Adaptive monitoring for multiple artificial intelligence / machine learning (AIML) positioning models

Abstract

In some implementations, a monitoring entity in a wireless network may monitor a first AIML positioning model in one or more operations used to determine a position estimate of a user equipment (UE) or object based on one or more wireless reference signals. In addition, the monitoring entity may detect, based on the monitoring of the first AIML positioning model, a trigger condition for monitoring a second AIML positioning model. The monitoring entity may, responsive to the detection of the trigger condition, perform at least one of: monitoring the second AIML positioning model, or sending, from the monitoring entity, a message indicative of the detection of the trigger condition.

Description

Qualcomm Ref. No. 2404046 WO -1-ADAPTIVE MONITORING FOR MULTIPLE ARTIFICIAL INTELLIGENCE / MACHINE LEARNING (AIML) POSITIONING MODELSBACKGROUND

[0001] This application claims the benefit of Greek Application No. 20240100862, filed December 10, 2024, entitled “ADAPTIVE MONITORING FOR MULTIPLE ARTIFICIAL INTELLIGENCE / MACHINE LEARNING (AIML) POSITIONING MODELS,” 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 adaptive monitoring of multiple artificial intelligence / machine learning (AIML or AI / ML) models used in positioning operations. Description of Related Art

[0003] Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, positioning, and broadcasts. Typical wireless communication systems may employ multiple-access technologies capable of supporting communication with multiple users by sharing available system resources (e.g., bandwidth, transmit power). Examples of such multipleaccess systems include fourth-generation (4G) systems such as Long-Term Evolution (LTE) systems, LTE-Advanced (LTE-A) systems, or LTE-A Pro systems, and fifthgeneration (5G) systems which may be referred to as New Radio (NR) systems.

[0004] In some examples, a wireless multiple-access communication system may include a number of base stations, each simultaneously supporting communication for multiple communication devices, otherwise known as user equipment (UEs). A base station may communicate with a set of UEs on downlink channels (e.g., for transmissions from a base station to a UE) and uplink channels (e.g., for transmissions from a UE to a base station). Additionally, UEs may communicate directly with each other using sidelink channels.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -2-

[0005] A location of a UE may be useful or essential to a number of applications including emergency calls, navigation, direction finding, asset tracking and Internet service. The UE may compute an estimate of its own location using positioning measurements in UE-based positioning or send the positioning measurements to a network entity (e.g., a location server), which may compute the UE location based on the positioning measurements in UE-assisted positioning. Further, a UE can be used to perform radio frequency (RF) sensing, which may be used to determine the presence and location of objects.BRIEF SUMMARY

[0006] An example method of enabling monitoring of multiple artificial intelligence / machine learning (AIML) positioning models in a wireless network, according to this disclosure, comprises monitoring, with a monitoring entity, a first AIML positioning model in one or more operations used to determine a position estimate of a user equipment (UE) or object based on one or more wireless reference signals. The method further may comprise detecting, based on the monitoring of the first AIML positioning model and with the monitoring entity, a trigger condition for monitoring a second AIML positioning model. The method further may comprise, responsive to the detection of the trigger condition, performing at least one of: monitoring the second AIML positioning model with the monitoring entity, or sending, from the monitoring entity, a message indicative of the detection of the trigger condition.

[0007] Another example method of enabling monitoring of multiple artificial intelligence / machine learning (AIML) positioning models in a wireless network, according to this disclosure, comprises obtaining, with a configuring entity, relationship information indicative of a relationship between a first AIML positioning model and a second AIML positioning model. The method further may comprise determining, with the configuring entity, a monitoring configuration for monitoring the first AIML positioning model in one or more operations used to determine a position estimate of a user equipment (UE) or object based on one or more wireless reference signals, wherein the monitoring configuration includes a trigger condition, detectable by a monitoring entity, for monitoring a second AIML positioning model based at least in part on the relationship information. The method further may comprise sending the monitoring configuration from the configuring entity.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -3-

[0008] An example monitoring entity, according to this disclosure, comprises at least one transceiver; at least one memory; and at least one processor communicatively coupled with the at least one transceiver and at least one memory. The at least one processor is configured to monitor a first artificial intelligence / machine learning (AIML) positioning model in one or more operations used to determine a position estimate of a user equipment (UE) or object based on one or more wireless reference signals. The at least one processor further may be configured to detect, based on the monitoring of the first AIML positioning model, a trigger condition for monitoring a second AIML positioning model. The at least one processor further may be configured to, responsive to the detection of the trigger condition, perform at least one of the following operations: monitor the second AIML positioning model, or send, via the at least one transceiver, a message indicative of the detection of the trigger condition.

