrating an example data monitoring related to AI / ML air interface and AI / ML positioning in accordance with various aspects of the present disclosure. In some implementations, as shown at 1002, in addition to performing AI / ML data training at 1010 and / or data inf erencing at 1020, an AI / ML model may also be configured to perform data monitoring. For purposes of the present disclosure, at a high-level, AI / ML performance monitoring, AI / ML model monitor, and / or AI / ML data monitoring, etc. (collectively as “AI / ML monitoring” hereafter) may refer to monitoring the overall quality of at least one AI / ML model, which may also include monitoring inputs for the at least one AI / ML model and / or outputs from 1he at least one AI / ML model. For example, AI / ML monitoring may include monitoring the accuracy of positioning or positioning measurements performed by an AI / ML model, monitoring data that is used for training an AI / ML model, monitoring whether an AI / ML model is suitable under a set of specified conditions or under a specified environment, etc. There may be a variety of configurations for AI / ML model monitoring in lifecycle management, which may include: (1) monitoring based on inference accuracy (including metrics related to intermediate key performance indicators (KPIs)), (2) monitoring based on system performance (including metrics related to system performance KPIs), (3) monitoringbased on data distribution, which may be input-based, e.g., monitoring the validity of the AI / ML input, e.g., out-of- distribution detection, drift detection of input data, or SNR, delay spread, etc., and / or output-based: e.g., drift detection of output data, (4) monitoring based on applicable condition. The monitoring metric calculation may be performed at the network (e.g, an LMF, a base station, etc.) or at the UE.

[0112] Methods to assess or monitor the applicability and expected performance of an inactive AI / ML model / functionality may including the following examples for the purpose of activation, selection, and / or switching of UE-side models / UE-part of two- sided models / functionalities (if applicable): (1) assessment and / or monitoring based on the additional conditions associated with the model / functionality, (2) assessment129025-2405W001Qualcomm Ref. No. 2404738WO 36 and / or monitoring based on input / output data distribution, (3) assessment and / or monitoring using the inactive model / functionality for monitoring purpose and measuring the inference accuracy, and / or (4) assessment and / or monitoring based on past knowledge of the performance of the same model / functionality (e.g., based on other UEs).

[0113] AI / ML positioning (both direct and assisted) has been shown to provide high positioning accuracy in stringent NLOS conditions. As such, deployments and applications of AI / ML models for positioning are expected to increase. 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, an “AI / ML functionality” may refer to a functionality of an AI / ML model or a functionality related to AI / ML-based positioning. Depending on the context, sometimes the term “AI / ML model” may be used interchangeably with the term “AI / ML functionality.”

[0114] In some scenarios, UEs in one area and from one vendor are most likely to share one or more common AI / ML positioning model(s). However, UEs may differ in their capabilities and complexity in apply AI / ML monitoring. For example, depending on battery status, some UEs may not be able to perform a heavy or complex monitoring process. In another example, depending on the processing capability and / or memory capability, some UEs may have limited buffer or processing capacities to perform frequent AI / ML monitoring. For example, when a plurality of UEs are configured to perform positioning over sidelink (SL) (which may be referred to as “sidelink (SL) positioning” hereafter) based on AI / ML, each of the plurality of UEs may be specified to run at least one AI / ML positioning model. However, due to different memory and processing capabilities, different class capabilities, different priorities, and / or different sub scription plans, different UEs are likely to select / apply different AI / ML models for the SL positioning.

[0115] Aspects presented herein may improve the overall performance and efficiency of AI / ML positioning by enabling one or more AI / ML models to be transferred to UEs for positioning based on the capabilities of the UEs. Aspects presented herein may also promote UEs that are in proximity to each other or in a specified area to use the129025-2405W001Qualcomm Ref. No. 2404738WO 37 same or similar AI / ML model(s) for positioning, thereby enabling the AI / ML training and inferencingto be more consistent. In one aspect of the present disclosure, a first UE or a network entity may be configured to transfer a set of AI / ML positioning models to at least a second UE based on a set of criteria, such as the capability of the second UE related to AI / ML processing, the range between the first UE and the second UE, the priority associated with the set of AI / ML positioning models and / or the second UE, and / or the power class of the second UE, etc.

[0116] FIG. 11 is a communication flow 1100 illustrating an example of an AI / ML model transfer procedure for UEs 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. For purposes of illustrations, a first entity that is configured to transfer AI / ML model(s) to a plurality of second entities may be referred to as an anchor UE or a first UE. However, depending on implementations or the context, the first entity may also be a network entity, such as an LMF, a base station, a TRP of a base station, etc. The second entity may be referred to as an SL UE. In some scenarios, an SL UE may also be configured / specified to provide AI / ML model(s) to an anchor UE (discussed below). The signaling between the entities may be based on a wireless communication channel, such as Uu (a wide area network LIE interface), PC5, and / or sidelink, etc. Also, while examples below show AI / ML model(s) are transferred directly between entities (e.g., from a first UE to a second UE via a direct SL link), the AI / ML model(s) may also be transferred from one entity to another entity via a relay (e.g., via SL relay node(s) if supported).

[0117] At 1120, one or more UEs, such as a first SL UE 1104, a second SL UE, a third SL UE, and up to an NthSL UE, may be configured to providetheir capability information related to AI / ML positioning (e.g., using at least one AI / ML model in association with positioning of a target) to an anchor UE 1102. For ease of illustration, the following aspects are described from the perspective of the first SL UE 1104. However, they are also applicable to other UEs in the one or more UEs (e.g., to the second SL UE, the third SL UE, and / or to the NthSL UE, etc.). In some examples, the capability information related to AI / ML positioning may also be referred to as “AI / ML processing capabilities.”129025-2405W001Qualcomm Ref. No. 2404738WO 38

[0118] Depending on implementations, the capability information / AI / ML processing capabilities of the first SL UE 1104 may include:(1) AI / ML processing capabilities of the first SL UE 1104 (e.g., the ability of the first SL UE 1104 to process an AI / ML model with certain sizes, complexities, etc.),(2) a number of AI / ML models supported by the first SL UE 1104 (e.g., a maximum number of AI / ML models that may be run by the first SL UE 1104 currently or for a period of time, a number of AI / ML models that may be run by the first SL UE 1104 simultaneously, etc.),(3) size(s) of AI / ML models supported by the first SL UE 1104 (e.g., a maximum size of an AI / ML model supported by the first SL UE 1104, a maximum aggregated size of multiple AI / ML models supported by the first SL UE 1104, etc.),(4) a complexity of AI / ML models supported (e.g., an indication of whether the first SL UE 1104 is capable of running an AI / ML model that demands a large amount of measurements, computation, and / or processing, etc.),(5) types of AI / ML models supported by the first SL UE 1104 (e.g., an AI / ML model that derives the position of atargetusing a specified type of input or measurement, an AI / ML model thatoutputs a specified data orpositionformat, an AI / MLmodel that is suitable for smartphone, vehicle, or smartwatches, etc.),(6) reference signal (RS) processing capabilities (e.g., types of RS that may be processed by the first SL UE 1104, an amount of RS that may be process by the first SL UE 1104 simultaneously, the maximum bandwidth for receiving the RS, etc.),(7) memory capabilities for processing AI / ML models (e.g., a maximum volatile / non-volatile memory of the first SL UE 1104 that may be used for processing AI / ML model(s)),(8) a list of positioning methods supported for AI / ML-based processing (e.g., positioning mechanism(s) supported by the first SL UE 1104 such as discussed in connection with FIG. 4),(9) the capability to receive one or more AI / ML models from the anchor UE 1102 (e.g., an indication of whether the first SL UE 1104 is capable of receiving and running an AI / ML model from another UE), or(10) a combination of above.129025-2405W001Qualcomm Ref. No. 2404738WO 39

[0119] At 1122, based on the capability information of the first SL UE 1104, the anchor UE 1102 may transmit, to the first SL UE 1104, a request for transferring a set of AI / ML models. The request for transferring a set of AI / ML models may be a request to transmit at least one AI / ML model from the anchor UE 1102 to the first SL UE 1104, a request to receive at least one AI / ML model from the first SL UE 1104 (e.g., for monitoring the at least one AI / ML model at the anchor UE 1102, for distributing to other SL UEs, etc.), or both.

[0120] In some implementations, at 1122, the first SL UE 1104 may also transmit the request for transmitting the set of AI / ML models to the anchor UE 1102 (and without transmitting its capability information at 1120). For example, the first SL UE 1104 may just request the anchor UE 1102 to provide the set of AI / ML models to the first SL UE 1104, and / or to receive the set of AI / ML models from first SL UE 1104, etc.

[0121] In some examples, if the request for transferring the set of AI / ML models is based on the capability information of the first SL UE 1104, the request may also include: (1) type(s) of the AI / ML model(s) to be transferred, (2) size(s) of the AI / ML model(s) to be transferred, (3) input(s) for the AI / ML model(s) to be transferred, (4) output(s) for the AI / ML model(s) to be transferred, (5) a list of use cases for the AI / ML model(s) to be transferred (e.g., a list of conditions, environments or scenarios that is suitable for using the AI / ML model(s)), (6) an age or a validity period of the AI / ML model(s) to be transferred (e.g., a time or a duration in which an AI / ML model can be used), (7) a list of tasks to be performedby the AI / ML model to be transferred (e.g., a list of tasks that is capable of performed by the AI / ML model(s)), or a combination thereof.

[0122] At 1124, based on the request for transferring the set of AI / ML models, the anchor UE 1102 and the first SL UE 1104 may perform the transferring of the set of AI / ML models (e.g., for SL positioning related operations). For example, if the request includes transmitting at least one AI / ML model from the anchor UE 1102 to the first SL UE 1104, then the anchor UE 1102 may transmit the at least one AI / ML model to the first SL UE 1104. If the request includes receiving at least one AI / ML model from the first SL UE 1104, then the first SL UE 1104 may transmit the at least one AI / ML model to the anchor UE 1102.

