Monitoring artificial intelligence / machine learning models used in positioning operations

Abstract

An example method performed by a management entity to facilitate monitoring of one or more artificial intelligence / machine learning (AI / ML) models used in positioning operations can include determining whether a first user equipment (UE) is configured with a first capability to monitor at least a first AI / ML model that is useable in a positioning operation; determining whether a second UE is configured with a second capability to monitor at least the first AI / ML model that is useable in the positioning operation; and assigning a monitoring operation to the first UE or the second UE based, at least in part, on determining whether the first UE has the first capability to monitor at least the first AI / ML model and determining whether the second UE has the second capability to monitor at least the first AI / ML model.

Description

Qualcomm Ref. No. 2404383WO -1-MONITORING ARTIFICIAL INTELLIGENCE / MACHINE LEARNINGMODELS USED IN POSITIONING OPERATIONSRELATED APPLICATIONS

[0001] This application claims the benefit of Greek Application No. 20240100647, filed September 23, 2024, entitled “MONITORING ARTIFICIAL INTELLIGENCE / MACHINE LEARNING MODELS USED IN POSITIONING OPERATIONS,” which is assigned to the assignee hereof, and incorporated herein in its entirety by reference.BACKGROUND Field of Disclosure

[0002] The present disclosure relates generally to the field of position determination and more specifically pertains to monitoring artificial intelligence / machine learning (AI / ML) models used in position determination operations. Description of Related Art

[0003] A position or location of a user equipment (UE) such as a mobile phone, for example, can be determined based on performing various types of position determination procedures. In an example scenario, a position determination procedure can be performed by the UE for determining the position of the UE. The position determination procedure can be performed either independently by the UE or by using assistance provided to the UE by another device (such as, for example, an access node of a cellular network).

[0004] In general, position determination procedures performed by a UE can be categorized under two broad categories as either non-cellular position determination procedures or cellular position determination procedures.

[0005] Some examples of non-cellular position determination procedures include satellite-based position determination procedures, sensor-based position determination procedures, and time-of-flight ranging procedures (for detecting objects).

[0006] Cellular-based position determination procedures typically involve interaction between the UE and one or more devices that may be a part of a cellular network (such as, for example, the Long Term Evolution (LTE) network and / or the 5GWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -2-New Radio (NR) network). An example cellular-based position determination procedure involves positioning measurements made by use of, what is known as a positioning reference signal (PRS) that may be provided to the UE by an access node of a cellular network. The PRS may be used for performing measurements associated with techniques such as, for example, time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AO A), angle of departure (AOD), and received signal strength indicator (RSSI).

[0007] The various position determination procedures indicated above provide various levels of accuracy. In some cases, such as, for example, for cellular-based position determination procedures based on the 5G New Radio (NR) network, standards-based performance requirements may be specified on the basis of various service levels. Each service level may be characterized by parameters such as, for example, accuracy, service availability, and latency. Meeting such performance requirements typically requires the use of various types of technologies and techniques. Some of these technologies and techniques may be susceptible to failures due to various causes.BRIEF SUMMARY

[0008] Embodiments described herein generally pertain to monitoring artificial intelligence / machine learning (AI / ML) models used in position determination operations. An example method performed by a management entity to facilitate monitoring of one or more artificial intelligence / machine learning (AI / ML) models used in positioning operations, the method comprising determining whether a first user equipment (UE) is configured with a first capability to monitor at least a first AI / ML model that is useable in a positioning operation; determining whether a second UE is configured with a second capability to monitor at least the first AI / ML model that is useable in the positioning operation; and assigning a monitoring operation to one of the first UE or the second UE based, at least in part, on determining whether the first UE has the first capability to monitor at least the first AI / ML model and determining whether the second UE has the second capability to monitor at least the first AI / ML model.

[0009] An example method performed by a first user equipment (UE) to facilitate monitoring of one or more artificial intelligence / machine learning (AI / ML) modelsWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -3- used in positioning operations, the method comprising determining whether the first UE has a first capability to monitor at least a first AI / ML model that is useable in a positioning operation; determining whether a second UE has a second capability to monitor at least the first AI / ML model that is useable in the positioning operation; and assigning a monitoring operation to one of the first UE or the second UE based, at least in part, on determining whether the first UE has the first capability to monitor at least the first AI / ML model and on determining whether the second UE has the second capability to monitor at least the first AI / ML model.

[0010] An example management entity that facilitates monitoring of one or more artificial intelligence / machine learning (AI / ML) models used in positioning operations, comprising at least one memory; and one or more processors communicatively coupled with the at least one memory, the one or more processors configured to determine whether a first user equipment (UE) has a first capability to monitor at least a first AI / ML model that is useable in a positioning operation; determine whether a second UE has a second capability to monitor at least the first AI / ML model that is useable in the positioning operation; and assign a monitoring operation to one of the first UE or the second UE based, at least in part, on the determination whether the first UE has the first capability to monitor at least the first AI / ML model and on the determination whether the second UE has the second capability to monitor at least the first AI / ML model.

[0011] An example first user equipment (UE) that performs positioning operations based on one or more artificial intelligence / machine learning (AI / ML) models, comprising at least one transceiver; at least one memory; and one or more processors communicatively coupled with the at least one memory, the one or more processors configured to determine whether the first UE has a first capability to monitor at least a first AI / ML model that is useable in a positioning operation; transmit, via the at least one transceiver, to a second UE, a capability request; receive, via the at least one transceiver, from the second UE, a capability report comprising a second capability of the second UE to monitor at least a first AI / ML model that is useable in the positioning operation; determine, based on the capability report, whether the second UE has the second capability to monitor at least the first AI / ML model; and assign a monitoring operation to one of the first UE or the second UE based on determining whether the first UE has the first capability to monitor at least the first AI / ML model and on determiningWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -4- whether the second UE has the second capability to monitor at least the first AI / ML model.

[0012] This summary is neither intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in isolation to determine the scope of the claimed subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification of this disclosure, any or all drawings, and each claim. The foregoing, together with other features and examples, will be described in more detail below in the following specification, claims, and accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The detailed description below pertains to a few example embodiments that are illustrated in the accompanying drawings. However, it must be understood that the description is equally relevant to various other variations of the embodiments described herein. Such embodiments may utilize objects and / or components other than those illustrated in the drawings. It must also be understood that like reference numerals used in the various figures indicate similar or identical objects.

[0014] FIG. l is a simplified illustration of a positioning system according to an embodiment.

[0015] FIG. 2 is a diagram of a 5th Generation (5G) New Radio (NR) positioning system, illustrating an embodiment of a positioning system (e.g., the positioning system of FIG. 1) implemented within a 5G NR communication system.

[0016] FIG. 3 is a block diagram for describing the concept of direct AI / ML operations for generating location information.

[0017] FIG. 4 is a block diagram for describing the concept of assisted AI / ML operations for generating location information.

[0018] FIG. 5 illustrates a first example scenario where AI / ML operations may be performed for obtaining location information.

[0019] FIG. 6 illustrates a second example scenario where AI / ML operations may be performed for obtaining location information.WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -5-

[0020] FIG. 7 illustrates a third example scenario where AI / ML operations may be performed for obtaining location information.

[0021] FIG. 8 illustrates a fourth example scenario where AI / ML operations may be performed for obtaining location information.

[0022] FIG. 9 illustrates a fifth example scenario where AI / ML operations may be performed for obtaining location information.

[0023] FIG. 10 illustrates some example elements of an AI / ML model that includes monitoring in accordance with the disclosure.

[0024] FIG. 11 illustrates a group monitoring scenario for monitoring an AI / ML model in accordance with the disclosure.

[0025] FIG. 12 illustrates a first monitoring handover scenario where a first device hands over monitoring of an AI / ML model to a second device in accordance with the disclosure.

[0026] FIG. 13 illustrates a second monitoring handover scenario where more than one device may hand over monitoring of an AI / ML model to a monitoring device in accordance with the disclosure.

[0027] FIG. 14 illustrates a first example signal flow scenario for configuring a user equipment to perform monitoring operations upon an AI / ML model in accordance with the disclosure.

[0028] FIG. 15 illustrates a second example signal flow scenario for configuring a user equipment to perform monitoring operations upon an AI / ML model in accordance with the disclosure.

[0029] FIG. 16 shows a flowchart of an example method performed by a management entity or a UE to facilitate monitoring of one or more AI / ML models used in positioning operations.

[0030] FIG. 17 is a diagram showing some example functional elements of a user equipment according to an embodiment.

[0031] FIG. 18 is a diagram showing some example functional elements of an access node according to an embodiment.WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -6-

[0032] FIG. 19 is a block diagram of an embodiment of a computer system which may be used, in whole or in part, to provide the functions of one or more components and / or devices in accordance with the disclosure.DETAILED DESCRIPTION

[0033] Several illustrative examples will now be described with respect to the accompanying drawings, which form a part hereof. While particular examples, in which one or more aspects of the disclosure may be implemented, are described below, other examples may be used, and various modifications may be made without departing from the scope of the disclosure or the spirit of the appended claims.

[0034] Reference throughout this specification to “one example” or “an example” means that a particular feature, structure, or characteristic described in connection with the example is included in at least one example of claimed subject matter. Thus, the appearances of the phrase “in one example” or “an example” in various places throughout this specification are not necessarily all referring to the same example. Furthermore, particular features, structures, or characteristics described herein may be combined in one or more examples. It must be understood that words such as “position” and “positioning” are used in this disclosure interchangeably with words / phrases such as “location” and “location determination” and are intended to be equivalent in meaning and context. The word “data” as used herein generally refers to information obtained from any of various sources such as, for example, a data signal, a wireless signal, a message, a database, or a memory.

[0035] The methodologies described herein may be implemented by various means depending upon applications according to particular examples. For example, such methodologies may be implemented in hardware, firmware, software, and / or combinations thereof. In a hardware implementation, for example, a processing unit may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, electronic devices, other devices units designed to perform the functions described herein, and / or combinations thereof.WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -7-

[0036] Various aspects described herein generally relate to systems and methods for monitoring an AI / ML model used for position determination of a device. It must be understood that the word “model” as used herein in this disclosure is intended to be non-limiting. Accordingly various aspects that are described herein with reference to an “AI / ML model” are equally applicable to, and encompass, various aspects associated with various types of AI / ML usage in positioning operations, such as, for example, AI / ML functions, AI / ML functionality, AI / ML physical models, AI / ML logical models, AI / ML computational implementations, and AI / ML hardware implementations. Thus, for example, a description pertaining to monitoring of an AI / ML model is equally applicable to monitoring of an AI / ML functionality or monitoring of AI / ML hardware, for example.

[0037] More particularly, the systems and methods described herein may be used for monitoring and evaluating data associated with one or more AI / ML models that may be used for operating upon wireless positioning signals as a part of a position determination procedure. Typically, an AI / ML model uses various types of data such as, for example, training data and inference data, for obtaining information from an input signal. In accordance with the disclosure, an AI / ML model may use data such as, for example, training data and inference data, to extract information from wireless signals (cellular as well as non-cellular wireless signals). The extracted information can be, for example, pertaining to determine a position / location of a device such as, for example, a user equipment (a cellphone, smartphone, laptop, tablet, personal data assistant (PDA), navigation device, Internet of Things (loT) device, or some other portable or moveable device).

[0038] The AI / ML model may operate efficiently if the various types of data used by the AI / ML model is stable. However, the operation of an AI / ML model may require monitoring and adjustment when data provided to the AI / ML model is erroneous or fluctuates over time in an undesirable way. Specifically, in the context of position determination, the operation of an AI / ML model may require monitoring and adjustment if, for example, training data and / or inference data provided to the AI / ML model is erroneous and / or varies in an undesirable manner over time.

[0039] Traditional monitoring scenarios associated with various position determination operations performed without the use of an AI / ML model may fail toWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -8- detect and remedy problems associated with an AI / ML model when used as a part of a position determination operation. The various example embodiments disclosed herein generally pertain to monitoring various aspects of an AI / ML model used as a part of a position determination operation and to generate a monitoring report that can be useful, for example, to address an issue or problem associated with using the AI / ML model.

[0040] Accordingly, particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the potential advantages described below.

[0041] An example advantage associated with monitoring and generating a monitoring report relates to preventing an erroneous position determination operation caused by issues associated with using an AI / ML model. Some of these issues may be subtle in nature and undetectable by conventional monitoring methods, such as, for example, changes in network conditions causing changes in wireless signals and change s / errors in training / inference data used by the AI / ML model. Furthermore, monitoring the AI / ML model and detecting an issue with the AI / ML model operation may avoid time and effort being spent elsewhere searching for a cause of an issue with a position determination operation.

[0042] FIG. 1 is a simplified illustration of a positioning / sensing system 100, which may be implemented in conjunction with and / or as part of a wireless communication system (e.g., cellular communication network) and can include a mobile device 105, a location server 160, and / or other components. One or more components of the positioning / sensing system 100 can be used for implementing the techniques disclosed herein for monitoring an AI / ML model used for operating upon information associated with wireless positioning signals, as a part of a position determination procedure to determine a position of a device, or a sensing procedure performed to sense one or more objects.

[0043] However, the techniques described herein are not limited to such components and may be implemented in other types of systems (not shown). The positioning / sensing system 100 can include: the mobile device 105 (which is one example of an UE); one or more satellites 110 (also referred to as space vehicles (SVs)) for a Global Navigation Satellite System (GNSS) (such as the Global Positioning System (GPS), GLONASS, Galileo or Beidou) and / or Non-Terrestrial Network (NTN)WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -9- functionality; base stations 120; access points (APs) 130; location server 160; network 170; and external client 180. Generally put, the positioning / sensing system 100 can estimate a location of the mobile device 105 based on RF signals received by and / or sent from the mobile device 105 and known locations of other components (e.g., GNSS satellites 110, base stations 120, APs 130) transmitting and / or receiving the RF signals. Additionally or alternatively, wireless devices such as the mobile device 105, base stations 120, and satellites 110 can be utilized to perform positioning (e.g., of one or more wireless devices) and / or to perform RF sensing (e.g., of one or more objects by using RF signals transmitted by one or more wireless devices).

[0044] It should be noted that FIG. 1 provides only a generalized illustration of various components, any or all of which may be utilized as appropriate, and each of which may be duplicated, as necessary. Specifically, although only one mobile device 105 is illustrated, it will be understood that many UEs (e.g., hundreds, thousands, millions, etc.) may utilize the positioning / sensing system 100. Similarly, the positioning / sensing system 100 may include a larger or smaller number of base stations 120 and / or APs 130 than illustrated in FIG. 1. The illustrated connections that connect the various components in the positioning / sensing system 100 comprise data and signaling connections which may include additional (intermediary) components, direct or indirect physical and / or wireless connections, and / or additional networks.Furthermore, components may be rearranged, combined, separated, substituted, and / or omitted, depending on desired functionality. In some embodiments, for example, the external client 180 may be directly connected to location server 160. A person of ordinary skill in the art will recognize many modifications to the components illustrated.

[0045] Depending on desired functionality, the network 170 may comprise any of a variety of wireless and / or wireline networks. The network 170 can, for example, comprise any combination of public and / or private networks, local and / or wide-area networks, and the like. Furthermore, the network 170 may utilize one or more wired and / or wireless communication technologies. In some embodiments, the network 170 may comprise a cellular or other mobile network, a wireless local area network (WLAN), a wireless wide-area network (WWAN), and / or the Internet, for example. Examples of network 170 include a Long-Term Evolution (LTE) wireless network, a Fifth Generation (5G) wireless network (also referred to as New Radio (NR) wireless network or 5G NR wireless network), a Wi-Fi WLAN, and the Internet. LTE, 5G, andWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -10-NR are wireless technologies defined, or being defined, by the 3rd Generation Partnership Project (3GPP). In an LTE, 5G, or other cellular network, mobile device 105 may be referred to as a user equipment (UE). Network 170 may also include more than one network and / or more than one type of network.

[0046] The base stations 120 and access points (APs) 130 may be communicatively coupled to the network 170. In some embodiments, the base stations 120 may be owned, maintained, and / or operated by a cellular network provider, and may employ any of a variety of wireless technologies, as described herein below. Depending on the technology of the network 170, a base station 120 may comprise a node B, an Evolved Node B (eNodeB or eNB), a base transceiver station (BTS), a radio base station (RBS), a New Radio (NR) NodeB, a Next Generation Node B (gNB), a Next Generation eNB (ng-eNB), or the like. A base station 120 that is a gNB or ng-eNB may be part of a Next Generation Radio Access Network (NG-RAN) which may connect to a 5G Core Network (5GC) in the case that Network 170 is a 5G network. The functionality performed by a base station 120 in earlier-generation networks (e.g., 3G and 4G) may be separated into different functional components (e.g., radio units (RUs), distributed units (DUs), and central units (CUs)) and layers (e.g., L1 / L2 / L3) in view Open Radio Access Networks (O-RAN) and / or Virtualized Radio Access Network (V-RAN or vRAN) in 5G or later networks, which may be executed on different devices at different locations connected, for example, via fronthaul, midhaul, and backhaul connections. As referred to herein, a “base station” (or ng-eNB, gNB, etc.) may include any or all of these functional components.

[0047] An AP 130 may comprise a Wi-Fi AP or a Bluetooth® AP or an AP having cellular capabilities (e.g., 4G LTE and / or 5GNR), for example. Thus, mobile device 105 can send and receive information with network-connected devices, such as location server 160, by accessing the network 170 via a base station 120 using a first communication link 133. Additionally or alternatively, because APs 130 also may be communicatively coupled with the network 170, mobile device 105 may communicate with network-connected and Internet-connected devices, including location server 160, using a second communication link 135, or via one or more other mobile devices 145. As used herein, the term “base station” may generically refer to a single physical transmission point, or multiple co-located physical transmission points, which may be located at a base station 120. A Transmission Reception Point (TRP) (also known asWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -11- transmit / receive point) corresponds to this type of transmission point, and the term “TRP” may be used interchangeably herein with the terms “gNB,” “ng-eNB,” and “base station.” In some cases, a base station 120 may comprise multiple TRPs - e.g. with each TRP associated with a different antenna or a different antenna array for the base station 120. As used herein, the transmission functionality of a TRP may be performed with a transmission point (TP) and / or the reception functionality of a TRP may be performed by a reception point (RP), which may be physically separate or distinct from a TP. That said, a TRP may comprise both a TP and an RP. Physical transmission points may comprise an array of antennas of a base station 120 (e.g., as in a Multiple Input-Multiple Output (MIMO) system and / or where the base station employs beamforming). According to aspects of applicable 5G cellular standards, a base station 120 (e.g., gNB) may be capable of transmitting different “beams” in different directions and performing “beam sweeping” in which a signal is transmitted in different beams, along different directions (e.g., one after the other). The term “base station” used herein may additionally refer to multiple non-co-located physical transmission points, the physical transmission points may be a Distributed Antenna System (DAS) (a network of spatially separated antennas connected to a common source via a transport medium) or a Remote Radio Head (RRH) (a remote base station connected to a serving base station).