[0009] An example configuring entity, according to this disclosure, comprises at least one transceiver; at least one memory; and at least one processor communicatively coupled with the at least one transceiver and at least one memory. The at least one processor is configured to obtain relationship information indicative of a relationship between a first artificial intelligence / machine learning (AIML) positioning model and a second AIML positioning model. The at least one processor further may be configured to determine a monitoring configuration for monitoring the first AIML positioning model in one or more operations used to determine a position estimate of a user equipment (UE) or object based on one or more wireless reference signals, wherein the monitoring configuration includes a trigger condition, detectable by a monitoring entity, for monitoring a second AIML positioning model based at least in part on the relationship information. The at least one processor further may be configured to send, via the at least one transceiver, the monitoring configuration from the configuring entity.

[0010] 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.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -4-BRIEF DESCRIPTION OF THE DRAWINGS

[0011] FIG. 1 is a diagram of a positioning / sensing system, according to an embodiment.

[0012] FIG. 2 is a diagram of a 5G NR positioning / sensing system, according to an embodiment.

[0013] FIG. 3 is a diagram of a direct artificial intelligence / machine learning (AIML) positioning model, according to an embodiment.

[0014] FIG. 4 is a diagram of an assisted AIML positioning model, according to an embodiment.

[0015] FIG. 5 is a diagram of a first example scenario in which an AIML positioning model may be used to determine position information, according to an embodiment.

[0016] FIG. 6 is a diagram of a second example scenario in which an AIML positioning model may be used to determine position information, according to an embodiment.

[0017] FIG. 7 is a diagram of a third example scenario in which an AIML positioning model may be used to determine position information, according to an embodiment.

[0018] FIG. 8 is a diagram of a fourth example scenario in which an AIML positioning model may be used to determine position information, according to an embodiment.

[0019] FIG. 9 is a diagram of a fifth example scenario in which an AIML positioning model may be used to determine position information, according to an embodiment.

[0020] FIG. 10 is a graph of an example hierarchical / multistage approach to an adaptive monitoring scheme for monitoring multiple AIML positioning models, according to an embodiment.

[0021] FIG. 11 is a graph of an example parallel / simultaneous approach to an adaptive monitoring scheme for monitoring multiple AIML positioning models, according to an embodiment.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -5-

[0022] FIG. 12 is a graph of another example parallel / simultaneous approach to an adaptive monitoring scheme for monitoring multiple AIML positioning models, according to an embodiment.

[0023] FIG. 13 is a signal flow diagram illustrating a first example of how various entities may communicate to implement the monitoring of multiple AIML positioning models as described herein, particularly between multiple monitoring entities.

[0024] FIG. 14 is a signal flow diagram illustrating a second example of how various entities may communicate to implement the monitoring of multiple AIML positioning models as described herein.

[0025] FIG. 15 is a signal flow diagram illustrating a third example of how entities may communicate to implement the monitoring of multiple AIML positioning models as described herein.

[0026] FIG. 16 is a block diagram of an embodiment of a UE.

[0027] FIG. 17 is a block diagram of an embodiment of an access node.

[0028] FIG. 18 is a block diagram of an embodiment of a computer system.

[0029] FIG. 19 is a flow diagram of an example method of enabling monitoring of multiple AIML positioning models in a wireless network.

[0030] FIG. 20 is a flow diagram of another example method of enabling monitoring of multiple AIML positioning models in a wireless network.