[0123] In some examples, as shown at 1126, prior to transmitting the request for transferring the set of AI / ML models, the anchor UE 1102 may determinethe set of AI / ML models to be transferred to the first SL UE 1104 based on the capability information of the129025-2405W001Qualcomm Ref. No. 2404738WO 40 first SL UE 1104 and also based on a set of conditions. Depending on implementations, the set of conditions may include:(1) a range / distance between the anchor UE 1102 and the first SL UE 1104,(2) a priority associated with the set of AI / ML models,(3) a priority associated with the first SL UE 1104,(4) the set of AI / ML models being a set of transferable AI / ML models, a set of standard AI / ML models, or a set of non-premier AI / ML models,(5) a power class associated with the set of AI / ML models,(6) a power class associated with the first SL UE 1104, or(7) a combination thereof (each one of them will be discussed in details below). In other words, the anchor UE 1102 is configured to determine at least one AI / ML model to be sent to the first SL UE 1104 based on the capability information of the first SL UE 1104, and then provide the at least one AI / ML model to the first SL UE 1104 through direct signaling as discussed in connection with 1122 and 1124.

[0124] In some examples, as shown at 1128, the anchor UE 1102 may receive, from a network entity 1106 (e.g., a server, a base station, a TRP, a location management function (LMF), an AI / ML management function, a sensing function, an operations, administration, and maintenance (0AM) entity, or a core entity, etc.), an indication of a list of AI / ML models that may be transferred to the anchor UE 1102 and / or the set of conditions. For example, the set of conditions may indicate a range restriction associated with each AI / ML model in the set of AI / ML models, a set of priorities associated with the set of AI / ML models, a set of priorities associated with a set of UEs, and / or a set of power classes specifications associated with the set of AI / ML models, etc. (discussed below).

[0125] In other words, the anchor UE 1102 may send the capability information of the first SL UE 1104 to the network entity 1106. In some scenarios, the anchor UE 1102 may also be the network entity 1106 (e.g., an LMF). Then, based on the capability information received, the network entity 1106 may provide, to the anchor UE 1102, a list of AI / ML models that may be transferred to the first SL UE 1104. In addition, the network entity 1106 may configure the anchor UE 1102 with instructions (e.g. , the set of conditions) on which AI / ML model(s) to pass on to other / different SL UEs (e.g., as the anchor UE 1102 may be configured to provide AI / ML models to N SL UEs),129025-2405W001Qualcomm Ref. No. 2404738WO 41 or the network entity 1106 may also directly configure those AI / ML models on the individual UEs.

[0126] In some examples, while not shown in the communication flow 1100, the network entity 1106 may also be able to provide one or more AI / ML models to the first SL UE 1104 directly, and the network entity 1106 may receive the one or more AI / ML models from the anchor UE 1102 (e.g., the one or more AI / ML models may be existing AI / ML model(s) running at the anchor UE 1102 or received by the anchor UE 1102 from other SL UE(s)).

[0127] In another example, as shown at 1130, after the anchor UE 1102 or the first SL UE 1104 transmits the requestfortransferringthe set of AI / ML models at 1122 (and prior to the transferring of the set of AI / ML models at 1124), the first SL UE 1104 or the first SL UE 1104 may be configured to transmit an indication of whether it accepts the request. For example, the first SL UE 1104 or the anchor UE 1102 may transmit an indication of an acknowledgement (ACK) or a negative acknowledgement (NACK) to indicate whether it accepts the request. Then, the entity that transmits the request (e.g., the anchor UE 1102 or the first SL UE 1104) may start the transferring of the set of AI / ML models after receiving an ACK from the entity that receives the request.

[0128] In another example, as shown at 1132, the anchor UE 1102 may be configured to transmitthe capability information ofthe first SLUE 1104 to the network entity 1106, or as an alternative, the first SL UE 1104 may be configured to transmit its capability information directly to the network entity 1106 as shown at 1134. Then, based on the capability information of the first SL UE 1104, the network entity 1106 may provide, to the anchor UE 1102, a list of AI / ML models that may be transferred from the anchor UE 1102 to the first SL UE 1104 (e.g., at 1128). In other words, the network entity 1106 may determine the set of AI / ML models to be transferred to the first SL UE 1104 (instead of determined by the anchor UE 1102). However, the network entity 1106 may also just provide a list of AI / ML models that can be transferred to the first SL UE 1104. Then, the anchor UE 1102 may select the set of AI / ML models to be transferred to the first SL UE 1104 from this list (e.g., such as based on the set of conditions at 1126).

[0129] In some examples, to reduce signaling overhead, each AI / ML model or each AI / ML model with a specific set of configurations may be associated with a model129025-2405W001Qualcomm Ref. No. 2404738WO 42 identification (ID) or a function (e.g., for positioning based on AoA, TDoA, RTT, etc.). For example, an ID X0001 may indicate an AI / ML model that is capable of performing SL positioning in a first geographical area based on AoA, an ID X0002 may indicate an AI / ML model that is capable of performing SL positioning in a second geographical area based on AoA, etc. Then, to provide the list of AI / ML models that may be transferred to the first SL UE 1104, the network entity 1106 may just indicate a list of IDs / functions that corresponds to the list of AI / ML models. Each AI / ML model in the list of AI / ML models may include a validity timer (e.g., provided / configured by the network entity 1106), where the validity timer may be indicative of a time or a duration in which the first SL UE 1104 is able to use the AI / ML model, e.g., until a specific time / date (e.g., until August 12, 2025), for a specific duration (e.g., for 10 hours, 24 hours, etc.), or just for a specific duration of a day (e.g., between 10 AM to 8 PM), etc.

[0130] In some examples, as shown at 1136, after transferring of the set of AI / ML models, the entity that receives the set of AI / ML models may be configured to run the set of AI / ML models, and provide, a set of feedback / monitoring reports related to the set of AI / ML models, and / or a set of measurements performed by the set of AI / ML models. For example, if the anchor UE 1102 transfers an AI / ML model for SL positioning to the first SL UE 1104, the first SL UE 1104 may be configured to run the AI / ML for the SL positioning, and then provide a feedback for the AI / ML model to the anchor UE 1102 (e.g., an indication of whether the AI / ML model is accurate, is able to be successfully run by the first SL UE 1104, is able to generate outputs specified, etc.).

[0131] FIG. 12 is a communication flow 1200 illustrating an example of configuring one or more default AI / ML models at a UE in accordance with various aspects of the present disclosure. The numberings associated with the communication flow 1200 do not specify a particular temporal order and are merely used as references for the communication flow 1200.

[0132] In another aspect of the present disclosure, in addition to (or as an alternative to) aspects described in connection with FIG. 11 , the first SL UE 1104 may be configured with one or more default / exceptional AI / ML models. For example, as shown at 1202 and 1204, the anchor UE 1102 and / or the network entity 1106 may configure one or more default / exceptional AI / ML models for the first SL UE 1104. In some examples,129025-2405W001Qualcomm Ref. No. 2404738WO 43 the one or more default / exceptional AI / ML models may also be pre-configured at the first SL UE 1104 (e.g., as a default factory setting). For purposes of the present disclosure, a default AI / ML model or an exceptional AI / ML model may refer to an AI / ML model that is used in exceptional use-cases, where exceptional may mean that a specified event has to happen in order for the first SL UE 1104 to use the AI / ML model.

[0133] As shown at 1206, the first SL UE 1104 may be configured to switch to the default / exception AI / ML model(s) based on specified condition(s) or whenever specified. For example, the first SL UE 1104 may be configured to use a default / exceptional AI / ML model when the first SL UE 1104 is going through a radio resource control (RRC) configuration / reconfiguration, a cell-change, and / or a network synchronization change, etc. In another example, the first SL UE 1104 may be configured to use a default / exceptional AI / ML model when the validity timer for other available AI / ML model(s) has expired (e.g., there are no AI / ML model(s) available for the first SL UE 1104 to use except for the default / exceptional AI / ML model). For example, the first SL UE 1104 may be currently running just one AI / ML model (referring to as the “current AI / ML model”) that is associated with a validity timer. After the validity timer for the current AI / ML model expires, the first SL UE 1104 may be configured to use a default / exceptional AI / ML model (or move to a set of default / exception AI / ML model configurations) if a new AI / ML model is not configured and / or if the validity timer is not extended. In some examples, the validity timer may be configured to be automatically extended based on an activity (e.g., model-monitoring reports indicating the model is still valid), and / or based of explicit signaling between the anchor UE 1102 / network entity 1106 and the first SL UE 1104.

[0134] FIG. 13 is a communication flow 1300 illustrating an example of a network entity configuring one or more default AI / ML models at a UE in accordance with various aspects of the present disclosure. The numberings associated with the communication flow 1300 do not specify a particular temporal order and are merely used as references for the communication flow 1300.

[0135] In one aspect, the network entity 1106 may receive, from one or more UEs, such as the first SL UE 1104, a second SL UE, a third SL UE, and up to an NthSL UE, capability information related to AI / ML positioning. For ease of illustration, the129025-2405W001Qualcomm Ref. No. 2404738WO 44 following aspects are described from the perspective of the first SL UE 1104. For example, as shown at 1302, the network entity 1106 may receive the capability information of the first SLUE 1104 via the anchor UE 1102, and / or as shown at 1304, the network entity 1106 may receive the capability information of the first SL UE 1104 directly from the first SL UE 1104 directly (or via at least one relay node).

[0136] The capability information / AI / ML processing capabilities of the first SL UE 1104 may include: (1) AI / ML processing capabilities of the first SL UE 1104, (2) a number of AI / ML models supported by the first SL UE 1104, (3) size(s) of AI / ML models supported by the first SL UE 1104, (4) a complexity of AI / ML models supported, (5) types of AI / ML models supported by the first SL UE 1104, (6) RS processing capabilities, (7) memory capabilities for processing AI / ML models, (8) a list of positioning methods supported for AI / ML-based processing, (9) the capability to receive one or more AI / ML models from the anchor UE 1102, or (10) a combination thereof.