[0048] As noted, satellites 110 may be used to implement NTN functionality, extending communication, positioning, and potentially other functionality (e.g., RF sensing) of a terrestrial network. As such, one or more satellites may be communicatively linked to one or more NTN gateways 150 (also known as “gateways,” “earth stations,” or “ground stations”). The NTN gateways 150 may be communicatively linked with base stations 120 via link 155. In some embodiments, NTN gateways 150 may function as DUs of a base station 120, as described previously. Not only can this enable the mobile device 105 to communicate with the network 170 via satellites 110, but this can also enable network-based positioning, RF sensing, etc.

[0049] Satellites 110 may be utilized in one or more way. For example, satellites 110 (also referred to as space vehicles (SVs)) may be part of a Global Navigation Satellite System (GNSS) such as the Global Positioning System (GPS), GLONASS, Galileo or Beidou. Positioning using RF signals from GNSS satellites may comprise measuring multiple GNSS signals at a GNSS receiver of the mobile device 105 to perform code-based and / or carrier-based positioning, which can be highly accurate.WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -12-Additionally or alternatively, satellites 110 may be utilized for NTN-based positioning, in which satellites 110 may functionally operate as TRPs (or TPs) of a network (e.g., LTE and / or NR network) and may be communicatively coupled with network 170. In particular, reference signals (e.g., PRS) transmitted by satellites 110 NTN-based positioning may be similar to those transmitted by base stations 120 and may be coordinated by a network function server that may operate as a location server. In some embodiments, satellites 110 used for NTN-based positioning may be different than those used for GNSS-based positioning. In some embodiments NTN nodes may include non-terrestrial vehicles, which may be in addition or as an alternative to NTN satellites. NTN satellites 110 and / or other NTN platforms may be further leveraged to perform RF sensing. As described in more detail hereafter, satellites may use a JCS symbol in an Orthogonal Frequency-Division Multiplexing (OFDM) waveform to allow both RF sensing and / or positioning, and communication.

[0050] As used herein, the term “cell” may generically refer to a logical communication entity used for communication with a base station 120 and may be associated with an identifier for distinguishing neighboring cells (e.g., a Physical Cell Identifier (PCID), a Virtual Cell Identifier (VCID)) operating via the same or a different carrier. In some examples, a carrier may support multiple cells, and different cells may be configured according to different protocol types (e.g., Machine-Type Communication (MTC), Narrowband Internet-of-Things (NB-IoT), Enhanced Mobile Broadband (eMBB), or others) that may provide access for different types of devices. In some cases, the term “cell” may refer to a portion of a geographic coverage area (e.g., a sector) over which the logical entity operates.

[0051] The location server 160 may comprise a server and / or other computing device configured to determine an estimated location of mobile device 105 and / or provide data (e.g., “assistance data”) to mobile device 105 to facilitate location measurement and / or location determination by mobile device 105. According to some embodiments, location server 160 may comprise a Home Secure User Plane Location (SUPL) Location Platform (H-SLP), which may support the SUPL user plane (UP) location solution defined by the Open Mobile Alliance (OMA) and may support location services for mobile device 105 based on subscription information for mobile device 105 stored in location server 160. In some embodiments, the location server 160 may comprise, a Discovered SLP (D-SLP) or an Emergency SLP (E-SLP). The locationWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -13- server 160 may also comprise an Enhanced Serving Mobile Location Center (E-SMLC) that supports location of mobile device 105 using a control plane (CP) location solution for LTE radio access by mobile device 105. The location server 160 may further comprise a Location Management Function (LMF) that supports location of mobile device 105 using a control plane (CP) location solution for NR or LTE radio access by mobile device 105.

[0052] In a CP location solution, signaling to control and manage the location of mobile device 105 may be exchanged between elements of network 170 and with mobile device 105 using existing network interfaces and protocols and as signaling from the perspective of network 170. In a UP location solution, signaling to control and manage the location of mobile device 105 may be exchanged between location server 160 and mobile device 105 as data (e.g. data transported using the Internet Protocol (IP) and / or Transmission Control Protocol (TCP)) from the perspective of network 170.

[0053] As previously noted, an estimated location of the mobile device 105 may be based on measurements of RF signals sent from and / or received by the mobile device 105. In particular, these measurements can provide information regarding the relative distance and / or angle of the mobile device 105 from one or more components in the positioning / sensing system 100 (e.g., satellites 110, APs 130, base stations 120). The estimated location of the mobile device 105 can be estimated geometrically (e.g., using multi angulation and / or multilateration), based on the distance (range) and / or angle measurements, along with known position of the one or more components.

[0054] Additionally or alternatively, the location server 160, may function as a sensing server. A sensing server can be used to coordinate and / or assist in the coordination of sensing of one or more objects (also referred to herein as “targets”) by one or more wireless devices in the positioning / sensing system 100. This can include the mobile device 105, base stations 120, APs 130, other mobile devices 145, satellites 110, or any combination thereof. Wireless devices capable of performing RF sensing may be referred to herein as “sensing nodes.” To perform RF sensing, a sensing server may coordinate sensing sessions in which one or more RF sensing nodes may perform RF sensing by transmitting RF signals (e.g., reference signals (RSs)), and measuring reflected signals, or “echoes,” comprising reflections of the transmitted RF signals off of one or more objects / targets. Reflected signals and object / target detection may beWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -14- determined, for example, from channel state information (CSI) received at a receiving device. Sensing may comprise (i) monostatic sensing using a single device as a transmitter (of RF signals) and receiver (of reflected signals); (ii) bistatic sensing using a first device as a transmitter and a second device as a receiver; or (iii) multi-static sensing using a plurality of transmitters and / or a plurality of receivers. To facilitate sensing (e.g., in a sensing session among one or more sensing nodes), a sensing server may provide data (e.g., “assistance data”) to the sensing nodes to facilitate RS transmission and / or measurement, object / target detection, or any combination thereof. Such data may include an RS configuration indicating which resources (e.g., time and / or frequency resources) may be used (e.g., in a sensing session) to transmit RS for RF sensing. According to some embodiments, a sensing server may comprise a Sensing Management Function (SMF or SnMF).

[0055] Although terrestrial components such as APs 130 and base stations 120 may be fixed, embodiments are not so limited. Mobile components may be used. For example, in some embodiments, a location of the mobile device 105 may be estimated at least in part based on measurements of RF signals 140 communicated between the mobile device 105 and one or more other mobile devices 145, which may be mobile or fixed. As illustrated, other mobile devices may include, for example, a mobile phone 145-1, vehicle 145-2, static communication / positioning device 145-3, or other static and / or mobile device capable of providing wireless signals used for positioning the mobile device 105, or a combination thereof. Wireless signals from mobile devices 145 used for positioning of the mobile device 105 may comprise RF signals using, for example, Bluetooth® (including Bluetooth Low Energy (BLE)), IEEE 802.1 lx (e.g., Wi-Fi®), Ultra-Wideband (UWB), IEEE 802.15x, or a combination thereof. Mobile devices 145 may additionally or alternatively use non-RF wireless signals for positioning of the mobile device 105, such as infrared signals or other optical technologies.

[0056] Mobile devices 145 may comprise other UEs communicatively coupled with a cellular or other mobile network (e.g., network 170). When one or more other mobile devices 145 comprising UEs are used in the position determination of a particular mobile device 105, the mobile device 105 for which the position is to be determined may be referred to as the “target UE,” and each of the other mobile devices 145 used may be referred to as an “anchor UE.” For position determination of a target UE, theWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -15- respective positions of the one or more anchor UEs may be known and / or jointly determined with the target UE. Direct communication between the one or more other mobile devices 145 and mobile device 105 may comprise sidelink and / or similar Device-to-Device (D2D) communication technologies. Sidelink, which is defined by 3GPP, is a form of D2D communication under the cellular-based LTE and NR standards.

[0057] According to some embodiments, such as when the mobile device 105 comprises and / or is incorporated into a vehicle, a form of D2D communication used by the mobile device 105 may comprise vehicle-to-everything (V2X) communication. V2X is a communication standard for vehicles and related entities to exchange information regarding a traffic environment. V2X can include vehicle-to-vehicle (V2V) communication between V2X-capable vehicles, vehicle-to-infrastructure (V2I) communication between the vehicle and infrastructure-based devices (commonly termed roadside units (RSUs)), vehicle-to-person (V2P) communication between vehicles and nearby people (pedestrians, cyclists, and other road users), and the like. Further, V2X can use any of a variety of wireless RF communication technologies. Cellular V2X (CV2X), for example, is a form of V2X that uses cellular-based communication such as LTE (4G), NR (5G) and / or other cellular technologies in a direct-communication mode as defined by 3 GPP. The mobile device 105 illustrated in FIG. 1 may correspond to a component or device on a vehicle, RSU, or other V2X entity that is used to communicate V2X messages. In embodiments in which V2X is used, the static communication / positioning device 145-3 (which may correspond with an RSU) and / or the vehicle 145-2, therefore, may communicate with the mobile device 105 and may be used to determine the position of the mobile device 105 using techniques similar to those used by base stations 120 and / or APs 130 (e.g., using multi angulation and / or multilateration). It can be further noted that mobile devices 145 (which may include V2X devices), base stations 120, and / or APs 130 may be used together (e.g., in a WWAN positioning solution) to determine the position of the mobile device 105, according to some embodiments.

[0058] An estimated location of mobile device 105 can be used in a variety of applications - e.g. to assist direction finding or navigation for a user of mobile device 105 or to assist another user (e.g. associated with external client 180) to locate mobile device 105. A “location” is also referred to herein as a “location estimate,” “estimatedWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -16- location,” “location,” “position,” “position estimate,” “position fix,” “estimated position,” “location fix” or “fix.” The process of determining a location may be referred to as “positioning,” “position determination,” “location determination,” or the like. A location of mobile device 105 may comprise an absolute location of mobile device 105 (e.g. a latitude and longitude and possibly altitude) or a relative location of mobile device 105 (e.g. a location expressed as distances north or south, east or west and possibly above or below some other known fixed location (including, e.g., the location of a base station 120 or AP 130) or some other location such as a location for mobile device 105 at some known previous time, or a location of a mobile device 145 (e.g., another UE) at some known previous time). A location may be specified as a geodetic location comprising coordinates which may be absolute (e.g. latitude, longitude and optionally altitude), relative (e.g. relative to some known absolute location) or local (e.g. X, Y and optionally Z coordinates according to a coordinate system defined relative to a local area such a factory, warehouse, college campus, shopping mall, sports stadium or convention center). A location may instead be a civic location and may then comprise one or more of a street address (e.g. including names or labels for a country, state, county, city, road and / or street, and / or a road or street number), and / or a label or name for a place, building, portion of a building, floor of a building, and / or room inside a building etc. A location may further include an uncertainty or error indication, such as a horizontal and possibly vertical distance by which the location is expected to be in error or an indication of an area or volume (e.g. a circle or ellipse) within which mobile device 105 is expected to be located with some level of confidence (e.g. 95% confidence).

[0059] The external client 180 may be a web server or remote application that may have some association with mobile device 105 (e.g. may be accessed by a user of mobile device 105) or may be a server, application, or computer system providing a location service to some other user or users which may include obtaining and providing the location of mobile device 105 (e.g. to enable a service such as friend or relative finder, or child or pet location). Additionally or alternatively, the external client 180 may obtain and provide the location of mobile device 105 to an emergency services provider, government agency, etc.

[0060] As previously noted, the example positioning / sensing system 100 can be implemented using a wireless communication network, such as an LTE-based or 5GWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -17-NR-based network, or a future 6G network.

[0061] FIG. 2 shows a diagram of a 5GNR positioning / sensing system 200, illustrating an embodiment of a positioning / sensing system (e.g., positioning / sensing system 100) implementing 5GNR. The 5G NR positioning / sensing system 200 may be configured to determine the location of a UE, such as, for example, the mobile device 105, by using access nodes, which may include NR NodeB (gNB) 210-1 and 210-2 (collectively and generically referred to herein as gNBs 210), ng-eNB 214, and / or WLAN 216 to implement one or more positioning methods. The gNBs 210 and / or the ng-eNB 214 may correspond with base stations 120 of FIG. 1, and the WLAN 216 may correspond with one or more access points 130 of FIG. 1. Optionally, the 5GNR positioning / sensing system 200 additionally may be configured to determine the location of a UE 205 (such as, for example, the mobile device 105 shown in FIG. 1) by using a location server 220 (which may correspond with location server 160) to implement the one or more positioning methods. Here, the 5G NR positioning / sensing system 200 comprises the UE 205, and components of a 5G NR network comprising a Next Generation (NG) Radio Access Network (RAN) (NG-RAN) 235 and a 5G Core Network (5G CN) 240. A 5G network may also be referred to as an NR network; NG- RAN 235 may be referred to as a 5G RAN or as an NR RAN; and 5G CN 240 may be referred to as an NG Core network. The 5G NR positioning / sensing system 200 may further utilize information from GNSS satellites 110 from a GNSS system like Global Positioning System (GPS) or similar system (e.g. GLONASS, Galileo, Beidou, Indian Regional Navigational Satellite System (IRNSS)). Additional components of the 5G NR positioning / sensing system 200 are described below. The 5G NR positioning / sensing system 200 may include additional or alternative components.

[0062] It should be noted that FIG. 2 provides only a generalized illustration of various components, any or all of which may be utilized as appropriate, and each of which may be duplicated or omitted as necessary. Specifically, although only one UE 205 is illustrated, it will be understood that many UEs (e.g., hundreds, thousands, millions, etc.) may utilize the 5G NR positioning / sensing system 200. Similarly, the 5G NR positioning / sensing system 200 may include a larger (or smaller) number of GNSS satellites 110, gNBs 210, ng-eNBs 214, Wireless Local Area Networks (WLANs) 216, Access and mobility Management Functions (AMF)s 215, external client 230, and / or other components. The illustrated connections that connect the various components inWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -18- the 5GNR positioning / sensing system 200 include data and signaling connections which may include additional (intermediary) components, direct or indirect physical and / or wireless connections, and / or additional networks. Furthermore, components may be rearranged, combined, separated, substituted, and / or omitted, depending on desired functionality.

[0063] The UE 205 may comprise and / or be referred to as a device, a mobile device, a wireless device, a mobile terminal, a terminal, a mobile station (MS), a Secure User Plane Location (SUPL)-Enabled Terminal (SET), or by some other name. Moreover, UE 205 may correspond to a cellphone, smartphone, laptop, tablet, personal data assistant (PDA), navigation device, Internet of Things (loT) device, or some other portable or moveable device. Typically, though not necessarily, the UE 205 may support wireless communication using one or more Radio Access Technologies (RATs) such as using GSM, CDMA, W-CDMA, LTE, High Rate Packet Data (HRPD), IEEE 802.11 Wi-Fi®, Bluetooth, Worldwide Interoperability for Microwave Access (WiMAX™), 5G NR (e.g., using the NG-RAN 235 and 5G CN 240), etc. The UE 205 may also support wireless communication using a WLAN 216 which (like the one or more RATs, and as previously noted with respect to FIG. 1) may connect to other networks, such as the Internet. The use of one or more of these RATs may allow the UE 205 to communicate with an external client 230 (e.g., via elements of 5G CN 240 not shown in FIG. 2, or possibly via a Gateway Mobile Location Center (GMLC) 225) and / or allow the external client 230 to receive location information regarding the UE 205 (e.g., via the GMLC 225). The external client 230 of FIG. 2 may correspond to external client 180 of FIG. 1, as implemented in or communicatively coupled with a 5G NR network.

[0064] The UE 205 may include a single entity or may include multiple entities, such as in a personal area network where a user may employ audio, video and / or data I / O devices, and / or body sensors and a separate wireline or wireless modem. An estimate of a location of the UE 205 may be referred to as a location, location estimate, location fix, fix, position, position estimate, or position fix, and may be geodetic, thus providing location coordinates for the UE 205 (e.g., latitude and longitude), which may or may not include an altitude component (e.g., height above sea level, height above or depth below ground level, floor level or basement level). Alternatively, a location of the UE 205 may be expressed as a civic location (e.g., as a postal address or the designation of some point or small area in a building such as a particular room or floor). A locationWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -19- of the UE 205 may also be expressed as an area or volume (defined either geodetically or in civic form) within which the UE 205 is expected to be located with some probability or confidence level (e.g., 67%, 95%, etc.). A location of the UE 205 may further be a relative location comprising, for example, a distance and direction or relative X, Y (and Z) coordinates defined relative to some origin at a known location which may be defined geodetically, in civic terms, or by reference to a point, area, or volume indicated on a map, floor plan or building plan. In the description contained herein, the use of the term location may comprise any of these variants unless indicated otherwise. When computing the location of a UE, it is common to solve for local X, Y, and possibly Z coordinates and then, if needed, convert the local coordinates into absolute ones (e.g. for latitude, longitude and altitude above or below mean sea level).

[0065] Base stations in the NG-RAN 235 shown in FIG. 2 may correspond to base stations 120 in FIG. 1 and may include gNBs 210. Pairs of gNBs 210 in NG-RAN 235 may be connected to one another (e.g., directly as shown in FIG. 2 or indirectly via other gNBs 210). The communication interface between base stations (gNBs 210 and / or ng-eNB 214) may be referred to as an Xn interface 237. Access to the 5G network is provided to UE 205 via wireless communication between the UE 205 and one or more of the gNBs 210, which may provide wireless communications access to the 5G CN 240 on behalf of the UE 205 using 5G NR. The wireless interface between base stations (gNBs 210 and / or ng-eNB 214) and the UE 205 may be referred to as a Uu interface 239. 5G NR radio access may also be referred to as NR radio access or as 5G radio access. In FIG. 2, the serving gNB for UE 205 is assumed to be gNB 210-1, although other gNBs (e.g. gNB 210-2) may act as a serving gNB if UE 205 moves to another location or may act as a secondary gNB to provide additional throughput and bandwidth to UE 205.

[0066] Base stations in the NG-RAN 235 shown in FIG. 2 may also or instead include a next generation evolved Node B, also referred to as an ng-eNB, 214. Ng-eNB 214 may be connected to one or more gNBs 210 in NG-RAN 235-e.g. directly or indirectly via other gNBs 210 and / or other ng-eNBs. An ng-eNB 214 may provide LTE wireless access and / or evolved LTE (eLTE) wireless access to UE 205. Some gNBs 210 (e.g. gNB 210-2) and / or ng-eNB 214 in FIG. 2 may be configured to function as positioning-only beacons which may transmit signals (e.g., Positioning Reference Signal (PRS)) and / or may broadcast assistance data to assist positioning of UE 205 butWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -20- may not receive signals from UE 205 or from other UEs. Some gNBs 210 (e.g., gNB 210-2 and / or another gNB not shown) and / or ng-eNB 214 may be configured to function as detecting-only nodes may scan for signals containing, e.g., PRS data, assistance data, or other location data. Such detecting-only nodes may not transmit signals or data to UEs but may transmit signals or data (relating to, e.g., PRS, assistance data, or other location data) to other network entities (e.g., one or more components of 5G CN 240, external client 230, or a controller) which may receive and store or use the data for positioning of at least UE 205. It is noted that while only one ng-eNB 214 is shown in FIG. 2, some embodiments may include multiple ng-eNBs 214. Base stations (e.g., gNBs 210 and / or ng-eNB 214) may communicate directly with one another via an Xn communication interface. Additionally or alternatively, base stations may communicate directly or indirectly with other components of the 5G NR positioning / sensing system 200, such as the location server 220 and AMF 215.