[0031] Like reference symbols in the various drawings indicate like elements, in accordance with certain example implementations. In addition, multiple instances of an element may be indicated by following a first number for the element with a letter or a hyphen and a second number. For example, multiple instances of an element 110 may be indicated as 110-1, 110-2, 110-3, etc., or as 110a, 110b, 110c, etc. When referring to such an element using only the first number, any instance of the element is to be understood (e.g., element 110 in the previous example would refer to elements 110-1, 110-2, and 110- 3 or to elements 110a, 110b, and 110c).WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -6-DETAILED DESCRIPTION

[0032] The following description is directed to certain implementations for the purposes of describing innovative aspects of various embodiments. However, a person having ordinary skill in the art will readily recognize that the teachings herein can be applied in a multitude of different ways. The described implementations may be implemented in any device, system, or network that is capable of transmitting and receiving radio frequency (RF) signals according to any communication standard, such as any of the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 standards for ultra-wideband (UWB), IEEE 802.11 standards (including those identified as Wi-Fi® technologies), the Bluetooth® standard, code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), Global System for Mobile communications (GSM), GSM / General Packet Radio Service (GPRS), Enhanced Data GSM Environment (EDGE), Terrestrial Trunked Radio (TETRA), Wideband-CDMA (W-CDMA), Evolution Data Optimized (EV-DO), IxEV- DO, EV-DO Rev A, EV-DO Rev B, High Rate Packet Data (HRPD), High Speed Packet Access (HSPA), High Speed Downlink Packet Access (HSDPA), High Speed Uplink Packet Access (HSUPA), Evolved High Speed Packet Access (HSPA+), Long Term Evolution (LTE), Advanced Mobile Phone System (AMPS), or other known signals that are used to communicate within a wireless, cellular or internet of things (loT) network, such as a system utilizing 3G, 4G, 5G, 6G, or further implementations thereof, technology.

[0033] 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.

[0034] Reference throughout this specification to “one example” or “an example” means that a particular feature, structure, or characteristic described in connection withWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -7- 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,” “location determination” and “position determination” and are intended to be equivalent in meaning and context. Further, unless otherwise specified, the term “positioning” as used herein may include absolute location determination, relative location determination, ranging, or a combination thereof. Such positioning may include and / or be based on timing, angular, phase, or power measurements, or a combination thereof (which may include RF sensing measurements) for the purpose of location or sensing services.

[0035] As used herein, an “RF signal” comprises an electromagnetic wave that transports information through the space between a transmitter (or transmitting device) and a receiver (or receiving device). As used herein, a transmitter may transmit a single “RF signal” or multiple “RF signals” to a receiver. However, the receiver may receive multiple “RF signals” corresponding to each transmitted RF signal due to the propagation characteristics of RF signals through multiple channels or paths.

[0036] Additionally, unless otherwise specified, references to “reference signals,” “RS,” “positioning reference signals,” “reference signals for positioning,” and the like may be used to refer to signals used for positioning of a user equipment (UE) in a 5G new radio (NR) network. As described in more detail herein, such signals may comprise any of a variety of signal types but may not necessarily be limited to a Positioning Reference Signal (PRS) as defined in relevant wireless standards.

[0037] As previously noted, a UE location can be determined based on measurements of RF signals transmitted by and / or received from a UE. These measurements may be made by the UE itself, other UEs, and / or wireless nodes in a wireless network (e.g., base stations). Further, the determination of the UE’s location may be made by the UE or another entity that collects these measurements (e.g., another UE, a base station, a location server, etc.). Additionally or alternatively, radio frequency (RF) sensing may be performed by wireless devices, including a UE, to determine the location of an object. The determination of the location of a UE and / or the position of an object (e.g., sensing)WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -8- may be referred to herein simply as “positioning.”

