Data collection for ai / ML-based positioning

Abstract

Aspects presented herein may enable a device to collect data, such as reference signal measurements, for one or more artificial intelligence (AI) or machine learning (ML) (AI / ML) models based on a set of data quality and condition configurations. In one aspect, a device receives a request to collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data. The device obtains, based on the request and the configuration, collected data for the at least one AI / ML-based model. The device transmits the collected data or an indication of the collected data, or training the at least one AI / ML-based model based on the collected data.

Description

DATA COLLECTION FOR AI / ML-BASED POSITIONINGCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of Greece Patent Application Serial No. 20240100528, entitled “DATA COLLECTION FOR AI / ML-BASED POSITIONING” and filed on July 29, 2024, which is expressly incorporated by reference herein in its entirety.TECHNICAL FIELD

[0002] The present disclosure relates generally to communication systems, and more particularly, to wireless communication involving artificial intelligence (Al) or machine learning (ML) (AI / ML) positioning.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 NR includes services associated with enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable low latencycommunications (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 a request to collect data for at least one artificial intelligence (Al) or machine learning (ML) (AI / ML)-based model, where the requestis indicative of a configuration for collectingthe data. The apparatus obtains, based on the request and the configuration, collected data for the at least one AI / ML-based model. The apparatus transmits the collected data or an indication of the collected data, ortrains the at least one AI / ML-based model based on the collected data.

[0008] In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus transmits a request to collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collectingthe data. The apparatus receives, based on the request and the configuration, collected data for the at least one AI / ML-based model. The apparatus transmits the collected data or an indication of the collected data, ortrains the at least one AI / ML- based model based on the collected data.

[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.

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

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

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

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

[0025] FIG. 10 is a communication flow illustrating an example procedure of configuring a UE to prioritize measurements in accordance with various aspects of the present disclosure.

[0026] FIG. 11 is a communication flow illustrating an example list of data which a UE may be requested to collect in accordance with various aspects of the present disclosure.

[0027] FIG. 12 is a communication flow illustrating an example list of data which a base station may be requested to collect in accordance with various aspects of the present disclosure.

[0028] FIG. 13 is a communication flow illustrating an example list of data which a location server (e.g., an LMF, a sensing management function, an AI / ML management function, etc.) may be requested to collect 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 diagram illustrating an example of a hardware implementation for an example apparatus and / or network entity.

[0031] FIG. 16 is a diagram illustrating an example of a hardware implementation for an example network entity.

[0032] FIG. 17 is a diagram illustrating an example of a hardware implementation for an example network entity.

[0033] FIG. 18 is a flowchart of a method of wireless communication.

[0034] FIG. 19 is a diagram illustrating an example of a hardware implementation for an example apparatus and / or network entity.

[0035] FIG. 20 is a diagram illustrating an example of a hardware implementation for an example network entity.

[0036] FIG. 21 is a diagram illustrating an example of a hardware implementation for an example network entity.DETAILED DESCRIPTION

[0037] Various aspects relate generally to wireless communication and more particularly to positioning based on wireless communication. Some aspects more specifically relate to improve the overall performance and efficiency of artificial intelligence (Al) / machine learning (ML) (AI / ML) positioning operations (e.g., data collection, AI / ML model training / retraining, inferencing, etc.) by enabling various conditions (or criteria) to be considered for data collection selection / prioritization at an entity / node side (e.g., at a user equipment (UE) / positioning reference unit (PRU) side, at a transmission reception point (TRP) side, and an location managem ent function (LMF) side, etc.). For example, a location server (e.g., an LMF, a sensing management function, an AI / ML management function, etc.) may configure data quality and conditions at a UE (e.g., a training entity, such as a base station or another UE, may request data quality directly from the location server, and the location server may also configure data collection at the UE to ensure the requested quality and conditions). After the UE receives, from the location server, configurations on the requested data quality and conditions, the UE may collect measurements (e.g., measuring reference signals) accordingto the configured quality and conditions. In some implementations, the UE may also apply labeling accordingto the configured quality and conditions. In another example, a UE may request a location server to provide assistance to ensure data quality and conditions (e.g., the training entity is at a UE side but UE demands certain RS configurations and / or location server labeling assistance). For example, the UE may request RS configurations that it believes is able to ensure data quality and conditions are met. If the UE specifies labeling assistance from the location server, the UE (e.g., as a data collection / source entity) may also request labeling assistance according to specified quality and conditions.

[0038] Particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the following potential advantages. For example, ensuring a high quality of measurements and labels is key fortraining and achieving the excellent performance for AI / ML positioning. As such, data collection entities may be configured to request data (measurements andpositioning labels) with specified quality and conditions. Likewise, data source entities (e.g., UEorPRU) may be specified to properly filter out measurements and label and just report clean positioning measurement and labels. Aspects presented herein provide signal conditions (or criteria) for data collection selection / prioritization atUE and / or PRU side. In one aspect, an LMF configures data quality and conditions at UE side (e.g., training entity may request data quality directly from LMF; and LMF configures data collection at UE side to ensure the requested quality and conditions). In another aspect, a UE requests LMF assistance to ensure data quality and conditions (e.g., the training entity is at UE side butUE demands certain RS configurations and / or LMF labeling assistance).

[0039] The detailed description set forth below in connection with the drawings describes various configurations and does not represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of various concepts. However, these concepts may be practiced without these specific details. In some instances, well known structures and components are shown in block diagram form in order to avoid obscuring such concepts.

[0040] Several aspects of telecommunication systems are presented with referenceto 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.

[0041] 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 mayperform 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.

[0042] Accordingly, in one or more example aspects, implementations, and / or use cases, the functions described may be implemented in hardware, software, or any combination thereof. If implemented in software, the functions may be stored on or encoded as one or more instructions or code on a computer-readable medium. Computer-readable media includes computer storage media. Storage media may be any available media that can be accessed by a computer. By way of example, such computer-readable media can include a random-access memory (RAM), a read-only memory (ROM), an electrically erasable programmable ROM (EEPROM), optical disk storage, magnetic disk storage, other magnetic storage devices, combinations of the types of computer- readable media, or any other medium that can be used to store computer executable code in the form of instructions or data structures that can be accessed by a computer.

[0043] 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 / or use 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 incorp oratingone 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.

[0044] 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.

[0045] 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 virtuallydistributed 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).

[0046] 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 0-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.

[0047] FIG. 1 is a diagram 100 illustrating an example of a wireless communications system and an access network. The illustrated wireless communications system includes a disaggregated base station architecture. The disaggregated base station architecture may include one or more CUs 110 that can communicate directly with a core network 120 via a backhaul link, or indirectly with the core network 120 through one or more disaggregated base station units (such as a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC) 125 via an E2 link, or a Non-Real Time (Non-RT) RIC 115 associated with a Service Management and Orchestration (SMO) Framework 105, or both). A CU 110 may communicate with one or more DUs 130 via respective midhaul links, such as an Fl interface. The DUs 130 may communicate with one or more RUs 140 via respective fronthaul links. The RUs 140 may communicate with respective UEs 104 via one or more radio frequency (RF) access links. In some implementations, the UE 104 may be simultaneously served by multiple RUs 140.

[0048] 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 configuredto 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.

[0049] 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 can communicate bidirectionally with the CU-CP unit via an interface, such as an El interface when implemented in an 0-RAN configuration. The CU 110 can be implemented to communicate with the DU 130, as necessary, for network control and signaling.

[0050] 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.

[0051] 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 performingfast 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.

[0052] The SMO Framework 105 may be configured to support RAN deployment and provisioning of non-virtualizedand virtualized network elements. For non-virtualized network elements, the SMO Framework 105 may be configured to support the deployment of dedicatedphysical 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) to perform network element life cycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface (such as an 02 interface). Such virtualized network elements can include, but are not limited to, CUs 110, DUs 130, RUs 140 andNear-RTRICs 125. In some implementations, the SMO Framework 105 can communicate with a hardware aspect of a 4G RAN, such as an open eNB (O- eNB) 111, via an 01 interface. Additionally, in some implementations, the SMO Framework 105 can communicate directly with one or more RUs 140 via an 01 interface. The SMO Framework 105 also may include a Non-RT RIC 115 configured to support functionality of the SMO Framework 105.

[0053] 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 (suchas 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.

[0054] 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).

[0055] 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 base station 102 provides an access point to the core network 120 for a UE 104. The base station 102 may include macrocells (high power cellular base station) and / or small cells (low power cellular base station). The small cells include femtocells, picocells, and microcells. Anetwork thatincludes 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 fMHz (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).

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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-bandfrequencies 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 5GNR operation beyond 52.6 GHz. For example, three higher op erating bands 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.

[0060] 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.

[0061] 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.

[0062] 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 adisaggregated 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).

[0063] 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 Location Center (GMLC) 165 and a Location Management Function (LMF) 166. However, generally, the one or more location servers 168 may include one or more location / positioning servers, which may include one or more of the GMLC 165, the LMF 166, a position determination entity (PDE), a serving mobile location center (SMLC), a mobile positioning center (MPC), or the like. The GMLC 165 and the LMF 166 support UE location services. The GMLC 165 provides an interface for clients / applications (e.g., emergency services) for accessing UE positioning information. The LMF 166 receives measurements and assistance information from the NG-RAN and the UE 104 via the AMF 161 to compute the position of the UE 104. The NG-RAN may utilize one or more positioningmethods 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.

[0064] 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, a wireless communications device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile client, a client, or some other suitable terminology. In some scenarios, the term UE may also apply to one or more companion devices such as in a device constellation arrangement. One or more of these devices may collectively access the network and / or individually access the network.

[0065] Referring again to FIG. 1, in certain aspects, the UE 104 may have a data collection component 198 that may be configured to receive a request to collect data for at least one artificial intelligence (Al) or machine learning (ML) (AI / ML)-based model, where the request is indicative of a configuration for collecting the data; obtain, based on the request and the configuration, collected data for the at least one AI / ML-based model; and transmitthe collected data or an indication of the collected data, or training the at least one AI / ML-based model based on the collected data. In certain aspects, the base station 102 may have a data collection component 199 that may be configured to receive a request to collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data; obtain, based on the request and the configuration, collected data for the at least one AI / ML-based model; and transmitthe collected data or an indication of the collected data, or training the at least one AI / ML-based model based on the collected data. In certain aspects, the oneor more location servers 168 may have a data collection configuration component 197 that may be configured to receive a request to collect data for at least one AI / ML- based model, where the request is indicative of a configuration for collecting the data; obtain, based on the request and the configuration, collected data for the at least one AI / ML-based model; and transmit the collected data or an indication of the collected data, or training the at least one AI / ML-based model based on the collected data. In certain aspects, the data collection configuration component 197 may also be configured to transmit a request to collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data; receive, based on the request and the configuration, collected data for the at least one AI / ML-based model; and transmitthe collected data or an indication of the collected data, or training the at least one AI / ML-based model based on the collected data.

[0066] 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 orUL, or may be time division duplexed (TDD) in which for a particular set of subcarriers (carrier system bandwidth), subframes within the set of subcarriers are dedicated for both DL andUL. In the examples provided by FIGs. 2A, 2C, the 5G NR frame structure is assumed to be TDD, with subframe 4 being configured with slot format 28 (with mostly DL), where D is DL, U is UL, and F is flexible for use between DL / UL, and subframe 3 being configured with slot format 1 (with all UL). While subframes 3, 4 are shown with slot formats 1, 28, respectively, any particular subframe may be configured with any of the various available slot formats 0-61 . Slot formats 0, 1 are all DL, UL, respectively. Other slot formats 2-61 include a mix of DL, UL, and flexible symbols. UEs are configured with the slot format (dynamically through DL control information (DCI), or semi- statically / statically through radio resource control (RRC) signaling) through a received slot format indicator (SFI). Note that the description infra applies also to a 5G NR frame structure that is TDD.

[0067] 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.Table 1: Numerology, SCS, and CP

[0068] 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^ si 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 / duration is 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).

[0069] 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.

[0070] As illustrated in FIG. 2A, 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 may also include beam measurement RS (BRS), beam refinement RS (BRRS), and phase tracking RS (PT-RS).

[0071] 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 determinethe 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.

[0072] 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 the 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.

[0073] 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.

[0074] 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), demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through HARQ, priority handling, and logical channel prioritization.

[0075] The transmit (TX) processors 16 and the receive (RX) processor 370 implement layer1 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 streammay 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.

[0076] 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 a 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.

[0077] 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.

[0078] 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, integrityprotection, 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.

[0079] 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.

[0080] The UL transmission is processed at the base station 310 in a manner similar to that described in connection with the receiver fun ction 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.

[0081] 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.

[0082] 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 data collection component 198 of FIG. 1.

[0083] 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 data collection component 199 of FIG. 1.

[0084] FIG. 4 is a diagram 400 illustrating an example of a UE positioningbased on reference signal measurements (which may also be referred to as “network -based positioning”)in accordance with variousaspects ofthe present disclosure. The 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 TpRSTX- 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 - TPRSTX| - |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 404 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.

[0085] 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-RS or 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.

[0086] 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 measurementfrequency bandwidth. In some examples, for FR1, the referencepoint for the DLPRS- 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 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 corresponding to a given receiver branch. For FR1 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.

[0087] 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.

[0088] 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.

[0089] 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, andthe resulting measurements are used along with other configuration information to locate the UE 404 in relation to the neighboring TRPs 402, 406.

[0090] 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.

[0091] 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 to 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 may be described as“UE-based ,” “UE-based positioning,” and / or “UE-based position calculation.”

[0092] 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.

[0093] 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.

[0094] 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 #i received from the TP. Multiple DL PRS or CSLRS 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.

[0095] “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.

[0096] “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 theDL 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.

[0097] “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 receiver 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

[0098] 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.

[0099] “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.

[0100] 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.).

[0101] 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 an LMF, 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.”

[0102] 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 / MLpositioning, 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.

[0103] FIG. 5B is a diagram 500B illustrating an example of AI / ML assisted positioning in accordance with various aspects of the present disclosure. For AI / ML assisted positioning, an entity / node (e.g., a UE, a network entity / node such as a base station, etc.) may use at least one AI / ML model to assistthe measurement of reference signals (e.g., positioningreference signals such as PRS, SRS, etc.). Then, the entity / node may transmit the reference signal measurements to a location server, such as an LMF. 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.

[0104] 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 secondTRP, . . . , 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.

[0105] 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 / ML Model Bi) for inferring the ToA of the first TRP, CIR of the second TRP may be input to a second AI / ML model (e.g., AI / ML Model B2 that is differentfrom AI / ML Model Bi) 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.

[0106] 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).

[0107] 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 signaltime 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.