[0067] 5G NR positioning / sensing system 200 may also include one or more WLANs 216 which may connect to a Non-3GPP InterWorking Function (N3IWF) 250 in the 5G CN 240 (e.g., in the case of an untrusted WLAN 216). For example, the WLAN 216 may support IEEE 802.11 Wi-Fi access for UE 205 and may comprise one or more Wi-Fi APs (e.g., APs 130 of FIG. 1). Here, the N3IWF 250 may connect to other elements in the 5G CN 240 such as AMF 215. In some embodiments, WLAN 216 may support another RAT such as Bluetooth. The N3IWF 250 may provide support for secure access by UE 205 to other elements in 5G CN 240 and / or may support interworking of one or more protocols used by WLAN 216 and UE 205 to one or more protocols used by other elements of 5G CN 240 such as AMF 215. For example, N3IWF 250 may support IPSec tunnel establishment with UE 205, termination of IKEv2 / IPSec protocols with UE 205, termination of N2 and N3 interfaces to 5G CN 240 for control plane and user plane, respectively, relaying of uplink (UL) and downlink (DL) control plane Non-Access Stratum (NAS) signaling between UE 205 and AMF 215 across an N1 interface. In some other embodiments, WLAN 216 may connect directly to elements in 5G CN 240 (e.g. AMF 215 as shown by the dashed line in FIG.2) and not via N3IWF 250. For example, direct connection of WLAN 216 to 5GCN 240 may occur if WLAN 216 is a trusted WLAN for 5GCN 240 and may be enabled using a Trusted WLAN Interworking Function (TWIF) (not shown in FIG. 2) which may be an element inside WLAN 216. It is noted that while only one WLAN 216 is shown in FIG.WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -21-2, some embodiments may include multiple WLANs 216.

[0068] Access nodes may comprise any of a variety of network entities enabling communication between the UE 205 and the AMF 215. As noted, this can include gNBs 210, ng-eNB 214, WLAN 216, and / or other types of cellular base stations. However, access nodes providing the functionality described herein may additionally or alternatively include entities enabling communications to any of a variety of RATs not illustrated in FIG. 2, which may include non-cellular technologies. Thus, the term “access node,” as used in the embodiments described herein below, may include but is not necessarily limited to a gNB 210, ng-eNB 214 or WLAN 216.

[0069] In some embodiments, an access node, such as a gNB 210, ng-eNB 214, and / or WLAN 216 (alone or in combination with other components of the 5G NR positioning / sensing system 200), may be configured to, in response to receiving a request for location information from the location server 220, obtain location measurements of uplink (UL) signals received from the UE 205) and / or obtain downlink (DL) location measurements from the UE 205 that were obtained by UE 205 for DL signals received by UE 205 from one or more access nodes. As noted, while FIG. 2 depicts access nodes (gNB 210, ng-eNB 214, and WLAN 216) configured to communicate according to 5G NR, LTE, and Wi-Fi communication protocols, respectively, access nodes configured to communicate according to other communication protocols may be used, such as, for example, a Node B using a Wideband Code Division Multiple Access (WCDMA) protocol for a Universal Mobile Telecommunications Service (UMTS) Terrestrial Radio Access Network (UTRAN), an eNB using an LTE protocol for an Evolved UTRAN (E-UTRAN), or a Bluetooth® beacon using a Bluetooth protocol for a WLAN. For example, in a 4G Evolved Packet System (EPS) providing LTE wireless access to UE 205, a RAN may comprise an E- UTRAN, which may comprise base stations comprising eNBs supporting LTE wireless access. A core network for EPS may comprise an Evolved Packet Core (EPC). An EPS may then comprise an E-UTRAN plus an EPC, where the E-UTRAN corresponds to NG-RAN 235 and the EPC corresponds to 5GCN 240 in FIG. 2. The methods and techniques described herein for obtaining a civic location for UE 205 may be applicable to such other networks.

[0070] The gNBs 210 and ng-eNB 214 can communicate with an AMF 215, which,WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -22- for positioning functionality, communicates with a location server 220. The AMF 215 may support mobility of the UE 205, including cell change and handover of UE 205 from an access node (e.g., gNB 210, ng-eNB 214, or WLAN 216)of a first RAT to an access node of a second RAT. The AMF 215 may also participate in supporting a signaling connection to the LE 205 and possibly data and voice bearers for the LE 205. The location server 220 may support positioning of the LIE 205 using a CP location solution when LE 205 accesses the NG-RAN 235 or WLAN 216 and may support position procedures and methods, including LE assisted / UE based and / or network based procedures / methods, such as Assisted GNSS (A-GNSS), Observed Time Difference Of Arrival (OTDOA) (which may be referred to in NR as Time Difference Of Arrival (TDOA)), Real Time Kinematic (RTK), Precise Point Positioning (PPP), Differential GNSS (DGNSS), Enhance Cell ID (ECID), angle of arrival (AOA), angle of departure (AoD), WLAN positioning, round trip signal propagation delay (RTT), multi-cell RTT, and / or other positioning procedures and methods. The location server 220 may also process location service requests for the LE 205, e.g., received from the AMF 215 or from the GMLC 225. The location server 220 may be connected to AMF 215 and / or to GMLC 225. In some embodiments, a network such as 5GCN 240 may additionally or alternatively implement other types of location-support modules, such as an Evolved Serving Mobile Location Center (E-SMLC) or a SUPL Location Platform (SLP). It is noted that in some embodiments, at least part of the positioning functionality (including determination of a LE 205’s location) may be performed at the LE 205 (e.g., by measuring downlink PRS (DL-PRS) signals transmitted by wireless nodes such as gNBs 210, ng-eNB 214 and / or WLAN 216, and / or using assistance data provided to the LE 205, e.g., by location server 220).

[0071] The Gateway Mobile Location Center (GMLC) 225 may support a location request for the UE 205 received from an external client 230 and may forward such a location request to the AMF 215 for forwarding by the AMF 215 to the location server 220. A location response from the location server 220 (e.g., containing a location estimate for the LE 205) may be similarly returned to the GMLC 225 either directly or via the AMF 215, and the GMLC 225 may then return the location response (e.g., containing the location estimate) to the external client 230.

[0072] A Network Exposure Function (NEF) 245 may be included in 5GCN 240. The NEF 245 may support secure exposure of capabilities and events concerning 5GCNWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -23-240 and UE 205 to the external client 230, which may then be referred to as an Access Function (AF) and may enable secure provision of information from external client 230 to 5GCN 240. NEF 245 may be connected to AMF 215 and / or to GMLC 225 for the purposes of obtaining a location (e.g. a civic location) of UE 205 and providing the location to external client 230.

[0073] As further illustrated in FIG. 2, the location server 220 may communicate with the gNBs 210 and / or with the ng-eNB 214 using an NR Positioning Protocol annex (NRPPa) as defined in 3GPP Technical Specification (TS) 38.455. NRPPa messages may be transferred between a gNB 210 and the location server 220, and / or between an ng-eNB 214 and the location server 220, via the AMF 215. As further illustrated in FIG. 2, location server 220 and UE 205 may communicate using an LTE Positioning Protocol (LPP) as defined in 3GPP TS 37.355. Here, LPP messages may be transferred between the UE 205 and the location server 220 via the AMF 215 and a serving gNB 210-1 or serving ng-eNB 214 for UE 205. For example, LPP messages may be transferred between the location server 220 and the AMF 215 using messages for service-based operations (e.g., based on the Hypertext Transfer Protocol (HTTP)) and may be transferred between the AMF 215 and the UE 205 using a 5GNAS protocol. The LPP protocol may be used to support positioning of UE 205 using UE assisted and / or UE based position methods such as A-GNSS, RTK, TDOA, multi-cell RTT, AoD, and / or ECID. The NRPPa protocol may be used to support positioning of UE 205 using network based position methods such as ECID, AO A, uplink TDOA (UL-TDOA) and / or may be used by location server 220 to obtain location related information from gNBs 210 and / or ng-eNB 214, such as parameters defining DL-PRS transmission from gNBs 210 and / or ng-eNB 214.

[0074] In the case of UE 205 access to WLAN 216, location server 220 may use NRPPa and / or LPP to obtain a location of UE 205 in a similar manner to that just described for UE 205 access to a gNB 210 or ng-eNB 214. Thus, NRPPa messages may be transferred between a WLAN 216 and the location server 220, via the AMF 215 and N3IWF 250 to support network-based positioning of UE 205 and / or transfer of other location information from WLAN 216 to location server 220. Alternatively, NRPPa messages may be transferred between N3IWF 250 and the location server 220, via the AMF 215, to support network-based positioning of UE 205 based on location related information and / or location measurements known to or accessible to N3IWF 250 andWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -24- transferred from N3IWF 250 to location server 220 using NRPPa. Similarly, LPP and / or LPP messages may be transferred between the UE 205 and the location server 220 via the AMF 215, N3IWF 250, and serving WLAN 216 for UE 205 to support UE assisted or UE based positioning of UE 205 by location server 220.

[0075] In a 5G NR positioning / sensing system 200, positioning methods can be categorized as being “UE assisted” or “UE based.” This may depend on where the request for determining the position of the UE 205 originated. If, for example, the request originated at the UE (e.g., from an application, or “app,” executed by the UE), the positioning method may be categorized as being UE based. If, on the other hand, the request originates from an external client or AF 230, location server 220, or other device or service within the 5G network, the positioning method may be categorized as being UE assisted (or “network-based”).

[0076] With a UE-assisted position method, UE 205 may obtain location measurements and send the measurements to a location server (e.g., location server 220) for computation of a location estimate for UE 205. For RAT-dependent position methods location measurements may include one or more of a Received Signal Strength Indicator (RSSI), Round Trip signal propagation Time (RTT), Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Reference Signal Time Difference (RSTD), Time of Arrival (TOA), AO A, Receive Time-Transmission Time Difference (Rx-Tx), Differential AOA (DAO A), AOD, or Timing Advance (TA) for gNBs 210, ng-eNB 214, and / or one or more access points for WLAN 216. Additionally or alternatively, similar measurements may be made of sidelink signals transmitted by other UEs, which may serve as anchor points for positioning of the UE 205 if the positions of the other UEs are known. The location measurements may also or instead include measurements for RAT -independent positioning methods such as GNSS (e.g., GNSS pseudorange, GNSS code phase, and / or GNSS carrier phase for GNSS satellites 110), WLAN, etc.

[0077] With a UE-based position method, UE 205 may obtain location measurements (e.g., which may be the same as or similar to location measurements for a UE assisted position method) and may further compute a location of UE 205 (e.g., with the help of assistance data received from a location server such as location server 220, an SLP, or broadcast by gNBs 210, ng-eNB 214, or WLAN 216).WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -25-

[0078] With a network based position method, one or more base stations (e.g., gNBs 210 and / or ng-eNB 214), one or more APs (e.g., in WLAN 216), or N3IWF 250 may obtain location measurements (e.g., measurements of RSSI, RTT, RSRP, RSRQ, AOA, or TOA) for signals transmitted by UE 205, and / or may receive measurements obtained by UE 205 or by an AP in WLAN 216 in the case of N3IWF 250, and may send the measurements to a location server (e.g., location server 220) for computation of a location estimate for UE 205.

[0079] Positioning of the UE 205 also may be categorized as UL, DL, or DL-UL based, depending on the types of signals used for positioning. If, for example, positioning is based solely on signals received at the UE 205 (e.g., from a base station or other UE), the positioning may be categorized as DL based. On the other hand, if positioning is based solely on signals transmitted by the UE 205 (which may be received by a base station or other UE, for example), the positioning may be categorized as UL based. Positioning that is DL-UL based includes positioning, such as RTT-based positioning, that is based on signals that are both transmitted and received by the UE 205. Sidelink (SL)-assisted positioning comprises signals communicated between the UE 205 and one or more other UEs. According to some embodiments, UL, DL, or DL- UL positioning as described herein may be capable of using SL signaling as a complement or replacement of SL, DL, or DL-UL signaling.

[0080] Depending on the type of positioning (e.g., UL, DL, or DL-UL based) the types of reference signals used can vary. For DL-based positioning, for example, these signals may comprise PRS (e.g., DL-PRS transmitted by base stations or SL-PRS transmitted by other UEs), which can be used for TDOA, AoD, and RTT measurements. Other reference signals that can be used for positioning (UL, DL, or DL-UL) may include Sounding Reference Signal (SRS), Channel State Information Reference Signal (CSI-RS), synchronization signals (e.g., synchronization signal block (SSB) Synchronizations Signal (SS)), Physical Uplink Control Channel (PUCCH), Physical Uplink Shared Channel (PUSCH), Physical Sidelink Shared Channel (PSSCH), Demodulation Reference Signal (DMRS), etc. Moreover, reference signals may be transmitted in a Tx beam and / or received in an Rx beam (e.g., using beamforming techniques), which may impact angular measurements, such as AOD or AOA.

[0081] In an example implementation, the UE 205 may be referred to as aWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -26- positioning reference unit (PRU). When operating as a PRU, the UE 205 can perform positioning measurements (e.g., RSTD, RSRP, UE Rx-Tx Time Difference measurements, DL-RSCPD, DL-RSCP, etc.) from a known location and report these measurements to the location server 220. In addition, the UE 205 can transmit SRS to enable TRPs to measure and report UL positioning measurements (e.g., RTOA, UL- AoA, gNB Rx-Tx Time Difference, UL-RSCP, etc.) from the known location. The positioning measurements can be compared by the location server 220 with measurements expected to be made at the known location in order to determine correction terms for other nearby target devices. The DL and / or UL location measurements for other target devices can then be corrected based on the previously determined correction terms. In some cases, measurements made by the UE 205 operating as a PRU may also be provided to a target device in the form of assistance data.

[0082] FIG. 3 is a block diagram for describing the concept of direct AI / ML operations for generating location information. More particularly, direct AI / ML operations 305 represents an AI / ML operation performed in accordance with various embodiments of the disclosure. An example embodiment in accordance with the disclosure pertains to determining a location of the UE 205 (shown in FIG. 2) based on measurements of RF signals sent from, and / or received by, the UE 205 in the manner described above. In this case, the procedure that is performed for determining the location of the mobile device 105 / UE 205 includes an AI / ML operation. More particularly, the direct AI / ML operations 305 are performed upon position determination measurements obtained by the UE 205, such as, for example, measurements obtained via RF signals received by the UE 205.

[0083] Various aspects that are described herein with reference to an “AI / ML model” are equally applicable to, and encompass, various aspects associated with direct AI / ML operations 305, such as, for example, AI / ML functions, AI / ML functionality, AI / ML physical models, AI / ML logical models, AI / ML computational implementations, and AI / ML hardware implementations, used for obtaining position information based on position determination measurements.

[0084] The direct AI / ML operations 305 may be particularly useful when conventional procedures for deriving location information from measurements involveWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -27- uncertainties and ambiguities. The uncertainties and ambiguities may arise as a result of various conditions that may be present when the measurements are / were made. Such uncertainties and ambiguities may be minimal or absent when the measurements are made under line-of-sight (LOS) conditions that are present between the UE 205 and various devices and objects that are a part of the measurement procedure. Such uncertainties and ambiguities may be higher when the measurements are made under conditions where some or all of the devices / objects are either completely blocked from view of the UE 205 (referred to as a non-LOS (NLOS) condition) or partially obstructed from view of the UE 205 (referred to as an obstructed-LOS (OLOS) condition).

[0085] In an example scenario, the direct AI / ML operations 305 successfully produce position information generated based on the position determination measurements provided as input. In another example scenario, the direct AI / ML operations 305 fail to produce accurate position information generated based on the position determination measurements provided as input. The failure may be attributed to various factors associated with the direct AI / ML operations 305 such as, for example, errors in training data and / or in inference data. Systems and methods associated with monitoring the direct AI / ML operations 305 can be employed in accordance with the disclosure to detect improper operations. In an example implementation, a monitoring report can be generated based on a monitoring operation. In an example implementation, results of monitoring can be used to modify the direct AI / ML operations 305 in order to minimize or eliminate errors and produce a satisfactory positioning result.

[0086] FIG. 4 is a block diagram for describing the concept of assisted AI / ML operations for generating location information. More particularly, assisted AI / ML operations 405 represents another type of AI / ML operation that is performed in accordance with various embodiments of the disclosure. Various aspects that are described herein with reference to an “AI / ML model” are equally applicable to, and encompass, various aspects associated with assisted AI / ML operations 405, such as, for example, AI / ML functions, AI / ML functionality, AI / ML physical models, AI / ML logical models, AI / ML computational implementations, and AI / ML hardware implementations, used for obtaining AI / ML intermediate measurements.

[0087] An example embodiment in accordance with the disclosure pertains toWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -28- determining a location of the UE 205 (shown in FIG. 2) partly based on assisted AI / ML operations 405 performed upon position determination measurements. The position determination measurements are provided to the UE 205 by another device, such as, for example, by an access node (for example, gNB 210-1 that is shown in FIG. 2). The output produced by the assisted AI / ML operations 405 are AI / ML intermediate measurements that can be a refinement of the position determination measurements provided as input. The refinements may be based on taking into consideration various conditions that may have been present when the measurements were made (LOS, NLOS, and / or OLOS).

[0088] In an example scenario, the assisted AI / ML operations 405 successfully produce the AI / ML intermediate measurements based on refining the position determination measurements provided as input. Conventional non-AI / ML operations 410 may then be employed to operate upon the AI / ML intermediate measurements and produce location information. In another example scenario, the assisted AI / ML operations 405 fail to produce accurate intermediate measurements. The failure may be attributed to various factors associated with the assisted AI / ML operations 405 such as, for example, errors in training data and / or in inference data. Systems and methods associated with monitoring the assisted AI / ML operations 405 can be employed in accordance with the disclosure to detect improper operations. In an example implementation, a monitoring report can be generated based on a monitoring operation. In an example implementation, results of monitoring can be used to modify the assisted AI / ML operations 405 in order to minimize or eliminate errors and produce accurate intermediate measurements.

[0089] FIG. 5 illustrates a first example scenario where AI / ML operations may be performed for obtaining location information in accordance with the disclosure. In this scenario, the UE 205 can perform AI / ML operations upon input provided to the UE 205 by an access node. The access node 505 can be, as indicated above, any of a variety of network entities such as, for example, gNBs 210, ng-eNB 214, WLAN 216, that are shown in FIG. 2, and / or other types of cellular base stations. In the illustrated scenario, the access node 505 provides a positioning reference signal (PRS) to the UE 205. The PRS may be used by the UE 205 for performing position measurements associated with techniques such as, for example, time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AO A), angle of departure (AOD), and received signalWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -29- strength indicator (RSSI).