[0038] The use of artificial intelligence and / or machine learning models (referred to herein as “AIML functionalities”, “AIML methods”, “AIML procedures”, “AIML positioning functionalities”, “AIML positioning methods”, “AIML positioning procedures”, “AIML sensing functionalities”, “AIML sensing methods”, “AIML sensing procedures”, “AIML models,” “AIML positioning models,” “AIML sensing models,” or simply “AIML”) in such positioning has been shown to increase accuracy, particularly in non-line-of-sight (NLOS) conditions. However, AIML positioning models can be sensitive to changes in a wireless environment (e.g., changes in clutter settings) and / or changes in network conditions (e.g., reference signal configurations, network synchronization and / or timing errors, transmission (Tx) power, etc.). Thus, to take advantage of the increased accuracy that AIML positioning models can provide to positioning, a wireless network may frequently monitor AIML positioning models to ensure they are valid and function properly.

[0039] Issues can arise, however, when monitoring AIML positioning models. The monitoring process can be quite burdensome for network operation and AIML positioning. The monitoring of multiple AIML positioning models can compound these problems. Thus, it is desirable to optimize the AIML positioning monitoring process for multiple AIML positioning models to help reduce the burden on the network while ensuring that AIML positioning models continue to function properly.

[0040] Embodiments address these and other issues by implementing (1) a hi erarchical / multi- stage monitoring process in which the monitoring burden can be increased incrementally (if needed) to confirm the presence of an issue and / or (2) a parallel monitoring process for (subset) models. For example, in some implementations, embodiments may include monitoring a first AIML positioning model with a monitoring entity, in which the monitoring of the first AIML positioning model may comprise monitoring functionality of the first AIML positioning model in one or more operations used to determine a position estimate of a UE based on one or more wireless reference signals. Embodiments may further include detecting a trigger condition for monitoring a second AIML positioning model, wary detecting the trigger condition is based at least in part on the monitoring of the first AIML positioning model. Responsive to the detection of the trigger condition, embodiments may then monitor a second AIML positioningWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -9- model. Depending on the desired functionality, the second AIML positioning model can be monitored by the same or a different entity than the one that monitors the first AIML positioning model. Additional details are provided in the embodiments described below.

[0041] Particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the following potential advantages. In some examples, by employing a hierarchical / multi-stage monitoring process, the described techniques can be used to efficiently monitor multiple related AIML positioning models with one or more monitoring entities. Further, in some examples, by utilizing existing protocols (e.g., long-term evolution (LTE) positioning protocol (LPP) or new radio positioning protocol A (NRPPa)), embodiments may enable such monitoring with limited impact on signaling overhead. In some examples, by first exchanging capability information with a configuring entity, a monitoring entity can allow the configuring entity to determine and optimize monitoring configuration that can effectively monitor multiple AIML positioning models with one or more monitoring entities configured by the configuring entity. These and other advantages will be apparent to a person of ordinary skill in the art in view of the disclosure below. A detailed discussion of various embodiments will be provided after a review of relevant technology.

[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., a 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 multiple AIML models that may be used in a position determination procedure to determine a position of a device.

[0043] The positioning / sensing system 100 can include: the mobile device 105 (which is one example of a 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) 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.,WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -10-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 (and / or other NTN platforms) 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). However, the techniques described herein are not limited to such components and may be implemented in other types of systems (not shown).

[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, and 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 equipmentWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -11-(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 as 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 associatedWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -12- 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 codebased and / or carrier-based positioning, which can be highly accurate. 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, referenceWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -13- 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 location 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)WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -14- 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 be 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 ofWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -15- 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, the respective positions of the one or more anchor UEs may be known and / or jointly determined with the target UE. An anchor UE may also be known as a positioning reference unit (PRU), which is a unit with a known location that can be used for positioning. 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) communicationWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -16- 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 3GPP. 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 multiangulation 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,” “estimated 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 locationWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -17- 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 5G NR- based network, or a future 6G network.