[0108] FIG. 8A is a diagram 800A illustrating an example of UE-assisted / LMF-based positioning with UE-side AI / ML model, AI / ML assisted positioning in accordance with various aspects of the present disclosure. In another implementation, a UE 702 may be associated with at least one AI / ML model 708, and the UE 702 may use 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).

[0109] 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.

[0110] FIG. 9A is a diagram 900 A illustrating an example of network (e.g., NG-RAN) node assisted positioning with gNB-side AI / ML model, AI / ML assisted positioning inaccordance 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).

[0111] FIG. 9B is a diagram 900B illustrating an example of network (e.g., NG-RAN) node assisted positioning with LMF-side AI / ML model, direct AI / ML positioning in accordance with various aspects of the present disclosure. In another implementation, a network node, such as a base station 706, may not include an AI / ML model, and a location server 704 may use at least one AI / ML model 708 to determine the position of a UE 702. For example, the UE 702 may transmit a set of SRSs to the base station 706, and the base station 706 may receive and measure the 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.

[0112] Table 2 below provides an example list of UE positioning methods that may be supported by a network.Table 2 - Supported UE Positioning Methoc

[0113] AI / ML positioning (both direct and assisted) has been shown to provide high positioning accuracy in stringent NLOS conditions. However, to achieve an excellent performance for AI / ML positioning, an important key is to ensure that there are high quality measurements (which may also be referred to as “training data”) and labels for training AI / ML models (which may also be referred to as “AI / ML positioning models”). For example, to ensure the quality of measurements and labels during data collection (e.g., the measurements and collection of training data), an entity / nodethat is configured to collect training data (for training one or more AI / ML models) may be expected to request data (e.g., measurements and positioning labels) from other entities / nodes with specified quality and conditions. In response, entities / nodes (e.g, UEs, positioning reference units (PRUs), TRPs, etc.) that are requested to provide data may be expected to properly filter out (unsuitable / improper) measurements) and / or label(s), and just report clean positioning measurements and labels. For purposes of the present disclosure and for the simplicity of illustration, “data collection” may refer to the process of an entity / node collecting data that is to be used for one or more AI / ML positioning operations, such as for training and / or retraining / fine-tuning an AI / ML model. An entity / node that is configured to performthe data collection may collectively be referred to as a “data collection entity.” On the other hand, an entity that is configured to provide data to be collected by the data collection entity may be referred to as a “data source entity.” Also, a positioning reference unit (PRU) may refer to an entity / node at a known location that is capable of performing positioning measurements (e.g., RSTD, RSRP, UE Rx-Tx Time Difference measurements, DL-RSCPD, DL-RSCP, etc.) and report these measurements to a location server. The term PRU may be used interchangeably with the term UE depending on the context. For example, if a location server (e.g., anLMF) is configured to collect training data (e.g., reference signal measurements) from a set of UEs / PRUs and / or a set of TRPs for training / retraining one or more AI / ML models at the location server, the location server may be referred to as the data collection entity, and the set of UEs / PRUs / TRPs may be referred to as the data source entities.

[0114] For example, regarding training data collection for AI / ML based positioning associated information of training data may be specified to include a quality indicator at least forthe ground truth label. Other information associated with training data may also be specified such as information related training dataset / samples, information related to scenario, resource configuration and mapping, timing for training data, information on implementation imperfections, etc. Some networks may also be configured with assistance signaling and procedure to facilitate generating / collecting training data, which may include potential determination of the UE / PRU / TRP which may provide the training data, configuration of reference signal (e.g., formeasurement and / or label), and / or other types of signaling unrelated to data collection (e.g., requesting quality of training data).

[0115] Aspects presented herein may improve the overall performance and efficiency of AI / ML positioning operations (e.g., data collection, AI / ML model training / retraining inferencing, etc.) by enabling various conditions (or criteria) to be considered for data collection selection / prioritization at an entity / node side (e.g., at a UE / PRU side, at a TRP side, and an LMF side, etc.). For example, a location server (e.g., an LMF) may configure data quality and conditions at a UE (e.g., a training entity, such as a base station or another UE, may request data quality directly from the location server, and the location server may configure data collection at the UE to ensure the requested quality and conditions). After the UE receives, from the location server, configurations on the requested data quality and conditions, the UE may collectmeasurements (e.g., measuring reference signals) accordingto the configured quality and conditions. In some implementations, the UE may also apply labeling according to the configured quality and conditions. In another example, a UE may request a location server to provide assistance to ensure data quality and conditions (e.g., the training entity is at a UE side but UE demands certain RS configurations and / or location server labeling assistance). For example, the UE may request RS configurations that it believes is able to ensure data quality and conditions are met. If the UE specifies labeling assistance from the location server, the UE (e.g., as a data collection / source entity) may also request labeling assistance accordingto specified quality and conditions.

[0116] For purposes of the present disclosure, measurement(s) that may be performed by a data source entity (e.g., by a UE / PRU, a TRP, etc.) may include reference signal time difference (RSTD), reference signal receive power (RSRP), additional (signal) path information (e.g., timing / power - reference signal received path power (RSRPP)), timing information of channel impulse response (CIR), power information of CIR, phase information of CIR, frequency information of channel frequency response (CFR), power information of CFR, and / or phase information of CFR, etc. The positioningmethods used by an entity / nodemay include bothradio access technology (RAT) positioning methods and non-RAT methods. Examples of RAT positioning methods, as described on connection with FIG. 4, may include DL-TDoA, DL-AoD, multi RTT, UL-TDoA, and / or UL-AoA, etc. Examples of non-RAT positioning methods may include GNSS / GPS, light detection and ranging (Lidar), WLAN positioning, motion sensors (e.g., inertial measurement unit (IMU)), etc. In some implementations, the entity / node may also be configured to use a hybrid approach (if supported), such as combining / deriving a position estimate using both RAT and non- RAT methods.

[0117] FIG. 10 is a communication flow 1000 illustrating an example procedure of configuring a UE to prioritize measurements in accordance with various aspects of the present disclosure. The numberings associated with the communication flow 1000 do not specify a particular temporal order and are merely used as references for the communication flow 1000. Note while the communication flow 1000 is illustrated with a UE, aspects presented herein may also apply to other entities / nodes, such as to a PRU, a TRP, etc.

[0118] In one example, a UE 1002 may receive one or more configurations to prioritize measurements. For purposes of the present disclosure, “measurements” described herein may referto measurement of reference signals or parameters that are to be used for AI / ML positioning operation(s). For example, measurements may include measuring of positioningreference signal(s), oritmay include physical characteristics of an object (e.g., the speed, direction, etc.). Depending on the implementations, the UE 1002 may receive the one or more configurations from a location server 1004 (e.g., an LMF), from a base station 1006 (or via one of its TRPs), from another UE, or from a data collection entity, etc.

[0119] The one or more configurations to prioritize measurements (and / or labels) may include a list of criteria in which the UE 1002 may be configured to prioritize measurements based on at least one of:(1) PRS measurements with RSRP satisfying an RSRP threshold,(2) PRS measurements with signal-to-noise ratio (SNR) / signal -to-interference- plus-noise ratio (SINR) satisfying a SNR / SINR threshold,(3) PRS measurements with delay spread satisfying a delay spread threshold,(4) PRS measurements with Rician factor satisfying a Rician factor threshold,(5 ) PRS measurements with Doppler spread satisfying a Doppler spread threshold, or(6) PRS measurements with a defined number of multipath components satisfying a number threshold, etc.

[0120] In some examples, the UE 1002 may receive multiple sets of configurations to generate multiple sets of measurements and labels, and the UE 1002 may receive corresponding multiple sets of reference signals (RSs) and obtain multiple sets of measurements and labels. For purpose of the present disclosure and in the context of AI / ML, label / labelling, which may also be referred to as “data labeling” and / or “label estimates,” may refer to a process of identifying data (e.g., measurements, images, text files, etc.) and adding one or more meaningful and informative labels to provide context so that an AI / ML model is able to learn from it. After obtain multiple measurements and labels, the UE 1002 may report, e.g., to the location server 1004 and / or the base station 1006, the multiple measurements, the multiple labels, indicator(s) on measurement quality, and / or indicator(s) on labeling quality based on the multiple label estimates, etc.

[0121] For example, at 1010, the UE 1002 may receive a first set of configurations to prioritize measurements from the location server 1004 or the base station 1006 (e.g, may be a serving base station of the UE 1002). At 1012, the UE 1002 may receive a first set of PRS (e.g., PRS #l) from a base station (e.g., the base station 1006 or one of its TRPs), and the UE 1002 may measure the first set of PRS based on the first set of configurations to obtain a first set of measurements and labels. Then, at 1014, the UE 1002 may transmitthe obtained first set of measurements and labels to the location server 1004 and / or the base station 1006. Similarly, at 1016, the UE 1002 may receive a second set of configurations to prioritize measurements from the location server 1004 or the base station 1006. At 1018, the UE 1002 may receive a second set of PRS (e.g., PRS #2) from a base station (e.g., thebase station 1006 or one ofits TRPs), and the UE 1002 may measure the second set of PRS based on the second set of configurations to obtain a second set of measurements and labels. Then, at 1020, the UE 1002 may transmit the obtained second set of measurements and labels to the location server 1004 and / or the base station 1006.

[0122] Note while the example in FIG. 10 shows the first set of configurations andthe second set of configurations are received via different signaling / messages, it is merely for illustration purposes. Depending on implementations, the location server 1004 and / or the base station 1006 may also be configured to transmit multiple sets of configurations via one signaling / message, and then the UE 1002 may apply different sets of configurations to different sets of RSs received and measured. Similarly, in some implementations, the UE 1002 may also be configured to report multiple sets of measurements and labels in one signaling / message. For example, the UE 1002 may be configured to report the first set of measurements and labels and the second set of measurements and labels via one signaling / message.

[0123] At 1022, depending on implementations, the location server 1004 and / or the base station 1006 may use the multiple measurements, multiple labels, indicator(s) on measurement quality, and / or indicator(s) on labeling quality based on the multiple label estimates, etc. to assess the feasibility of the UE 1002 to collect data related to AI / ML positioning operations (e.g., data collection, training, inferencing, etc.), and the location server 1004 and / or the base station 1006 may send the UE 1002 further signaling. For example, the location server 1004 and / or the base station 1006 may requestthe UE 1002 to collect additional data if the UE 1002 is assessed to be feasiblefor data collection, or request the UE 1002 to cease collecting data if the UE 1002 is assessed not to be feasible for data collection. In another example, the location server 1004 and / or the base station 1006 may send further signaling / request to the UE 1002 to tune / update data collection prioritization rule at the UE 1002, and / or send further signaling / requestto the UE 1002 to activate / deactivate data collection atthe UE 1002. While the communication flow 1000 shows the UE 1002 providingthe measurements and labels to the location server 1004 and / or the base station 1006, the UE 1002 may also provide data collected (e.g., the measurements and labels) to other data collection entities, such another UE, a data repository, or a training entity, etc.

[0124] FIG. 11 is a communication flow 1100 illustrating an example list of data which a UE may be requested to collect 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.

[0125] At 1110, a UE 1102 (which may also be referred to as a “device” or a “target device” for purposes of illustration) may receive a request, from a first entity 1104 (e.g., another device or a “requesting device”), to collect data (e.g., to perform data collection(s)) for at least one AI / ML model (which may also be referred to as an “AI / ML-based model” and / or “AI / ML-based positioning model”). Examples of an AI / ML model may include an AI / ML positioning model, an AI / ML positioning model, and / or an AI / ML channel state feedback (CSF) model, etc. Depending on the implementations, the request may also indicate at least one configuration that is to be applied to the data collection, such as a prioritization of measurements and / or labels described in connection with FIG. 10. The first entity 1104 may be a data collection entity, a network entity / node (e.g., a server, a location server, an LMF, a sensing management function, an AI / ML management function, a base station, a TRP, etc.), a PRU, or another UE, etc.

[0126] At 1112, the UE 1102 may obtain / collect a set of data (hereafter “collected data”) based on the request (and also based on the at least one configuration), such as receiving and measuring reference signals (RS) transmitted from an entity (e.g., the first entity 1104, a second entity 1106, or another entity). For example, as shown at 1126, if the first entity 1104 is a base station, the UE 1102 may receive and measure a set of RS / PRS (collectively as “DL-RS”) from the base station (or from one or moreof its TRPs). In another example, as shown at 1128, the first entity 1104 may request a second entity 1106 to transmit reference sign al (s) to the UE 1102. For example, if the first entity 1104 is a location server / LMF, the first entity 1104 may request a second entity 1106 (e.g., a base station, a TRP, another UE, etc.) to transmit a set of RS (e.g., PRS, sidelink RS, etc.) to the UE 1102. As shown at 1130, the second entity 1106 may transmit a set of RS to the UE 1102 in response to the quest from the first entity 1104.

[0127] Table 3 below provides an example list of data in which the first entity 1104 may request the UE 1102 to collect and also the potential advantage of collecting such data. Note this is just an exemplary list and is not meant to be exhaustive.Table 3 - Example Data a UE is Requested to Collect

[0128] In one example, as shown at 1114, after obtaining the collected data, the UE 1102 may transmit the collected data (or an indication of the collected data) to the first entity 1104 (e.g., to the entity that requests it).

[0129] In another example, the UE 1102 maybe configured to transmit the collected data to a second entity 1106 (e.g., a data collection entity, a network entity / node (e.g., a location server, an LMF, a base station, a TRP, etc.), a PRU, or another UE, etc.). For example, as shown at 1116, the second entity 1106 (e.g., a base station, a TRP, etc.) may request the first entity 1104 (e.g., a location server, an LMF, a data collection entity, etc.) to collect data from the UE 1102 (or from a UE selected by the first entity 1104). Then, based on the request from the second entity 1106, the first entity 1104 may request the UE 1102 to collect the data, such as described in connection with 1110. Thus, after the UE 1102 collected the data, as shown at 1118, theUE 1102 may transmit the collected data directly to the second entity 1106. Alternatively, as shown at 1114 and 1120, theUE 1102 may transmitthe collected data to the first entity 1104, and the first entity 1104 may transmit / forward the data to the second entity 1106.

[0130] In another example, as shown at 1122, the UE 1102 may be configured to train one or more AI / ML-based model based on the collected data (e.g., the UE 1102 may not transmit the collected data to another entity in this example). As shown at 1136, after receiving the collected data, the first entity 1104 may also be configured to train one or more AI / ML-based model based on the collected data.