[0090] In one implementation, the UE 205 applies direct AI / ML operations 305 upon the position measurements obtained by use of the PRS. In an example scenario, location information obtained after successful application of the direct AI / ML operations 305 may be transmitted by the UE 205 to the location server 220 via the access node 505. In another example scenario, the direct AI / ML operations 305 may fail and the UE 205 may be unable to determine location information. In this scenario, monitoring performed upon the direct AI / ML operations 305 can produce a monitoring report and / or monitoring results used to modify the direct AI / ML operations 305 in order to minimize or eliminate errors and produce accurate location information. The UE 205 may transmit the monitoring report to the access node 505, the location server 220 (via the access node 505), and / or convey the monitoring report to another entity such as, for example, a user of the UE 205 (via a display, for example).

[0091] FIG. 6 illustrates a second example scenario where AI / ML operations may be performed for obtaining location information in accordance with the disclosure. In this scenario, the UE 205 can perform AI / ML operations upon input provided to the UE 205 by an access node 505. The access node 505 can be, as indicated above, any of a variety of network entities such as, for example, gNBs 210, ng-eNB 214, WLAN 216, that are shown in FIG. 2, and / or other types of cellular base stations. In the illustrated scenario, the access node 505 provides a positioning reference signal (PRS) to the UE 205. The PRS may be used by the UE 205 for performing position measurements associated with techniques such as, for example, time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AO A), angle of departure (AOD), and received signal strength indicator (RSSI).

[0092] In this example scenario, the UE 205 applies assisted AI / ML operations 405 upon the position measurements obtained by use of the PRS. The assisted AI / ML operations 405 may be used, for example, when the PRS received from the access node 505 is poor, such as, for example, due to RF signal fading, RF signal reflections, or NLOS / OLOS conditions between the UE 205 and a cell tower transmitting the PRS. In an example scenario, the UE 205 may perform assisted AI / ML operations 405 upon position measurements obtained by use of the PRS. The UE 205 may, upon successful application of the assisted AI / ML operations 405, transmit AI / ML intermediateWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -30- measurements to the access node 505 and / or the location server 220 (via the access node 505). The access node 505 and / or the location server 220 may then perform additional operations to determine location information of the UE 205 such as, for example, by using non-AI / ML operations 410. In another example scenario, the assisted AI / ML operations 405 may fail. Monitoring performed upon the assisted AI / ML operations 405 can produce a monitoring report and / or monitoring results used to modify the assisted AI / ML operations 405 in order to minimize or eliminate errors in the intermediate measurement results. The UE 205 may transmit the monitoring report to the access node 505, the location server 220 (via the access node 505), and / or convey the monitoring report to another entity such as, for example, a user of the UE 205 (via a display, for example).

[0093] FIG. 7 illustrates a third example scenario where AI / ML operations may be performed for obtaining location information in accordance with the disclosure. In this scenario, the location server 220 can perform AI / ML operations upon input provided to the location server 220 by the UE 205. The access node 505 can be, as indicated above, any of a variety of network entities such as, for example, gNBs 210, ng-eNB 214, WLAN 216, that are shown in FIG. 2, and / or other types of cellular base stations. In the illustrated scenario, the access node 505 provides a positioning reference signal (PRS) to the UE 205. The PRS may be used by the UE 205 for performing position measurements associated with techniques such as, for example, time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AO A), angle of departure (AOD), and received signal strength indicator (RS SI).

[0094] The UE 205 may determine location information of an entity (such as, for example, the UE 205 or an object sensed by the UE 205) based on techniques that may not necessarily include AI / ML and transmit the location information to the location server 220 (in the form of a PRS-based measurement). The location server 220 may then operate upon the PRS-based measurement by using direct AI / ML operations 305 in order to determine the location of the entity. In an example scenario, the direct AI / ML operations 305 may fail. Monitoring performed upon the direct AI / ML operations 305 can produce a monitoring report and / or monitoring results used to modify the direct AI / ML operations 305 in order to minimize or eliminate errors in the intermediate measurement results. The location server 220 may transmit the monitoring report to the access node 505, the UE 205 (via the access node 505), and / or convey the monitoringWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -31- report to another entity such as, for example, a user of the UE 205.

[0095] FIG. 8 illustrates a fourth example scenario where AI / ML operations may be performed for obtaining location information in accordance with the disclosure. In this scenario, the access node 505 can perform AI / ML operations upon input provided to the access node 505 by the UE 205. The access node 505 can be, as indicated above, any of a variety of network entities such as, for example, gNBs 210, ng-eNB 214, WLAN 216, that are shown in FIG. 2, and / or other types of cellular base stations. In the illustrated scenario, the access node 505 receives a sounding reference signal (SRS) from the UE 205. The SRS may be used by the access node 505 for performing position measurements based on assisted AI / ML operations 405.

[0096] The access node 505 may, upon successful application of the assisted AI / ML operations 405, transmit intermediate measurements to the location server 220. The location server 220 may then perform additional operations to determine location information of the UE 205 and / or the object, such as, for example, by using non- AI / ML operations 410. In an example scenario, the assisted AI / ML operations 405 may fail. Monitoring performed upon the assisted AI / ML operations 405 can produce a monitoring report and / or monitoring results used to modify the assisted AI / ML operations 405 in order to minimize or eliminate errors in the intermediate measurement results. The location server 220 may transmit the monitoring report to the access node 505 and / or the UE 205.

[0097] FIG. 9 illustrates a fifth example scenario where AI / ML operations may be performed for obtaining location information in accordance with the disclosure. In this scenario, the location server 220 may receive a location request from a client device (not shown) and may initiate a location determination procedure based on communications with the UE 205. In the illustrated scenario, the access node 505 receives a sounding reference signal (SRS) from the UE 205 and transmits SRS-based measurements to the location server 220. The SRS-based measurements may be used by the location server 220 for performing position measurements based on direct AI / ML operations 305.

[0098] The location server 220 may, upon successful application of the direct AI / ML operations 305, transmit location information of the UE 205 and / or an object sensed by the UE 205, to the client device. In an example scenario, the direct AI / ML operations 305 may fail. Monitoring performed upon the direct AI / ML operations 305WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -32- can produce a monitoring report and / or monitoring results used to modify the direct AI / ML operations 305 in order to minimize or eliminate errors in the positioning result. The location server 220 may transmit the monitoring report to the access node 505 and / or the UE 205.

[0099] FIG. 10 illustrates some example elements of an AI / ML model 1000 that includes monitoring in accordance with the disclosure. The AI / ML model 1000 includes functional blocks such as, a data collection functional block 1005 that provides training data for model training 1010, inference data for inference 1025, and monitoring data for management 1015. Trained / updated model produced by model training 1010 may be stored in data storage 1030 and the contents of data storage 1030, which includes the trained / updated model, can be transferred / delivered to perform inference 1025. The transfer / delivery may be carried out based on a model transfer / delivery request from management 1015. Management 1015 may also provide management instructions to inference 1025 based on inference output from inference 1025.

[0100] A monitoring report may be produced by management 1015 based on monitoring 1020 performed in accordance with the disclosure upon data provided to management 1015 by data collection 1005. The data provided to management 1015 can include data associated with an AI / ML model that is used as a part of a position determination procedure performed by an entity such as, for example, the UE 205 or an access node of a cellular network.

[0101] Monitoring of the AI / ML model can be based on various methods and metrics, and can include, for example, monitoring based on inference accuracy, monitoring based on system performance, monitoring based on data distribution, monitoring based on validity of input provided to the AI / ML model (for example, out- of-distribution detection, drift detection of input data, or SNR, and / or delay spread), and monitoring based on AI / ML model output (drift detection, for example). Applicability and / or expected performance of an AI / ML model may also be carried out upon an inactive AI / ML model for purposes of activating / selecting / switching the AI / ML model. Actions associated with assessment / monitoring of an inactive AI / ML model may be based, for example, on input / output data distribution, historic performance, and performance accuracy.WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -33-

[0102] More particularly, an AI / ML model used in a position determination procedure may require frequent monitoring as a result of various variables, such as, for example, changes in a wireless environment (changes in clutter setting, for example) and / or changes in network conditions (changes in devices, configuration changes, data errors, timing errors, and / or wireless signal amplitude variations). Monitoring of the AI / ML model may be broadly classified under two categories as label-based mode of monitoring and label-free mode of monitoring. Label-based monitoring may be ground truth based and may be based on model input measurements and expected model output, for example. Label-free monitoring may be performed without using ground truths and may include monitoring based on model input measurements (statistics, for example), and / or model outputs (statistics and / or drift over time).

[0103] Monitoring of AI / ML models used by various devices of a system such as, for example, the positioning / sensing system 100 or the 5G NR positioning / sensing system 200 described above, can be performed based on at least two methodologies in accordance with the disclosure. A first methodology that may be referred to as group cooperative monitoring, involves the use of a group of devices to conduct monitoring based on their individual capabilities. A second methodology that may be referred to as offloading monitoring, involves offloading monitoring functions from a first device to a second device based on a monitoring capability of the second device being higher than that of the first device. Various aspects related to these two methodologies are described below using various figures.

[0104] FIG. 11 illustrates a group monitoring scenario for monitoring an AI / ML model in accordance with the disclosure. The group monitoring scenario may be implemented by use of multiple UEs that may, or may not, be identical to each other. Thus, in a first example implementation, each of the multiple UEs in a group is a cellular phone. The capabilities of some or all of the cellular phones can be different from each other in various respects. For example some of the cellular phones may have more powerful hardware (faster processor, larger memory, larger battery capacity, etc.) than one or more of the other cellular phones.

[0105] In a second example implementation (illustrated in FIG. 11), the multiple UEs in a group are different from one another. More particularly, some UEs are cellular phones, some UEs are a first type of wearable devices (smart watches), some UEs are aWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -34- second type of wearable devices (smart goggles), some UEs are tablet computers, and some UEs are terrestrial robotic vehicles (wheeled robotic vehicles, for example). The capabilities of some of the UEs such as, for example, the tablet computers, can be different in comparison to the capabilities of some other UEs such as, for example, the smart goggles. More particularly with respect to the illustrated example scenario, it would be typical for the tablet computers to have more powerful hardware (faster processors, larger memory, larger battery capacity, etc.) and better computing capabilities (software, firmware etc.) than the smart goggles. Furthermore, two or more UEs such as, for example, multiple cellular phones may be provided by a single vendor who has installed identical AI / ML models in each of the multiple cellular phones. The AI / ML models may be selected by the vendor for use in position determination operations.

[0106] FIG. 11 illustrates several UEs that may be selected from multiple UEs and designated as a group 1105 for performing a group monitoring procedure. The group 1105 may be selected on the basis of various criteria such as, for example, a specified hardware capability, a specified software capability, and a spatial distribution. In an example implementation, the spatial distribution may be defined in terms of a geographical area in which a specified level of cellular signal coverage is available (via base stations 120) to each UE in the group 1105. The example group 1105 in the illustrated scenario includes a UE 205-3 having a specified AI / ML model, a UE 205-4 (a tablet computer) having the specified AI / ML model, a UE 205-6 having the specified AI / ML model, and a UE 205-8 (a cellular phone) having the specified AI / ML model. The other UEs in the multiple UEs shown in FIG. 11 may, or may not, have the specified AI / ML model. For example, in some cases, UEs that are precluded from the group 1105 may use the specified AI / ML model used by the group 1105 but may not be directly involved in performing monitoring procedures upon the specified AI / ML model.

[0107] In an example implementation, the group 1105 may be selected based on factors such as, for example, a number of position monitoring metrics supported by a UE, a nature of one or more position monitoring metrics supported by a UE, supported AI / ML positioning monitoring procedures (for example, label-based or label-free) for a UE, memory capacity of a UE, processing capabilities of a UE, label generation capabilities of a UE (for example, ground truth label generation capability), and spatialWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -35- distribution of a UE with respect to other UEs. As for spatial distribution, UEs dispersed over a larger geographical area may be selected in order to validate a particular AI / ML model, UEs clustered together in a smaller geographical area may be selected in order to obtain higher monitoring accuracy in the smaller geographical area.

[0108] A management entity may be designated (for example, a location server such as the LMF 220, the AMF 215, an access node, or an UE) for performing functions such as selecting UEs to include in the group 1105 of UEs, specifying the AI / ML model to be used, assigning monitoring tasks and procedures to each UE in the group 1105 of UEs, aggregating monitoring outcomes, and taking monitoring decisions (for example, to modify an AI / ML operation in one or more UEs, transmit monitoring outcomes to one or more UEs or other entities, evaluate monitoring outcomes, etc.)..

[0109] In an example implementation, the management entity may assign to each of the UEs in the group 1105, various types of monitoring procedures, various monitoring metrics, various monitoring conditions (thresholds, for example). The assignment may be based on a capability of each UE to perform a monitoring operation. Thus, for example, a first UE may be assigned a label-based monitoring procedure and a first set of monitoring tasks, and a second UE may be assigned a label-free monitoring procedure and a second set of monitoring tasks that may, or may not, be identical to the first set of monitoring tasks. Assigning of monitoring procedures can include, for example, a specified number of monitoring occasions and trigger conditions for initiating a monitoring operation.

[0110] The UEs of group 1105 may provide to the management entity, information related to the monitoring tasks. In an example implementation, information may be reported to the management entity in accordance with a real-time routine, a near realtime routine, an intermittent routine, an as-needed routine, or a cumulative routine. A cumulative routine may involve the UE collecting monitoring information over a period of time and / over multiple monitoring occasions before providing the accumulated information to the management entity. In an example scenario, the real-time routine may be used to report a status of the AI / ML model, such as, for example, whether active, in good health, failing, failure rate, metric falling below a preset threshold, etc.). Detecting a failure may be based on evaluating various metrics and using various techniques such as, for example, using a majority rule of outcomes technique to detect aWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -36- failure or an impending failure in an AI / ML model, using an averaging routine, and / or using a best-of-“n” detection technique (i.e., at least “m” out of “n” outcomes indicates a failure or impending failure).[OHl] The management entity may perform various actions based on evaluating monitoring information provided by one or more UEs of the group 1105. For example, the management entity may, based on the evaluation, activate a monitoring operation, terminate a monitoring operation, initiate handover of a monitoring operation to another UE, instruct a UE to switch to another AI / ML model or a non- AI / ML routine. Instructions and / or information provided by the management entity to one or more UEs in the group 1105 may be conveyed in various ways such as, for example, via a radio resource control (RRC) broadcast message, a lower layer protocol (LPP) signaling, or a sidelink message.

[0112] FIG. 12 illustrates a first monitoring handover scenario where a first device hands over monitoring of an AI / ML model to a second device in accordance with the disclosure. In an example implementation, one or both of the first device and the second device can be a positioning reference unit (PRU) or a road-side unit (RSU) that can be used by a UE or a network entity for performing a positioning operation.

[0113] In the illustrated example scenario, the first device is UE 205-6 (a smart watch) and the second device is UE 205-8 (a cellular phone). In this scenario, a management entity (and / or the UE 205-6) may make a determination that a capability of the UE 205-6 to monitor an AI / ML model is less than a capability of the UE 205-8 to monitor the AI / ML model. The determination may be made, for example, based on evaluating various hardware (for example, faster processor, larger memory, larger battery capacity, etc.) and / or software capabilities of the UE 205-6 (for example, a capability to execute / monitor the AI / ML model etc.).

[0114] The management entity (and / or the UE 205-6) may also determine a suitability / compatibility of the UE 205-8 to perform the monitoring operation (detecting whether label-based or label-free based, for example), monitoring output capabilities of the UE 205-8 (a label generation capability), and a spatial separation distance between the UE 205-8 and the UE 205-6.

[0115] At this time, the UE 205-6 may either be using the AI / ML model for a positioning operation or intending to use the AI / ML model for a positioning operation.WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -37-Upon making the determination that the capability of the UE 205-8 exceeds the capability of the UE 205-6, the management entity may instruct the UE 205-6 to handover a monitoring operation of the AI / ML model to the UE 205-8 as indicated by an arrow 1205. The management entity may further instruct the UE 205-6 to stop a monitoring operation if the monitoring operation is in progress. The monitoring operation performed by the UE 205-6 may or may not be identical to a monitoring operation performed by the UE 205-8 upon completion of the handover operation. In some cases, the UE 205-8 may have a capability to perform a monitoring operation upon another AI / ML model in addition to performing a monitoring operation upon the AI / ML model transferred from the UE 205-6. In some cases, the UE 205-8 (and / or the UE 205-6) may be assigned another monitoring operation in addition to, or in lieu of, monitoring the AI / ML model that is based on the hand-over operation.

[0116] The management entity may configure the UE 205-8 to perform the monitoring operation by providing various instructions to the UE 205-8 such as, for example, various monitoring metrics, various monitoring conditions (thresholds, for example), and a specified number of monitoring occasions.

[0117] The UE 205-8 may provide to the management entity, information related to the monitoring tasks. In an example implementation, information may be reported to the management entity in accordance with a real-time routine, a near real-time routine, an intermittent routine, an as-needed routine, or a cumulative routine. A cumulative routine may involve the UE collecting monitoring information over a period of time and / over multiple monitoring occasions before providing the accumulated information to the management entity. In an example scenario, the real-time routine may be used to report a status of the AI / ML model, such as, for example, whether active, in good health, failing, failure rate, metric falling below a preset threshold, etc.). Detecting a failure may be based on evaluating various metrics and using various techniques such as, for example, using a majority rule of outcomes technique to detect a failure or an impending failure in an AI / ML model, using an averaging routine, and / or using a best- of-“n” detection technique (i.e., at least “m” out of “n” outcomes indicates a failure or impending failure).

[0118] The management entity may perform various actions based on evaluating monitoring information provided by the UE 205-8. For example, the managementWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -38- entity may, based on the evaluation, activate a monitoring operation, terminate a monitoring operation, initiate handover of a monitoring operation to another UE, instruct the UE 205-8 to switch to another AI / ML model or a non-AI / ML routine. Instructions and / or information provided by the management entity to the UE 205-8 may be conveyed in various ways such as, for example, via a radio resource control (RRC) broadcast message, a lower layer protocol (LPP) signaling, or a sidelink message.

[0119] FIG. 13 illustrates a second monitoring handover scenario where one or more devices hand over monitoring of an AI / ML model to a device that is a part of a group, in accordance with the disclosure. The description provided above with reference to the first monitoring handover scenario illustrated in FIG. 12 is equally applicable to the second monitoring handover scenario except for the second device (the UE 205-8) being a part of the group 1105 that is described above with reference to FIG. 11. Furthermore, in the second monitoring handover scenario, the UE 205-8 may accept a handover from more than one device such as for example a first handover from the UE 205-6 (indicated by the arrow 1305) as well as a second handover from the UE 205-7 (indicated by an arrow 1310), either concurrently or at different times. In this example, the UE 205-7 is another wearable (smart goggles) having a capability to monitor an AI / ML model that is less than a capability of the UE 205-8 to monitor the AI / ML model. Any of the devices that do not belong to the group 1105 may perform a handover operation to transfer a monitoring operation of an AI / ML model to any device belonging to the group 1105.

[0120] The second monitoring handover scenario described with reference to FIG. 13 may be implemented in various ways. In a first example approach, a management entity may form the group 1105 first, identify details pertaining to offloading operations that may be performed by members (UEs) of the group 1105, followed by managing offloading operations.