[0061] FIG. 2 shows a diagram of a 5G NR positioning / sensing system 200, illustrating an embodiment of a positioning / sensing system (e.g., positioning / sensing system 100) implementing 5G NR. The 5G NR positioning / sensing system 200 may be configured to determine the location of a UE, such as, for example, the mobile deviceWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -18-105, 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. The location server 220 may also manage sensing functions and / or sensing functions may be by a separate server (e.g., a sensing management server (SMF), not shown. 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 in the 5GNR positioning / sensing system 200 include data and signaling connections which may include additional (intermediary) components, direct or indirect physical and / or wirelessWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -19- 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™), 5GNR (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 location 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 locationWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -20- 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 5GNR. 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 but 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 transmitWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -21- 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. 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,WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -22- 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, 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 UE 205 and possibly data and voice bearers for the UE 205. The location server 220 may support positioning of the UE 205 using a CP location solution when UEWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -23-205 accesses the NG-RAN 235 or WLAN 216 and may support position procedures and methods, including UE 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 UE 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 UE 205’s location) may be performed at the UE 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 UE 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 UE 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. TheNEF 245 may support secure exposure of capabilities and events concerning 5GCN 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.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -24-

[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 5G NAS 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, AOA, 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 and 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 requestWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -25- 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 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), AOA, 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).

[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.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -26-

[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 (CSL 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] FIG. 3 is a diagram illustrating how some embodiments may use an AIML positioning model 305 to perform direct AIML positioning, which is the use of an AIML positioning model to produce position information using positioning measurements (e.g., measurements to determine the location of a UE and / or RF sensing measurements to determine the location of an object). (Direct AIML positioning also may be referred to herein as “D-AIML,” and AIML positioning models used in direct AIML positioning may be referred to as “D-AIML models.”) As previously noted, a UE and / or object location may be determined based on measurements of RF signals transmitted by and / or received from a UE. These positioning measurements can be performed by one or more wirelessWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -27- devices in the positioning / sensing system 100 of FIG. 1 and / or the 5G NR positioning / sensing system 200 of FIG. 2, for example, and obtained by an entity (e.g., the UE, another UE, an access node, location server, etc.) to determine the UE and / or object’s location (e.g., location of the mobile device 105 / UE 205 or an object that may be nearby). As shown in FIG. 3, an entity may perform direct AIML positioning using an AIML positioning model 305 to output positioning information (e.g., the UE and / or object’s location, and / or indication thereof) using the positioning measurements. Thus, according to some embodiments, the direct AIML model 305 may replace traditional geometrically-based algorithms for position determination.

[0082] According to some embodiments, the direct AIML positioning may be useful particularly when conventional procedures for deriving location information from measurements involve uncertainties and / or ambiguities. These uncertainties and / or ambiguities may arise as a result of various conditions that may be present when the positioning measurements are made. Such uncertainties and / or ambiguities may be minimal or absent when the measurements are made under line-of-sight (LOS) conditions that are present between the UE and various devices and objects that are a part of the measurement procedure. Such uncertainties and / or ambiguities may be higher, however, when the measurements are made under conditions where some or all of the devices / objects are either completely blocked from view of the UE (referred to as a non- LOS (NLOS) condition) or partially obstructed from view of the UE (referred to as an obstructed-LOS (OLOS) condition). The use of AIML models can alleviate such issues.

[0083] FIG. 4 is a diagram illustrating how some embodiments may use an AIML positioning model 405 to perform assisted AIML positioning, which is the use of an AIML positioning model to produce “intermediate” (or enhanced) positioning measurements with which position information can be determined at block 410. (Assisted AIML positioning also may be referred to herein as “A-AIML,” and AIML positioning models used in direct AIML positioning may be referred to as “A-AIML models.” Referred) Here, the AIML positioning model 405 may be executed by a first device (e.g., a device performing the positioning measurements, such as the UE, another UE, or access node), and the position determination 410 may be performed by a second device (e.g., an access node or location server). Further, the position determination 410 may utilize one or more traditional geometrically-based algorithms for determining the position information or may use another AIML positioning model. The intermediateWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -28- measurements output by the AIML positioning model 405 may comprise a refined or modified version of the positioning measurements provided as input. These 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).