[0131] In some implementations, as shown at 1124, after the UE 1102 receives the request to collect data (e.g., from the first entity 1104 at 1110), the UE 1102 may be configured to first determine or verify whether the request is associated with AI / ML positioning related operation(s) and / or model(s). If the UE 1102 determines / verifies that the request is associated with AI / ML positioning related operation(s) and / or model(s), the UE 1102 may collect data based on the request (and the configuration(s)). On the other hand, if the UE 1102 determines / verifies that the requestis not associated with AI / ML positioningrelated operation(s) and / or model(s), the UE 1102 may collect data in a different way, such as without performing labelling for the collected data.

[0132] In some examples, as shown at 1132, the UE 1102 may request for assistance data from the first entity 1104. For purposes of the present disclosure and in the context of positioning, “assistance data” may refer to information / configuration(s) that are capable of enhancing the performance and / or accuracy of positioning (e.g., the positioning of aUE). For example, depending on the implementations, assistance data may assist a UE (e.g., a GNSS device / receiver) to acquire satellite signals more quickly and accurately, thereby reducing the time to first fix (TTFF) and improving overall positioning accuracy, or assist a UE to obtain information related to a base station and / or its TRP(s), thereby enabling the UE to transmit reference signal(s) to and / or receive reference signal(s) from the base station and / or its TRP(s) more effectively, etc.

[0133] For example, at 1132, if the first entity 1104 is an LMF and based on the request to collect data (e.g., received at 1110), theUE 1102 may transmit a requestthe first entity 1104 to provide assistance data (e.g., PRS assistance data) for performing the data collection. Then, as shown at 1134, based on the request to provide assistance data, the UE 1102 may receive the assistance data from the first entity 1104. In some examples, based on the request to provide assistance data, the first entity 1104 may also request a second entity 1106 to provide reference signal(s), such as shown and discussed in connection with 1128. Then, the first entity 1104 may indicate to the UE 1102 information related to the reference signal(s) that is to be transmitted from 1he second entity 1106.

[0134] FIG. 12 is a communication flow 1200 illustrating an example list of data which a base station may be requested to collect 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.

[0135] At 1210, abase station 1202 (whichmay alsobereferredto as a“device / targetdevice” and / or a “network entity” for purposes of illustration) may receive a request, from a first entity 1204 (e.g., another device or a “requesting device”), to collect data (e.g., to perform data collection(s)) for at least one AI / ML model. Examples of an AI / ML model may include an AI / ML positioningmodel, an AI / ML positioningmodel, and / or an AI / ML CSF model, etc. Depending on the implementations, the request may also indicate at least one configuration that is to be applied to the data collection, such asa prioritization of measurements and / or labels described in connection with FIG. 10. Depending on the configurations or the context, the first entity 1204 may be a data collection entity, a server, a location server, anLMF, a sensing management function, an AI / ML management function, a base station, a PRU, a UE, etc.

[0136] At 1212, the base station 1202 may obtain / collect a set of data (hereafter “collected data”) based on the request (and also based on the at least one configuration), such as receiving and measuringRS transmitted from an entity (e.g., the first entity 1204, a second entity 1206, or another entity, etc.). For example, as shown at 1226, if the first entity 1204 is a location server (e.g., an LMF) and the second entity 1206 is a UE, the base station 1202 may receive and measure a set of RS (e.g., a set of SRS) from the second entity 1206 (e.g., the UE), such as shown at 1230. Thus, as shown at 1228, in some scenarios, the first entity 1204 may request the second entity 1206 to transmit reference signal(s)tothebase station 1202. Then, the second entity 1206 may transmit a set of RS to the base station 1202 in response to the quest from the first entity 1204 such as shown at 1230.

[0137] In some examples, if the first entity 1204 is a UE, then the first entity 1204 may transmit a set of RS / SRS (collectively as “UL-RS”) to the base station 1202 directly such as shown at 1226. In addition, if the first entity 1204 or the second entity 1206 is a UE, the base station 1202 may also be able to directly requestthe first entity 1204, the second entity 1206, or another UE to transmit a set of UL-RS to the base station 1202 (e.g., without going through another entity).

[0138] Table 4 below provides an example list of data in which the first entity 1204 may requestthe base station 1202 to collect and also the potential advantage of collecting such data. Note this is just an exemplary list and is not meant to be exhaustive.Table 4 - Example Data a Base Station is Requested to Collect

[0139] In one example, as shown at 1214, after obtaining the collected data, the base station 1202 may transmit the collected data (or an indication of the collected data) to the first entity 1204 (e.g., to the entity that requests it).

[0140] In another example, the base station 1202 may be configured to transmit the collected data to a second entity 1206 (e.g., a data collection entity, a network entity / node (e.g, a location server, an LMF, another base station, a TRP, etc.), a PRU, or a UE, etc.). For example, as shown at 1216, the second entity 1206 (e.g., a UE, a PRU, etc.) may request the first entity 1204 (e.g., a location server, an LMF, a data collection entity, etc.) to collect data from the base station 1202 (or from a base station selected by the first entity 1204). Then, based on the request from the second entity 1206, the first entity 1204 may request the base station 1202to collect the data, such as describedin connection with 1210. Thus, after the base station 1202 collected the data, as shown at 1218, the base station 1202 may transmit the collected data directly to the second entity 1206. Alternatively, as shown at 1214 and 1220, the base station 1202 may transmit the collected data to the first entity 1204, and the first entity 1204 may forward the data to the second entity 1206.

[0141] In another example, as shown at 1222, the base station 1202 may be configured to train one or more AI / ML-based model based on the collected data (e.g., the base station 1202 may not transmit the collected data to another entity in this example). As shown at 1236, after receiving the collected data, the first entity 1204 may also be configured to train one or more AI / ML-based model based on the collected data.

[0142] In some implementations, as shown at 1224, after the base station 1202 receives the request to collect data (e.g., from the first entity 1204 at 1210), the base station 1202 may be configured to first determine or verify whether the request is associated with AI / ML positioning related operation(s) and / or model(s). If the base station 1202 determines / verifies that the request is associated with AI / ML positioning related operation(s) and / or model(s), the base station 1202 may collect data based on the request (and the configuration(s)). On the other hand, if the base station 1202 determines / verifies that the request is not associated with AI / ML positioning related operation(s) and / or model(s), the base station 1202 may collect data in a different way, such as without performing labelling for the collected data.

[0143] In some examples, as shown at 1232, the base station 1202 may request for assistance data from the first entity 1204. For example, at 1232, if the first entity 1204 is an LMF and based on the request to collect data (e.g., received at 1210), the base station 1202 may transmit a requestthe first entity 1204 to provide assistance dataand / or additional configuration(s) for performing the data collection. Then, as shown at 1234, based on the request to provide assistance data and / or the additional configuration(s), the base station 1202 may receive the assistance data and / or the additional configurations) from the first entity 1204. In some examples, based on the request to provide assistance data and / or the additional configuration^), the first entity 1204 may also request a second entity 1206 to provide reference signal(s), such as shown and discussed in connection with 1228. Then, the first entity 1204 may indicate to the base station 1202 information related to the reference signal(s) that is to be transmitted from the second entity 1206.

[0144] FIG. 13 is a communication flow 1300 illustrating an example list of data which a location server (e.g., an LMF, a sensing management function, an AI / ML management function, etc,) may be requested to collect in accordance with various aspects of the present disclosure. The numberings associated with the communication flow 1300 do not specify aparticular temporal order and are merely used as references for the communication flow 1300.

[0145] At 1310, a location server 1302 (which may also be referred to as a “device / target device” and / or a “network entity” for purposes of illustration) may receive a request, from a first entity 1304 (e.g., another device or a “requesting device”), to collect data (e.g., to perform data collection(s)) for at least one AI / ML model. Examples of anAI / ML model may include an AI / ML positioning model, an AI / ML positioning model, and / or an AI / ML channel state feedback (CSF) model, etc. Depending on the implementations, the request may also indicate at least one configuration that is to be applied to the data collection, such as a prioritization of measurements and / or labels described in connection with FIG. 10. Depending on the configurations orthe context, the location server 1302 may be a general server, an LMF, a sensing management function, an AI / ML management function, etc., and the first entity 1304 may be a data collection entity, a base station, a PRU, or a UE, etc.

[0146] At 1312, the location server 1302 may obtain / collect a set of data (hereafter “collected data”) based on the request (and also based on the at least one configuration). In one example, as shown at 1314, the location server 1302 may request a second entity 1306 to perform the data collection, where the second entity 1306 may be a data collection entity, a base station, a PRU, or a UE, etc.

[0147] For example, at 1314, if the second entity 1306 is a UE, the location server 1302 may request the second entity 1306 to collect data by measuring a set of PRS from a base station (or from one or more TRPs of the base station), or measuring a set of sidelink RS from another UE, etc. At 1316, in response to the request from the location server 1302, the second entity 1306 may perform the data collection (e.g., perform measurements).

[0148] In some examples, as shown at 1318, the second entity 1306 may also requestthe first entity 1304 (or another entity) to provide reference signals to the second entity 1306. For example, the second entity 1306 may be a UE and the first entity 1304 may be a serving base station of the second entity 1306. In response, at 1320, the first entity 1304 may configure a set of RS to be transmitted to the second entity 1306 (e.g, directly from the first entity 1304 or from another entity / node, etc.).

[0149] At 1322, the second entity 1306 may transmit the collected data to the location server 1302. At 1324, after obtaining the collected data, the location server 1302 may transmit / forward the collected data to the first entity 1304 (e.g., to the entity that requests it). In some examples, as shown at 1326, to reduce signaling overhead, the location server 1302 may also request the second entity 1306 to provide the collected data to the first entity 1304 directly.

[0150] Table 5 below provides an example list of data in which the first entity 1304 may request the location server 1302 to collect and also the potential advantage ofcollecting such data. Note this is just an exemplary list and is not meant to be exhaustive.Table 5 - Example Data a Base Station is Requested to Collect

[0151] In another example, as shown at 1328, the location server 1302 may be configured to train one or more AI / ML-based model based on the collected data. As shown at 1330, after receiving the collected data, the first entity 1304 may also be configured to train one or more AI / ML-based model based on the collected data.

[0152] In some implementations, as shown at 1330, after the location server 1302 receives the request to collect data (e.g., from the first entity 1304 at 1310), the location server 1302 may be configured to first determine or verify whether the request is associated with AI / ML positioning related operation(s) and / or model(s). If the location server 1302 determines / verifies thatthe request is associated with AI / ML positioning related operation(s) and / or model(s), the location server 1302 may collect data based on the request (and the configuration(s)). On the other hand, if the location server 1302 determines / verifies that the request is not associated with AI / ML positioning related operation(s) and / or model(s), the location server 1302 may collect data in a different way, such as without performing labelling for the collected data.

[0153] FIG. 14 is a flowchart 1400 of wireless communication. The method may be performed by a device (e.g., the UE 104, 404, 602, 702, 1002, 1102; the base station 102, 706, 1006, 1202; the location server 1302; the apparatus 1504; the network entity 1602, 1760). The method may enable the device to collect data, such as reference signal measurements, for one or more AI / ML models based on a set of data quality and condition configurations.

[0154] At 1402, the device may receive a requestto collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data, such as described in connection with FIGs. 10 to 13. For example, as discussed in connection with 1110 of FIG. 11, a UE 1102 (which may also be referred to as a “device / target device” for purposes of illustration) may receive a request, from a first entity 1104, to collect data (e.g., to perform data collection(s)) for at least one AI / ML model. The reception of the request may be performed by, e.g., the data collection component 198, the transceiver(s) 1522, the cellular baseband processor(s) 1524, and / or the application processor(s) 1506 of the apparatus 1504 in FIG. 15. The reception of the requestmay also be performed by, e.g., the data collection component 199, the transceiver(s) 1646, the RU processor(s) 1642, the DU processor(s) 1632, and / orthe CU processor(s) 1612, of the network entity 1602 in FIG. 16. Thereception of the request may also be performed by, e.g., the data collection configurationcomponent 197, the network processor(s) 1712, and / or the network interface 1780 of the network entity 1760 in FIG. 17.

[0155] At 1404, the device may obtain, based on the request and the configuration, collected data for the at least one AI / ML-based model, such as described in connection with FIGs. 10 to 13. For example, as discussed in connection with 1112 of FIG. 11 , the UE 1102 may obtain / collect a set of data (hereafter “collected data”) based on the request (and also based on the at least one configuration), such as receiving and measuring reference signals (RS) transmitted from an entity (e.g., the first entity 1104, a second entity 1106, or another entity). The obtainment of the collected data may be performed by, e.g., the data collection component 198, the transceiver(s) 1522, the cellular baseband processor(s) 1524, and / or the application processor(s) 1506 ofthe apparatus 1504 in FIG. 15. The obtainment of the collected data may also be performed by, e.g, the data collection component 199, the transceiver(s) 1646, the RU processor(s) 1642, the DU processor(s) 1632, and / or the CU processor(s) 1612, of the network entity 1602 in FIG. 16. The obtainment of the collected data may also be performed by, e.g, the data collection configuration component 197, the network processor(s) 1712, and / or the network interface 1780 of the network entity 1760 in FIG. 17.

[0156] At 1406, the device may transmit the collected data or an indication of the collected data, or train the at least one AI / ML-based model based on the collected data, such as described in connection with FIGs. 10 to 13. For example, as discussed in connection with 1114 of FIG. 11, after obtaining the collected data, the UE 1102 may transmit the collected data (or an indication of the collected data) to the first entity 1104 (e.g, to the entity that requests it). In another example, as shown at 1122, the UE 1102 may be configured to train one or more AI / ML-based model based on the collected data (e.g., the UE 1102 may not transmit the collected data to another entity in this example. The transmission of the collected data and / or the training of the at least one AI / ML-based model may be performed by, e.g., the data collection component 198, the transceiver(s) 1522, the cellular baseband processor(s) 1524, and / or the application processor(s) 1506 of the apparatus 1504 in FIG. 15. The transmission of the collected data and / or the training of the at least one AI / ML-based model may also be performed by, e.g., the data collection component 199, thetransceiver(s) 1646, the RU processor(s) 1642, the DU processor(s) 1632, and / or the CU processor(s) 1612, of the network entity 1602 in FIG. 16. The transmission of the collected data and / orthe training of the at least one AI / ML-based model may also be performed by, e.g, the data collection configuration component 197, the network processor(s) 1712, and / or the network interface 1780 of the network entity 1760 in FIG. 17.

[0157] In one example, the device may determine or verify the request is associated with an AI / ML-based method or model, where obtainment of the collected data is further based on determination or verification that the request is associated with the AI / ML- based method or model.