[0137] At 1306, based on the capability information of the first SL UE 1104, the network entity 1106 may transmit, to the anchor UE 1102, a list of AI / ML models that can be transferred to the first SL UE 1104 and also a set of conditions for transmitting the AI / ML model(s) to SL UE(s). In some examples, as an alternative, or in addition to, at 1308, the network entity 1106 may transmit one or more AI / ML models to the first SL UE 1104 directly (e.g., AI / ML model(s) in the list of AI / ML models that can be transferred to the first SL UE 1104). Depending on implementations, as shown at 1310, the one or more AI / ML models may come from the anchor UE 1102 and / or (directly / indirectly) from other SL UE(s). In addition, each AI / ML model in the list of AI / ML models may include a validity timer, where the validity timer is indicative of a time or a duration in which the first SL UE 1104 is able to use an AI / ML model in the list of AI / ML models. Also, to reduce signaling overhead, each AI / ML model or each AI / ML model with a specific set of configurations may be associated with a model ID or a function. Then, to provide the list of AI / ML models that may be transferred to the first SL UE 1104, the network entity 1106 may just indicate a list of IDs / functions that corresponds to the list of AI / ML models.

[0138] The set of conditions for transmitting the AI / ML model(s) to SL UE(s) may include a range restriction associated with an AI / ML model to be transferred, a priority associated with an AI / ML model to be transferred, a set of priorities associated with129025-2405W001Qualcomm Ref. No. 2404738WO 45 the SL UEs, and / or a set of power classes specifications associated with the AI / ML model(s) and / or the SL UE(s), etc. (discussed below).

[0139] In some implementations (e.g., as discussed in connection with 1128 of FIG. 11 and 1306 of FIG. 13), the network entity 1106 may configure the anchor UE 1102 to transmit the set of AI / ML models to one or more SL UEs based on the range between the anchor UE 1102 and the one or more SL UEs. In other words, the set of conditions may include may include a range restriction that provides a validity range for which an AI / ML model is valid for the SL UE(s). For example, the network entity 1106 may configure / indicate that a first AI / ML model is valid with 1 kilometer (km) of the radius or the anchor UE 1102, and a second AI / ML model is valid with 50 km of the radius or the anchor UE 1102, etc. As such, if the range between the anchor UE 1102 and the first SL UE 1104 is less than 1 km, the anchor UE 1102 may provide the first SL UE 1104 with both the first AI / ML model and the second AI / ML model. If the range between the anchor UE 1102 and the first SL UE 1104 is 30 km, the anchor UE 1102 may provide the first SL UE 1104 with just the second AI / ML model. If the range between the anchor UE 1102 and the first SL UE 1104 is 60 km, the anchor UE 1102 may not provide both the first AI / ML model and the second AI / ML model to the first SL UE 1104.

[0140] Dependingon implementations, the anchor UE 1102 and / orthe first SL UE 1104 may calculate the range between them using one or more positioning methods. For example, to estimate whether the first SL UE 1104 is within a validity range of an AI / ML model, the anchor UE 1102 may be configured to perform the range calculation, or the first SL UE 1104 may be configured to perform the range calculation and report the calculated range to the anchor UE 1102. Similarly, each validity range / AI / ML model may further be associated with a validity timer. After the validity time expires, the anchor UE 1102 and / orthe first SL UE 1104 may be specified to perform the range estimation procedure again. In some examples, the anchor UE 1102 and / or the first SL UE 1104 may also be configured to perform proximity -based determination. For example, based on the range between the anchor UE 1102 and the first SL UE 1104, the anchor UE 1102 and / or the first SL UE 1104 may determine whether to respond to a request, to provide a report, what AI / ML model(s) to report back, and to whom should the AI / ML model(s) be addressed, etc.129025-2405W001Qualcomm Ref. No. 2404738WO 46

[0141] In some implementations (e.g., as discussed in connection with 1128 of FIG. 11 and 1306 of FIG. 13), the network entity 1106 may configure the anchor UE 1102 to transmit the set of AI / ML models to one or more SL UEs based on the priorities associated with the set of AI / ML models. In other words, the set of conditions may include may include a priority restriction / indication that provides a priority for each AI / ML model. In one example, if the first SL UE 1104 request the anchor UE 1102 to provide one or more AI / ML models to the first SL UE 1104 (e.g., at 1122), the first SL UE 1104 may include a requested priority for the one or more AI / ML models (e.g, an indication that the first SL UE 1104 just want to receive AI / ML model(s) with specified priorities). Then, based on the request, the anchor UE 1102 may transmit AI / ML model(s) with the requested priorities to the first SL UE 1104 (e.g., at 1124).

[0142] Depending on implementations, the network entity 1106 may configure the AI / ML models with their priorities (such as based a subscription plan). For example, a first AI / ML model may be configured with a first priority (priority 1), a second AI / ML model may be configured with a second priority (priority 2), where the first priority takes overthe second priority (e.g., priority 1 > priority 2) and so on. Also, the priority of the anchor UE 1102 (in the context of AI / ML model priority) may be set by the network entity 1106, and the priority of the SL UEs (in the context of AI / ML model priority) may be set by the network entity 1106 or the anchor UE 1102. Then, the anchor UE 1102 may be configuredto transfer the AI / ML model(s) to SL UE(S) based on the priority rule. For example, if the first SL UE 1104 is configured with the priority X during the registration, the anchor UE 1102 may transfer AI / ML model(s) with priority greater than X to the first SL UE 1104.

[0143] In some implementations (e.g., as discussed in connection with 1128 of FIG. 11 and 1306 of FIG. 13), the network entity 1106 may configure the anchor UE 1102 to transmit the set of AI / ML models to one or more SL UEs based on the priorities associated with the SL UEs. In other words, the set of conditions may include may include a priority restriction / indication that provides a priority for each SL UE, which may be used for determining which UE(s) may provide AI / ML model(s) to the SL UE(s). For example, a positioning reference unit (PRU) may have a higher priority to transfer AI / ML model(s) to SL UE(s) compared to a regular (anchor) UE (e.g., a mobile phone). In another example, a high-tier UE may be a high priority UE to perform the AI / ML model transfer to another UE.129025-2405W001Qualcomm Ref. No. 2404738WO 47

[0144] In another configuration, the network entity 1106 may configure AI / ML models with the priority of the SL UEs. For example, a first set of SL UEs may have most top priority for the AI / ML model transfer, and a second set of SL UEs may have a next priority for the AI / ML model transfer, and so on. Then, the anchor UE 1102 may start transfer one or more AI / ML models to the top priority UE set first (e.g., the first set of SL UEs), and then to the next priority UE set (e.g., the second set of SL UEs).

[0145] In another example, each set of priority UEs may also be mapped to the priority of the AI / ML models discussed above. For example, a top priority (e.g., priority 1) AI / ML model may be associated with the first and second priority UE sets, and a next priority (e.g., priority 2) AI / ML model may be associated with a third priority UE sets, etc. In some scenarios, it may be useful to divide / classify UEs into different priority sets when some UEs are more important than others due to their service level agreements.

[0146] In some implementations (e.g., as discussed in connection with 1128 of FIG. 11 and 1306 of FIG. 13), the network entity 1106 may configure the anchor UE 1102 to transmit the set of AI / ML models to one or more SL UEs based on whether the set of AI / ML models are standard models or premier models. In other words, the set of conditions may include may include a model transfer restriction / indication. For purposes of the present disclosure, a standard model may refer to an AI / ML model that is transferrable (e.g., can be transferred from one UE to another UE), and a premier model may refer to an AI / ML model that is not transferrable (e.g., cannot be transferred from one UE to another UE). For example, the anchor UE 1102 may have a first set of AI / ML models that is tagged as “non-transferable” and a second set of AI / ML models that is tagged as “transferable.” The first set of AI / ML models may be the “premier models” and the second set of AI / ML models may the “standard models.”

[0147] When there are standard and premier models available for transfer, the network entity 1106 may be configured to define standard and premier models, which may be based on a direct flag defining the standard and premier models, and / or based on the AI / ML model priority (e.g., AI / ML models with the priority less than or equal to X is consider as premier models, and AI / ML models with the priority greater than X is consider as standard models, etc.). Then, for a positioning session (e.g., an SL positioning session), the anchor UE 1102 may not be allowed to transfer the premier models, and is allowed to just transfer the standards models. The model transfer protocol may be129025-2405W001Qualcomm Ref. No. 2404738WO 48 range-based, priority-based, and / or class-based, etc., which may be up to the implementation of the anchor UE 1102 and / or the SL UEs.

[0148] In some implementations (e.g., as discussed in connection with 1128 of FIG. 11 and 1306 of FIG. 13), the network entity 1106 may configure the anchor UE 1102 to transmit the set of AI / ML models to one or more SL UEs based on the power class associated with the SL UE(s) and / or the AI / ML model(s). In other words, the set of conditions may include may include a power class restriction / indication. For examples, the SL UEs may be classified with different power classes (e.g., power class 1, power class 1.5, power class 2, power class 3, etc.), which may be based on the UE capability and its static. Then, the network entity 1106 may configure AI / ML models with power class specifications. For example, a first AI / ML model may be configured to work with just SL UEs having a power class less than 2, and a second AI / ML model may be configured to work with just SL UEs having a power class greater than 2, etc. In addition, the SL UEs may be configured to share their power classes with the anchor UE 1102, and the anchor UE 1102 may transfer the AI / ML model(s) to the SL UEs based on their power classes. For example, if the first SL UE 1104 belongs to the power class 3, then the anchor UE 1102 may transfer the second AI / ML model to the first SL UE 1104 but not the first AI / ML model. In other words, the anchor UE 1102 may just transfer the AI / ML model(s) which satisfy the power class specifications.

[0149] Depending on implementations, the AI / ML model transfer described in connection with FIGs. 11 to 13 may be happening for an SL UE (e.g., the first SL UE 1104) to perform inference using the provided AI / ML model(s), or it may also be happening for the SL UE to monitor the provided AI / ML model(s). For example, a smartwatch (e.g., a lower tier UE) may be requested by a smartphone (e.g., a higher tier UE) to transfer to the smart phone an AI / ML model for the smart phone to monitor the performance of the AI / ML model or to train / retrain / fine-tuningthe AI / ML model, etc.