[0121] In a second example approach, a management entity may first determine the nature of one or more offloading operations that may be performed by members of a group, followed by forming of the group 1105, and managing offloading operations. Managing offloading operations may include providing information to the members about procedures and rules for performing the offloading operations.WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -39-

[0122] Information pertaining to the monitoring (with respect to one or both of the approaches described above) may be shared among members of the group 1105 and / or with non-members. Sharing of information may be caried out via unicast messages, groupcast messages, broadcast messages,

[0123] Managing can further include registration procedures by which various UEs register with the management entity for forming the group 1105. Registration and capability reporting may be performed by use of one or more messaging formats and techniques. In some scenarios, a registered UE may be designated as a monitoring reference unit (MRU).

[0124] In an example implementation, a UE may register with a management entity such as, for example, the AMF 215, specifically for the purpose of participating in the grouping and / or offloading operations. As a part of the registration, the UE can provide an indication to the AMF 215 (via an access node), whether the UE can function as an AI / ML offloading / monitoring unit or simply as an MRU. The AI / ML offloading / monitoring unit may register with the LMF 220 using a new AI / ML offloading / monitoring unit Registration Request service operation directed towards the LMF 220.

[0125] In another example implementation, an AI / ML offloading / monitoring unit initiates a service registration procedure with the AMF 215 and informs the AMF 215 of an AI / ML offloading / monitoring capability of one or more UEs. In an example scenario, information provided to the AI / ML offloading / monitoring unit may be dynamically updated by the UEs. Subsequently, the AMF 215 may send a request to the AI / ML offloading / monitoring unit seeking information about the availability of UEs for performing offloading / monitoring operations.

[0126] In another example implementation, an AI / ML offloading / monitoring unit registers with the AMF 215 using a new Supplementary Services message pair. The LMF 220 and the AI / ML offloading / monitoring unit can then exchange LPP messages via the AMF 215.

[0127] In another example implementation, the LMF 220 may obtain information about the availability of UEs for performing offloading / monitoring operations via LPP procedures that may be based on capability exchanges.WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -40-

[0128] FIG. 14 illustrates a first example signal flow scenario for configuring a user equipment to perform monitoring operations upon an AI / ML model in accordance with the disclosure. The interactions between the UE 205-1, the UE 205-2 and a management entity 1405 (for example, a location server such as the LMF 220) may be carried out using any of various communication formats that support messaging and data transport. In one scenario, the UE 205-1 can be a standalone device and in another scenario, the UE 205-1 can be a part of the group 1105 that is described above.

[0129] In a first example implementation, the management entity 1405 may transmit a query (arrow 1410) to the UE 205-1 to obtain information about a capability of the UE 205-1 for monitoring an AI / ML model useable for a positioning operation. In a second example implementation, the query 1410 may be a capability request without an explicit indication of a monitoring capability. In a third example implementation, the query may be omitted.

[0130] The UE 205-1 may transmit to the management entity 1405, a monitoring capability report (arrow 1415) that indicates a capability of the UE 205-1 to monitor an AI / ML model useable for a positioning operation. In a first case, the capability report is transmitted in response to receiving the query (arrow 1410) from the management entity 1405. In another case, the capability report is transmitted irrespective of receiving the query (arrow 1410) such as, for example, in order to initiate a positioning procedure. In yet another case, the capability report can include the monitoring capability of the UE 205-1 as well as other capabilities.

[0131] The description above of actions indicated by arrow 1410 and arrow 1415 with respect to communications between the management entity 1405 and the UE 205-1, is equally applicable to actions indicated by arrow 1420 and arrow 1425 respectively with respect to communications between the management entity 1405 and the UE 205-2.

[0132] Block 1430 indicates an action performed by the management entity 1405 based at least in part on the monitoring capability report received from UE 205-1 (arrow 1415) and the monitoring capability report received from UE 205-2 (arrow 1425). More particularly, the action indicated by block 1430 pertains to evaluating the capabilities of the UE 205-1 and UE 205-2. Evaluating the capabilities of the UE 205-1 and UE 205-2 can include determining a capability of the UE 205-1 and / or the UE 205-2 to monitorWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -41- one or more AI / ML models useable in a positioning operation. Evaluating the capabilities of the UE 205-1 and UE 205-2 can further include comparing the capability of the UE 205-1 to the capability of the UE 205-2.

[0133] The management entity 1405 may then transmit to either the UE 205-1 or UE 205-2, a monitoring assignment for monitoring an AI / ML model. In the illustrated example signal flow scenario, the management entity 1405 transmits the monitoring assignment to the UE 205-1 (indicated by arrow 1435) based on determining in block 1430 that the capability of the UE 205-1 to monitor the AI / ML model exceeds the capability of the UE 205-2 to monitor the AI / ML model.

[0134] Based on the monitoring assignment, the UE 205-1 performs a monitoring operation (indicated by block 1440) upon the AI / ML model. Monitoring of the AI / ML model can be based on various methods and metrics and can include, for example, monitoring based on inference accuracy, monitoring based on system performance, monitoring based on data distribution, monitoring based on validity of input provided to the AI / ML model (for example, out-of-distribution detection, drift detection of input data, or SNR, and / or delay spread), and monitoring based on AI / ML model output (drift detection, for example). Applicability and / or expected performance of an AI / ML model may also be carried out upon an inactive AI / ML model for purposes of activating / selecting / switching the AI / ML model. Actions associated with assessment / monitoring of an inactive AI / ML model may be based, for example, on input / output data distribution, historic performance, and performance accuracy.

[0135] More particularly, an AI / ML model used in a position determination procedure may require frequent monitoring as a result of various variables, such as, for example, changes in a wireless environment (changes in clutter setting, for example) and / or changes in network conditions (changes in devices, configuration changes, data errors, timing errors, and / or wireless signal amplitude variations). Monitoring of the AI / ML model may be broadly classified under two categories as label-based monitoring and label-free monitoring. Label-based monitoring may be ground truth based and may be based on model input measurements and expected model output, for example. Label- free monitoring may be performed without using ground truths and may include monitoring based on model input measurements (statistics, for example), and / or model outputs (statistics and / or drift over time).WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -42-

[0136] The UE 205-1 may transmit a monitoring report (arrow 1445) to the management entity 1405 based on the monitoring operation indicated by block 1440. In an example implementation, the monitoring report may be transmitted to the management entity 1405 in accordance with a real-time routine, a near real-time routine, an intermittent routine, an as-needed routine, or a cumulative routine. A cumulative routine may involve the UE 205-1 collecting monitoring information over a period of time and / over multiple monitoring occasions before providing the accumulated information to the management entity 1405. In an example scenario, the real-time routine may be used to report a status of the AI / ML model, such as, for example, whether active, in good health, failing, failure rate, metric falling below a preset threshold, etc.). Detecting a failure may be based on evaluating various metrics and using various techniques such as, for example, using a majority rule of outcomes technique to detect a failure or an impending failure in an AI / ML model, using an averaging routine, and / or using a best-of-“n” detection technique (i.e., at least “m” out of “n” outcomes indicates a failure or impending failure).

[0137] Block 1450 indicates an evaluation performed by the management entity 1405 upon the monitoring report received from the UE 205-1. The evaluation may be aimed at preventing an erroneous position determination caused by issues associated with using the AI / ML model. Some of these issues may be subtle in nature and undetectable by conventional monitoring methods, such as, for example, changes in network conditions causing changes in wireless signals and change s / err or s in training / inference data used by the AI / ML model. Furthermore, monitoring the AI / ML model and detecting an issue with the AI / ML model operation may avoid time and effort being spent elsewhere searching for a cause of an issue with a position determination operation.

[0138] The management entity 1405 may then perform various actions based on evaluating the monitoring report. For example, the management entity 1405 may transmit a message to the UE 205-1 (arrow 1455) that indicates one or more actions to be performed by the UE 205-1. The actions can include, for example, terminating a monitoring operation, initiating a handover of a monitoring operation to another UE such as, the UE 205-2, switching from using one AI / ML model to another AI / ML model, or switching from an AI / ML model to a non- AI / ML model.WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -43-

[0139] Arrow 1460 indicates a message that may be sent by the management entity 1405 to the UE 205-2 and / or measurement results of the monitoring operation performed by the UE 205-1. The message may inform the UE 205-2 to perform actions such as, for example, cooperating with the UE 205-1 to perform a handover operation associated with monitoring of an AI / ML model, switching from using one AI / ML model to another AI / ML model, or switching from an AI / ML model to a non- AI / ML model.

[0140] FIG. 15 illustrates a second example signal flow scenario for configuring a user equipment to perform monitoring operations upon an AI / ML model in accordance with the disclosure. Unlike the first example signal flow scenario described above, the second example scenario pertains to actions performed by two UEs, specifically, the UE 205-1 and the UE 205-2. In this example, the UE 205-2 operates as a configuring entity to configure the UE 205-1 to perform a monitoring operation upon an AI / ML model. In one scenario, the UE 205-1 can be a standalone device and in another scenario, the UE 205-1 can be a part of the group 1105 that is described above.

[0141] In a first example implementation, the UE 205-2 may transmit a query (arrow 1505) to the UE 205-1 to obtain information about a capability of the UE 205-1 for monitoring an AI / ML model useable for a positioning operation. In a second example implementation, the query may be omitted.

[0142] The UE 205-1 may transmit to the UE 205-2, a monitoring capability report (arrow 1510) that indicates a capability of the UE 205-1 to monitor an AI / ML model useable for a positioning operation. In a first case, the capability report is transmitted in response to receiving the query (arrow 1505) from the UE 205-2. In another case, the capability report is transmitted irrespective of receiving the query (arrow 1505) such as, for example, in order to initiate a positioning procedure.

[0143] Block 1515 indicates an action performed by the UE 205-2 based at least in part on the monitoring capability report received from UE 205-1 (arrow 1510). More particularly, the action indicated by block 1515 pertains to evaluating the capabilities of the UE 205-1, which can include determining a capability of the UE 205-1 to monitor one or more AI / ML models useable in a positioning operation.

[0144] Based on the evaluation, the UE 205-2 may transmit to the UE 205-1 (arrow 1520), a monitoring assignment for monitoring an AI / ML model.WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -44-

[0145] The UE 205-1 responds to the monitoring assignment by performing a monitoring operation (indicated by block 1525) upon the AI / ML model. Monitoring of the AI / ML model can be based on various methods and metrics as described above.

[0146] The UE 205-1 may transmit a monitoring report (arrow 1530) to the UE 205- 2 based on the monitoring operation indicated by block 1525. In an example implementation, the monitoring report may be transmitted to the UE 205-2 in accordance with a real-time routine, a near real-time routine, an intermittent routine, an as-needed routine, or a cumulative routine as described above.

[0147] Block 1535 indicates an evaluation performed by the UE 205-2 upon the monitoring report received from the UE 205-1. The evaluation may be aimed at preventing an erroneous position determination caused by issues associated with using the AI / ML model. Some of these issues may be subtle in nature and undetectable by conventional monitoring methods, such as, for example, changes in network conditions causing changes in wireless signals and change s / errors in training / inference data used by the AI / ML model. Furthermore, monitoring the AI / ML model and detecting an issue with the AI / ML model operation may avoid time and effort being spent elsewhere searching for a cause of an issue with a position determination operation.

[0148] The UE 205-2 may then perform various actions based on evaluating the monitoring report. For example, the UE 205-2 may transmit a message to the UE 205-1 (arrow 1540) that indicates one or more actions to be performed by the UE 205-1. The actions can include, for example, terminating a monitoring operation, initiating a handover of a monitoring operation to another UE, switching from using one AI / ML model to another AI / ML model, or switching from an AI / ML model to a non- AI / ML model.

[0149] FIG. 16 shows a flowchart 1600 of an example method performed by a management entity or a UE to facilitate monitoring of one or more artificial intelligence / machine learning (AI / ML) models used in positioning operations. Means for performing the functionality illustrated in one or more of the blocks of the flowchart 1600 may be performed by hardware and / or software components of a management entity such as, example, the LMF 220, or a UE such as, for example, the UE 205 described herein.

[0150] At block 1605, the functionality can include determining whether a first userWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -45- equipment (UE) is configured with a first capability to monitor at least a first AI / ML model that is useable in a positioning operation. In an example implementation, the functionality can include determining whether, for example, the first UE has a capability to monitor a particular AI / ML model. The particular AI / ML model can be, for example, an AI / ML model that is configured to operate upon position measurements, such as, for example, position measurements made by the first UE, position measurements received by the first UE from an access node, a PRS received by the first UE from an access node, or an SRS. The particular AI / ML model is referred to below as the first AI / ML model.

[0151] In an example scenario, the first UE (UE 205-3, for example) may have a capability to monitor only the first AI / ML model and may either be unable to monitor other AI / ML models or may lack access to additional AI / ML models. In this scenario, the functionality included in block 1605 may pertain to determining whether the first UE has the capability to monitor the first AI / ML model.

[0152] In another scenario, the first UE (UE 205-3, for example) may have a capability to monitor not only the first AI / ML model but to also monitor one or more other AI / ML models. In this scenario, the functionality included in block 1605 may pertain to determining whether the first UE has the capability to monitor a number of AI / ML models, including the first AI / ML model.

[0153] In an example implementation, the functionality indicated at block 1605 may be performed in the form of a one-time format, such as, for example, once prior to, or once during, a positioning operation. The one-time format may be alternatively referred to as a static format. In another example implementation, the functionality indicated at block 1605 may be performed in a dynamic format, such as, for example, more than once during a positioning operation.

[0154] At block 1610, the functionality can include determining whether a second UE is configured with a second capability to monitor at least the first AI / ML model. In an example scenario, the functionality can include determining whether, for example, the UE 205-4 (shown in FIG. 11) has a capability to monitor the first AI / ML model.

[0155] In one cases, the second UE (UE 205-4, for example) may have a capability to monitor only the first AI / ML model and may either be unable to monitor other AI / ML models, or may lack access to additional AI / ML models. In this case, theWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -46- functionality included in block 1610 may pertain to determining whether the second UE has the capability to monitor the first AI / ML model.

[0156] In another case, the second UE (UE 205-4, for example) may have a capability to monitor not only the first AI / ML model but to monitor one or more other AI / ML models as well. In this case, the functionality included in block 1610 may pertain to determining whether the second UE has the capability, in general, to monitor one or more AI / ML models, including the first AI / ML model.

[0157] The description provided above with reference to the static and dynamic formats can be applicable to block 1610 as well.

[0158] At block 1615, the functionality can include assigning a monitoring operation to one of the first UE or the second UE based, at least in part, on determining whether the first UE has the first capability to monitor at least the first AI / ML model and determining whether the second UE has the second capability to monitor at least the first AI / ML model.

[0159] The functionality indicated at block 1615 can lead to three possible outcomes. A first outcome of the determining action indicated at block 1615 provides an indication that both the first UE and the second UE lack the capability to monitor the first AI / ML model. In some cases, the determination may further indicate that both the first UE and the second UE lack the ability to monitor one or more other AI / ML models and / or lack the ability to monitor any AI / ML model. The first outcome eliminates the need to perform a monitoring operation in accordance with the disclosure.

[0160] A second outcome of the determining action indicated at block 1615 provides an indication that only one of two UEs has the capability to monitor the first AI / ML model. For example, the second outcome may indicate that the first UE (UE 205-3, for example) has the capability to monitor the first AI / ML model and that the second UE (UE 205-4, for example) lacks the capability to monitor the first AI / ML model. In this case, a monitoring operation can be assigned to the first UE that has the capability to monitor the first AI / ML model.

[0161] A third outcome of the determining action indicated at block 1615 provides an indication that both the UEs have the capability to monitor the first AI / ML model. The capability of each of the first UE and the second UE may then be evaluated in orderWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -47- to make a determination whether to assign a monitoring operation to one of the two UEs, or to both UEs. The evaluation can be performed in various ways, some of which are described below.

[0162] In an example implementation, the monitoring operation can be assigned exclusively to the first UE based on determining that the first UE has a better capability than the second UE for performing the monitoring operation.

[0163] In another example implementation, some, or all, of the monitoring operation can be assigned to the first UE as well as the second UE in what may be referred to a load-sharing operation (the load here referring to the monitoring operation). In one case, an identical monitoring operation may be assigned to both the UEs. In another case, a monitoring operation may be assigned on a proportional basis to the two UEs. Thus, for example, the first UE may have a higher capability to monitor the first AI / ML model, than the second UE, and may, consequently, be assigned the monitoring operation in its entirety (or may be assigned certain portions of the monitoring operation that are significant or important). The second UE in this case, may be assigned a portion of the monitoring operation that is less significant or less important.

[0164] In an example implementation, evaluating the capability of each of the first UE and the second UE in order to make a determination whether to assign a monitoring operation to one of the two UEs, or to both UEs, may involve evaluating various factors / parameters. For example, in one case, determining that the first UE has the capability to monitor at least the first AI / ML model can be based, at least in part, on evaluating at least one of an AI / ML model operation capability of the first UE, a data processing capability of the first UE, a data storage capacity of the first UE, a battery capacity of the first UE, or a label generating capability of the first UE. The second UE may be similarly evaluated.

[0165] Further, in an example implementation, a first comparison metric may be determined based, for example, on evaluating the AI / ML model operation capability of the first UE, the data processing capability of the first UE, the data storage capacity of the first UE, the battery capacity of the first UE, and / or the label generating capability of the first UE.

[0166] A second comparison metric may be similarly determined based, for example, on evaluating the AI / ML model operation capability of the second UE, theWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -48- data processing capability of the second UE, the data storage capacity of the second UE, the battery capacity of the second UE, and / or the label generating capability of the second UE. The monitoring operation may then be assigned based on comparing the first comparison metric to the second comparison metric.

[0167] Each of the first comparison metric and the second comparison metric can be a quantitative metric, a qualitative metric, a binary metric, or an aggregation of a set of quantitative metrics.

[0168] Some examples of quantitative metrics can include a data storage capacity of a UE and a battery capacity of a UE. Thus, when expressed in terms of a quantitative metric, the first comparison metric corresponding to the first UE may be indicated as a 10 TB storage capacity of the first UE, and the second comparison metric corresponding to the second UE may be indicated as a 5 TB storage capacity of the second UE. In this case, the monitoring operation may then be assigned to the first UE based on the first UE having a larger storage capacity than the second UE.

[0169] An aggregation of a set of quantitative metrics may be based, for example, on determining a statistical parameter associated with one or more quantitative metrics of a UE. The statistical parameter can be, for example, a mean value of a set of quantitative metrics, a median value of a set of quantitative metrics, an average of a set of quantitative metrics, a distribution characteristic of a set of quantitative metrics, or a range of a set of quantitative metrics. At least some metrics such as, for example, a data storage capacity of a UE and a battery capacity of a UE can vary over time. In the case of time-variant quantitative metrics, the statistical parameter may be determined and specified over one or more periods of time.