[0158] In another example, the device is a UE. In some implementations, to receive the request, the device may be configured to receive the request from a server, an LMF, a sensing management function, an AI / ML management function, a second UE, or a base station, and to transmit the collected data or the indication of the collected data, the device may be configured to transmit the collected data or the indication of the collected data to the server, the LMF, the second UE, or the base station. In some implementations, the device may transmit, to an LMF based on the request, a second request for PRS assistance data for collecting the data, and receive, from the LMF based on the request, the PRS assistance data or a second indication of a set of PRS resources for collecting the data. In some implementations, the configuration for collectingthe data includes at least one of: (1) collecting data that is associated with a specific set of DL-RS resource IDs, a specific set of DL-RS resource set IDs, or a specific set of beams associated with a TRP, (2) collecting data that includes one or more joint measurements across a set of aggregated frequency layers, component carriers, or bands, (3) collecting data in which one or more measurements with additional paths are performed, (4) collecting data in which one or more measurements are associated with a RxTEG, a specific RxTEG, or a specific number of Rx TEGs, or (5) collecting data in which one or more measurements include an estimated LOS or NLOS indicator. In some implementations, the configuration for collecting the data includes at least one of: (1) collecting data with RSRPP measurements for one or more additional paths, (2) collecting data with multiple measurement instances in a single measurement report, (3) collecting data with DLRS Rx hopping measurement or reporting, (4) collecting data that includes DL PRS Rx hoppingwith a specified bandwidth, (5) collecting data with a specified maximum number of UE-Rx-Tx time difference measurements for differentDL-RS resources or different DL-RS resource sets per TRP, or (6) collecting data that includes RSCPmeasurements. In some implementations, the configuration for collecting the data includes at least one of : (1 ) collecting data that includes RSCPD measurements, (2) collecting data that includes a specified maximum number of DL-RS RSTD measurements per pair of TRPs, (3) collecting data with a specified reporting granularity, (4) collecting data with a same DL-RS resource of a TRP with a specified number of different UE RxTEGs, (5) collecting data that includes measurements with reduced number of samples, (6) collecting data that uses a lower Rx beam sweeping factor than eight for FR2, or (7) collecting data that includes measurements for a specific set of DL-RS resource sets in a specified time window.

[0159] In another example, the device is a base station. In some implementations, to receive the request, the device may be configured to receive the request from a server, an LMF, a sensing management function, an AI / ML management function, a UE, or a second base station, and to transmitthe collected data or the indication of the collected data, the device may be configured to transmitthe collected data or the indication of the collected data to the server, the LMF, the sensing management function, the AI / ML management function, the UE, or the second base station. In some implementations, the device may transmit, to a UE based on the request, a second configuration to transmission a set of reference signals, and receive, from the UE based on the configuration, the set of reference signals, where to obtain the data for the at least one AI / ML-based model, the device may be configured to measure the set of reference signals. In some implementations, the device may transmit, to an LMF based on the request, a second request for assistance data for collecting the data, and receive, from the LMF based on the request, the assistance data for collectingthe data. In some implementations, the configuration for collectingthe data includes at least one of: (1) collecting data that is associated with a specific set of UL-RS IDs, a specific set of UL-RS resource set IDs, or a specific set of beams associated with a TRP, (2) collecting data that includes one or more joint measurements across a set of UL-RSs, (3 ) collecting data in which one or more measurements with additional paths are performed, (4) collecting data in which one or more measurements are associated with a RxTEG, a specific RxTEG, or a specific number of Rx TEGs, or (5) collecting data in which one or more measurements include an estimated LOS or NLOS indicator. In some implementations, the configuration for collectingthe data includes at least one of: (1) collecting data with RSRPP measurements for one or moreadditional paths, (2) collecting data with multiple measurement instances in a single measurement report, (3) collecting data with UL-RS Tx hopping measurement or reporting, (4) collecting data that includes UL-RS Tx hopping with a specified bandwidth, (5) collecting data with a specified maximum number of base station-Rx- Tx time difference measurements for different UL-RS resources or different UL-RS resource sets per TRP, or (6) collecting data that includes RSCP measurements. In some implementations, the configuration for collecting the data includes at least one of : (1) collecting data that includes RSCPD measurements, (2) collecting data with a specified reporting granularity, (3) collecting data with a same UL-RS resource of a TRP with a specified number of different base station RxTEGs, (4) collecting data that includes measurements with reduced number of samples, (5) collecting data that uses a lower Rx beam sweeping factor than eight for FR2, or (6) collecting data that includes measurements for a specific set of UL-RS resource sets in a specified time window.

[0160] In another example, the device is a server or an LMF. In some implementations, to receive the request, the device may be configured to receive the request from a UE or a base station, and to transmit the collected data or the indication of the collected data, the device may be configured to transmit the collected data or the indication of the collected data to the UE or the base station. In some implementations, the device may transmit, to at least one second UE or at least one second base station based on the request, a second request for collecting the data, and receive, from the at least one second UE or the at least one second base station, the data. In some implementations, the configuration for collecting the data includes at least one of: (1) collecting data that is associated with a specific set of DL-RS resource IDs, a specific set of DL-RS resource set IDs, a specific set of UL-RS IDs, a specific set of UL-RS resource set IDs, or a specific set of beams associated with a TRP, (2) collecting data that includes one or more joint measurements across a set of aggregated frequency layers, component carriers, or bands or UL-RSs, (3) collecting data in which one or more measurements with additional paths are performed, (4) collecting data in which one or more measurements are associated with a RxTEG, a specific RxTEG, or a specific number of Rx TEGs, or (5) collecting data in which one or more measurements include an estimated LOS or NLOS indicator. In some implementations, the configuration for collecting the data includes at least one of: (1) collecting data withRSRPP measurements for one or more additional paths, (2) collecting data with multiple measurement instances in a single measurement report, (3) collecting data with DL-RS Rx or UL-RS Tx hopping measurement or reporting, (4) collecting data that includes DL PRS Rx hopping or UL-RS Tx hopping with a specified bandwidth, (5) collecting data with a specified maximum number of UE-Rx-Tx time difference measurements for different DL-RS or UL-RS resources or differentDL-RS or UL-RS resource sets, or (6) collecting data that includes RSCP measurements. In some implementations, the configuration for collecting the data includes at least one of: (1) collecting data that includes RSCPD measurements, (2) collecting data that includes a specified maximum number of DL-RS RSTD measurements per pair of TRPs, (3) collecting data with a specified reporting granularity, (4) collecting data with a same UL-RS with a specified number of differentRxTEGs, (5) collecting data that includes measurements with reduced number of samples, (6) collecting data that uses a lower Rx beam sweeping factor than eight for FR2, or (7) collecting data that includes measurements for a specific set of DL-RS or UL-RS resource sets in a specified time window.

[0161] FIG. 15 is a diagram 1500 illustrating an example of a hardware implementation for an apparatus 1504. The apparatus 1504 may be a UE, a component of a UE, or may implement UE functionality. In some aspects, the apparatus 1504 may include at least one cellular baseband processor 1524 (also referred to as a modem) coupled to one or more transceivers 1522 (e.g., cellular RF transceiver). The cellular baseband processor(s) 1524 may include at least one on-chip memory 1524'. In some aspects, the apparatus 1504 may further include one or more subscriber identity modules (SIM) cards 1520 and at least one application processor 1506 coupled to a secure digital (SD) card 1508 and a screen 1510. The application processor(s) 1506 may include on-chip memory 1506'. In some aspects, the apparatus 1504 may further include a Bluetooth module 1512, a WLAN module 1514, an ultrawide band (UWB) module 1538 (e.g., a UWB transceiver), an SPS module 1516 (e.g., GNSS module), one or more sensors 1518 (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 1526, a power supply1530, and / or a camera 1532. The Bluetooth module 1512, the UWB module 1538, the WLAN module 1514, and the SPS module 1516 may include an on-chip transceiver (TRX) (or in some cases, just a receiver (RX)). The Bluetooth module 1512, the WLAN module 1514, and the SPS module 1516 may include their own dedicated antennas and / or utilize the antennas 1580 for communication. The cellular baseband processor(s) 1524 communicates through the transceiver(s) 1522 via one or more antennas 1580 with the UE 104 and / or with an RU associated with a network entity 1502. The cellular baseband processor(s) 1524 and the application processor(s) 1506 may each include a computer-readable medium / memory 1524', 1506', respectively. The additional memory modules 1526 may also be considered a computer-readable medium / memory. Each computer-readable medium / memory 1524', 1506', 1526 may be non-transitory. The cellular baseband processor(s) 1524 and the application processor(s) 1506 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) 1524 / application processor(s) 1506, causes the cellular baseband processor(s) 1524 / application processor(s) 1506 to perform the various functions described supra. The cellular baseband processors) 1524 and the application processor(s) 1506 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) 1524 and the application processor(s) 1506 may be configuredto perform a first sub set of the various functions described supra with out information storedin the memory and may be configured to perform a second subset of the various functions described supra based on the information stored in the memory. The computer-readable medium / memory may also be used for storing data that is manipulated by the cellular baseband processors) 1524 / application processor(s) 1506 when executing software. The cellular baseband processor(s) 1524 / application processor(s) 1506 may be a component of the UE 350 and may include the at least one memory 360 and / or at least one of the TX processor 368, the RX processor 356, and the controller / processor 359. In one configuration, the apparatus 1504 may be at least one processor chip (modem and / or application) and include just the cellular baseband processor(s) 1524 and / or the application processor(s) 1506, and in another configuration, the apparatus 1504 may be the entireUE (e.g., see UE 350 of FIG. 3) and include the additional modules of the apparatus 1504.

[0162] As discussed supra, the data collection component 198 may be configured to receive a request to collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data. The data collection component 198 may also be configured to obtain, based on the request and the configuration, collected data for the at least one AI / ML-based model. The data collection component 198 may also be configured to transmit the collected data or an indication of the collected data, or training the at least one AI / ML-based model based on the collected data. The data collection component 198 may be within the cellular baseband processor(s) 1524, the application processor(s) 1506, or both the cellular baseband processor(s) 1524 and the application processor(s) 1506. The data collection 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 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. As shown, the apparatus 1504 may include a variety of components configuredforvarious functions. In one configuration, the apparatus 1504, and in particular the cellular baseband processor(s) 1524 and / or the application processor(s) 1506, may include means for receiving a request to collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data. The apparatus 1504 may further include means for obtaining, based on the request and the configuration, collected data for the at least one AI / ML-based model. The apparatus 1504 may further include means for transmitting the collected data or an indication of the collected data, or training the at least one AI / ML-based model based on the collected data.

[0163] In one configuration, the apparatus 1504 may further include means for determining or means for verifying the request is associated with an AI / ML-based method or model, where obtainment of the collected data is further based on determination or verification that the request is associated with the AI / ML-based method or model.

[0164] In another configuration, the means for receiving the request may include configuring the apparatus 1504 to receive the request from a server, an LMF, a second UE, or a base station, and the means for transmitting the collected data or the indication of the collected data may include configuring the apparatus 1504 to transmit the collected data or the indication of the collected data to the server, the LMF, the second UE, or the base station.

[0165] In another configuration, the apparatus 1504 may further include means for transmitting, to an LMF based on the request, a second request for PRS assistance data for collecting the data, and means for receiving, from the LMF based on the request, the PRS assistance data or a second indication of a set of PRS resources for collecting the data.

[0166] In another configuration, the configuration for collecting the data includes atleast one of: (1) collecting data that is associated with a specific set of DL-RS resource IDs, a specific set of DL-RS resource set IDs, or a specific set of beams associated with a TRP, (2) collecting data that includes one or more joint measurements across a set of aggregated frequency layers, component carriers, or bands, (3) collecting data in which one or more measurements with additional paths are performed, (4) collecting data in which one or more measurements are associated with a RxTEG, a specific RxTEG, ora specific number of Rx TEGs, or (5) collecting data in which one or more measurements include an estimated LOS or NLOS indicator.

[0167] In another configuration, the configuration for collecting the data includes atleast one of: (1) collecting data with RSRPP measurements for one or more additional paths, (2) collecting data with multiple measurement instances in a single measurement report, (3) collecting data with DL-RS Rx hopping measurement or reporting, (4) collecting data that includes DL PRS Rx hopping with a specified bandwidth, (5) collecting data with a specified maximum number of UE-Rx-Tx time difference measurements for different DL-RS resources or different DL-RS resource sets per TRP, or (6) collecting data that includes RSCP measurements.

[0168] In another configuration, the configuration for collecting the data includes atleast one of: (1) collecting data that includes RSCPD measurements, (2) collecting data that includes a specified maximum number of DL-RS RSTD measurements per pair of TRPs, (3) collecting data with a specified reporting granularity, (4) collecting data with a same DL-RS resource of a TRP with a specified number of different UERxTEGs, (5) collecting data that includes measurements with reduced number of samples, (6) collecting data that uses a lower Rx beam sweeping factor than eight for FR2, or (7) collecting data that includes measurements for a specific set of DL-RS resource sets in a specified time window.

[0169] The means may be the data collection component 198 of the apparatus 1504 configured to perform the functions recited by the means. As described supra, the apparatus 1504 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.

[0170] FIG. 16 is a diagram 1600 illustrating an example of a hardware implementation for a network entity 1602. The network entity 1602 may be a BS, a component of a BS, or may implementBS functionality . The network entity 1602 may include at least one of a CU 1610, a DU 1630, or an RU 1640. For example, depending on the layer functionality handled by the data collection component 199, the network entity 1602 may include the CU 1610; both the CU 1610 and the DU 1630; each ofthe CU 1610, the DU 1630, and the RU 1640; the DU 1630; both the DU 1630 and the RU 1640; or the RU 1640. The CU 1610 may include at least one CU processor 1612. The CU processor(s) 1612 may include on-chip memory 1612'. In some aspects, the CU 1610 may further include additional memory modules 1614 and a communications interface 1618. The CU 1610 communicates with the DU 1630 through a midhaul link, such as an Fl interface. The DU 1630 may include at least one DU processor 1632. The DU processor(s) 1632 may include on-chip memory 1632'. In some aspects, the DU 1630 may further include additional memory modules 1634 and a communications interface 1638. The DU 1630 communicates with the RU 1640 through a fronthaul link. The RU 1640 may include at least one RU processor 1642. The RU processor(s) 1642 may include on-chip memory 1642'. In some aspects, the RU 1640 may further include additional memory modules 1644, one or more transceivers 1646, antennas 1680, and a communications interface 1648. The RU 1640 communicates with the UE 104. The on-chip memory 1612', 1632', 1642' and the additional memory modules 1614, 1634, 1644 may each be considered a computer-readable medium / memory. Each computer-readable medium / memory may be non-transitory. Each of the processors 1612, 1632, 1642 is responsible forgeneral 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.