[0150] FIG. 14 is a flowchart 1400 of wireless communication. The method may be performedby a first user equipment (UE) (e.g., the UE 104, 404, 602, 702; the anchor UE 1102; the apparatus 1604). The method may enable the first UE and a second UE to transfer AI / ML models to each other based on specified conditions to promote a consistency in AI / ML models used by the UEs in the same area or from the same vendor.129025-2405W001Qualcomm Ref. No. 2404738WO 49

[0151] At 1402, a first UE may receive, from a second UE, capability information of the second UE related to AI / ML for SL positioning, such as described in connection with FIG. 11 . For example, at 1120, the anchor UE 1102 may receive, from one or more UEs, such as a first SL UE 1104, a second SL UE, a third SL UE, and up to an NthSL UE, their capability information related to AI / ML positioning. The reception of the capability information may be performed by, e.g., the model transfer component 198, the transceiver(s) 1622, the cellular baseband processor(s) 1624, and / or the application processor(s) 1606 of the apparatus 1604 in FIG. 16.

[0152] In one example, the capability information includes one or more of a set of SL AI / ML processing capabilities, a number of AI / ML models supported, a size of AI / ML models supported, a complexity of AI / ML models supported, a set of types of AI / ML models supported, a set of RS processing capabilities, a set of memory capabilities for processing AI / ML models, a list of positioning methods supported for AI / ML- based processing, or the capability to receive one or more AI / ML models from the first UE.

[0153] In another example, the first UE is an LMF, a sensing management function, an AI / ML management function, a base station, an 0AM entity, or a core entity.

[0154] At 1408, the first UEmay communicate, based on the capability information, a request for a transferring of a set of AI / ML models, such as described in connection with FIG. 11. For example, at 1122, based on the capability information ofthe first SL UE 1104, the anchor UE 1102 may transmit, to the first SL UE 1104, a request for transferring a set of AI / ML models. The request for transferring a set of AI / ML models may be a request to transmit at least one AI / ML model from the anchor UE 1102 to the first SL UE 1104, a request to receive at least one AI / ML model from the first SL UE 1104 (e.g., for monitoring the at least one AI / ML model at the anchor UE 1102, for distributing to other SL UEs, etc.), or both. The communication of the request may be performed by, e.g., the model transfer component 198, the transceiver(s) 1622, the cellular baseband processor(s) 1624, and / or the application processor(s) 1606 of the apparatus 1604 in FIG. 16.

[0155] In one example, to communicate the request for the transferring of the set of AI / ML models, the first UE may transmit, to the second UE, the request for transferring the set of AI / ML models from the first UE to the second UE, and / or receive, from the129025-2405W001Qualcomm Ref. No. 2404738WO 50 second UE, the request for transferring the set of AI / ML models from the second UE to the first UE.

[0156] In another example, the request includes one or more of : a type of an AI / ML model to be transferred, a size of the AI / ML model to be transferred, an input for the AI / ML model to be transferred, an output for the AI / ML model to be transferred, a list of use cases for the AI / ML model to be transferred, an age or a validity period of the AI / ML model to be transferred, or a list of tasks to be performed by the AI / ML model to be transferred.

[0157] At 1410, the first UE may perform, with the second UE based on the request, the transferring of the set of AI / ML models for the SL positioning, such as described in connection with FIG. 11 . For example, at 1124, based on the request for transferring the set of AI / ML models, the anchor UE 1102 and the first SL UE 1104 may perform the transferring of the set of AI / ML models (e.g., for SL positioning related operations). For example, if the request includes transmitting at least one AI / ML model from the anchor UE 1102 to the first SL UE 1104, then the anchor UE 1102 may transmit the at least one AI / ML model to the first SL UE 1104. If the request includes receiving at least one AI / ML model from the first SL UE 1104, then the first SL UE 1104 may transmit the at least one AI / ML model to the anchor UE 1102. The transferringofthe set of AI / ML models may be performed by, e.g., the model transfer component 198, the transceiver(s) 1622, the cellular baseband processor(s) 1624, and / or the application processor(s) 1606 of the apparatus 1604 in FIG. 16.

[0158] In one example, to perform, with the second UE based on the request, the transferring of the set of AI / ML models for the SL positioning, the first UE may transmit, to the second UE based on the request, the set of AI / ML models, and / or receive, from the second UE based on the request, the set of AI / ML models.

[0159] In another example, the first UE may determine, based on the capability information, the set of AI / ML models to be transferred from the first UE to the second UE, such as described in connection with FIG. 11. For example, at 1126, prior to transmitting the request for transferring the set of AI / ML models, the anchor UE 1102 may determine the set of AI / ML models to be transferred to the first SL UE 1104 based on the capability information of the first SL UE 1104 and also based on a set of conditions. The determination of the set of AI / ML models to be transferred may be performedby, e.g., the model transfer component 198, thetransceiver(s) 1622, the cellular baseband129025-2405W001Qualcomm Ref. No. 2404738WO 51 processor(s) 1624, and / or the application processor(s) 1606 of the apparatus 1604 in FIG. 16. In some implementations, to communicate the request for the transferring of the set of AI / ML models and perform the transferring of the set of AI / ML models for the SL positioning, the first UE may be configured to transmit, to the second UE, the request to transmit the set of AI / ML models from the first UE to the second UE, receive, from the UE, an indication or an acknowledgement to receive the set of AI / ML models, and transmit, to the second UE based on the indication or the acknowledgement, the set of AI / ML models. In some implementation, the determination of the set of AI / ML models to be transferred from the first UE to the second UE is further based on: a distance between the first UE and the second UE, a first priority associated with the set of AI / ML models, a second priority associated with the second UE, the set of AI / ML models being a set of transferable AI / ML models, a set of standard AI / ML models, or a set of non-premier AI / ML models, a first power class associated with the set of AI / ML models, a second power class associated with the second UE, or a combination thereof. In some implementations, the first UE may receive, from a network entity, an indication indicative at least one of : a range restriction associated with each AI / ML model in the set of AI / ML models, a first set of priorities associated with the set of AI / ML models, a second set of priorities associated with a set of UEs, or a set of power classes specifications associated with the set of AI / ML models.

[0160] In another example, the first UE may transmit, to a network entity, the capability information, receive, from the network entity based on the capability information, an indication of a list of AI / ML models that can be transferred to the second UE, and select the set of AI / ML models to be transferred from the first UE to the second UE from the list of AI / ML models, such as described in connection with FIG. 11 . For example, at 1132, the anchor UE 1102 may be configured to transmit the capability information of the first SL UE 1104 to the network entity 1106. Then, based on the capability information of the first SL UE 1104, the network entity 1106 may provide, to the anchor UE 1102, a list of AI / ML models that may be transferred from the anchor UE 1102 to the first SL UE 1104 (e.g., at 1128). In other words, the network entity 1106 may determine the set of AI / ML models to be transferred to the first SL UE 1104 (instead of determined by the anchor UE 1102). Then, the anchor UE 1102 may select the set of AI / ML models to be transferred to the first SL UE 1104 from this list129025-2405W001Qualcomm Ref. No. 2404738WO 52(e.g., such as based on the set of conditions at 1126). The transmission of the capability information, the reception of the indication, and / or the selection of the set of AI / ML models to be transferred may be performed by, e.g., the model transfer component 198, the transceiver(s) 1622, the cellular baseband processor(s) 1624, and / or the application processor(s) 1606 of the apparatus 1604 in FIG. 16. In some implementations, to communicate the request for the transferring of the set of AI / ML models and perform the transferring of the set of AI / ML models for the SL positioning, the first UE may be configured to transmit, to the second UE, the request to transmit the set of AI / ML models from the first UE to the second UE, receive, from the UE, a second indication or an acknowledgement to receive the set of AI / ML models, and transmit, to the second UE based on the indication or the acknowledgement, the set of AI / ML models. In some implementation, the first UE is an anchor UE, the second UE is an SL UE, and the network entity is an LMF, a sensing management function, an AI / ML management function, a base station, an 0AM entity, or a core entity. In some implementations, the indication of the list of AI / ML models includes at least one of an ID or a function for each AI / ML model in the list of AI / ML models. In some implementations, the indication of the list of AI / ML models that can be transferred to the second UE includes a validity timer for each AI / ML model in the list of AI / ML models, where the validity timer is indicative of a time or a duration in which the second UE is able to use an AI / ML model in the list of AI / ML models.

[0161] In another example, the first UE may communicate, with the second UE after the transferring of the set of AI / ML models, at least one of: a set of feedback, a set of monitoring reports, or a set of measurements related to the set of AI / ML models, such as described in connection with FIG. 11. For example, at 1136, after transferring of the set of AI / ML models, the entity that receives the set of AI / ML models may be configured to run the set of AI / ML models, and provide, a set of feedback / monitoring reports related to the set of AI / ML models, and / or a set of measurements performed by the set of AI / ML models. For example, if the anchor UE 1102 transfers an AI / ML model for SL positioning to the first SL UE 1104, the first SL UE 1104 may be configured to run the AI / ML for the SL positioning, and then provide a feedback for the AI / ML model to the anchor UE 1102 (e.g., an indication of whether the AI / ML model is accurate, is able to be successfully run by the first SL UE 1104, is able to129025-2405W001Qualcomm Ref. No. 2404738WO 53 generate outputs specified, etc.). The communication may be performed by, e.g., the model transfer component 198, the transceiver(s) 1622, the cellular baseband processor(s) 1624, and / or the application processor(s) 1606 of the apparatus 1604 in FIG. 16.

[0162] FIG. 15 is a flowchart 1500 of wireless communication. The method may be performed by a first user equipment (UE) (e.g., the UE 104, 404, 602, 702; the anchor UE 1102; the apparatus 1604). The method may enable the first UE and a second UE to transfer AI / ML models to each other based on specified conditions to promote a consistency in AI / ML models used by the UEs in the same area or from the same vendor.

[0163] At 1502, a first UE may receive, from a second UE, capability information of the second UE related to AI / ML for SL positioning, such as described in connection with FIG. 11 . For example, at 1120, the anchor UE 1102 may receive, from one or more UEs, such as a first SL UE 1104, a second SL UE, a third SL UE, and up to an NthSL UE, their capability information related to AI / ML positioning. The reception of the capability information may be performed by, e.g., the model transfer component 198, the transceiver(s) 1622, the cellular baseband processor(s) 1624, and / or the application processor(s) 1606 of the apparatus 1604 in FIG. 16.