[0084] The monitoring of the AIML positioning model 305 in direct AIML positioning and / or AIML positioning model 405 in assisted AIML positioning can help ensure that the position information is accurate and reliable. As described in more detail below, model monitoring can determine whether a model fails (e.g., an input of the model fails to meet one or more predetermined input criteria and / or an output of the model fails to meet one or more predetermined output criteria) and / or whether certain conditions may be present in the trigger model failure. When a model fails or when such conditions are present, the model may be replaced with a different model that may be more robust and / or may be more capable of providing satisfactory output in such conditions. FIGS. 5-10, described below, provide additional details regarding how D-AIML and A-AIML positioning models may be implemented and monitored, according to some embodiments.

[0085] FIG. 5 illustrates a first example scenario in which an AIML positioning model may be used to determine position information, according to some embodiments. In this scenario, a UE 205 can implement a D-AIML model 305 to process measurements made by the UE 205 of a reference signal (e.g., PRS) transmitted by an access node 505. The access node 505 can be, as indicated above, any of a variety of wireless network entities such as a gNB 210, ng-eNB 214, WLAN 216, which are shown in FIG. 2, or another type of base station or TRP. (The location server 220 may be the LMF 220 of FIG. 2, although an additional or alternative server or device could be used.) Particular types of measurement may vary depending on the type of positioning performed. Measurements utilized by a D-AIML model in this scenario (and / or other scenarios described below) can include, for example, a channel impulse response (CIR), power delay profile (PDP), reference signal received power (RSRP), RSRP per path (RSRPP), reference signal time difference (RSTD), and / or other such positioning measurements described elsewhere herein.

[0086] FIG. 6 illustrates a second example scenario in which an AIML positioning model may be used to determine position information, according to some embodiments. As can be seen, the scenario is similar to the first example scenario, in which the UE 205WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -29- implements an AIML model to process measurements made of a reference signal transmitted by an access node 505. In this example scenario, however, the UE 205 uses an A-AIML model 405 to provide one or more PRS-based measurements to the location server 220, rather than the location information provided in the example of FIG. 5. Again, the use of an A-AIML model 405 in this manner can be particularly helpful when the PRS received from the access node 505 is poor, which may be due to due to RF signal fading, RF signal reflections, or NLOS / OLOS conditions between the UE 205 and access node 505 transmitting the PRS (or other reference signal). Measurements utilized by an A- AIML model in this scenario (and / or other scenarios described below) can include, for example, time of arrival (TOA), reference signal time difference (RSTD), RSRPP, and / or other such positioning measurements described elsewhere herein. The PRS-based measurements sent to the location server 220 in this example may correspond with the intermediate measurements described above with respect to FIG. 4. In some embodiments, the access node 505 may relay the PRS-based measurement(s) from the user equipment 205 to the location server 220. In some embodiments, the A-AIML model 405 in this scenario (and / or other scenarios described herein) may include additional information with the PRS-based measurements (or other refined measurements as described herein), including an LOS / NLOS indicator, high resolution of RSTD, timing estimation, or the like. The location server 220 may then perform additional operations on the PRS-based measurement(s) to determine location information (e.g., an estimated location of a UE and / or sensed object, and / or information derived therefrom) of the user equipment 205.

[0087] FIG. 7 illustrates a third example scenario in which an AIML positioning model may be used to determine position information, according to some embodiments. As with the scenarios in FIGS. 5 and 6, the scenario in FIG. 7 includes a location server 220, user equipment 205, and access node 505, as described above. In this scenario, however, the location server 220 implements a D-AIML model 305 to determine location information based on one or more PRS-based measurements received from the UE 205. The measurements themselves may be obtained using techniques that do not necessarily include the use of an AIML positioning model. (That said, it can be noted that the scenarios illustrated in FIGS. 6 and 7 are not mutually exclusive. In some embodiments, for example, the A-AIML model 405 as used in FIG. 6 may be used in conjunction with a D-AIML model 305 as used in FIG. 7.)WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -30-

[0088] FIGS. 8 and 9 illustrate additional examples of scenarios in which AIML models may be used to determine positioning information. In contrast with the examples in FIGS. 5-7, which measurements of downlink (DL) signals transmitted by the access node 505 are made by the UE 205, the examples in FIGS. 8 and 9 illustrate examples in which the access node 505 makes measurements of uplink (UL) signals (e.g., SRS) transmitted by the UE 205. Additional details are provided below.