[0171] As discussed supra, the data collection component 199 may be configured to receive a request to collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data. The data collection component 199 may also be configured to obtain, based on the request and the configuration, collected data for the at least one AI / ML-based model. The data collection component 199 may also be configured to transmit the collected data or an indication of the collected data, or training the at least one AI / ML-based model based on the collected data. The data collection component 199 may be within one or more processors of one or more of the CU 1610, DU 1630, and the RU 1640. The data collection component 199 may be one or more hardware components specifically configured to carry out the stated processes / algorithm, implemented by one or more processors configured to perform the stated processes / algorithm, stored within a computer- readable medium for 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 1602 may include a variety of components configured for various functions. In one configuration, the network entity 1602 may include means for receiving a request to collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data. The network entity 1602 may further include means for obtaining, based on the request and the configuration, collected data for the at least one AI / ML-based model. The network entity 1602 may further include means for transmitting the collected data or an indication of the collected data, or training the at least one AI / ML-based model based on the collected data.

[0172] In one configuration, the network entity 1602 may further include means for determining or means for verifying the request is associated with an AI / ML-based method or model, where obtainment of the collected data is further based on determination or verification that the request is associated with the AI / ML-based method or model.

[0173] In another configuration, the means for receiving the request may include configuring the network entity 1602 to receive the request from a server, an LMF, a UE, or a second base station, and the means fortransmittingthe collected data orthe indication of the collected data may include configuring the network entity 1602 to transmit the collected data or the indication of the collected data to the server, the LMF, the UE, or the second base station.

[0174] In another configuration, the network entity 1602 may further include means for transmitting, to a UE based on the request, a second configuration to transmission a set of reference signals, and means for receiving, from the UE based on the configuration, the set of reference signals, where the means for obtaining the data for the at least one AI / ML-based model may include configuring the network entity 1602 to measure the set of reference signals.

[0175] In another configuration, the network entity 1602 may further include means for transmitting, to an LMF based on the request, a second request for assistance data for collecting the data, and means for receiving, from the LMF based on the request, the assistance data for collecting the data.

[0176] In another configuration, the configuration for collecting the data includes at least one of: (1) collecting data that is associated with a specific set of UL-RS IDs, a specific set of UL-RS resource set IDs, or a specific set of beams associated with a TRP, (2) collecting data that includes one or more joint measurements across a set of UL-RSs, (3) collecting data in which one or more measurements with additional paths are perf ormed, (4) collecting data in which one or more measurements are associated with a RxTEG, a specific RxTEG, or a specific number of Rx TEGs, or (5) collecting data in which one or more measurements include an estimated LOS or NLOS indicator.

[0177] In another configuration, the configuration for collecting the data includes at least one of: (1) collecting data with RSRPP measurements for one or more additional paths, (2) collecting data with multiple measurement instances in a single measurement report, (3) collecting data with UL-RS Tx hopping measurement or reporting, (4) collecting data that includes UL-RS Tx hopping with a specified bandwidth, (5) collecting data with a specified maximum number of base station-Rx-Tx time difference measurements for different UL-RS resources or different UL-RS resource sets per TRP, or (6) collecting data that includes RSCP measurements.

[0178] In another configuration, the configuration for collecting the data includes atleast one of : (1) collecting data that includes RSCPD measurements, (2) collecting data with a specified reporting granularity, (3) collecting data with a same UL-RS resource of a TRP with a specified number of different base station RxTEGs, (4) collecting data that includes measurements with reduced number of samples, (5) collecting data that uses a lower Rx beam sweeping factor than eight for FR2, or (6) collecting data that includes measurements for a specific set of UL-RS resource sets in a specified time window.

[0179] The means may be the data collection component 199 of the network entity 1602 configured to perform the functions recited by the means. As described supra, the network entity 1602 may include the TX processor 316, the RX processor 370, and the controller / processor 375. As such, in one configuration, the means may be the TX processor 316, the RX processor 370, and / or the controller / processor 375 configured to perform the functions recited by the means.

[0180] FIG. 17 is a diagram 1700 illustrating an example of a hardware implementation for a network entity 1760. In one example, the network entity 1760 may be within the core network 120. The network entity 1760 may include at least one network processor 1712. The network processor(s) 1712 may include on-chip memory 1712'. In some aspects, the network entity 1760 may further include additional memory modules 1714. The network entity 1760 communicates via thenetwork interface 1780 directly (e.g., backhaul link) or indirectly (e.g., through a RIC) with the CU 1702. The on-chip memory 1712' and the additional memory modules 1714 may each be considered a computer-readable medium / memory. Each computer-readable medium / memory may be non-transitory. The network processor(s) 1712 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.

[0181] As discussed supra, the data collection configuration component 197 may be configured to receive a request to collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data. The data collection configuration component 197 may also be configured to obtain, based onthe request and the configuration, collected data for the at least one AI / ML-based model. The data collection configuration component 197 may also be configured to transmitthe collected data or an indication of the collected data, ortrainingthe at least one AI / ML-based model based on the collected data. The data collection configuration component 197 maybe within the network processor(s) 1712. The data collection 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 implementation by one or more processors, or some combination thereof. When multiple processors are implemented, the multiple processors may perform the stated processes / algorithm individually or in combination. The network entity 1760 may include a variety of components configured for various functions. In one configuration, the network entity 1760 may include means for receiving a request to collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data. The network entity 1760 may further include means for obtaining, based on the request and the configuration, collected data for the at least one AI / ML-based model. The network entity 1760 may further include means for transmitting the collected data or an indication of the collected data, or training the at least one AI / ML-based model based on the collected data.

[0182] In one configuration, the network entity 1760 may further include means for determining or means for verifying the request is associated with an AI / ML-based method or model, where obtainment of the collected data is further based on determination or verification that the request is associated with the AI / ML-based method or model.

[0183] In another configuration, the means for receiving the request may include configuring the network entity 1760 to receive the requestfrom a UE or a base station, and the means for transmitting the collected data or the indication of the collected data may include configuring the network entity 1760 to transmit the collected data or the indication of the collected data to the UE or the base station.

[0184] In another configuration, the network entity 1760 may further include means for transmitting, to at least one second UE or at least one second base station based onthe request, a second request for collecting the data, and means for receiving, from the at least one second UE or the at least one second base station, the data.

[0185] In another configuration, the configuration for collectingthe data includes atleast one of: (1) collecting data that is associated with a specific set of DL-RS resource IDs, a specific set of DL-RS resource set IDs, a specific set of UL-RS IDs, a specific set of UL-RS resource set IDs, or a specific set of beams associated with a TRP, (2) collecting data that includes one or more joint measurements across a set of aggregated frequency layers, component carriers, or bands orUL-RSs, (3) collecting data in which one or more measurements with additional paths are performed, (4) collecting data in which one or more measurements are associated with a RxTEG, a specific Rx TEG, ora specific numb er of Rx TEGs, or(5) collecting data in which one or more measurements include an estimated LOS or NLOS indicator.

[0186] In another configuration, the configuration for collectingthe data includes atleast one of: (1) collecting data with RSRPP measurements for one or more additional paths, (2) collecting data with multiple measurement instances in a single measurement report, (3) collecting data with DL-RS Rx or UL-RS Tx hopping measurement or reporting, (4) collecting data that includes DLPRS Rx hopping or UL-RS Tx hopping with a specified bandwidth, (5) collecting data with a specified maximum number of UE-Rx-Tx time difference measurements for different DL-RS or UL-RS resources or different DL-RS or UL-RS resource sets, or (6) collecting data that includes RSCP measurements.

[0187] In another configuration, the configuration for collectingthe data includes atleast one of: (1) collecting data that includes RSCPD measurements, (2) collecting data that includes a specified maximum number of DL-RS RSTD measurements per pair of TRPs, (3) collecting data with a specified reporting granularity, (4) collecting data with a same UL-RS with a specified number of different RxTEGs, (5) collecting data that includes measurements with reduced number of samples, (6) collecting data that uses a lower Rx beam sweeping factor than eight for FR2, or (7) collecting data that includes measurements for a specific set of DL-RS or UL-RS resource sets in a specified time window.

[0188] The means may be the data collection configuration component 197 of the network entity 1760 configured to perform the functions recited by the means.

[0189] FIG. 18 is a flowchart 1800 of a method of wireless communication. The methodmay be performedby a device (e.g., the base station 102, 706; the location server 1004; the UE 104, 404, 602, 702; the one or more location servers 168; the first entity 1104, 1204, 1304; the apparatus 1904; the network entity 2002, 2160). The method may enable the device to configure a set of data quality and condition configurations for a device, such that the device may collect data for one or more AI / ML models based on the set of data quality and condition configurations.

[0190] At 1802, the device may transmit a request to collect data for at least one AI / ML- based model, where the request is indicative of a configuration for collecting the data, such as described in connection with FIGs. 10 to 13. For example, as discussed in connection with 1110 of FIG. 11 , the first entity 1104 may transmit, to a UE 1102, a request to collect data (e.g., to perform data collection(s)) for at least one AI / ML model. The transmission of the request may be performed by, e.g., the data collection component 198, the transceiver(s) 1922, the cellular baseband processor(s) 1924, and / or the application processor(s) 1906 of the apparatus 1904 in FIG. 19. The transmission of the request may also be performed by, e.g., the data collection component 199, the transceiver(s) 2046, the RU processor(s) 2042, the DU processor(s) 2032, and / or the CU processor(s) 2012, of the network entity 2002 in FIG. 20. The transmission of the request may also be performed by, e.g., the data collection configuration component 197, the network processor(s) 2112, and / or the network interface 2180 of the network entity 2160 in FIG. 21.

[0191] At 1804, the device may receive, based on the request and the configuration, collected data for the at least one AI / ML-based model, such as described in connection with FIGs. 10 to 13. For example, as discussed in connection with 1114 of FIG. 11, the first entity 1104 may receive, from the UE 1102, the collected data (or an indication of the collected data). The reception of the collected data may be performedby, e.g, the data collection component 198, the transceiver(s) 1922, the cellular baseband processor(s) 1924, and / or the application processor(s) 1906 of the apparatus 1904 in FIG. 19. The reception of the collected data may also be performedby, e.g., the data collection component 199, the transceiver(s) 2046, the RU processor(s) 2042, the DU processor(s) 2032, and / or the CU processor(s) 2012, of the network entity 2002 in FIG. 20. The reception of the collected data may also be performedby, e.g., the datacollection configuration component 197, the network processor(s) 2112, and / or the network interface 2180 of the network entity 2160 in FIG. 21.

[0192] At 1806, the device may transmit the collected data or an indication of the collected data, or train the at least one AI / ML-based model based on the collected data, such as described in connection with FIGs. 10 to 13. For example, as discussed in connection with 1120 of FIG. 11 , the UE 1102 may transmit the collected data to the first entity 1104, and the first entity 1104 may transmit / forward the data to the second entity 1106 As shown at 1136, after receiving the collected data, the first entity 1104 may also be configured to train one or more AI / ML-based model based on the collected data. The transmission of the collected data and / or the training of the at least one AI / ML-based model may be performed by, e.g., the data collection component 198, the transceiver(s) 1922, the cellular baseband processor(s) 1924, and / or the application processor(s) 1906 of the apparatus 1904 in FIG. 19. The transmission of the collected data and / or the training of the at least one AI / ML-based model may also be performed by, e.g., the data collection component 199, the transceiver(s) 2046, the RU processor(s) 2042, the DU processor(s) 2032, and / or the CU processor(s) 2012, of the network entity 2002 in FIG. 20. The transmission of the collected data and / or the training of the at least one AI / ML-based model may also be performed by, e.g., the data collection configuration component 197, the network processor(s) 2112, and / or the network interface 2180 of the network entity 2160 in FIG. 21.

[0193] In one example, the device may receive, from a first UE or a first base station, a second request to collect the data for the at least one AI / ML-based model, where to transmit the request, the device may be configured to transmit the request to a second UE or a second base station, where to receive the collected data, the device may be configured to receive the collected data from the second UE or the second base station, where to transmit the collected data, the device may be configured to transmit the collected data to the first UE or the first base station.

[0194] In another example, the device is a UE. In some implementations, to transmit the request, the device may be configured to transmit the request to a server, an LMF, a second UE, or a base station, and to receive the collected data or the indication of the collected data, the device may be configured to receive the collected data or the indication of the collected data from the server, the LMF, the second UE, or the base station.

[0195] In another example, the device is a base station. In some implementations, to transmit the request, the device may be configured to transmit the request to a server, anLMF, a UE, or a second base station, and to receive the collected data or the indication of the collected data, the device may be configured to receive the collected data or the indication of the collected data from the server, the LMF, the UE, or the second base station.

[0196] In another example, the device is a server or an LMF. In some implementations, to transmit the request comprises, the device may be configured to transmit the request to a UE or a base station, and to receive the collected data or the indication of the collected data, the device may be configured to receive the collected data or the indication of the collected data from the UE or the base station. In some implementations, the configuration for collecting the data includes at least one of (1) collecting data that is associated with a specific set of DL-RS resource IDs, a specific set of DL-RS resource set IDs, a specific set of UL-RS IDs, a specific set of UL-RS resource set IDs, or a specific set of beams associated with a TRP, (2) collecting data that includes one or more joint measurements across a set of aggregated frequency layers, component carriers, or bands orUL-RSs, (3) collecting data in which one or more measurements with additional paths are performed, (4) collecting data in which one or more measurements are associated with a RxTEG, a specific RxTEG, or a specific number of Rx TEGs, or (5) collecting data in which one or more measurements include an estimated LOS or NLOS indicator. In some implementations, the configuration for collecting the data includes at least one of (1) collecting data with RSRPP measurements for one or more additional paths, (2) collecting data with multiple measurement instances in a single measurement report,(3) collecting data with DL-RS Rx or UL-RS Tx hopping measurement or reporting(4) collecting data that includes DL PRS Rx hopping or UL-RS Tx hopping with a specified bandwidth, (5) collecting data with a specified maximum number of UE- Rx-Tx time difference measurements for different DL-RS or UL-RS resources or different DL-RS or UL-RS resource sets, or (6) collecting data that includes RSCP measurements. In some implementations, the configuration for collecting the data includes at least one of : (1 ) collecting data that includes RSCPD measurements, (2) collecting data that includes a specified maximum number of DL-RS RSTD measurements per pair of TRPs, (3) collecting data with a specified reportinggranularity, (4) collecting data with a same UL-RS with a specified number of different RxTEGs, (5) collecting data that includes measurements with reduced number of samples, (6) collecting data that uses a lower Rx beam sweeping factor than eight for FR2, or (7) collecting data that includes measurements for a specific set of DL-RS or UL-RS resource sets in a specified time window.