[0164] In one example, the capability information includes one or more of a set of SL AI / ML processing capabilities, a number of AI / ML models supported, a size of AI / ML models supported, a complexity of AI / ML models supported, a set of types of AI / ML models supported, a set of RS processing capabilities, a set of memory capabilities for processing AI / ML models, a list of positioning methods supported for AI / ML- based processing, or the capability to receive one or more AI / ML models from the first UE.

[0165] In another example, the first UE is an LMF, a sensing management function, an AI / ML management function, a base station, an 0AM entity, or a core entity.

[0166] At 1508, the first UEmay communicate, based on the capability information, a request fora transf erring of a set of AI / ML models, such as described in connection with FIG. 11 . For example, at 1122, based on the capability information of the first SL UE 1104, the anchor UE 1102 may transmit, to the first SL UE 1104, a request for transferring a set of AI / ML models. The request for transferring a set of AI / ML models may be a request to transmit at least one AI / ML model from the anchor UE 1102 to the first SL129025-2405W001Qualcomm Ref. No. 2404738WO 54UE 1104, a request to receive at least one AI / ML model from the first SL UE 1104 (e.g., for monitoring the at least one AI / ML model at the anchor UE 1102, for distributing to other SL UEs, etc.), or both. The communication of the request may be performed by, e.g., the model transfer component 198, the transceiver(s) 1622, the cellular baseband processor(s) 1624, and / or the application processor(s) 1606 of the apparatus 1604 in FIG. 16.

[0167] In one example, to communicate the request for the transferring of the set of AI / ML models, the first UE may transmit, to the second UE, the request for transferring the set of AI / ML models from the first UE to the second UE, and / or receive, from the second UE, the request for transferring the set of AI / ML models from the second UE to the first UE.

[0168] In another example, the request includes one or more of : a type of an AI / ML model to be transferred, a size of the AI / ML model to be transferred, an input for the AI / ML model to be transferred, an output for the AI / ML model to be transferred, a list of use cases for the AI / ML model to be transferred, an age or a validity period of the AI / ML model to be transferred, or a list of tasks to be performed by the AI / ML model to be transferred.

[0169] At 1510, the first UE may perform, with the second UE based on the request, the transferring of the set of AI / ML models for the SL positioning, such as described in connection with FIG. 11 . For example, at 1124, based on the request for transferring the set of AI / ML models, the anchor UE 1102 and the first SL UE 1104 may perform the transferring of the set of AI / ML models (e.g., for SL positioning related operations). For example, if the request includes transmitting at least one AI / ML model from the anchor UE 1102 to the first SL UE 1104, then the anchor UE 1102 may transmit the at least one AI / ML model to the first SL UE 1104. If the request includes receiving at least one AI / ML model from the first SL UE 1104, then the first SL UE 1104 may transmit the at least one AI / ML model to the anchor UE 1102. The transferring of the set of AI / ML models may be performed by, e.g., the model transfer component 198, the transceiver(s) 1622, the cellular baseband processor(s) 1624, and / or the application processor(s) 1606 of the apparatus 1604 in FIG. 16.

[0170] In one example, to perform, with the second UE based on the request, the transferring of the set of AI / ML models for the SL positioning, the first UE may transmit, to the129025-2405W001Qualcomm Ref. No. 2404738WO 55 second UE based on the request, the set of AI / ML models, and / or receive, from the second UE based on the request, the set of AI / ML models.

[0171] In another example, at 1504, the first UE may determine, based on the capability information, the set of AI / ML models to be transferred fromthe first UE to the second UE, such as described in connection with FIG. 11 . For example, at 1126, prior to transmitting the requestfortransferringthe set of AI / ML models, the anchor UE 1102 may determine the set of AI / ML models to be transferred to the first SL UE 1104 based on the capability information of the first SL UE 1104 and also based on a set of conditions. The determination of the set of AI / ML models to be transferred may be performed by, e.g., the model transfer component 198, the transceiver(s) 1622, the cellular baseband processor(s) 1624, and / or the application processor(s) 1606 of the apparatus 1604 in FIG. 16. In some implementations, to communicate the request for the transferring of the set of AI / ML models and perform the transferring of the set of AI / ML models for the SL positioning, the first UE may be configured to transmit, to the second UE, the request to transmit the set of AI / ML models from the first UE to the second UE, receive, from the UE, an indication or an acknowledgement to receive the set of AI / ML models, and transmit, to the second UE based on the indication or the acknowledgement, the set of AI / ML models. In some implementation, the determination of the set of AI / ML models to be transferred fromthe first UE to the second UE is further based on: a distance between the first UE and the second UE, a first priority associated with the set of AI / ML models, a second priority associated with the second UE, the set of AI / ML models being a set of transferable AI / ML models, a set of standard AI / ML models, or a set of non-premier AI / ML models, a first power class associated with the set of AI / ML models, a second power class associated with the second UE, or a combination thereof. In some implementations, the first UE may receive, from a network entity, an indication indicative at least one of : a range restriction associated with each AI / ML model in the set of AI / ML models, a first set of priorities associated with the set of AI / ML models, a second set of priorities associated with a set of UEs, or a set of power classes specifications associated with the set of AI / ML models.

[0172] In another example, at 1506, the first UE may transmit, to a network entity, the capability information, receive, from the network entity based on the capability information, an indication of a list of AI / ML models that can be transferred to the129025-2405W001Qualcomm Ref. No. 2404738WO 56 second UE, and select the set of AI / ML models to be transferred from the first UE to the second UE from the list of AI / ML models, such as described in connection with FIG. 11 . For example, at 1132, the anchor UE 1102 may be configured to transmit the capability information of the first SLUE 1104 to the network entity 1106. Then, based on the capability information of the first SL UE 1104, the network entity 1106 may provide, to the anchor UE 1102, a list of AI / ML models that may be transferred from the anchor UE 1102 to the first SL UE 1104 (e.g., at 1128). In other words, the network entity 1106 may determine the set of AI / ML models to be transferred to the first SL UE 1104 (instead of determined by the anchor UE 1102). Then, the anchor UE 1102 may select the set of AI / ML models to be transferred to the first SL UE 1104 from this list (e.g., such as based on the set of conditions at 1126). The transmission of the capability information, the reception of the indication, and / or the selection of the set of AI / ML models to be transferred may be performed by, e.g., the model transfer component 198, the transceiver(s) 1622, the cellular baseband processors) 1624, and / or the application processor(s) 1606 of the apparatus 1604 in FIG. 16. In some implementations, to communicate the request for the transferring of the set of AI / ML models and perform the transferring of the set of AI / ML models for the SL positioning, the first UE may be configured to transmit, to the second UE, the request to transmit the set of AI / ML models from the first UE to the second UE, receive, from the UE, a second indication or an acknowledgement to receive the set of AI / ML models, and transmit, to the second UE based on the indication or the acknowledgement, the set of AI / ML models. In some implementation, the first UE is an anchor UE, the secondUEis an SL UE, and the network entity is an LMF, a sensing management function, an AI / ML management function, a base station, an 0AM entity, or a core entity. In some implementations, the indication of the list of AI / ML models includes at least one of an ID or a function for each AI / ML model in the list of AI / ML models. In some implementations, the indication of the list of AI / ML models that can be transferred to the second UE includes a validity timer for each AI / ML model in the list of AI / ML models, where the validity timer is indicative of a time or a duration in which the second UE is able to use an AI / ML model in the list of AI / ML models.

[0173] In another example, at 1512, the first UE may communicate, with the second UE after the transferring of the set of AI / ML models, at least one of a set of feedback, a set of129025-2405W001Qualcomm Ref. No. 2404738WO 57 monitoring reports, or a set of measurements related to the set of AI / ML models, such as described in connection with FIG. 11. For example, at 1136, after transferring of the set of AI / ML models, the entity that receives the set of AI / ML models may be configured to run the set of AI / ML models, and provide, a set of feedback / monitoring reports related to the set of AI / ML models, and / or a set of measurements performed by the set of AI / ML models. For example, if the anchor UE 1102 transfers an AI / ML model for SL positioning to the first SL UE 1104, the first SL UE 1104 may be configured to run the AI / ML for the SL positioning, and then provide a feedback for the AI / ML model to the anchor UE 1102 (e.g., an indication of whether the AI / ML model is accurate, is able to be successfully run by the first SL UE 1104, is able to generate outputs specified, etc.). The communication may be performed by, e.g., the model transfer component 198, the transceiver(s) 1622, the cellular baseband processor(s) 1624, and / or the application processor(s) 1606 of the apparatus 1604 in FIG. 16.

[0174] FIG. 16 is a diagram 1600 illustrating an example of a hardware implementation for an apparatus 1604. The apparatus 1604 may be a UE, a component of a UE, or may implement UE functionality. In some aspects, the apparatus 1604 may include at least one cellular baseband processor 1624 (also referred to as a modem) coupled to one or more transceivers 1622 (e.g., cellular RF transceiver). The cellular baseband processor(s) 1624 may include at least one on-chip memory 1624'. In some aspects, the apparatus 1604 may further include one or more subscriber identity modules (SIM) cards 1620 and at least one application processor 1606 coupled to a secure digital (SD) card 1608 and a screen 1610. The application processor(s) 1606 may include on-chip memory 1606'. In some aspects, the apparatus 1604 may further include a Bluetooth module 1612, a WLAN module 1614, an ultrawide band (UWB) module 1638 (e.g., a UWB transceiver), an SPS module 1616 (e.g., GNSS module), one or more sensors 1618 (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 1626, a power supply 1630, and / oracamera 1632. The Bluetooth module 1612, the UWB module 1638, the WLAN module 1614, and the SPS module 1616 may include an on-chip transceiver129025-2405W001Qualcomm Ref. No. 2404738WO 58(TRX) (or in some cases, just a receiver (RX)). The Bluetooth module 1612, the WLAN module 1614, and the SPS module 1616 may include their own dedicated antennas and / or utilize the antennas 1680 for communication. The cellular baseband processor(s) 1624 communicates through the transceiver(s) 1622 via one or more antennas 1680 with the UE 104 and / or with an RU associated with a network entity 1602. The cellular baseband processor(s) 1624 and the application processor(s) 1606 may each include a computer-readable medium / memory 1624', 1606', respectively. The additional memory modules 1626 may also be considered a computer-readable medium / memory. Each computer-readable medium / memory 1624', 1606', 1626 may be non-transitory. The cellular baseband processor(s) 1624 and the application processor(s) 1606 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) 1624 / application processor(s) 1606, causes the cellular baseband processor(s) 1624 / application processor(s) 1606 to perform the various functions described supra. The cellular baseband processors) 1624 and the application processor(s) 1606 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) 1624 and the application processor(s) 1606 may be configuredto perform a first sub set of the various functions described supra without information stored in 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) 1624 / application processor(s) 1606 when executing software. The cellular baseband processor(s) 1624 / application processor(s) 1606 may be a component of the LE 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 1604 may be at least one processor chip (modem and / or application) and include just the cellular baseband processor(s) 1624 and / or the application processor(s) 1606, and in another configuration, the apparatus 1604 may be the entire UE (e.g., see UE 350 of FIG. 3) and include the additional modules of the apparatus 1604.129025-2405W001Qualcomm Ref. No. 2404738WO 59