[0170] One example of a qualitative metric can pertain to a data processing capability. Thus, when expressed in terms of a qualitative metric, the first comparison metric corresponding to the first UE may be indicated in the form of a processor operating at a first clock rate and the second comparison metric corresponding to the second UE may be indicated in the form of a processor operating at a second clock rate that is lower than the first clock rate. The data processing capability can directly correspond to the clock rate, in this example. The monitoring operation may be assigned to the first UE based on the first UE having a better data processing capability than the second UE.WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -49-

[0171] One example of a binary metric can pertain to a label generating capacity of a UE. Thus, when a comparison of the first UE to the second UE is expressed in terms of a binary metric (present / absent, having / lacking, etc.), the first comparison metric corresponding to the first UE may be indicated in the form of the first UE having a label generating capacity and the second comparison metric corresponding to the second UE may be indicated in the form of the second UE lacking a label generating capacity. The monitoring operation may be assigned to the first UE based on a binary assessment of the label generating capability of the first UE that indicates that the first UE has the label generating capacity and a binary assessment of the label generating capability of the second UE that indicates that the second UE lacks the label generating capacity.

[0172] In some implementations, evaluating the capability of each of the first UE and the second UE in order to make a determination whether to assign a monitoring operation to one of the two UEs or to both UEs, may involve evaluating various factors / parameters in various ways. For example, a prioritization factor and / or a weighting factor may be applied to one or more of a plurality of capabilities of each UE, and the monitoring operation may be assigned based on comparing one or more prioritized / weighted capabilities of the first UE to one or more prioritized / weighted capabilities of the second UE.

[0173] Thus, for example, a data processing capability of a UE may be assigned a higher priority and / or a higher weight than a battery capacity of the UE. Consequently, comparison of the first comparison metric to the second comparison metric that is referred to above, can involve primarily comparing the data processing capability of the first UE to the data processing capability of the second UE, and either disregarding, or placing less importance, on the battery capacity of the first UE and the battery capacity of the second UE.

[0174] The functionalities indicated in the flowchart 1600 described above can be extended to include additional functionalities. Such additional functionalities may be directed at other aspects of the disclosure, such as, for example, forming a group and performing a group monitoring procedure (such as described above with reference to FIG. 11), and / or for performing a monitoring handover operation where a first device hands over monitoring of an AI / ML model to a second device (such as described above with reference to FIG. 12).WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -50-

[0175] In an example implementation, functionalities associated with forming a group and performing a group monitoring procedure can include determining that a first UE (UE 205-3 shown in FIG. 11, for example) has the capability to monitor a first AI / ML model, determining that a second UE (UE 205-4 shown in FIG. 11, for example) also has the capability to monitor the first AI / ML model, and determining that a third UE (UE 205-5 shown in FIG. 11, for example) lacks the capability to monitor the first AI / ML model.

[0176] In this example, a group may be formed that includes the first UE and the second UE but does not include the third UE. An example group 1105 is described above with reference to FIG. 11 where the UE 205-3 and the UE 205-4 are included in a group 1105 and the UE 205-5 is excluded from the group 1105.

[0177] Example functionalities associated with the group monitoring operation described above with reference to a group such as the group 1105, can include obtaining a group monitoring result and sharing the result with non-members of the group.

[0178] Example functionalities associated with a monitoring handover operation (described above with reference to FIG. 12) can include determining that a first UE (UE 205-8 shown in FIG. 12, for example) has a first level of capability to monitor a first AI / ML model and determining that a second UE (UE 205-6 shown in FIG. 12, for example) has a lower level of capability for monitoring the first AI / ML model. In this case, the second UE may perform a monitoring handover operation to handover monitoring operations to the first UE as described above with reference to FIG. 12.

[0179] FIG. 17 illustrates an embodiment of the UE 205 which can be utilized as described herein with reference to FIGS. 1- 16. It should be noted that FIG. 17 is meant only to provide a generalized illustration of various components, any or all of which may be utilized as appropriate. It can be noted that, in some instances, components illustrated by FIG. 17 can be localized to a single physical device and / or distributed among various networked devices, which may be disposed at different physical locations. Furthermore, as previously noted, the functionality of the UE discussed in the previously described embodiments may be executed by one or more of the hardware and / or software components illustrated in FIG. 17.

[0180] The UE 205 is shown comprising hardware elements that can be electrically coupled via a bus 1705 (or may otherwise be in communication, as appropriate). TheWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -51- hardware elements may include a processing unit(s) 1710 which can include without limitation one or more general-purpose processors, one or more special-purpose processors (such as DSP chips, graphics acceleration processors, application specific integrated circuits (ASICs), and / or the like), and / or other processing structures or means. As shown in FIG. 17, some embodiments may have a separate DSP 1720, depending on desired functionality. Location determination authentication, spoofing detection, and / or other operations that may be based on wireless communication can be provided in the processing unit(s) 1710 and / or wireless communication interface 1730 (discussed below). The UE 205 can also include one or more input devices 1770, which can include without limitation one or more keyboards, touch screens, touch pads, microphones, buttons, dials, switches, and / or the like; and one or more output devices 1715, which can include without limitation one or more displays (e.g., touch screens), light emitting diodes (LEDs), speakers, and / or the like.

[0181] The UE 205 may also include a wireless communication interface 1730, which may comprise without limitation a modem, a network card, an infrared communication device, a wireless communication device, and / or a chipset (such as a Bluetooth® device, an IEEE 802.11 device, an IEEE 802.15.4 device, a Wi-Fi device, a WiMAX device, a WAN device, and / or various cellular devices, etc.), and / or the like, which may enable the UE 205 to communicate with other devices such as, for example, the base stations 120 described in the embodiments above. The wireless communication interface 1730 may permit data and signaling to be communicated (e.g., transmitted and received) with TRPs of a network, for example, via eNBs, gNBs, ng- eNBs, access points, various base stations and / or other access node types, and / or other network components, computer systems, and / or any other electronic devices communicatively coupled with TRPs, as described herein. The communication can be carried out via one or more wireless communication antenna(s) 1732 that send and / or receive wireless signals 1734. According to some embodiments, the wireless communication antenna(s) 1732 may comprise a plurality of discrete antennas, antenna arrays, or a combination thereof.

[0182] Depending on desired functionality, the wireless communication interface 1730 may comprise a separate receiver and transmitter, or a combination of transceivers, transmitters, and / or receivers to communicate with base stations (e.g., ng- eNBs and gNBs) and other terrestrial transceivers, such as wireless devices and accessWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -52- points. The UE 205 may communicate with different data networks that may comprise various network types. For example, a Wireless Wide Area Network (WWAN) may be a CDMA network, a Time Division Multiple Access (TDMA) network, a Frequency Division Multiple Access (FDMA) network, an Orthogonal Frequency Division Multiple Access (OFDMA) network, a Single-Carrier Frequency Division Multiple Access (SC-FDMA) network, a WiMAX (IEEE 802.16) network, and so on. A CDMA network may implement one or more RATs such as CDMA2000, WCDMA, and so on. CDMA2000 includes IS-95, IS-2000 and / or IS-856 standards. A TDMA network may implement GSM, Digital Advanced Mobile Phone System (D-AMPS), or some other RAT. An OFDMA network may employ LTE, LTE Advanced, 5G NR, and so on. 5G NR, LTE, LTE Advanced, GSM, and WCDMA are described in documents from 3GPP. Cdma2000 is described in documents from a consortium named “3rd Generation Partnership Project 3” (3GPP2). 3GPP and 3GPP2 documents are publicly available. A WLAN may also be an IEEE 802.1 lx network, and a wireless personal area network (WPAN) may be a Bluetooth network, an IEEE 802.15x, or some other type of network. The techniques described herein may also be used for a combination of WWAN, WLAN and / or WPAN.

[0183] The UE 205 can further include sensor(s) 1740. Sensors 1740 may comprise, without limitation, one or more inertial sensors and / or other sensors (e.g., accelerometer(s), gyroscope(s), camera(s), magnetometer(s), altimeter(s), microphone(s), proximity sensor(s), light sensor(s), barometer(s), and the like), some of which may be used to authenticate location determination and to detect position spoofing, for example.

[0184] Embodiments of the UE 205 may also include a Global Navigation SatelliteSystem (GNSS) receiver 1780 capable of receiving signals 1784 from one or more GNSS satellites using an antenna 1782 (which could be the same as antenna 1732). Positioning based on GNSS signal measurement can be utilized to complement and / or incorporate the techniques described herein. The GNSS receiver 1780 can extract a position of the UE 205, using conventional techniques, from GNSS satellites 110 of a GNSS system, such as Global Positioning System (GPS), Galileo, GLONASS, QuasiZenith Satellite System (QZSS) over Japan, Indian Regional Navigational Satellite System (IRNSS) over India, BeiDou Navigation Satellite System (BDS) over China, and / or the like. Moreover, the GNSS receiver 1780 can be used with variousWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -53- augmentation systems (e.g., a Satellite Based Augmentation System (SBAS)) that may be associated with or otherwise enabled for use with one or more global and / or regional navigation satellite systems, such as, e.g., Wide Area Augmentation System (WAAS), European Geostationary Navigation Overlay Service (EGNOS), Multi-functional Satellite Augmentation System (MSAS), and Geo Augmented Navigation system (GAGAN), and / or the like.

[0185] It can be noted that, although GNSS receiver 1780 is illustrated in FIG. 17 as a distinct component, embodiments are not so limited. As used herein, the term “GNSS receiver” may comprise hardware and / or software components configured to obtain GNSS measurements (measurements from GNSS satellites). In some embodiments, therefore, the GNSS receiver may comprise a measurement engine executed (as software) by one or more processing units, such as processing unit(s) 1710, DSP 1720, and / or a processing unit within the wireless communication interface 1730 (e.g., in a modem). A GNSS receiver may optionally also include a positioning engine, which can use GNSS measurements from the measurement engine to determine a position of the GNSS receiver using an Extended Kalman Filter (EKF), Weighted Least Squares (WLS), a hatch filter, particle filter, or the like. The positioning engine may also be executed by one or more processing units, such as processing unit(s) 1710 or DSP 1720.

[0186] The UE 205 may further include and / or be in communication with a memory 1760. The memory 1760 can include, without limitation, local and / or network accessible storage, a disk drive, a drive array, an optical storage device, a solid-state storage device, such as a random access memory (RAM), and / or a read-only memory (ROM), which can be programmable, flash-updateable, and / or the like. Such storage devices may be configured to implement any appropriate data stores, including without limitation, various file systems, database structures, and / or the like.

[0187] The memory 1760 of the UE 205 also can comprise software elements (not shown in FIG. 17), including an operating system, device drivers, executable libraries, and / or other code, such as one or more application programs, which may comprise computer programs provided by various embodiments, and / or may be designed to implement methods, and / or configure systems, provided by other embodiments, as described herein. Merely by way of example, one or more procedures described with respect to the method(s) discussed above may be implemented as code and / orWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -54- instructions in memory 1760 that are executable by the UE 205 (and / or processing unit(s) 1710 or DSP 1720 within UE 205). In an aspect, then such code and / or instructions can be used to configure and / or adapt a general-purpose computer (or other device) to perform one or more operations in accordance with the described methods.

[0188] FIG. 18 illustrates an embodiment of an access node 1800. As described above with reference to FIG. 2, an 5GNR positioning / sensing system may be configured to determine the location of a UE, such as, for example, the UE 205, by using access nodes, which may include NR NodeB (gNB) 210-1 and 210-2 (collectively and generically referred to herein as gNBs 210), ng-eNB 214, and / or WLAN 216 to implement one or more positioning methods. The gNBs 210 and / or the ng-eNB 214 may correspond with base stations 120 of FIG. 1, and the WLAN 216 may correspond with one or more access points 130 of FIG. 1.

[0189] The access node 1800 can be utilized in various ways as described above (e.g., in association with FIGS. 1-16). It should be noted that FIG. 18 is meant only to provide a generalized illustration of various components, any or all of which may be utilized as appropriate. The access node 1800 is shown comprising hardware elements that can be electrically coupled via a bus 1805 (or may otherwise be in communication, as appropriate). The hardware elements may include a processing unit(s) 1810 which can include without limitation one or more general-purpose processors, one or more special-purpose processors (such as DSP chips, graphics acceleration processors, ASICs, and / or the like), and / or other processing structure or means. As shown in FIG. 18, some embodiments may have a separate DSP 1820, depending on desired functionality. Operations such as authenticating location determination and detecting position spoofing, for example, that may be based on wireless communication can be provided in the processing unit(s) 1810 and / or wireless communication interface 1830 according to some embodiments.

[0190] The wireless communication interface 1830 may comprise without limitation a modem, a network card, an infrared communication device, a wireless communication device, and / or a chipset (such as a Bluetooth® device, an IEEE 802.11 device, an IEEE 802.15.4 device, a Wi-Fi device, a WiMAX device, cellular communication facilities, etc.), and / or the like, which may enable the access node 1800 to communicate as described herein. The wireless communication interface 1830 may permit data andWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -55- signaling to be communicated (e.g., transmitted and received) to UEs, other base stations / TRPs (e.g., eNBs, gNBs, and ng-eNBs), and / or other network components, computer systems, and / or any other electronic devices described herein. The communication can be carried out via one or more wireless communication antenna(s) 1832 that send and / or receive wireless signals 1834.

[0191] The access node 1800 may also include a network interface 1880, which can include support of wireline communication technologies. The network interface 1880 may include a modem, network card, chipset, and / or the like. The network interface 1880 may include one or more input and / or output communication interfaces to permit data to be exchanged with a network, communication network servers, computer systems, and / or any other electronic devices described herein.

[0192] In many embodiments, the access node 1800 may further comprise a memory 1860. The memory 1860 can include, without limitation, local and / or network accessible storage, a disk drive, a drive array, an optical storage device, a solid-state storage device, such as a RAM, and / or a ROM, which can be programmable, flash- updateable, and / or the like. Such storage devices may be configured to implement any appropriate data stores, including without limitation, various file systems, database structures, and / or the like.

[0193] The memory 1860 of the access node 1800 also may comprise software elements (not shown in FIG. 18), including an operating system, device drivers, executable libraries, and / or other code, such as one or more application programs, which may comprise computer programs provided by various embodiments, and / or may be designed to implement methods, and / or configure systems, provided by other embodiments, as described herein. Merely by way of example, one or more procedures described with respect to the method(s) discussed above may be implemented as code and / or instructions in memory 1860 that are executable by the access node 1800 (and / or processing unit(s) 1810 or DSP 1820 within access node 1800). In an aspect, then such code and / or instructions can be used to configure and / or adapt a general-purpose computer (or other device) to perform one or more operations in accordance with the described methods.

[0194] FIG. 19 is a block diagram of an embodiment of a computer system 1900, which may be used, in whole or in part, to provide the functions of one or moreWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -56- components and / or devices as described in the embodiments herein. The computer system 1900, for example, may be utilized within and / or executed by a server (e.g., location server 220). It should be noted that FIG. 19 is meant only to provide a generalized illustration of various components, any or all of which may be utilized as appropriate. FIG. 19, therefore, broadly illustrates how individual system elements may be implemented in a relatively separated or relatively more integrated manner. In addition, it can be noted that components illustrated by FIG. 19 can be localized to a single device and / or distributed among various networked devices, which may be disposed at different geographical locations.

[0195] The computer system 1900 is shown comprising hardware elements that can be electrically coupled via a bus 1905 (or may otherwise be in communication, as appropriate). The hardware elements may include processor(s) 1910, which may comprise without limitation one or more general-purpose processors, one or more special-purpose processors (such as digital signal processing chips, graphics acceleration processors, and / or the like), and / or other processing structure, which can be configured to perform one or more of the methods described herein. The computer system 1900 also may comprise one or more input devices 1920, which may comprise without limitation a mouse, a keyboard, a camera, a microphone, and / or the like; and one or more output devices 1925, which may comprise without limitation a display device, a printer, and / or the like.

[0196] The computer system 1900 may further include (and / or be in communication with) one or more non-transitory storage devices 1915, which can comprise, without limitation, local and / or network accessible storage, and / or may comprise, without limitation, a disk drive, a drive array, an optical storage device, a solid-state storage device, such as a random-access memory (RAM) and / or read-only memory (ROM), which can be programmable, flash-updateable, and / or the like. Such storage devices may be configured to implement any appropriate data stores, including without limitation, various file systems, database structures, and / or the like. Such data stores may include database(s) and / or other data structures used store and administer messages and / or other information to be sent to one or more devices via hubs, as described herein.

[0197] The computer system 1900 may also include a communications subsystem 1930, which may comprise wireless communication technologies managed andWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -57- controlled by a wireless communication interface 1935, as well as wired technologies (such as Ethernet, coaxial communications, universal serial bus (USB), and the like). The wireless communication interface 1935 may comprise one or more wireless transceivers that may send and receive wireless signals 1937 (e.g., signals according to 5G NR or LTE) via wireless antenna(s) 1936. Thus the communications subsystem 1930 may comprise a modem, a network card (wireless or wired), an infrared communication device, a wireless communication device, and / or a chipset, and / or the like, which may enable the computer system 1900 to communicate on any or all of the communication networks described herein to any device on the respective network, including UE, base stations and / or other transmission reception points (TRPs), satellites, and / or any other electronic devices described herein. Hence, the communications subsystem 1930 may be used to receive and send data as described in the embodiments herein.

[0198] In many embodiments, the computer system 1900 will further comprise a working memory 1940, which may comprise a RAM or ROM device, as described above. Software elements, shown as being located within the working memory 1940, may comprise an operating system 1945, device drivers, executable libraries, and / or other code, such as one or more applications 1950, which may comprise computer programs provided by various embodiments, and / or may be designed to implement methods, and / or configure systems, provided by other embodiments, as described herein. Merely by way of example, one or more procedures described with respect to the method(s) discussed above might be implemented as code and / or instructions executable by a computer (and / or a processor within a computer); in an aspect, then, such code and / or instructions can be used to configure and / or adapt a general purpose computer (or other device) to perform one or more operations in accordance with the described methods.

[0199] A set of these instructions and / or code might be stored on a non-transitory computer-readable storage medium, such as the storage device(s) 1915 described above. In some cases, the storage medium might be incorporated within a computer system, such as computer system 1900. In other embodiments, the storage medium might be separate from a computer system (e.g., a removable medium, such as an optical disc), and / or provided in an installation package, such that the storage medium can be used to program, configure, and / or adapt a general-purpose computer with the instructions / codeWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -58- stored thereon. These instructions might take the form of executable code, which is executable by the computer system 1900 and / or might take the form of source and / or installable code, which, upon compilation and / or installation on the computer system 1900 (e.g., using any of a variety of generally available compilers, installation programs, compression / decompression utilities, etc.), then takes the form of executable code.

[0200] It will be apparent to those skilled in the art that substantial variations may be made in accordance with specific requirements. For example, customized hardware might also be used and / or particular elements might be implemented in hardware, software (including portable software, such as applets, etc.), or both. Further, connection to other computing devices such as network input / output devices may be employed.

[0201] With reference to the appended figures, components that can include memory can include non-transitory machine-readable media. The term “machine- readable medium” and “computer-readable medium” as used herein, refer to any storage medium that participates in providing data that causes a machine to operate in a specific fashion. In embodiments provided hereinabove, various machine-readable media might be involved in providing instructions / code to processors and / or other device(s) for execution. Additionally or alternatively, the machine-readable media might be used to store and / or carry such instructions / code. In many implementations, a computer- readable medium is a physical and / or tangible storage medium. Such a medium may take many forms, including but not limited to, non-volatile media and volatile media. Common forms of computer-readable media include, for example, magnetic and / or optical media, any other physical medium with patterns of holes, a RAM, a programmable ROM (PROM), erasable PROM (EPROM), a FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer can read instructions and / or code.