[0197] FIG. 19 is a diagram 1900 illustrating an example of a hardware implementation for an apparatus 1904. The apparatus 1904 may be a UE, a component of a UE, or may implement UE functionality. In some aspects, the apparatus 1904 may include at least one cellular baseband processor 1924 (also referred to as a modem) coupled to one or more transceivers 1922 (e.g., cellular RF transceiver). The cellular baseband processor(s) 1924 may include at least one on-chip memory 1924'. In some aspects, the apparatus 1904 may further include one or more subscriber identity modules (SIM) cards 1920 and at least one application processor 1906 coupled to a secure digital (SD) card 1908 and a screen 1910. The application processor(s) 1906 may include on-chip memory 1906'. In some aspects, the apparatus 1904 may further include a Bluetooth module 1912, a WLAN module 1914, an ultrawide band (UWB) module 1938 (e.g., a UWB transceiver), an SPS module 1916 (e.g., GNSS module), one or more sensors 1918 (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 1926, a power supply 1930, and / oracamera 1932. The Bluetooth module 1912, the UWB module 1938, the WLAN module 1914, and the SPS module 1916 may include an on-chip transceiver (TRX) (or in some cases, just a receiver (RX)). The Bluetooth module 1912, the WLAN module 1914, and the SPS module 1916 may include their own dedicated antennas and / or utilize the antennas 1980 for communication. The cellular baseband processor(s) 1924 communicates through the transceiver(s) 1922 via one or more antennas 1980 with the UE 104 and / or with an RU associated with a network entity 1902. The cellular baseband processor(s) 1924 and the application processor(s) 1906 may each include a computer-readable medium / memory 1924', 1906', respectively. The additional memory modules 1926 may also be considered a computer-readable medium / memory. Each computer-readable medium / memory 1924', 1906', 1926may be non-transitory. The cellular baseband processor(s) 1924 and the application processor(s) 1906 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) 1924 / application processor(s) 1906, causes the cellular baseband processor(s) 1924 / application processor(s) 1906 to perform the various functions described supra. The cellular baseband processors) 1924 and the application processor(s) 1906 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) 1924 and the application processor(s) 1906 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) 1924 / application processor(s) 1906 when executing software. The cellular baseband processor(s) 1924 / application processor(s) 1906 may be a component of the UE 350 and may include the at least one memory 360 and / or at least one of the TX processor 368, the RX processor 356, and the controller / processor 359. In one configuration, the apparatus 1904 may be at least one processor chip (modem and / or application) and include just the cellular baseband processor(s) 1924 and / or the application processor(s) 1906, and in another configuration, the apparatus 1904 may be the entire UE (e.g., see UE 350 of FIG. 3) and include the additional modules of the apparatus 1904.

[0198] As discussed supra, the data collection component 198 may be configured to transmit a request to collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data. The data collection component 198 may also be configured to receive, based on the request and the configuration, collected data for the at least one AI / ML-based model. The data collection component 198 may also be configured to transmit the collected data or an indication of the collected data, or train the at least one AI / ML-based model based on the collected data. The data collection component 198 may be within the cellular baseband processor(s) 1924, the application processor(s) 1906, or both the cellular baseband processor(s) 1924 and the application processor(s) 1906. The data collectioncomponent 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 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. As shown, the apparatus 1904 may include a variety of components configuredforvarious functions. In one configuration, the apparatus 1904, and in particular the cellular baseband processor(s) 1924 and / or the application processor(s) 1906, may include means for transmitting a request to collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data. The apparatus 1904 may further include means for receiving, based on the request and the configuration, collected data for the at least one AI / ML-based model. The apparatus 1904 may further include means for transmitting the collected data or an indication of the collected data, or means for training the at least one AI / ML-based model based on the collected data.

[0199] In one configuration, the apparatus 1904 may further include means for receiving from a first UE or a first base station, a second request to collect the data for the at least one AI / ML-based model, where the means for transmitting the request may include configuring the apparatus 1904 to transmit the request to a second UE or a second base station, where the means for receiving the collected data may include configuring the apparatus 1904 to receive the collected data from the second UE or the second base station, where the means for transmitting the collected data may include configuring the apparatus 1904 to transmit the collected data to the first UE or the first base station.

[0200] In another configuration, the means for transmitting the request may include configuring the apparatus 1904 to transmit the request to a server, an LMF, a second UE, or a base station, and the means for receiving the collected data or the indication of the collected data may include configuring the apparatus 1904 to receive the collected data or the indication of the collected data from the server, the LMF, the second UE, or the base station.

[0201] The means may be the data collection component 198 of the apparatus 1904 configured to perform the functions recited by the means. As described supra, theapparatus 1904 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.

[0202] FIG. 20 is a diagram 2000 illustrating an example of a hardware implementation for a network entity 2002. The network entity 2002 may be a BS, a component of a BS, or may implementBS functionality. The network entity 2002 may include atleast one of a CU 2010, a DU 2030, or an RU 2040. For example, depending on the layer functionality handled by the data collection component 199, the network entity 2002 may include the CU 2010; both the CU 2010 and the DU 2030; each of the CU 2010, the DU 2030, and the RU 2040; the DU 2030; both the DU 2030 and the RU 2040; or the RU 2040. The CU 2010 may include at least one CU processor 2012. The CU processor(s) 2012 may include on-chip memory 2012'. In some aspects, the CU 2010 may further include additional memory modules 2014 and a communications interface 2018. The CU 2010 communicates with the DU 2030 through a midhaul link, such as an Fl interface. The DU 2030 may include at least one DU processor 2032. The DU processor(s)2032may include on-chip memory 2032'. In some aspects, the DU 2030 may further include additional memory modules 2034 and a communications interface 2038. The DU 2030 communicates with the RU 2040 through a fronthaul link. The RU 2040 may include atleast one RU processor 2042. The RU processor(s) 2042 may include on-chip memory 2042'. In some aspects, the RU 2040 may further include additional memory modules 2044, one or more transceivers 2046, antennas 2080, and a communications interface 2048. The RU 2040 communicates with the UE 104. The on-chip memory 2012', 2032', 2042' and the additional memory modules 2014, 2034, 2044 may each be considered a computer-readable medium / memory. Each computer-readable medium / memory may be non-transitory. Each of the processors 2012, 2032, 2042 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.

[0203] As discussed supra, the data collection component 199 may be configured to transmit a request to collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data. The data collection component 199 may also be configured to receive, based on the request and the configuration, collected data for the at least one AI / ML-based model. The data collection component 199 may also be configured to transmit the collected data or an indication of the collected data, or train the at least one AI / ML-based model based on the collected data. The data collection component 199 may be within one or more processors of one or more of the CU 2010, DU 2030, and the RU 2040. The data collection component 199 maybe 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 2002 may include a variety of components configured for various functions. In one configuration, the network entity 2002 may include means for transmitting a request to collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data. The network entity 2002 may further include means for receiving, based on the request and the configuration, collected data for the at least one AI / ML-based model. The network entity 2002 may further include means for transmitting the collected data or an indication of the collected data, or means for training the at least one AI / ML-based model based on the collected data.

[0204] In one configuration, the network entity 2002 may further include means for receiving, from a first UE or a first base station, a second request to collect the data for the at least one AI / ML-based model, where the means for transmitting the request may include configuring the network entity 2002 to transmit the request to a second UE or a second base station, where the means for receiving the collected data may include configuring the network entity 2002 to receive the collected data from the second UE or the second base station, where the means for transmitting the collected data may include configuring the network entity 2002 to transmit the collected data to the first UE or the first base station.

[0205] In another configuration, the means for transmitting the request may include configuringthe network entity 2002 to transmitthe requestto a server, an LMF, a UE, or a second base station, and the means for receiving the collected data or the indication of the collected data may include configuringthe network entity 2002 to receive the collected data or the indication of the collected data from the server, the LMF, the UE, or the second base station.

[0206] The means may be the data collection component 199 of the network entity 2002 configured to perform the functions recited by the means. As described supra, the network entity 2002 may include the TX processor 316, the RX processor 370, and the controller / processor 375. As such, in one configuration, the means may be the TX processor 316, the RX processor 370, and / or the controller / processor 375 configured to perform the functions recited by the means.

[0207] FIG. 21 is a diagram 2100 illustrating an example of a hardware implementation for a network entity 2160. In one example, the network entity 2160 may be within the core network 120. The network entity 2160 may include at least one network processor 2112. The network processor(s) 2112 may include on-chip memory 2112'. In some aspects, the network entity 2160 may further include additional memory modules 2114. The network entity 2160 communicates via the network interface 2180 directly (e.g., backhaul link) or indirectly (e.g., through a RIC) with the CU 2102. The on-chip memory 2112' and the additional memory modules 2114 may each be considered a computer-readable medium / memory. Each computer-readable medium / memory may be non-transitory. The network processor(s) 2112 is responsible for general processing, including the execution of software stored on the computer- readable medium / memory. The software, when executed by the corresponding processor(s) causes the processor(s) to perform the various functions described supra. The computer-readable medium / memory may also be used for storing data that is manipulated by the processor(s) when executing software.

[0208] As discussed supra, the data collection configuration component 197 may be configured to transmit a requestto collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data. The data collection configuration component 197 may also be configured to receive, based on the request and the configuration, collected data for the at least one AI / ML-based model. The data collection configuration component 197 may also be configured totransmit the collected data or an indication of the collected data, or train the at least one AI / ML-based model based on the collected data. The data collection configuration component 197 may be within the network processor(s) 2112. The data collection 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 implementation by one or more processors, or some combination thereof. When multiple processors are implemented, the multiple processors may perform the stated processes / algorithm individually or in combination. The network entity 2160 may include a variety of components configured for various functions. In one configuration, the network entity 2160 may include means for transmitting a request to collect data for at least one AI / ML-based model, where the request is indicative of a configuration for collecting the data. The network entity 2160 may further include means for receiving, based on the request and the configuration, collected data for the at least one AI / ML-based model. The network entity 2160 may further include means for transmitting the collected data or an indication of the collected data, or means for training the at least one AI / ML-based model based on the collected data.

[0209] In one configuration, the network entity 2160 may further include means for receiving, from a first UE or a first base station, a second request to collect the data for the at least one AI / ML-based model, where the means for transmitting the request may include configuring the network entity 2160 to transmit the request to a second UE or a second base station, where the means for receiving the collected data may include configuring the network entity 2160 to receive the collected data from the second UE or the second base station, where the means for transmitting the collected data may include configuring the network entity 2160 to transmit the collected data to the first UE or the first base station.

[0210] In another configuration, the means for transmitting the request may include configuring the network entity 2160 to transmit the request to a UE or a base station, and the means for receiving the collected data or the indication of the collected data may include configuring the network entity 2160 to receive the collected data or the indication of the collected data from the UE or the base station.

[0211] In another configuration, the configuration for collecting the data includes atleast one of: (1) collecting data that is associated with a specific set of DL-RS resource IDs, a specific set of DL-RS resource set IDs, a specific set of UL-RS IDs, a specific set of UL-RS resource set IDs, or a specific set of beams associated with a TRP, (2) collecting data that includes one or more joint measurements across a set of aggregated frequency layers, component carriers, or bands orUL-RSs, (3) collecting data in which one or more measurements with additional paths are performed, (4) collecting data in which one or more measurements are associated with a RxTEG, a specific Rx TEG, ora specific numb er of Rx TEGs, or(5) collecting data in which one or more measurements include an estimated LOS or NLOS indicator.

[0212] In another configuration, the configuration for collecting the data includes atleast one of: (1) collecting data with RSRPP measurements for one or more additional paths, (2) collecting data with multiple measurement instances in a single measurement report, (3) collecting data with DL-RS Rx or UL-RS Tx hopping measurement or reporting, (4) collecting data that includes DLPRS Rx hopping or UL-RS Tx hopping with a specified bandwidth, (5) collecting data with a specified maximum number of UE-Rx-Tx time difference measurements for different DL-RS or UL-RS resources or different DL-RS or UL-RS resource sets, or (6) collecting data that includes RSCP measurements.

[0213] In another configuration, the configuration for collecting the data includes atleast one of: (1) collecting data that includes RSCPD measurements, (2) collecting data that includes a specified maximum number of DL-RS RSTD measurements per pair of TRPs, (3) collecting data with a specified reporting granularity, (4) collecting data with a same UL-RS with a specified number of different RxTEGs, (5) collecting data that includes measurements with reduced number of samples, (6) collecting data that uses a lower Rx beam sweeping factor than eight for FR2, or (7) collecting data that includes measurements for a specific set of DL-RS or UL-RS resource sets in a specified time window.

[0214] The means may be the data collection configuration component 197 of the network entity 2160 configured to perform the functions recited by the means.

[0215] 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 theprocesses / flowcharts maybe rearranged. Further, some blocks may be combined or omitted. The accompanying method claims present elements of the various blocks in a sample order, and are not limited to the specific order or hierarchy presented.

[0216] 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 processorcircuitry. A memory / memory module may be referred to as memory circuitry. If a first apparatus receives data from or transmits data to a second apparatus, the data may be received / transmitted directly between the first and second apparatuses, or indirectly between the first and second apparatuses through a set of apparatuses. A device configured to “output” data or “provide” data, such as a transmission, signal, or message, may transmit the data, for example with a transceiver, or may send the data to a device that transmits the data. A device configured to “obtain” data, such as a transmission, signal, or message, may receive, for example with a transceiver, or may obtain the data from a device that receives the data. Information stored in a memory includes instructions and / or data. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are encompassed by the claims. Moreover, nothing disclosed herein is dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. The words “module,” “mechanism,” “element,” “device,” and the like may not be a substitute for the word “means.” As such, no claim element is to be construed as a means plus function unless the element is expressly recited using the phrase “means for.”

[0217] 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.

[0218] The following aspects are illustrative only and may be combined with other aspects or teachings described herein, without limitation.

[0219] Aspect 1 is a method of wireless communication at a device, comprising: receiving a request to collect data for at least one artificial intelligence (Al) or machine learning (ML) (AI / ML)-based model, wherein the request is indicative of a configuration for collecting the data; obtaining, based on the request and the configuration, collected data for the at least one AI / ML-based model; and transmitting the collected data or an indication of the collected data, or training the at least one AI / ML-based model based on the collected data.

[0220] Aspect 2 is the method of aspect 1 , further comprising: determining or verifying the request is associated with an AI / ML-based method or model, wherein obtainment of the collected data is further based on determination or verification that the request is associated with the AI / ML-based method or model.

[0221] Aspect 3 is the method of aspect 1 or aspect 2, wherein the device is a first user equipment (UE).

[0222] Aspect 4 is the method of aspect 3 , wherein receiving the request comprises receiving the request from a server, a location management function (LMF), a sensing management function, an AI / ML management function, a second UE, or a base station, and wherein transmitting the collected data or the indication of the collected data comprises transmitting the collected data or the indication of the collected data to the server, the LMF, the sensing management function, the AI / ML management function, the second UE, or the base station.