[0175] As discussed supra, the model transfer component 198 maybe configured to receive, from a second UE, capability information of the second UE related to AI / ML for SL positioning. The model transfer component 198 may also be configured to communicate, based on the capability information, a request for a transferring of a set of AI / ML models. The model transfer component 198 may also be configured to perform, with the second UEbased on the request, the transferring of the set of AI / ML models for the SL positioning. The model transfer component 198 may be within the cellular baseband processor(s) 1624, the application processor(s) 1606, or both the cellularbasebandprocessor(s) 1624andthe applicationprocessor(s) 1606. Themodel transfer 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 1604 may include a variety of components configured for various functions. In one configuration, the apparatus 1604, and in particular the cellular baseband processor(s) 1624 and / or the application processors) 1606, may include means for receiving, from a second UE, capability information of the second UE related to AI / ML for SL positioning. The apparatus 1604 may further include means for communicating, based on the capability information, a request for a transferring of a set of AI / ML models. The apparatus 1604 may further include means performing, with the second UE based on the request, the transferring of the set of AI / ML models for the SL positioning.

[0176] In one configuration, the capability information includes one or more of : a set of SL AI / ML processing capabilities, a number of AI / ML models supported, a size of AI / ML models supported, a complexity of AI / ML models supported, a set of types of AI / ML models supported, a set of RS processing capabilities, a set of memory capabilities for processing AI / ML models, a list of positioning methods supported for AI / ML-based processing, or the capability to receive one or more AI / ML models from the first UE.129025-2405W001Qualcomm Ref. No. 2404738WO 60

[0177] In another configuration, the first UE / apparatus 1604 is an LMF, a sensing management function, an AI / ML management function, a base station, an 0AM entity, or a core entity.

[0178] In another configuration, the means for communicatingtherequestfor the transferring of the set of AI / ML models may include configuring the apparatus 1604 to transmit, to the second UE, the request for transferring the set of AI / ML models from the first UE to the second UE, and / or receive, from the second UE, the request for transferring the set of AI / ML models from the second UE to the first UE.

[0179] In another configuration, the request includes one or more of: a type of an AI / ML model to be transferred, a size of the AI / ML model to be transferred, an input for the AI / ML model to be transferred, an output for the AI / ML model to be transferred, a list of use cases for the AI / ML model to be transferred, an age or a validity period of the AI / ML model to be transferred, or a list of tasks to be performed by the AI / ML model to be transferred.

[0180] In another configuration, the means for performing, with the second UE based on the request, the transferring of the set of AI / ML models for the SL positioning may include configuring the apparatus 1604 to transmit, to the second UE based on the request, the set of AI / ML models, and / or receive, from the second UE based on the request, the set of AI / ML models.

[0181] In another configuration, the apparatus 1604 may further include means for determining, based on the capability information, the set of AI / ML models to be transferred from the first UE to the second UE. In some implementations, the means for communicatingtherequestforthe transferring of the set of AI / ML models and the means for performing the transferring of the set of AI / ML models for the SL positioningmay include configuringthe apparatus 1604to transmit, to the second UE, the request to transmit the set of AI / ML models from the first UE to the second UE, receive, from the UE, an indication or an acknowledgement to receive the set of AI / ML models, and transmit, to the second UE based on the indication or the acknowledgement, the set of AI / ML models. In some implementation, the determination of the set of AI / ML models to be transferred from the first UE to the second UE is further based on: a distance between the first UE and the second UE, a first priority associated with the set of AI / ML models, a second priority associated with the second UE, the set of AI / ML models being a set of transferable AI / ML129025-2405W001Qualcomm Ref. No. 2404738WO 61 models, a set of standard AI / ML models, or a set of non-premier AI / ML models, a first power class associated with the set of AI / ML models, a second power class associated with the second UE, or a combination thereof. In some implementations, the apparatus 1604 may further include means for receiving, from a network entity, an indication indicative at least one of : a range restriction associated with each AI / ML model in the set of AI / ML models, a first set of priorities associated with the set of AI / ML models, a second set of priorities associated with a set of UEs, or a set of power classes specifications associated with the set of AI / ML models.

[0182] In another configuration, the apparatus 1604 may further include means for transmitting, to a network entity, the capability information, means for receiving, from the network entity based on the capability information, an indication of a list of AI / ML models that can be transferred to the second UE, and means for selecting the set of AI / ML models to be transferred from the first UE to the second UE from the list of AI / ML models. In some implementations, the means for communicating the request for the transferring of the set of AI / ML models and the means for performing the transferring of the set of AI / ML models for the SL positioning may include configuring the apparatus 1604 to transmit, to the second UE, the request to transmit the set of AI / ML models from the first UE to the second UE, receive, from the UE, a second indication or an acknowledgement to receive the set of AI / ML models, and transmit, to the second UE based on the indication or the acknowledgement, the set of AI / ML models. In some implementation, the first UE is an anchor UE, the second UE is an SL UE, and the network entity is an LMF, a sensing management function, an AI / ML management function, a base station, an 0AM entity, or a core entity. In some implementations, the indication of the list of AI / ML models includes at least one of an ID ora function for each AI / ML model in the list of AI / ML models. In some implementations, the indication of the list of AI / ML models that can be transferred to the second UE includes a validity timer for each AI / ML model in the list of AI / ML models, where the validity timer is indicative of a time or a duration in which the second UE is able to use an AI / ML model in the list of AI / ML models.

[0183] In another configuration, the apparatus 1604 may further include means for communicating, with the secondUEafterthetransferringof the setof AI / MLmodels, at least one of : a set of feedback, a set of monitoring reports, or a set of measurements related to the set of AI / ML models.129025-2405W001Qualcomm Ref. No. 2404738WO 62

[0184] The means may be the model transfer component 198 of the apparatus 1604 configured to perform the functions recited by the means. As described supra, the apparatus 1604 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.

[0185] FIG. 17 is a flowchart 1700 of a method of wireless communication. The methodmay be performed by a network entity (e.g., the base station 102; the one or more location servers 168; the location server 704; the network entity 1106, 1860). The methodmay enable the network entity to configure a first UE with a list of AI / ML models that can be transferred to a second UE, and also a set of conditions for transferring the AI / ML model(s).

[0186] At 1702, the network entity may receive, from a first UE, capability information of a second UE related to AI / ML for SL positioning, such as described in connection with FIGs. 11 and 13. For example, as discussed in connection with 1302 and 1304 of FIG. 13, the network entity 1106 may receive the capability information of the first SL UE 1104 via the anchor UE 1102, and / or as shown at 1304, the network entity 1106 may receive the capability information of the first SL UE 1104 directly from the first SL UE 1104 directly (or via at least one relay node). The reception of the capability information may also be performed by, e.g., the model transfer configuration component 197, the network processor(s) 1812, and / or the network interface 1880 of the network entity 1860 in FIG. 18.

[0187] At 1704, the network entity may transmit, based on the capability information, an indication of a list of AI / ML models that can be transferred to the second UE or at least one AI / ML model in list the of AI / ML models, such as describedin connection with FIGs. 11 and 13. For example, as discussed in connection with 1306 of FIG. 13, based on the capability information of the first SL UE 1104, the network entity 1106 may transmit, to the anchor UE 1102, a list of AI / ML models that can be transferred to the first SL UE 1104 and also a set of conditions for transmitting the AI / ML model(s) to SL UE(s). The transmission of the indication may also be performed by, e.g., the model transfer configuration component 197, the network processor(s) 1812, and / or the network interface 1880 of the network entity 1860 in FIG. 18.129025-2405W001Qualcomm Ref. No. 2404738WO 63

[0188] In one example, to transmit the indication of the list of AI / ML models that can be transferred to the second UE or the at least one AI / ML model in the list of AI / ML models, the network entity may be configured to transmit, to the first UE, the indication of the list of AI / ML models that can be transferred to the second UE, or transmit, to the second UE, the at least one AI / ML model in the list of AI / ML models.

[0189] In another example, the network entity may receive, from the first UE, the at least one AI / ML model.

[0190] In another example, the capability information includes one or more of: a set of SL AI / ML processing capabilities, a number of AI / ML models supported, a size of AI / ML models supported, a complexity of AI / ML models supported, a set of types of AI / ML models supported, a set of RS processing capabilities, a set of memory capabilities for processing AI / ML models, a list of positioning methods supported for AI / ML-based processing, or the capability to receive one or more AI / ML models from the first UE.

[0191] In another example, the network entity may transmit, to the first UE, a second indication indicative at least one of : a range restriction associated with each AI / ML model in the list of AI / ML models, a first set of priorities associated with the list of AI / ML models, a second set of priorities associated with a set of UEs, or a set of power classes specifications associated with the list of AI / ML models.

[0192] In another example, the indication of the list of AI / ML models that can be transferred to the second UE includes a validity timer for each AI / ML model in the list of AI / ML models, where the validity timer is indicative of a time or a duration in which the second UE is able to use an AI / ML model in the list of AI / ML models.

[0193] In another example, the first UE is an anchor UE, the second UE is an SL UE, and the network entity is an LMF, a sensing management function, an AI / ML management function, a base station, an 0AM entity, or a core entity.