[0202] The methods, systems, and devices discussed herein are examples. Various embodiments may omit, substitute, or add various procedures or components as appropriate. For instance, features described with respect to certain embodiments may be combined in various other embodiments. Different aspects and elements of the embodiments may be combined in a similar manner. The various components of the figures provided herein can be embodied in hardware and / or software. Also, technologyWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -59- evolves and, thus many of the elements are examples that do not limit the scope of the disclosure to those specific examples.

[0203] It has proven convenient at times, principally for reasons of common usage, to refer to such signals as bits, information, values, elements, symbols, characters, variables, terms, numbers, numerals, or the like. It should be understood, however, that all of these or similar terms are to be associated with appropriate physical quantities and are merely convenient labels. Unless specifically stated otherwise, as is apparent from the discussion above, it is appreciated that throughout this Specification discussion utilizing terms such as “processing,” “computing,” “calculating,” “determining,” “ascertaining,” “identifying,” “associating,” “measuring,” “performing,” or the like refer to actions or processes of a specific apparatus, such as a special purpose computer or a similar special purpose electronic computing device. In the context of this Specification, therefore, a special purpose computer or a similar special purpose electronic computing device is capable of manipulating or transforming signals, typically represented as physical electronic, electrical, or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the special purpose computer or similar special purpose electronic computing device.

[0204] Terms, “and” and “or” as used herein, may include a variety of meanings that also is expected to depend, at least in part, upon the context in which such terms are used. Typically, “or” if used to associate a list, such as A, B, or C, is intended to mean A, B, and C, here used in the inclusive sense, as well as A, B, or C, here used in the exclusive sense. In addition, the term “one or more” as used herein may be used to describe any feature, structure, or characteristic in the singular or may be used to describe some combination of features, structures, or characteristics. However, it should be noted that this is merely an illustrative example and claimed subject matter is not limited to this example. Furthermore, the term “at least one of’ if used to associate a list, such as A, B, or C, can be interpreted to mean any combination of A, B, and / or C, such as A, AB, AA, AAB, AABBCCC, etc.

[0205] Having described several embodiments, various modifications, alternative constructions, and equivalents may be used without departing from the scope of the disclosure. For example, the above elements may merely be a component of a larger system, wherein other rules may take precedence over or otherwise modify theWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -60- application of the various embodiments. Also, a number of steps may be undertaken before, during, or after the above elements are considered. Accordingly, the above description does not limit the scope of the disclosure.

[0206] In view of this description embodiments may include different combinations of features. Implementation examples are described in the following numbered clauses:

[0207] Clause 1 A method performed by a management entity to facilitate monitoring of one or more artificial intelligence / machine learning (AI / ML) models used in positioning operations, the method comprising determining whether a first user equipment (UE) is configured with a first capability to monitor at least a first AI / ML model that is useable in a positioning operation; determining whether a second UE is configured with a second capability to monitor at least the first AI / ML model that is useable in the positioning operation; and assigning a monitoring operation to one of the first UE or the second UE based, at least in part, on determining whether the first UE has the first capability to monitor at least the first AI / ML model and determining whether the second UE has the second capability to monitor at least the first AI / ML model.

[0208] Clause 2 The method of clause 1, further comprising determining that the first UE is configured with the first capability to monitor at least the first AI / ML model; determining that the second UE is not configured with the second capability to monitor at least the first AI / ML model; and assigning the monitoring operation to the first UE based on the determination that the first UE has the first capability to monitor at least the first AI / ML model and based on the determination that the second UE is not configured with the second capability to monitor at least the first AI / ML model.

[0209] Clause 3 The method of clause 1, further comprising determining that the first UE has the first capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one of an AI / ML model operation capability of the first UE, a data processing capability of the first UE, a data storage capacity of the first UE, a battery capacity of the first UE, or a label generating capability of the first UE; and determining that the second UE has the second capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one of the AI / ML model operation capability of the second UE, a data processing capability of the second UE, aWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -61- data storage capacity of the second UE, a battery capacity of the second UE, or a label generating capability of the second UE.

[0210] Clause 4 The method of clause 3, further comprising determining a first comparison metric based, at least in part, on the evaluation of the at least one of the AI / ML model operation capability of the first UE, the data processing capability of the first UE, the data storage capacity of the first UE, the battery capacity of the first UE, or the label generating capability of the first UE; determining a second comparison metric based, at least in part, on the evaluation of the at least one of the AI / ML model operation capability of the second UE, the data processing capability of the second UE, the data storage capacity of the second UE, the battery capacity of the second UE, or the label generating capability of the second UE; and assigning the monitoring operation to one of the first UE or the second UE further based on comparing the first comparison metric to the second comparison metric.

[0211] Clause 5 The method of clause 3, wherein assigning the monitoring operation to the one of the first UE or the second UE is further based on performing at least one of a qualitative comparison of the AI / ML model operation capability of the first UE to the AI / ML model operation capability of the second UE, a qualitative comparison of the data processing capability of the first UE to the data processing capability of the second UE, a quantitative comparison of the data storage capacity of the first UE to the data storage capacity of the second UE, a quantitative comparison of the battery capacity of the first UE to the battery capacity of the second UE, a binary assessment of the label generating capability of the first UE, or a binary assessment of the label generating capability of the second UE.

[0212] Clause 6 The method of clause 1, further comprising applying at least one of a prioritization factor or a weighting factor to one or more of a plurality of capabilities of the first UE; selecting, based on applying the at least one of the prioritization factor or the weighting factor to the one or more of the plurality of capabilities of the first UE, at least one of an AI / ML model operation capability of the first UE, a data processing capability of the first UE, a data storage capacity of the first UE, a battery capacity of the first UE, or a label generating capability of the first UE; determining whether the first UE has the first capability to monitor at least the first AI / ML model based on the selected at least one of the AI / ML model operationWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -62- capability of the first UE, the data processing capability of the first UE, the data storage capacity of the first UE, the battery capacity of the first UE, or the label generating capability of the first UE; applying at least one of the prioritization factor or the weighting factor to one or more of a plurality of capabilities of the second UE; selecting, applying at least one of the prioritization factor or the weighting factor to one or more of a plurality of capabilities of the second UE, at least one of an AI / ML model operation capability of the second UE, a data processing capability of the second UE, a data storage capacity of the second UE, a battery capacity of the second UE, or a label generating capability of the second UE; and determining whether the second UE has the second capability to monitor at least the first AI / ML model based on the selected at least one of the AI / ML model operation capability of the second UE, the data processing capability of the second UE, the data storage capacity of the second UE, the battery capacity of the second UE, or the label generating capability of the second UE.

[0213] Clause 7 The method of any one of clause 1 through clause 6, wherein the monitoring operation comprises at least one of a label-based mode of monitoring, a label-free mode of monitoring, a first mode of monitoring that is based on determining inference accuracy, a second mode of monitoring that is based on one or more performance metrics for determining system performance, or a third mode of monitoring that is based on one or more data metrics for determining data distribution.

[0214] Clause 8 The method of clause 1, further comprising determining that the first UE has the first capability to monitor at least the first AI / ML model; determining that the second UE has the second capability to monitor at least the first AI / ML model; determining that a third UE lacks a third capability to monitor at least the first AI / ML model; assigning, based on the determination that the first UE has the first capability to monitor at least the first AI / ML model and the determination that the second UE has the second capability to monitor at least the first AI / ML model, to a first group of UEs configured to perform a group monitoring operation upon the at least the first AI / ML model; and assigning the third UE to a second group of UEs, the third UE configured to use the first AI / ML model in an unmonitored mode of operation.

[0215] Clause 9 The method of clause 8, further comprising obtaining, based at least on the group monitoring operation, at least one of a monitoring result of the group monitoring operation or a measurement obtained by performing the positioningWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -63- operation; and providing, to at least the third UE, the at least one of the monitoring result of the group monitoring operation or the measurement obtained by performing the positioning operation.

[0216] Clause 10 The method of clause 1, wherein the management entity is a network entity, and wherein determining whether the first UE has the first capability to monitor at least the first AI / ML model comprises transmitting, to the first UE, a capability request; receiving, from the first UE, a capability report comprising a monitoring capability of the first UE to monitor at least the first AI / ML model; and determining, based on the capability report, whether the first UE has the first capability to monitor at least the first AI / ML model.

[0217] Clause 11 The method of clause 10, wherein the capability request is transmitted via one of a radio resource control (RRC) broadcast message or a lower layer protocol (LPP) message, and wherein determining, based on the capability report, whether the first UE has the first capability to monitor at least the first AI / ML model, comprises determining, at least one of a number of position monitoring metrics supported by the first UE, a nature of one or more position monitoring metrics supported by the first UE, a memory capacity of the first UE, a processing capability of the first UE, or a label generation capability of the first UE.

[0218] Clause 12 A method performed by a first user equipment (UE) to facilitate monitoring of one or more artificial intelligence / machine learning (AI / ML) models used in positioning operations, the method comprising determining whether the first UE has a first capability to monitor at least a first AI / ML model that is useable in a positioning operation; determining whether a second UE has a second capability to monitor at least the first AI / ML model that is useable in the positioning operation; and assigning a monitoring operation to one of the first UE or the second UE based, at least in part, on determining whether the first UE has the first capability to monitor at least the first AI / ML model and on determining whether the second UE has the second capability to monitor at least the first AI / ML model.

[0219] Clause 13 The method of clause 12, wherein the determination whether the second UE has the second capability to monitor at least the first AI / ML model comprises transmitting, to the second UE, a capability request; receiving, from the second UE, a capability report comprising a monitoring capability of the second UE toWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -64- monitor at least the first AI / ML model; and determining, based on the capability report, whether the second UE has the second capability to monitor at least the first AI / ML model.

[0220] Clause 14 The method of clause 12, further comprising determining that the first UE has the first capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one of an AI / ML model operation capability of the first UE, a data processing capability of the first UE, a data storage capacity of the first UE, a battery capacity of the first UE, or a label generating capability of the first UE; and determining that the second UE has the second capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one of the AI / ML model operation capability of the second UE, a data processing capability of the second UE, a data storage capacity of the second UE, a battery capacity of the second UE, or a label generating capability of the second UE.

[0221] Clause 15 The method of clause 14, further comprising determining a first comparison metric based, at least in part, on the evaluation of the at least one of the AI / ML model operation capability of the first UE, the data processing capability of the first UE, the data storage capacity of the first UE, the battery capacity of the first UE, or the label generating capability of the first UE; determining a second comparison metric based, at least in part, on the evaluation of the at least one of the AI / ML model operation capability of the second UE, the data processing capability of the second UE, the data storage capacity of the second UE, the battery capacity of the second UE, or the label generating capability of the second UE; and assigning the monitoring operation to one of the first UE or the second UE further based on comparing the first comparison metric to the second comparison metric.

[0222] Clause 16 The method of clause 14, wherein assigning the monitoring operation to the one of the first UE or the second UE is further based on performing at least one of a qualitative comparison of the AI / ML model operation capability of the first UE to the AI / ML model operation capability of the second UE, a qualitative comparison of the data processing capability of the first UE to the data processing capability of the second UE, a quantitative comparison of the data storage capacity of the first UE to the data storage capacity of the second UE, a quantitative comparison of the battery capacity of the first UE to the battery capacity of the second UE, a binaryWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -65- assessment of the label generating capability of the first UE, or a binary assessment of the label generating capability of the second UE.

[0223] Clause 17 The method of clause 12, further comprising applying at least one of a prioritization factor or a weighting factor to one or more of a plurality of capabilities of the first UE; selecting, based on applying the at least one of the prioritization factor or the weighting factor to the one or more of the plurality of capabilities of the first UE, at least one of an AI / ML model operation capability of the first UE, a data processing capability of the first UE, a data storage capacity of the first UE, a battery capacity of the first UE, or a label generating capability of the first UE; determining whether the first UE has the first capability to monitor at least the first AI / ML model based on the selected at least one of the AI / ML model operation capability of the first UE, the data processing capability of the first UE, the data storage capacity of the first UE, the battery capacity of the first UE, or the label generating capability of the first UE; applying at least one of the prioritization factor or the weighting factor to one or more of a plurality of capabilities of the second UE; selecting, based on applying the at least one of the prioritization factor or the weighting factor to the one or more of the plurality of capabilities of the second UE, at least one of an AI / ML model operation capability of the second UE, a data processing capability of the second UE, a data storage capacity of the second UE, a battery capacity of the second UE, or a label generating capability of the second UE; and determining whether the second UE has the second capability to monitor at least the first AI / ML model based on the selected at least one of the AI / ML model operation capability of the second UE, the data processing capability of the second UE, the data storage capacity of the second UE, the battery capacity of the second UE, or the label generating capability of the second UE.

[0224] Clause 18 The method of clause 12, further comprising determining that the first UE lacks the first capability to monitor at least the first AI / ML model; determining that the second UE has the second capability to monitor at least the first AI / ML model; determining that a third UE has a third capability to monitor at least the first AI / ML model; assigning, based on the determination that the second UE has the second capability to monitor at least the first AI / ML model and that the third UE has the third capability to monitor at least the first AI / ML model, to a first group of UEs configured to perform a group monitoring operation upon the at least the first AI / ML model; andWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -66- assigning the first UE to a second group of UEs, the first UE configured to use the first AI / ML model in an unmonitored mode of operation.

[0225] Clause 19 The method of clause 18, further comprising obtaining, based at least on the group monitoring operation, at least one of a monitoring result of the group monitoring operation or a measurement obtained by performing the positioning operation.

[0226] Clause 20 A management entity that facilitates monitoring of one or more artificial intelligence / machine learning (AI / ML) models used in positioning operations, comprising at least one memory; and one or more processors communicatively coupled with the at least one memory, the one or more processors configured to determine whether a first user equipment (LE) has a first capability to monitor at least a first AI / ML model that is useable in a positioning operation; determine whether a second UE has a second capability to monitor at least the first AI / ML model that is useable in the positioning operation; and assign a monitoring operation to one of the first UE or the second UE based, at least in part, on the determination whether the first UE has the first capability to monitor at least the first AI / ML model and on the determination whether the second UE has the second capability to monitor at least the first AI / ML model.

[0227] Clause 21 The management entity of clause 20, wherein the one or more processors are further configured to determine that the first UE has the first capability to monitor at least the first AI / ML model; determine that the second UE lacks the second capability to monitor at least the first AI / ML model; and assign the monitoring operation to the first UE based on determining that the first UE has the first capability to monitor at least the first AI / ML model and that the second UE lacks the second capability to monitor at least the first AI / ML model.

[0228] Clause 22 The management entity of clause 20, further comprising determining that the first UE has the first capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one of an AI / ML model operation capability of the first UE, a data processing capability of the first UE, a data storage capacity of the first UE, a battery capacity of the first UE, or a label generating capability of the first UE; and determining that the second UE has the second capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one of the AI / ML model operation capability of the second UE, a data processingWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -67- capability of the second UE, a data storage capacity of the second UE, a battery capacity of the second UE, or a label generating capability of the second UE.

[0229] Clause 23 The management entity of clause 22, further comprising determining a first comparison metric based, at least in part, on evaluating the at least one of the AI / ML model operation capability of the first UE, the data processing capability of the first UE, the data storage capacity of the first UE, the battery capacity of the first UE, or the label generating capability of the first UE; determining a second comparison metric based, at least in part, on evaluating the at least one of the AI / ML model operation capability of the second UE, the data processing capability of the second UE, the data storage capacity of the second UE, the battery capacity of the second UE, or the label generating capability of the second UE; and assigning the monitoring operation to one of the first UE or the second UE further based on comparing the first comparison metric to the second comparison metric.

[0230] Clause 24 The management entity of clause 22, wherein assigning the monitoring operation to the one of the first UE or the second UE is further based on performing at least one of a qualitative comparison of the AI / ML model operation capability of the first UE to the AI / ML model operation capability of the second UE, a qualitative comparison of the data processing capability of the first UE to the data processing capability of the second UE, a quantitative comparison of the data storage capacity of the first UE to the data storage capacity of the second UE, a quantitative comparison of the battery capacity of the first UE to the battery capacity of the second UE, a binary assessment of the label generating capability of the first UE, or a binary assessment of the label generating capability of the second UE.

[0231] Clause 25 The management entity of clause 20, further comprising applying at least one of a prioritization factor or a weighting factor to one or more of a plurality of capabilities of the first UE; selecting, based on applying the at least one of the prioritization factor or the weighting factor to the one or more of the plurality of capabilities of the first UE, at least one of an AI / ML model operation capability of the first UE, a data processing capability of the first UE, a data storage capacity of the first UE, a battery capacity of the first UE, or a label generating capability of the first UE; determining whether the first UE has the first capability to monitor at least the first AI / ML model based on the selected at least one of the AI / ML model operationWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -68- capability of the first UE, the data processing capability of the first UE, the data storage capacity of the first UE, the battery capacity of the first UE, or the label generating capability of the first UE; applying at least one of the prioritization factor or the weighting factor to one or more of a plurality of capabilities of the second UE; selecting, based on applying the at least one of the prioritization factor or the weighting factor to the one or more of the plurality of capabilities of the second UE, at least one of an AI / ML model operation capability of the second UE, a data processing capability of the second UE, a data storage capacity of the second UE, a battery capacity of the second UE, or a label generating capability of the second UE; and determining whether the second UE has the second capability to monitor at least the first AI / ML model based on the selected at least one of the AI / ML model operation capability of the second UE, the data processing capability of the second UE, the data storage capacity of the second UE, the battery capacity of the second UE, or the label generating capability of the second UE.

[0232] Clause 26 The management entity of clause 20, wherein the one or more processors are further configured to determine that the first UE has the first capability to monitor at least the first AI / ML model; determine that the second UE has the second capability to monitor at least the first AI / ML model; determine that a third UE lacks a third capability to monitor at least the first AI / ML model; assign, based on determining that the first UE has the first capability to monitor at least the first AI / ML model and that the second UE has the second capability to monitor at least the first AI / ML model, to a first group of UEs configured to perform a group monitoring operation upon the at least the first AI / ML model; and assign the third UE to a second group of UEs, the third UE configured to use the first AI / ML model in an unmonitored mode of operation.

[0233] Clause 27 The management entity of clause 26, wherein the one or more processors are further configured to obtain, based at least on the group monitoring operation, at least one of a monitoring result of the group monitoring operation or a measurement obtained by performing the positioning operation; and provide, to at least the third UE, the at least one of the monitoring result of the group monitoring operation or the measurement obtained by performing the positioning operation.

[0234] Clause 28 A first user equipment (UE) that performs positioning operations based on one or more artificial intelligence / machine learning (AI / ML) models,WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -69- comprising at least one transceiver; at least one memory; and one or more processors communicatively coupled with the at least one memory, the one or more processors configured to determine whether the first UE has a first capability to monitor at least a first AI / ML model that is useable in a positioning operation; transmit, via the at least one transceiver, to a second UE, a capability request; receive, via the at least one transceiver, from the second UE, a capability report comprising a second capability of the second UE to monitor at least a first AI / ML model that is useable in the positioning operation; determine, based on the capability report, whether the second UE has the second capability to monitor at least the first AI / ML model; and assign a monitoring operation to one of the first UE or the second UE based on determining whether the first UE has the first capability to monitor at least the first AI / ML model and on determining whether the second UE has the second capability to monitor at least the first AI / ML model.