[0223] Aspect 5 is the method of aspect 3, further comprising: transmitting, to a location management function (LMF) based on the request, a second request for positioning reference signal (PRS) assistance data for collecting the data; and receiving, from the LMF based on the request, the PRS assistance data or a second indication of a set of PRS resources for collecting the data.

[0224] Aspect 6 is the method of aspect 3, wherein the configuration for collectingthe data includes at least one of: (1) collecting data that is associated with a specific set of downlink (DL)- reference signal (RS) (DL-RS) resource identifications (IDs), a specific set of DL-RS resource set IDs, or a specific set of beams associated with a transmission-reception point (TRP), (2) collecting data that includes one or more j oint measurements across a set of aggregated frequency layers, component carriers, or bands, (3) collecting data in which one or more measurements with additional paths are performed, (4) collecting data in which one or more measurements are associated with a reception timing error group (RxTEG), a specific Rx TEG, ora specific number of Rx TEGs, or (5) collecting data in which one or more measurements include an estimated line-of-sight (LOS) or non-line-of-sight (NLOS) indicator.

[0225] Aspect 7 is the method of aspect 3, wherein the configuration for collectingthe data includes at least one of : (1) collecting data with reference signal received path power (RSRPP) measurements for one or more additional paths, (2) collecting data with multiple measurement instances in a single measurement report, (3) collecting datawith downlink (DL)-reference signal (RS) (DL-RS) reception (Rx) hopping measurement or reporting, (4) collecting data that includes DL PRS Rx hopping with a specified bandwidth, (5) collecting data with a specified maximum number of UE- reception-transmission (Rx-Tx) (UE-Rx-Tx) time difference measurements for different DL-RS resources or different DL-RS resource sets per transmissionreception point (TRP), or (6) collecting data that includes received signal code power (RSCP) measurements.

[0226] Aspect 8 is the method of aspect 3, wherein the configuration for collectingthe data includes at least one of: (1 ) collecting data that includes reference signal carrier phase difference (RSCPD) measurements, (2) collecting data that includes a specified maximum number of downlink (DL)- reference signal (RS) (DL-RS) reference signal time difference (RSTD) measurements per pair of transmission-reception points (TRPs), (3) collecting data with a specified reporting granularity, (4) collecting data with a same DL-RS resource of a TRP with a specified number of different UE reception timing error groups (RxTEGs), (5) collecting data that includes measurements with reduced number of samples, (6) collecting data that uses a lower reception (Rx) beam sweeping factor than eight for frequency range 2 (FR2), or (7) collecting data that includes measurements for a specific set of DL-RS resource sets in a specified time window.

[0227] Aspect 9 is the method of aspect 1 or aspect 2, wherein the device is a base station.

[0228] Aspect 10 is the method of aspect 9, wherein receiving the request comprises receiving the request from a server, a location management function (LMF), a user equipment (UE), or a second base station, and wherein transmitting the collected data or the indication of the collected data comprises transmitting the collected data or the indication of the collected data to the server, the LMF, the UE, or the second base station.

[0229] Aspect 11 is the method of aspect 9, further comprising: transmitting, to a user equipment (UE) based on the request, a second configuration to transmission a set of reference signals; and receiving, from the UE based on the configuration, the set of reference signals, wherein obtaining the data for the at least one AI / ML-based model comprises measuring the set of reference signals.

[0230] Aspect 12 is the method of aspect 9, further comprising: transmitting, to a location management function (LMF) based on the request, a second request for assistancedata for collecting the data; and receiving, from the LMF based on the request, the assistance data for collecting the data.

[0231] Aspect 13 is the method of aspect 9, wherein the configuration for collecting the data includes at least one of: (1) collecting data that is associated with a specific set of uplink (UL) reference signal (RS) (UL-RS) identifications (IDs), a specific set of UL- RS resource set IDs, or a specific set of beams associated with a transmissionreception point (TRP), (2) collecting data that includes one or more joint measurements across a set of UL-RSs, (3) collecting data in which one or more measurements with additional paths are performed, (4) collecting data in which one or more measurements are associated with a reception timing error group (RxTEG), a specific RxTEG, or a specific number of Rx TEGs, or (5) collecting data in which one or more measurements include an estimated line-of-sight (LOS) or non-line-of- sight (NLOS) indicator.

[0232] Aspect 14 is the method of aspect 9, wherein the configuration for collecting the data includes at least one of : (1) collecting data with reference signal received path power (RSRPP) measurements for one or more additional paths, (2) collecting data with multiple measurement instances in a single measurement report, (3) collecting data with uplink (UL) reference signal (RS) (UL-RS) transmission (Tx) hopping measurement or reporting, (4) collecting data that includes UL-RS Tx hopping with a specified bandwidth, (5) collecting data with a specified maximum number of base station-reception-transmission (Rx-Tx) (base station-Rx-Tx) time difference measurements for different UL-RS resources or different UL-RS resource sets per transmission-reception point (TRP), or (6) collecting data that includes received signal code power (RSCP) measurements.

[0233] Aspect 15 is the method of aspect 9, wherein the configuration for collecting the data includes at least one of: (1 ) collecting data that includes reference signal carrier phase difference (RSCPD) measurements, (2) collecting data with a specified reporting granularity, (3) collecting data with a uplink (UL) reference signal (RS) (UL-RS) resource of a TRP with a specified number of different base station reception timing error groups (RxTEGs), (4) collecting data that includes measurements with reduced number of samples, (5) collecting data that uses a lower reception (Rx) beam sweeping factor than eight for frequency range 2 (FR2), or (6) collecting data thatincludes measurements for a specific set of UL-RS resource sets in a specified time window.

[0234] Aspect 16 is the method of aspect 1 or aspect 2, wherein the device is a server or a location management function (LMF).

[0235] Aspect 17 is the method of aspect 16, wherein receiving the request comprises receiving the request from a user equipment (UE) or a base station, and wherein transmitting the collected data or the indication of the collected data comprises transmitting the collected data or the indication of the collected data to the UE or the base station.

[0236] Aspect 18 is the method of aspect 16, further comprising: transmitting, to at least one second UE or at least one second base station based on the request, a second request for collecting the data; and receiving, from the at least one second UE or the at least one second base station, the data.

[0237] Aspect 19 is the method of aspect 16, wherein the configuration for collectingthe data includes at least one of: (1) collecting data that is associated with a specific set of downlink (DL)-reference signal (RS) (DL-RS) resource identifications (IDs), a specific set of DL-RS resource set IDs, a specific set of uplink (UL) reference signal (RS) (UL-RS) IDs, a specific set of UL-RS resource setIDs, or a specific set of beams associated with a transmission-reception point (TRP), (2) collecting data that includes one or more joint measurements across a set of aggregated frequency layers, component carriers, or bands or UL-RSs, (3) collecting data in which one or more measurements with additional paths are performed, (4) collecting data in which one or more measurements are associated with a reception timing error group (RxTEG), a specific RxTEG, or a specific number of Rx TEGs, or (5) collecting data in which one or more measurements include an estimated line-of-sight (LOS) or non-line-of- sight (NLOS) indicator.

[0238] Aspect20 is the method of aspect 16, wherein the configuration for collectingthe data includes at least one of : (1) collecting data with reference signal received path power (RSRPP) measurements for one or more additional paths, (2) collecting data with multiple measurement instances in a single measurement report, (3) collecting data with downlink (DL)-reference signal (RS) (DL-RS) reception (Rx) or uplink (UL) reference signal (RS) (UL-RS) transmission (Tx) hopping measurement or reporting (4) collecting data that includes DL PRS Rx hopping or UL-RS Tx hopping with aspecified bandwidth, (5) collecting data with a specified maximum number of UE- Rx-Tx time difference measurements for different DL-RS or UL-RS resources or different DL-RS or UL-RS resource sets, or (6) collecting data that includes received signal code power (RSCP) measurements.

[0239] Aspect21 is the method of aspect 16, wherein the configurationforcollectingthe data includes at least one of: (1 ) collecting data that includes reference signal carrier phase difference (RSCPD) measurements, (2) collecting data that includes a specified maximum number of downlink (DL)-reference signal (RS) (DL-RS) reference signal time difference (RSTD) measurements per pair of transmission-reception points (TRPs), (3) collecting data with a specified reporting granularity, (4) collecting data with a same uplink (UL) reference signal (RS) (UL-RS) with a specified number of different reception timing error groups (RxTEGs), (5) collecting data that includes measurements with reduced number of samples, (6) collecting data that uses a lower reception (Rx) beam sweeping factor than eight for frequency range 2 (FR2), or (7) collecting data that includes measurements for a specific set of DL-RS or UL-RS resource sets in a specified time window.

[0240] Aspect 22 is an apparatus for wireless communication at a device, including: at least one memory; and atleast one processor coupled to the atleast 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 21.

[0241] Aspect 23 is the apparatus of aspect 22, further including at least one transceiver coupled to the at least one processor.

[0242] Aspect24 is an apparatus forwireless communication atadeviceincludingmeans for implementing any of aspects 1 to 21.

[0243] Aspect 25 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 21 .

[0244] Aspect 26 is a method of wireless communication at a device, comprising: transmitting a request to collect data for at least one artificial intelligence (Al) or machine learning (ML) (AEML)-based model, wherein the request is indicative of a configuration for collecting the data; receiving, based on the request and the configuration, collected data forthe atleast one AI / ML-based model; and transmittingthe collected data or an indication of the collected data, or training the at least one AI / ML-based model based on the collected data.

[0245] Aspect 27 is the method of aspect 26, further comprising: receiving, from a first user equipment (UE) or a first base station, a second request to collect the data for the at least one AI / ML-based model, where transmitting the request comprises transmitting the request to a second UE or a second base station, wherein receiving the collected data comprises receiving the collected data from the second UE or the second base station, wherein transmitting the collected data comprises transmitting the collected data to the first UE or the first base station.

[0246] Aspect 28 is the method of aspect 26 or aspect 27, wherein the device is a user equipment (UE).

[0247] Aspect 29 is the method of aspect 28, wherein transmitting the request comprises transmitting the requestto a server, a location management function (LMF), a sensing management function, an AI / ML management function, a second UE, or a base station, and wherein receivingthe collected data or the indication of the collected data comprises receiving the collected data or the indication of the collected data from the server, the LMF, the sensingmanagementfunction, the AI / ML management function, the second UE, or the base station.

[0248] Aspect 30 is the method of aspect26 or aspect27, wherein the device is abase station.

[0249] Aspect 31 is the method of aspect 30, wherein transmitting the request comprises transmitting the requestto a server, a location management function (LMF), a sensing management function, an AI / ML management function, a user equipment (UE), or a second base station, and wherein receiving the collected data or the indication of the collected data comprises receiving the collected data or the indication of the collected data from the server, the LMF, the sensing management function, the AI / ML management function, the UE, or the second base station.

[0250] Aspect 32 is the method of aspect 26 or aspect 27, wherein the device is a server, a location management function (LMF), a sensing management function, an AI / ML management function.

[0251] Aspect 33 is the method of aspect 32, wherein transmitting the request comprises transmitting the request to a user equipment (UE) or a base station, and wherein receivingthe collected data orthe indication of the collected data comprises receivingthe collected data or the indication of the collected data from the UE or the base station.

[0252] Aspect 34 is the method of aspect 32, wherein the configuration for collectingthe data includes at least one of: (1) collecting data that is associated with a specific set of downlink (DL)- reference signal (RS) (DL-RS) resource identifications (IDs), a specific set of DL-RS resource set IDs, a specific set of uplink (UL) reference signal (RS) (UL-RS) IDs, a specific set of UL-RS resource setIDs, or a specific set of beams associated with a transmission-reception point (TRP), (2) collecting data that includes one or more joint measurements across a set of aggregated frequency layers, component carriers, or bands or UL-RSs, (3) collecting data in which one or more measurements with additional paths are performed, (4) collecting data in which one or more measurements are associated with a reception timing error group (RxTEG), a specific RxTEG, or a specific number of Rx TEGs, or (5) collecting data in which one or more measurements include an estimated line-of-sight (LOS) or non-line-of- sight (NLOS) indicator.

[0253] Aspect 35 is the method of aspect 32, wherein the configuration for collectingthe data includes at least one of : (1) collecting data with reference signal received path power (RSRPP) measurements for one or more additional paths, (2) collecting data with multiple measurement instances in a single measurement report, (3) collecting data with downlink (DL)- reference signal (RS) (DL-RS) reception (Rx) or uplink (UL) reference signal (RS) (UL-RS) transmission (Tx) hopping measurement or reporting (4) collecting data that includes DL PRS Rx hopping or UL-RS Tx hopping with a specified bandwidth, (5) collecting data with a specified maximum number of UE- Rx-Tx time difference measurements for different DL-RS or UL-RS resources or different DL-RS or UL-RS resource sets, or (6) collecting data that includes received signal code power (RSCP) measurements.

[0254] Aspect 36 is the method of aspect 32, wherein the configuration for collectingthe data includes at least one of: (1 ) collecting data that includes reference signal carrier phase difference (RSCPD) measurements, (2) collecting data that includes a specified maximum number of downlink (DL)- reference signal (RS) (DL-RS) reference signal time difference (RSTD) measurements per pair of transmission-reception points (TRPs), (3) collecting data with a specified reporting granularity, (4) collecting data with a same uplink (UL) reference signal (RS) (UL-RS) with a specified number ofdifferent reception timing error groups (RxTEGs), (5) collecting data that includes measurements with reduced number of samples, (6) collecting data that uses a lower reception (Rx) beam sweeping factor than eight for frequency range 2 (FR2), or (7) collecting data that includes measurements for a specific set of DL-RS or UL-RS resource sets in a specified time window.

[0255] Aspect 37 is an apparatus for wireless communication at a device, including: at least one memory; and atleast one processor coupled to the atleast 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 26 to 36.

[0256] Aspect 38 is the apparatus of aspect 37, further including at least one transceiver coupled to the at least one processor.

[0257] Aspect39 is an apparatus forwireless communication atadeviceincludingmeans for implementing any of aspects 26 to 36.

[0258] Aspect 40 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 26 to 36.

Claims

CLAIMSWHAT IS CLAIMED IS:1 . An apparatus for wireless communication at a device, comprising: at least one memory; and at least one processor coupled to the at least one memory, the at least one processor, individually or in any combination, is configured to: receive a request to collect data for at least one artificial intelligence (Al) or machine learning (ML) (AI / ML)-based model, wherein the request is indicative of a configuration for collecting the data; obtain, based on the request and the configuration, collected data for the at least one AI / ML-based model; and transmitthe collected data or an indication of the collected data, ortraining the at least one AI / ML-based model based on the collected data.