[0194] In another example, the indication of the list of AI / ML models includes at least one of an ID or a function for each AI / ML model in the list of AI / ML models.

[0195] FIG. 18 is a diagram 1800 illustrating an example of a hardware implementation for a network entity 1860. In one example, the network entity 1860 may be within the core network 120. The network entity 1860 may include at least one network processor 1812. The network processor(s) 1812 may include on-chip memory 1812'. In some aspects, the network entity 1860 may further include additional memory129025-2405W001Qualcomm Ref. No. 2404738WO 64 modules 1814. The network entity 1860communicatesviathenetworkinterface l880 directly (e.g., backhaul link) or indirectly (e.g., through a RIC) with the CU 1802. The on-chip memory 1812' and the additional memory modules 1814 may each be considered a computer-readable medium / memory. Each computer-readable medium / memory may be non-transitory. The network processor(s) 1812 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.

[0196] As discussed supra, the model transfer configuration component 197 may be configured to receive, from a first UE, capability information of a second UE related to AI / ML for SL positioning. The model transfer configuration component 197 may also be configured to transmit, based on the capability information, an indication of a list of AI / ML models that can be transferred to the second UE or at least one AI / ML model in list the of AI / ML models. The model transfer configuration component 197 may be within the network processor(s) 1812. The model transfer 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 for implementationby 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 1860 may include a variety of components configured for various functions. In one configuration, the network entity 1860 may include means for receiving, from a first UE, capability information of a second UE related to AI / ML for SL positioning The network entity 1860 may further include means for transmitting, based on the capability information, an indication of a list of AI / ML models that can be transferred to the second UE or at least one AI / ML model in list the of AI / ML models.

[0197] In one configuration, the means for transmitting the indication of the list of AI / ML models that can be transferred to the second UE or the at least one AI / ML model in the list of AI / ML models may include configuringthe network entity 1860 to transmit, to the first UE, the indication of the list of AI / ML models that can be transferred to129025-2405W001Qualcomm Ref. No. 2404738WO 65 the second UE, or transmit, to the second UE, the at least one AI / ML model in the list of AI / ML models.

[0198] In another configuration, the network entity 1860 may further include means for receiving, from the first UE, the at least one AI / ML model.

[0199] In another configuration, the capability information includes one or more of : a set of SL AI / ML processing capabilities, a number of AI / ML models supported, a size of AI / ML models supported, a complexity of AI / ML models supported, a set of types of AI / ML models supported, a set of RS processing capabilities, a set of memory capabilities for processing AI / ML models, a list of positioning methods supported for AI / ML-based processing, orthe capability to receive one or more AI / ML models from the first UE.

[0200] In another configuration, the network entity 1860 may further include means for transmitting, to the first UE, a second indication indicative at least one of: a range restriction associated with each AI / ML model in the list of AI / ML models, a first set of priorities associated with the list of AI / ML models, a second set of priorities associated with a set of UEs, or a set of power classes specifications associated with the list of AI / ML models.

[0201] In another configuration, the indication of the list of AI / ML models that can be transferred to the second UE includes a validity timer for each AI / ML model in the list of AI / ML models, where the validity timer is indicative of a time or a duration in which the second UE is able to use an AI / ML model in the list of AI / ML models.

[0202] In another configuration, the first UE is an anchor UE, the second UE is an SL UE, and the network entity is an LMF, a sensing management function, an AI / ML management function, a base station, an 0AM entity, or a core entity.

[0203] In another configuration, the indication of the list of AI / ML models includes at least one of an ID or a function for each AI / ML model in the list of AI / ML models.

[0204] The means may be the model transfer configuration component 197 of the network entity 1860 configured to perform the functions recited by the means.

[0205] 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 or129025-2405W001Qualcomm Ref. No. 2404738WO 66 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.

[0206] 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 memb er or members of A, B, or C. Sets should b e interpreted 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 a129025-2405W001Qualcomm Ref. No. 2404738WO 67 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.”

[0207] 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.

[0208] The following aspects are illustrative only and may be combined with other aspects or teachings described herein, without limitation.

[0209] Aspect 1 is a method of wireless communication at a first user equipment (UE), comprising: receiving, from a second UE, capability information of the second UE related to artificial intelligence (Al) or machine learning (ML) (AI / ML) for sidelink (SL) positioning; communicating, based on the capability information, a request for a transferring of a set of AI / ML models; and performing, with the second UE based on the request, the transferring of the set of AI / ML models for the SL positioning.

[0210] Aspect 2 is the method of aspect 1, wherein communicating the request for the transferring of the set of AI / ML models includes one of: transmitting, to the second UE, the request for transferring the set of AI / ML models from the first UE to the129025-2405W001Qualcomm Ref. No. 2404738WO 68 second UE; or receiving, from the second UE, the request for transferring the set of AI / ML models from the second UE to the first UE.

[0211] Aspect 3 is the method of aspect 1 or aspect 2, wherein the request includes one or more of : a type of an AI / ML model to be transferred, a size of the AI / ML model to be transferred, an input for the AI / ML model to be transferred, an output for the AI / ML model to be transferred, a list of use cases for the AI / ML model to be transferred, an age or a validity period of the AI / ML model to be transferred, or a list of tasks to be performed by the AI / ML model to be transferred.

[0212] Aspect 4 is the method of any of aspects 1 to 3, wherein the capability information includes one or more of: a set of SL AI / ML processing capabilities, a number of AI / ML models supported, a size of AI / ML models supported, a complexity of AI / ML models supported, a set of types of AI / ML models supported, a set of reference signal (RS) processing capabilities, a set of memory capabilities for processing AI / ML models, a list of positioning methods supported for AI / ML-based processing, or the capability to receive one or more AI / ML models from the first UE.

[0213] Aspect 5 is the method of any of aspects 1 to 4, wherein performing, with the second UE based on the request, the transferring of the set of AI / ML models for the SL positioning comprises one of : transmitting, to the second UEbased on the request, the set of AI / ML models; or receiving, from the second UEbased on the request, the set of AI / ML models.

[0214] Aspect 6 is the method of any of aspects 1 to 5, further comprising: determining, based on the capability information, the set of AI / ML models to be transferred from the first UE to the second UE.

[0215] Aspect 7 is the method of any of aspects 1 to 6, wherein communicating the request for the transferring of the set of AI / ML models and performing the transferring of the set of AI / ML models for the SL positioning comprises: transmitting, to the second UE, the request to transmit the set of AI / ML models from the first UE to the second UE; receiving, from the UE, an indication or an acknowledgement to receive the set of AI / ML models; and transmitting, to the second UEbased on the indication or the acknowledgement, the set of AI / ML models.

[0216] Aspect 8 is the method of any of aspects 1 to 7, wherein determination of the set of AI / ML models to be transferred from the first UE to the second UE is further based on: a distance between the first UE and the second UE, a first priority associated with129025-2405W001Qualcomm Ref. No. 2404738WO 69 the set of AI / ML models, a second priority associated with the second UE, the set of AI / ML models being a set of transferable AI / ML models, a set of standard AI / ML models, or a set of non-premier AI / ML models, a first power class associated with the set of AI / ML models, a second power class associated with the second UE, or a combination thereof.

[0217] Aspect 9 is the method of any of aspects 1 to 8, further comprising: receiving from a network entity, an indication indicative at least one of: a range restriction associated with each AI / ML model in the set of AI / ML models, a first set of priorities associated with the set of AI / ML models, a second set of priorities associated with a set of UEs, or a set of power classes specifications associated with the set of AI / ML models.

[0218] Aspect 10 is the method of any of aspects 1 to 9, further comprising: transmitting to a network entity, the capability information; receiving, from the network entity based on the capability information, an indication of a list of AI / ML models that can be transferred to the second UE; and selecting the set of AI / ML models to be transferred from the first UE to the second UE from the list of AI / ML models.

[0219] Aspect 11 is the method of any of aspects 1 to 10, wherein communicatingthe request for the transferring of the set of AI / ML models and performing the transferring of the set of AI / ML models for the SL positioning comprises: transmitting, to the second UE, the request to transmit the set of AI / ML models from the first UE to the second UE; receiving, from the UE, a second indication or an acknowledgement to receive the set of AI / ML models; and transmitting, to the second UE based on the indication or the acknowledgement, the set of AI / ML models.

[0220] Aspect 12 is the method of any of aspects 1 to 11, wherein the first UE is an anchor UE, the second UE is a sidelink (SL) UE, and the network entity is a location management function (LMF), a sensing management function, an AI / ML management function, a base station, an operations, administration, and maintenance (0AM) entity, or a core entity.

[0221] Aspect 13 is the method of any of aspects 1 to 12, wherein the indication of the list of AI / ML models includes at least one of a model identification (ID) or a function for each AI / ML model in the list of AI / ML models.

[0222] Aspect 14 is the method of any of aspects 1 to 13, wherein the indication of the list of AI / ML models that can be transferred to the second UE includes a validity timer for each AI / ML model in the list of AI / ML models, wherein the validity timer is129025-2405W001Qualcomm Ref. No. 2404738WO 70 indicative of a time or a duration in which the second UE is able to use an AI / ML model in the list of AI / ML models.

[0223] Aspect 15 is the method of any of aspects 1 to 14, further comprising: communicating with the second UE after the transferring of the set of AI / ML models, at least one of: a set of feedback, a set of monitoring reports, or a set of measurements related to the set of AI / ML models.

[0224] Aspect 16 is the method of any of aspects 1 to 15, wherein the first UE is a location management function (LMF), a sensing management function, an AI / ML management function, a base station, an operations, administration, and maintenance (0AM) entity, or a core entity.

[0225] Aspect 17 is an apparatus for wireless communication at a first user equipment (UE), 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 16.

[0226] Aspect 18 is the apparatus of aspect 17, further including at least one transceiver coupled to the at least one processor.

[0227] Aspect 19 is an apparatus for wireless communication at a first user equipment (UE) including means for implementing any of aspects 1 to 16.

[0228] Aspect 20 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 16.