[0235] Clause 29 The first UE of clause 28, further comprising determining that the first UE has the first capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one of an AI / ML model operation capability of the first UE, a data processing capability of the first UE, a data storage capacity of the first UE, a battery capacity of the first UE, or a label generating capability of the first UE; and determining that the second UE has the second capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one of the AI / ML model operation capability of the second UE, a data processing capability of the second UE, a data storage capacity of the second UE, a battery capacity of the second UE, or a label generating capability of the second UE.

[0236] Clause 30 The UE of clause 29, further comprising determining a first comparison metric based, at least in part, on the evaluation of the at least one of the AI / ML model operation capability of the first UE, the data processing capability of the first UE, the data storage capacity of the first UE, the battery capacity of the first UE, or the label generating capability of the first UE; determining a second comparison metric based, at least in part, on the evaluation of the at least one of the AI / ML model operation capability of the second UE, the data processing capability of the second UE, the data storage capacity of the second UE, the battery capacity of the second UE, or the label generating capability of the second UE; and assigning the monitoring operation toWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -70- one of the first UE or the second UE further based on comparing the first comparison metric to the second comparison metric.WAVS Ref. No. QLCM471WO

Claims

Qualcomm Ref. No. 2404383WO -71-CLAIMSWhat is claimed is:

1. A method performed by a management entity to facilitate monitoring of one or more artificial intelligence / machine learning (AI / ML) models used in positioning operations, the method comprising: determining whether a first user equipment (UE) is configured with a first capability to monitor at least a first AI / ML model that is useable in a positioning operation; determining whether a second UE is configured with a second capability to monitor at least the first AI / ML model that is useable in the positioning operation; and assigning a monitoring operation to one of the first UE or the second UE based, at least in part, on determining whether the first UE has the first capability to monitor at least the first AI / ML model and determining whether the second UE has the second capability to monitor at least the first AI / ML model.

2. The method of claim 1, further comprising: determining that the first UE is configured with the first capability to monitor at least the first AI / ML model; determining that the second UE is not configured with the second capability to monitor at least the first AI / ML model; and assigning the monitoring operation to the first UE based on the determination that the first UE has the first capability to monitor at least the first AI / ML model and based on the determination that the second UE is not configured with the second capability to monitor at least the first AI / ML model.

3. The method of claim 1, further comprising: determining that the first UE has the first capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one of an AI / ML model operation capability of the first UE, a data processing capability of the first UE, a data storage capacity of the first UE, a battery capacity of the first UE, or a label generating capability of the first UE; and determining that the second UE has the second capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one ofWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -72- the AI / ML model operation capability of the second UE, a data processing capability of the second UE, a data storage capacity of the second UE, a battery capacity of the second UE, or a label generating capability of the second UE.

4. The method of claim 3, further comprising: determining a first comparison metric based, at least in part, on the evaluation of the at least one of the AI / ML model operation capability of the first UE, the data processing capability of the first UE, the data storage capacity of the first UE, the battery capacity of the first UE, or the label generating capability of the first UE; determining a second comparison metric based, at least in part, on the evaluation of the at least one of the AI / ML model operation capability of the second UE, the data processing capability of the second UE, the data storage capacity of the second UE, the battery capacity of the second UE, or the label generating capability of the second UE; and assigning the monitoring operation to one of the first UE or the second UE further based on comparing the first comparison metric to the second comparison metric.

5. The method of claim 3, wherein assigning the monitoring operation to the one of the first UE or the second UE is further based on performing at least one of a qualitative comparison of the AI / ML model operation capability of the first UE to the AI / ML model operation capability of the second UE, a qualitative comparison of the data processing capability of the first UE to the data processing capability of the second UE, a quantitative comparison of the data storage capacity of the first UE to the data storage capacity of the second UE, a quantitative comparison of the battery capacity of the first UE to the battery capacity of the second UE, a binary assessment of the label generating capability of the first UE, or a binary assessment of the label generating capability of the second UE.

6. The method of claim 1, further comprising: applying at least one of a prioritization factor or a weighting factor to one or more of a plurality of capabilities of the first UE; selecting, based on applying the at least one of the prioritization factor orWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -73- the weighting factor to the one or more of the plurality of capabilities of the first UE, at least one of an AI / ML model operation capability of the first UE, a data processing capability of the first UE, a data storage capacity of the first UE, a battery capacity of the first UE, or a label generating capability of the first UE; determining whether the first UE has the first capability to monitor at least the first AI / ML model based on the selected at least one of the AI / ML model operation capability of the first UE, the data processing capability of the first UE, the data storage capacity of the first UE, the battery capacity of the first UE, or the label generating capability of the first UE; applying at least one of the prioritization factor or the weighting factor to one or more of a plurality of capabilities of the second UE; selecting, applying at least one of the prioritization factor or the weighting factor to one or more of a plurality of capabilities of the second UE, at least one of an AI / ML model operation capability of the second UE, a data processing capability of the second UE, a data storage capacity of the second UE, a battery capacity of the second UE, or a label generating capability of the second UE; and determining whether the second UE has the second capability to monitor at least the first AI / ML model based on the selected at least one of the AI / ML model operation capability of the second UE, the data processing capability of the second UE, the data storage capacity of the second UE, the battery capacity of the second UE, or the label generating capability of the second UE.

7. The method of claim 1, wherein the monitoring operation comprises at least one of a label-based mode of monitoring, a label-free mode of monitoring, a first mode of monitoring that is based on determining inference accuracy, a second mode of monitoring that is based on one or more performance metrics for determining system performance, or a third mode of monitoring that is based on one or more data metrics for determining data distribution.

8. The method of claim 1, further comprising: determining that the first UE has the first capability to monitor at least the first AI / ML model; determining that the second UE has the second capability to monitor atWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -74- least the first AI / ML model; determining that a third UE lacks a third capability to monitor at least the first AI / ML model; assigning, based on the determination that the first UE has the first capability to monitor at least the first AI / ML model and the determination that the second UE has the second capability to monitor at least the first AI / ML model, to a first group of UEs configured to perform a group monitoring operation upon the at least the first AI / ML model; and assigning the third UE to a second group of UEs, the third UE configured to use the first AI / ML model in an unmonitored mode of operation.

9. The method of claim 8, further comprising: obtaining, based at least on the group monitoring operation, at least one of a monitoring result of the group monitoring operation or a measurement obtained by performing the positioning operation; and providing, to at least the third UE, the at least one of the monitoring result of the group monitoring operation or the measurement obtained by performing the positioning operation.

10. The method of claim 1, wherein the management entity is a network entity, and wherein determining whether the first UE has the first capability to monitor at least the first AI / ML model comprises: transmitting, to the first UE, a capability request; receiving, from the first UE, a capability report comprising a monitoring capability of the first UE to monitor at least the first AI / ML model; and determining, based on the capability report, whether the first UE has the first capability to monitor at least the first AI / ML model.

11. The method of claim 10, wherein the capability request is transmitted via one of a radio resource control (RRC) broadcast message or a lower layer protocol (LPP) message, and wherein determining, based on the capability report, whether the first UE has the first capability to monitor at least the first AI / ML model, comprises: determining, at least one of a number of position monitoring metrics supported by the first UE, a nature of one or more position monitoring metricsWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -75- supported by the first UE, a memory capacity of the first UE, a processing capability of the first UE, or a label generation capability of the first UE.

12. A method performed by a first user equipment (UE) to facilitate monitoring of one or more artificial intelligence / machine learning (AI / ML) models used in positioning operations, the method comprising: determining whether the first UE has a first capability to monitor at least a first AI / ML model that is useable in a positioning operation; determining whether a second UE has a second capability to monitor at least the first AI / ML model that is useable in the positioning operation; and assigning a monitoring operation to one of the first UE or the second UE based, at least in part, on determining whether the first UE has the first capability to monitor at least the first AI / ML model and on determining whether the second UE has the second capability to monitor at least the first AI / ML model.

13. The method of claim 12, wherein the determination whether the second UE has the second capability to monitor at least the first AI / ML model comprises: transmitting, to the second UE, a capability request; receiving, from the second UE, a capability report comprising a monitoring capability of the second UE to monitor at least the first AI / ML model; and determining, based on the capability report, whether the second UE has the second capability to monitor at least the first AI / ML model.

14. The method of claim 12, further comprising: determining that the first UE has the first capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one of an AI / ML model operation capability of the first UE, a data processing capability of the first UE, a data storage capacity of the first UE, a battery capacity of the first UE, or a label generating capability of the first UE; and determining that the second UE has the second capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one of the AI / ML model operation capability of the second UE, a data processing capability of the second UE, a data storage capacity of the second UE, a batteryWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -76- capacity of the second UE, or a label generating capability of the second UE.

15. The method of claim 14, further comprising: determining a first comparison metric based, at least in part, on the evaluation of the at least one of the AI / ML model operation capability of the first UE, the data processing capability of the first UE, the data storage capacity of the first UE, the battery capacity of the first UE, or the label generating capability of the first UE; determining a second comparison metric based, at least in part, on the evaluation of the at least one of the AI / ML model operation capability of the second UE, the data processing capability of the second UE, the data storage capacity of the second UE, the battery capacity of the second UE, or the label generating capability of the second UE; and assigning the monitoring operation to one of the first UE or the second UE further based on comparing the first comparison metric to the second comparison metric.

16. The method of claim 14, wherein assigning the monitoring operation to the one of the first UE or the second UE is further based on performing at least one of a qualitative comparison of the AI / ML model operation capability of the first UE to the AI / ML model operation capability of the second UE, a qualitative comparison of the data processing capability of the first UE to the data processing capability of the second UE, a quantitative comparison of the data storage capacity of the first UE to the data storage capacity of the second UE, a quantitative comparison of the battery capacity of the first UE to the battery capacity of the second UE, a binary assessment of the label generating capability of the first UE, or a binary assessment of the label generating capability of the second UE.

17. The method of claim 12, further comprising: applying at least one of a prioritization factor or a weighting factor to one or more of a plurality of capabilities of the first UE; selecting, based on applying the at least one of the prioritization factor or the weighting factor to the one or more of the plurality of capabilities of the first UE, at least one of an AI / ML model operation capability of the first UE, a dataWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -77- processing capability of the first UE, a data storage capacity of the first UE, a battery capacity of the first UE, or a label generating capability of the first UE; determining whether the first UE has the first capability to monitor at least the first AI / ML model based on the selected at least one of the AI / ML model operation capability of the first UE, the data processing capability of the first UE, the data storage capacity of the first UE, the battery capacity of the first UE, or the label generating capability of the first UE; applying at least one of the prioritization factor or the weighting factor to one or more of a plurality of capabilities of the second UE; selecting, based on applying the at least one of the prioritization factor or the weighting factor to the one or more of the plurality of capabilities of the second UE, at least one of an AI / ML model operation capability of the second UE, a data processing capability of the second UE, a data storage capacity of the second UE, a battery capacity of the second UE, or a label generating capability of the second UE; and determining whether the second UE has the second capability to monitor at least the first AI / ML model based on the selected at least one of the AI / ML model operation capability of the second UE, the data processing capability of the second UE, the data storage capacity of the second UE, the battery capacity of the second UE, or the label generating capability of the second UE.

18. The method of claim 12, further comprising: determining that the first UE lacks the first capability to monitor at least the first AI / ML model; determining that the second UE has the second capability to monitor at least the first AI / ML model; determining that a third UE has a third capability to monitor at least the first AI / ML model; assigning, based on the determination that the second UE has the second capability to monitor at least the first AI / ML model and that the third UE has the third capability to monitor at least the first AI / ML model, to a first group of UEs configured to perform a group monitoring operation upon the at least the first AI / ML model; and assigning the first UE to a second group of UEs, the first UE configuredWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -78- to use the first AI / ML model in an unmonitored mode of operation.

19. The method of claim 18, further comprising: obtaining, based at least on the group monitoring operation, at least one of a monitoring result of the group monitoring operation or a measurement obtained by performing the positioning operation.

20. A management entity that facilitates monitoring of one or more artificial intelligence / machine learning (AI / ML) models used in positioning operations, comprising: at least one memory; and one or more processors communicatively coupled with the at least one memory, the one or more processors configured to: determine whether a first user equipment (UE) has a first capability to monitor at least a first AI / ML model that is useable in a positioning operation; determine whether a second UE has a second capability to monitor at least the first AI / ML model that is useable in the positioning operation; and assign a monitoring operation to one of the first UE or the second UE based, at least in part, on the determination whether the first UE has the first capability to monitor at least the first AI / ML model and on the determination whether the second UE has the second capability to monitor at least the first AI / ML model.

21. The management entity of claim 20, wherein the one or more processors are further configured to: determine that the first UE has the first capability to monitor at least the first AI / ML model; determine that the second UE lacks the second capability to monitor at least the first AI / ML model; and assign the monitoring operation to the first UE based on determining that the first UE has the first capability to monitor at least the first AI / ML model and that the second UE lacks the second capability to monitor at least the first AI / ML model.WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -79-22. The management entity of claim 20, further comprising: determining that the first UE has the first capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one of an AI / ML model operation capability of the first UE, a data processing capability of the first UE, a data storage capacity of the first UE, a battery capacity of the first UE, or a label generating capability of the first UE; and determining that the second UE has the second capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one of the AI / ML model operation capability of the second UE, a data processing capability of the second UE, a data storage capacity of the second UE, a battery capacity of the second UE, or a label generating capability of the second UE.

23. The management entity of claim 22, further comprising: determining a first comparison metric based, at least in part, on evaluating the at least one of the AI / ML model operation capability of the first UE, the data processing capability of the first UE, the data storage capacity of the first UE, the battery capacity of the first UE, or the label generating capability of the first UE; determining a second comparison metric based, at least in part, on evaluating the at least one of the AI / ML model operation capability of the second UE, the data processing capability of the second UE, the data storage capacity of the second UE, the battery capacity of the second UE, or the label generating capability of the second UE; and assigning the monitoring operation to one of the first UE or the second UE further based on comparing the first comparison metric to the second comparison metric.

24. The management entity of claim 22, wherein assigning the monitoring operation to the one of the first UE or the second UE is further based on performing at least one of a qualitative comparison of the AI / ML model operation capability of the first UE to the AI / ML model operation capability of the second UE, a qualitative comparison of the data processing capability of the first UE to the data processing capability of the second UE, a quantitative comparison of the data storage capacity of the first UE to the data storage capacity of the secondWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -80-UE, a quantitative comparison of the battery capacity of the first UE to the battery capacity of the second UE, a binary assessment of the label generating capability of the first UE, or a binary assessment of the label generating capability of the second UE.

25. The management entity of claim 20, further comprising: applying at least one of a prioritization factor or a weighting factor to one or more of a plurality of capabilities of the first UE; selecting, based on applying the at least one of the prioritization factor or the weighting factor to the one or more of the plurality of capabilities of the first UE, at least one of an AI / ML model operation capability of the first UE, a data processing capability of the first UE, a data storage capacity of the first UE, a battery capacity of the first UE, or a label generating capability of the first UE; determining whether the first UE has the first capability to monitor at least the first AI / ML model based on the selected at least one of the AI / ML model operation capability of the first UE, the data processing capability of the first UE, the data storage capacity of the first UE, the battery capacity of the first UE, or the label generating capability of the first UE; applying at least one of the prioritization factor or the weighting factor to one or more of a plurality of capabilities of the second UE; selecting, based on applying the at least one of the prioritization factor or the weighting factor to the one or more of the plurality of capabilities of the second UE, at least one of an AI / ML model operation capability of the second UE, a data processing capability of the second UE, a data storage capacity of the second UE, a battery capacity of the second UE, or a label generating capability of the second UE; and determining whether the second UE has the second capability to monitor at least the first AI / ML model based on the selected at least one of the AI / ML model operation capability of the second UE, the data processing capability of the second UE, the data storage capacity of the second UE, the battery capacity of the second UE, or the label generating capability of the second UE.

26. The management entity of claim 20, wherein the one or more processors are further configured to:WAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -81- determine that the first UE has the first capability to monitor at least the first AI / ML model; determine that the second UE has the second capability to monitor at least the first AI / ML model; determine that a third UE lacks a third capability to monitor at least the first AI / ML model; assign, based on determining that the first UE has the first capability to monitor at least the first AI / ML model and that the second UE has the second capability to monitor at least the first AI / ML model, to a first group of UEs configured to perform a group monitoring operation upon the at least the first AI / ML model; and assign the third UE to a second group of UEs, the third UE configured to use the first AI / ML model in an unmonitored mode of operation.

27. The management entity of claim 26, wherein the one or more processors are further configured to: obtain, based at least on the group monitoring operation, at least one of a monitoring result of the group monitoring operation or a measurement obtained by performing the positioning operation; and provide, to at least the third UE, the at least one of the monitoring result of the group monitoring operation or the measurement obtained by performing the positioning operation.

28. A first user equipment (UE) that performs positioning operations based on one or more artificial intelligence / machine learning (AI / ML) models, comprising: at least one transceiver; at least one memory; and one or more processors communicatively coupled with the at least one memory, the one or more processors configured to: determine whether the first UE has a first capability to monitor at least a first AI / ML model that is useable in a positioning operation; transmit, via the at least one transceiver, to a second UE, a capability request; receive, via the at least one transceiver, from the second UE, aWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -82- capability report comprising a second capability of the second UE to monitor at least a first AI / ML model that is useable in the positioning operation; determine, based on the capability report, whether the second UE has the second capability to monitor at least the first AI / ML model; and assign a monitoring operation to one of the first UE or the second UE based on determining whether the first UE has the first capability to monitor at least the first AI / ML model and on determining whether the second UE has the second capability to monitor at least the first AI / ML model.

29. The first UE of claim 28, further comprising: determining that the first UE has the first capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one of an AI / ML model operation capability of the first UE, a data processing capability of the first UE, a data storage capacity of the first UE, a battery capacity of the first UE, or a label generating capability of the first UE; and determining that the second UE has the second capability to monitor at least the first AI / ML model based, at least in part, on evaluating at least one of the AI / ML model operation capability of the second UE, a data processing capability of the second UE, a data storage capacity of the second UE, a battery capacity of the second UE, or a label generating capability of the second UE.

30. The first UE of claim 29, further comprising: determining a first comparison metric based, at least in part, on the evaluation of the at least one of the AI / ML model operation capability of the first UE, the data processing capability of the first UE, the data storage capacity of the first UE, the battery capacity of the first UE, or the label generating capability of the first UE; determining a second comparison metric based, at least in part, on the evaluation of the at least one of the AI / ML model operation capability of the second UE, the data processing capability of the second UE, the data storage capacity of the second UE, the battery capacity of the second UE, or the label generating capability of the second UE; andWAVS Ref. No. QLCM471WOQualcomm Ref. No. 2404383WO -83- assigning the monitoring operation to one of the first UE or the second UE further based on comparing the first comparison metric to the second comparison metric.WAVS Ref. No. QLCM471WO

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