2. The apparatus of claim 1, wherein the at least one processor, individually or in any combination, is further configured to: determine or verify the request is associated with an AI / ML-based method or model, wherein obtainment of the collected data is further based on determination or verification that the request is associated with the AI / ML-based method or model.

3. The apparatus of claim 1, wherein the device is a first user equipment (UE).

4. The apparatus of claim 3 , wherein to receive the request, the at least one processor, individually or in any combination, is configured to receive the request from a server, a location management function (LMF), a sensing management function, an AI / ML management function, a second UE, or a base station, and wherein to transmit the collected data or the indication of the collected data, the at least one processor, individually or in any combination, is configured to transmit the collected data or the indication of the collected data to the server, the LMF, the sensing management function, the AI / ML management function, the second UE, or the base station.

5. The apparatus of claim 3, wherein the at least one processor, individually or in any combination, is further configured to: transmit, to a location management function (LMF), a sensing management function, or an AI / ML management function based on the request, a second request for reference signal (RS) assistance data for collecting the data; and receive, from the LMF), the sensing management function, or the AI / ML management function based on the request, the RS assistance data or a second indication of a set of RS resources for collecting the data.

6. The apparatus of claim 3, wherein the configuration for collecting the data includes at least one of:(1) collecting data that is associated with a specific set of downlink (DL)- reference signal (RS) (DL-RS) resource identifications (IDs), a specific set of DL-RS resource set IDs, or a specific set of beams associated with a transmission-reception point (TRP),(2) collecting data that includes one or more joint measurements across a set of aggregated frequency layers, component carriers, or bands,(3) collecting data in which one or more measurements with additional paths are performed,(4) collecting data in which one or more measurements are associated with a reception timing error group (RxTEG), a specific Rx TEG, or a specific number of Rx TEGs, or(5) collecting data in which one or more measurements include an estimated line- of-sight (LOS) or non-line-of-sight (NLOS) indicator.

7. The apparatus of claim 3, wherein the configuration for collecting the data includes at least one of:(1) collecting data with reference signal received path power (RSRPP) measurements for one or more additional paths,(2) collecting data with multiple measurement instances in a single measurement report,(3) collecting data with downlink (DL)- reference signal (RS) (DL-RS) reception (Rx) hopping measurement or reporting,(4) collecting data that includes DL RS Rx hopping with a specified bandwidth,(5) collecting data with a specified maximum number of UE-reception- transmission (Rx-Tx) (UE-Rx-Tx) time difference measurements for different DL-RS resources or different DL-RS resource sets per transmission-reception point (TRP), or(6) collecting data that includes received signal code power (RSCP) measurements.

8. The apparatus of claim 3, wherein the configuration for collecting the data includes at least one of:(1) collecting data that includes reference signal carrier phase difference (RSCPD) measurements,(2) collecting data that includes a specified maximum number of downlink (DL)- reference signal (RS) (DL-RS) reference signal time difference (RSTD) measurements per pair of transmission-reception points (TRPs),(3) collecting data with a specified reporting granularity,(4) collecting data with a same DL-RS resource of a TRP with a specified number of different UE reception timing error groups (RxTEGs),(5) collecting data that includes measurements with reduced number of samples,(6) collecting data that uses a lower reception (Rx) beam sweeping factor than a threshold, or(7) collecting data that includes measurements for a specific set of DL-RS resource sets in a specified time window.

9. The apparatus of claim 1, wherein the device is a base station.

10. The apparatus of claim 9, wherein to receive the request, the atleast one processor, individually or in any combination, is configured to receive the request from a server, a location management function (LMF), a sensing management function, an AI / ML management function, a user equipment (UE), or a second base station, and wherein totransmit the collected data or the indication of the collected data, the at least one processor, individually or in any combination, is configured to transmit the collected data or the indication of the collected data to the server, the LMF, the sensing management function, the AI / ML management function, the UE, or the second base station.11 . The apparatus of claim 9, wherein the at least one processor, individually or in any combination, is further configured to: transmit, to a user equipment (UE) based on the request, a second configuration to transmission a set of reference signals; and receive, from the UE based on the configuration, the set of reference signals, wherein obtaining the data for the at least one AI / ML-based model comprises measuring the set of reference signals.

12. The apparatus of claim 9, wherein the at least one processor, individually or in any combination, is further configured to: transmit, to a location management function (LMF), a sensing management function, or an AI / ML management function based on the request, a second request for assistance data for collecting the data; and receive, from the LMF, the sensing management function, or the AI / ML management function based on the request, the assistance data for collecting the data.

13. The apparatus of claim 9, wherein the configuration for collecting the data includes at least one of:(1) collecting data that is associated with a specific set of uplink (UL)-reference signal (RS) (UL-RS) identifications (IDs), a specific set of UL-RS resource set IDs, or a specific set of beams associated with a transmission-reception point (TRP),(2) collecting data that includes one or more joint measurements across a set of UL-RSs,(3) collecting data in which one or more measurements with additional paths are performed,(4) collecting data in which one or more measurements are associated with a reception timing error group (RxTEG), a specific Rx TEG, or a specific number of Rx TEGs, or(5) collecting data in which one or more measurements include an estimated line- of-sight (LOS) or non-line-of-sight (NLOS) indicator.

14. The apparatus of claim 9, wherein the configuration for collecting the data includes at least one of:(1) collecting data with reference signal received path power (RSRPP) measurements for one or more additional paths,(2) collecting data with multiple measurement instances in a single measurement report,(3) collecting data with uplink (UL)-reference signal (RS) (UL-RS) transmission (Tx) hopping measurement or reporting,(4) collecting data that includes UL-RS Tx hopping with a specified bandwidth,(5) collecting data with a specified maximum number of base station-reception- transmission (Rx-Tx) (base station-Rx-Tx) time difference measurements for different UL-RS resources or different UL-RS resource sets per transmission-reception point (TRP), or(6) collecting data that includes received signal code power (RSCP) measurements.

15. The apparatus of claim 9, wherein the configuration for collecting the data includes at least one of:(1) collecting data that includes reference signal carrier phase difference (RSCPD) measurements,(2) collecting data with a specified reporting granularity,(3) collecting data with a same uplink (UL)-reference signal (RS) (UL-RS) resource of a TRP with a specified number of different base station reception timing error groups (RxTEGs),(4) collecting data that includes measurements with reduced number of samples,(5) collecting data that uses a lower reception (Rx) beam sweeping factor than eight for frequency range 2 (FR2), or(6) collecting data that includes measurements for a specific set of UL-RS resource sets in a specified time window.

16. The apparatus of claim 1, wherein the device is a server or a location management function (LMF).

17. The apparatus of claim 16, wherein to receive the request, the at least one processor, individually or in any combination, is configured to receive the request from a user equipment (UE) or a base station, and wherein to transmit the collected data or the indication of the collected data, the at least one processor, individually or in any combination, is configured to transmit the collected data or the indication of the collected data to the UE or the base station.

18. The apparatus of claim 17, wherein the at least one processor, individually or in any combination, is further configured to: transmit, to at least one second UE or at least one second base station based on the request, a second request for collecting the data; and receive, from the at least one second UE or the at least one second base station, the data.

19. The apparatus of claim 16, wherein the configuration for collecting the data includes at least one of:(1) collecting data that is associated with a specific set of downlink (DL)-reference signal (RS) (DL-RS) resource identifications (IDs), a specific set of DL-RS resource set IDs, a specific set of uplink (UL)-reference signal (RS) (UL-RS) IDs, a specific set of UL-RS resource set IDs, or a specific set of beams associated with a transmissionreception point (TRP),(2) collecting data that includes one or more joint measurements across a set of aggregated frequency layers, component carriers, or bands or UL-RSs,(3) collecting data in which one or more measurements with additional paths are performed,(4) collecting data in which one or more measurements are associated with a reception timing error group (RxTEG), a specific Rx TEG, or a specific number of Rx TEGs, or(5) collecting data in which one or more measurements include an estimated line- of-sight (LOS) or non-line-of-sight (NLOS) indicator.

20. The apparatus of claim 16, wherein the configuration for collecting the data includes at least one of:(1) collecting data with reference signal received path power (RSRPP) measurements for one or more additional paths,(2) collecting data with multiple measurement instances in a single measurement report,(3) collecting data with downlink (DL)-reference signal (RS) (DL-RS) reception (Rx) or uplink (UL)-reference signal (RS) (UL-RS) transmission (Tx) hopping measurement or reporting,(4) collecting data that includes DL RS Rx hopping or UL RS Tx hopping with a specified bandwidth,(5) collecting data with a specified maximum number of UE-Rx-Tx time difference measurements for different DL-RS or UL-RS resources or different DL-RS or UL-RS resource sets, or(6) collecting data that includes received signal code power (RSCP) measurements.

21. The apparatus of claim 16, wherein the configuration for collecting the data includes at least one of:(1) collecting data that includes reference signal carrier phase difference (RSCPD) measurements,(2) collecting data that includes a specified maximum number of downlink (Dereference signal (RS) (DL-RS) reference signal time difference (RSTD) measurements per pair of transmission-reception points (TRPs),(3) collecting data with a specified reporting granularity,(4) collecting data with a same uplink (UL)-reference signal (RS) (UL-RS) with a specified number of different reception timing error groups (RxTEGs),(5) collecting data that includes measurements with reduced number of samples,(6) collecting data that uses a lower reception (Rx) beam sweeping factor than eight for frequency range 2 (FR2), or(7) collecting data that includes measurements for a specific set of DL-RS or UL- RS resource sets in a specified time window.

22. A method of wireless communication at a device, comprising: receiving a request to collect data for at least one artificial intelligence (Al) or machine learning (ML) (AI / ML)-based model, wherein the request is indicative of a configuration for collecting the data; obtaining, based on the request andthe configuration, collected data forthe atleast one AI / ML-based model; and transmitting the collected data or an indication of the collected data, or training the at least one AI / ML-based model based on the collected data.

23. An apparatus for wireless communication at a device, comprising: at least one memory; and at least one processor coupled to the at least one memory, the at least one processor, individually or in any combination, is configured to: transmit a request to collect data for at least one artificial intelligence (Al) or machine learning (ML) (AI / ML)-based model, wherein the request is indicative of a configuration for collecting the data; receive, based on the request and the configuration, collected data forthe at least one AI / ML-based model; and transmitthe collected data or an indication of the collected data, ortraining the at least one AI / ML-based model based on the collected data.

24. The apparatus of claim 23, wherein the at least one processor, individually or in any combination, is further configured to: receive, from a first user equipment (UE) or a first base station, a second request to collectthe data forthe atleast one AI / ML-based model, wherein to transmitthe request, the at least one processor, individually or in any combination, is configured to transmit the request to a second UE or a second base station, wherein to receive the collected data, the at least one processor, individually or in any combination, is configured to receive the collected data from the second UE or the second base station, wherein to transmit the collected data comprises transmitting the collected data to the first UE or the first base station.

25. The apparatus of claim 23, wherein the device is a user equipment (UE), wherein to transmitthe request, the at least one processor, individually or in any combination, is configured to transmitthe request to a server, a location management function (LMF), a sensing management function, an AI / ML management function, a second UE, or a base station, and to receive the collected data or the indication of the collected data, the at least one processor, individually or in any combination, is configured to receive the collected data or the indication of the collected data from the server, the LMF, the sensing management function, the AI / ML management function, the second UE, or the base station.

26. The apparatus of claim 23, wherein the device is abase station, wherein to transmit the request, the at least one processor, individually or in any combination, is configured to transmit the request to a server, a location management function (LMF), a sensing management function, an AI / ML management function, a user equipment (UE), or a second base station, and to receive the collected data or the indication of the collected data, the at least one processor, individually or in any combination, is configured to receive the collected data or the indication of the collected data from the server, the LMF, the sensing management function, the AI / ML management function, the UE, or the second base station.

27. The apparatus of claim 23, wherein the device is a server, a location management function (LMF), a sensing management function, an AI / ML management function, wherein to transmit the request, the at least one processor, individually or in any combination, is configured to transmit the request to a user equipment (UE) or a base station, and to receive the collected data or the indication of the collected data, the at least one processor, individually or in any combination, is configured to receive the collected data or the indication of the collected data from the UE or the base station.

28. The apparatus of claim 23, wherein the configuration for collecting the data includes at least one of: (1) collecting data that is associated with a specific set of downlink (DL)-reference signal (RS) (DL-RS) resource identifications (IDs), a specific set of DL-RS resource set IDs, a specific set of uplink (UL)-reference signal (RS) (UL- RS) IDs, a specific set of UL-RS resource set IDs, or a specific set of beams associated with a transmission-reception point (TRP), (2) collecting data that includes one or more joint measurements across a set of aggregated frequency layers, component carriers, or bands or UL-RSs, (3) collecting data in which one or more measurements with additional paths are performed, (4) collecting data in which one or more measurements are associated with a reception timing error group (RxTEG), a specific RxTEG, or a specific number of Rx TEGs, (5) collecting data in which one or more measurements include an estimated line-of-sight (LOS) or non-line-of-sight (NLOS) indicator, (6) collecting data with reference signal received path power (RSRPP) measurements for one or more additional paths, (7) collecting data with multiple measurement instances in a single measurement report, (8) collecting data with downlink (DL-PRS Rx or UL-RS transmission (Tx) hopping measurement or reporting, (9) collecting data that includes DL PRS Rx hopping or UL-RS Tx hopping with a specified bandwidth, (10) collecting data with a specified maximum number of UE-Rx-Tx time difference measurements for different DL-RS or UL-RS resources or different DL-RS or UL-RS resource sets, or (11) collecting data that includes received signal code power (RSCP) measurements.

29. The apparatus of claim 23, wherein the configuration for collecting the data includes at least one of:(1) collecting data that includes reference signal carrier phase difference (RSCPD) measurements,(2) collecting data that includes a specified maximum number of downlink (Dereference signal (RS) (DL-RS) reference signal time difference (RSTD) measurements per pair of transmission-reception points (TRPs),(3) collecting data with a specified reporting granularity,(4) collecting data with a same uplink (UL)-reference signal (RS) (UL-RS) with a specified number of different reception timing error groups (RxTEGs),(5) collecting data that includes measurements with reduced number of samples,(6) collecting data that uses a lower reception (Rx) beam sweeping factor than eight for frequency range 2 (FR2), or(7) collecting data that includes measurements for a specific set of DL-RS or UL- RS resource sets in a specified time window.

30. A method of wireless communication at a device, comprising: transmitting a request to collect data for at least one artificial intelligence (Al) or machine learning (ML) (AI / ML)-based model, wherein the request is indicative of a configuration for collecting the data; receiving, based on the request and the configuration, collected dataforthe atleast one AI / ML-based model; and transmitting the collected data or an indication of the collected data, or training the at least one AI / ML-based model based on the collected data.

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