[0229] Aspect 21 is a method of wireless communication at a network entity, comprising: receiving, from a first user equipment (UE), capability information of a second UE related to artificial intelligence (Al) or machine learning (ML) (AI / ML) for sidelink (SL) positioning; and transmitting, based on the capability information, an indication of a list of AI / ML models that can be transferred to the second UE or at least one AI / ML model in list the of AI / ML models.

[0230] Aspect 22 is the method of aspect 21 , wherein transmitting the indication of the list of AI / ML models that can be transferred to the second UE or the at least one AI / ML model in the list of AI / ML models comprises: transmitting, to the first UE, the indication of the list of AI / ML models that can be transferred to the second UE; or129025-2405W001Qualcomm Ref. No. 2404738WO 71 transmitting, to the second UE, the at least one AI / ML model in the list of AI / ML models.

[0231] Aspect23 is the method of aspect21 oraspect22, further comprising: receiving, from the first UE, the at least one AI / ML model.

[0232] Aspect24 is the method of any of aspects 21 to 23, wherein the capability information includes one or more of: a set of SL AI / ML processing capabilities, a number of AI / ML models supported, a size of AI / ML models supported, a complexity of AI / ML models supported, a set of types of AI / ML models supported, a set of reference signal (RS) processing capabilities, a set of memory capabilities for processing AI / ML models, a list of positioning methods supported for AI / ML-based processing, or the capability to receive one or more AI / ML models from the first UE.

[0233] Aspect 25 is the method of any of aspects 21 to 24, further comprising: transmitting to the first UE, a second indication indicative at least one of : a range restriction associated with each AI / ML model in the list of AI / ML models, a first set of priorities associated with the list of AI / ML models, a second set of priorities associated with a set of UEs, or a set of power classes specifications associated with the list of AI / ML models.

[0234] Aspect 26 is the method of any of aspects 21 to 25, wherein the indication of the list of AI / ML models that can be transferred to the second UE includes a validity timer for each AI / ML model in the list of AI / ML models, wherein the validity timer is indicative of a time or a duration in which the second UE is able to use an AI / ML model in the list of AI / ML models.

[0235] Aspect 27 is the method of any of aspects 21 to 26, wherein the first UE is an anchor UE, the second UE is a sidelink (SL) UE, and the network entity is a location management function (LMF), a sensing management function, an AI / ML management function, a base station, an operations, administration, and maintenance (0AM) entity, or a core entity.

[0236] Aspect 28 is the method of any of aspects 21 to 27, wherein the indication of the list of AI / ML models includes at least one of a model identification (ID) or a function for each AI / ML model in the list of AI / ML models.

[0237] Aspect 29 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 one129025-2405W001Qualcomm Ref. No. 2404738WO 72 memory, the at least one processor, individually or in any combination, is configured to implement any of aspects 21 to 28.

[0238] Aspect 30 is the apparatus of aspect 29, further including at least one network interface coupled to the at least one processor.

[0239] Aspect 31 is an apparatus for wireless communication at a network entity including means for implementing any of aspects 21 to 29.

[0240] Aspect 32 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 21 to 29.129025-2405W001

Claims

Qualcomm Ref. No. 2404738WO 73CLAIMSWHAT IS CLAIMED IS:

1. An apparatus for wireless communication at a first user equipment (UE), 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 second UE, capability information of the second UE related to artificial intelligence (Al) or machine learning (ML) (AI / ML) for sidelink (SL) positioning; communicate, based on the capability information, a request for a transferring of a set of AI / ML models; and perform, with the second UE based on the request, the transferring of the set of AI / ML models for the SL positioning.

2. The apparatus of claim 1, wherein to communicate the request for the transferring of the set of AI / ML models, the atleast one processor, individually or in any combination, is configured to: transmit, to the second UE, the request for transferring the set of AI / ML models from the first UE to the second UE; or receive, from the second UE, the request for transferring the set of AI / ML models from the second UE to the first UE.

3. The apparatus of claim 1, wherein the request includes one or more of: a type of an AI / ML model to be transferred, a size of the AI / ML model to be transferred, an input for the AI / ML model to be transferred, an output for the AI / ML model to be transferred, a list of use cases for the AI / ML model to be transferred, an age or a validity period of the AI / ML model to be transferred, or a list of tasks to be performed by the AI / ML model to be transferred.129025-2405W001Qualcomm Ref. No. 2404738WO 744. The apparatus of claim 1, wherein the capability information includes oneormore of: a set of SL AI / ML processing capabilities, a number of AI / ML models supported, a size of AI / ML models supported, a complexity of AI / ML models supported, a set of types of AI / ML models supported, a set of reference signal (RS) processing capabilities, a set of memory capabilities for processing AI / ML models, a list of positioning methods supported for AI / ML-based processing, or the capability to receive one or more AI / ML models from the first UE.

5. The apparatus of claim 1, wherein to perform, with the second UE based on the request, the transferring of the set of AI / ML models for the SL positioning, the at least one processor, individually or in any combination, is configured to: transmit, to the second UE based on the request, the set of AI / ML models; or receive, from the second UE based on the request, the set of AI / ML models.

6. The apparatus of claim 1, wherein the at least one processor, individually or in any combination, is further configured to: determine, based on the capability information, the set of AI / ML models to be transferred from the first UE to the second UE.

7. The apparatus of claim 6, wherein to communicate the request for the transferring of the set of AI / ML models and perform the transferring of the set of AI / ML models for the SL positioning, the at least one processor, individually or in any combination, is configured to: transmit, to the second UE, the request to transmit the set of AI / ML models from the first UE to the second UE; receive, from the UE, an indication or an acknowledgement to receive the set of AI / ML models; and transmit, to the second UE based on the indication or the acknowledgement, the set of AI / ML models.129025-2405W001Qualcomm Ref. No. 2404738WO 758. The apparatus of claim 6, wherein determination of the set of AI / ML models to be transferred from the first UE to the second UE is further based on: a distance between the first UE and the second UE, a first priority associated with the set of AI / ML models, a second priority associated with the second UE, the set of AI / ML models being a set of transferable AI / ML models, a set of standard AI / ML models, or a set of non-premier AI / ML models, a first power class associated with the set of AI / ML models, a second power class associated with the second UE, or a combination thereof.

9. The apparatus of claim 8, wherein the at least one processor, individually or in any combination, is further configured to: receive, from a network entity, an indication indicative at least one of: a range restriction associated with each AI / ML model in the set of AI / ML models, a first set of priorities associated with the set of AI / ML models, a second set of priorities associated with a set of UEs, or a set of power classes specifications associated with the set of AI / ML models.

10. The apparatus of claim 1, wherein the at least one processor, individually or in any combination, is further configured to: transmit, to a network entity, the capability information; receive, from the network entity based on the capability information, an indication of a list of AI / ML models that can be transferred to the second UE; and select the set of AI / ML models to be transferred from the first UE to the second UE from the list of AI / ML models.

11. The apparatus of claim 10, wherein to communicate the request for the transferring of the set of AI / ML models and perform the transferring of the set of AI / ML129025-2405W001Qualcomm Ref. No. 2404738WO 76 models for the SL positioning, the at least one processor, individually or in any combination, is configured to: transmit, to the second UE, the request to transmit the set of AI / ML models from the first UE to the second UE; receive, from the UE, a second indication or an acknowledgement to receive the set of AI / ML models; and transmit, to the second UE based on the indication or the acknowledgement, the set of AI / ML models.

12. The apparatus of claim 10, wherein the indication of the list of AI / ML models includes at least one of a model identification (ID) or a function for each AI / ML model in the list of AI / ML models.

13. The apparatus of claim 10, wherein the indication of the list of AI / ML models that can be transferred to the second UE includes a validity timer for each AI / ML model in the list of AI / ML models, wherein the validity timer is indicative of a time or a duration in which the second UE is able to use an AI / ML model in the list of AI / ML models.

14. The apparatus of claim 1, wherein the at least one processor, individually or in any combination, is further configured to: communicate, with the second UE after the transferring of the set of AI / ML models, at least one of: a set of feedback, a set of monitoring reports, or a set of measurements related to the set of AI / ML models.

15. A method of wireless communication at a first user equipment (UE), comprising: receiving, from a second UE, capability information of the second UE related to artificial intelligence (Al) or machine learning (ML) (AI / ML) for sidelink (SL) positioning; communicating, based on the capability information, a request for a transferring of a set of AI / ML models; and performing, with the second UE based on the request, the transferring of the set of AI / ML models for the SL positioning.129025-2405W001Qualcomm Ref. No. 2404738WO 7716. 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 first user equipment (UE), capability information of a second UE related to artificial intelligence (Al) or machine learning (ML) (AI / ML) for sidelink (SL) positioning; and transmit, based on the capability information, an indication of a list of AI / ML models that can be transferred to the second UE or at least one AI / ML model in list the of AI / ML models.

17. The apparatus of claim 16, wherein to transmit the indication of the list of AI / ML models that can be transferred to the second UE or the at least one AI / ML model in the list of AI / ML models, the at least one processor, individually or in any combination's configured to: transmit, to the first UE, the indication of the list of AI / ML models that can be transferred to the second UE; or transmit, to the second UE, the at least one AI / ML model in the list of AI / ML models.

18. The apparatus of claim 16, wherein the at least one processor, individually or in any combination, is further configured to: receive, from the first UE, the at least one AI / ML model.

19. The apparatus of claim 16, wherein the capability information includes one or more of: a set of SL AI / ML processing capabilities, a number of AI / ML models supported, a size of AI / ML models supported, a complexity of AI / ML models supported, a set of types of AI / ML models supported, a set of reference signal (RS) processing capabilities, a set of memory capabilities for processing AI / ML models,129025-2405W001Qualcomm Ref. No. 2404738WO 78 a list of positioning methods supported for AI / ML-based processing, or the capability to receive one or more AI / ML models from the first UE.

20. The apparatus of claim 16, wherein the at least one processor, individually or in any combination, is further configured to: transmit, to the first UE, a second indication indicative at least one of : a range restriction associated with each AI / ML model in the list of AI / ML models, a first set of priorities associated with the list of AI / ML models, a second set of priorities associated with a set of UEs, or a set of power classes specifications associated with the list of AI / ML models.129025-2405W001

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