[0089] FIG. 8 illustrates a fourth example scenario in which an AIML positioning model may be used to determine position information, according to some embodiments. In this scenario, as noted above, the access node 505 may make one or more measurements of a reference signal (e.g., SRS) transmitted by the UE 205. Additionally, the access node 505 may implement an A-AIML model 405, which may be used as described above to create one or more intermediate measurements by refining or otherwise modifying the measurement(s). The intermediate measurement s) may then be sent to the location server 220 to determine the location information.

[0090] FIG. 9 illustrates a fifth example scenario in which an AIML positioning model may be used to determine position information, according to some embodiments. In this scenario, as noted above, the access node 505 may make one or more measurements of a reference signal (e.g., SRS) transmitted by the UE 205. In contrast with the scenario in FIG. 8, however, the access node 505 may not implement an A-AIML model 405 but instead provides the SRS-based measurements to the location server 220. As further illustrated, the location server 220 may implement a D-AIML model 305 that may then determine the location information based on the SRS-based measurement(s) received from the access node 505.

[0091] It can be noted that the AIML positioning models illustrated in FIGS. 8 and 9 are not necessarily mutually exclusive. That is, in some scenarios in which location information is determined from SRS-based measurements, embodiments may implement an A-AIML model 405 at the access node 505 and a D-AIML model 305 at the location server 220.

[0092] Alternative embodiments may implement additional or alternative variations to the scenarios illustrated in FIGS. 5-9. For example, because location information regarding the location of a UE and / or sensed object may be based on measurements from multiple access nodes 505 and / or UEs 205, some scenarios may involve a combination ofWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -31- the scenarios illustrated in FIGS. 5-9. Moreover, some scenarios performing positioning and / or sensing using both UL and DL reference signals (e.g., DL-UL-based positioning, as described above) may use a combination of one or more DL-based scenarios (FIGS. 5- 7) with one or more UL-based scenarios (FIGS. 8-9). A person of ordinary skill in the art will appreciate other such variations.

[0093] Further, monitoring may be performed of any / all of the AIML positioning models illustrated in the examples of FIGS. 5-9. In some scenarios, a D-AIML model 305 may not be able to provide location information, an A- AIML model 405 may not be able to provide refined measurements, the AIML models provided in the examples of FIGS. 5-9 may otherwise provide location information that is unsatisfactory (e.g., inaccurate or otherwise unreliable), the AIML models may be exposed to network conditions that may cause such errors, or any combination of these events might occur. Monitoring of the AIML positioning models in such scenarios may detect these types of failures. This can allow a network to implement corrective measures such as switching to another (e.g., more reliable) model and / or utilizing traditional positioning techniques. Additionally, as discussed in more detail below, this can allow a network to monitor other AIML positioning models.

[0094] The monitoring of AIML positioning models can comprise monitoring different aspects of AIML positioning model implementation. As noted above, this may include monitoring AIML positioning model input / output, network conditions, and / or other such aspects. According to some embodiments, monitoring may be based on data distribution, in which input-based monitoring may comprise monitoring the validity of the AIML input (e.g., out-of-distribution detection, direct detection of input data, SNR, delay spread, etc.), and / or monitoring the validity of the AIML output (e.g., drifted detection of output data). As such, data used for computing a monitoring metric may vary, depending on the type of monitoring implemented. For example, if monitoring is based on model input, a monitoring metric may comprise a measurement corresponding to a model inference input. If monitoring is based on model output, then the monitoring metric may comprise an estimated UE location corresponding to a model output for a D-AIML model, one or more estimated intermediate parameters responding to the output of a A- AIML model, the ground-truth (GT) label corresponding to model inference output for either / both D-AIML and A-AIML models, or the like.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -32-

[0095] It can be noted that AIML positioning model monitoring may be performed of AIML positioning models for training and inference operation. For example, when training AIML positioning models, label-based training (e.g., GT-based) monitoring may be performed for both model input and expected model output (e.g., label / GT) are available to validate the AIML positioning model. According to some embodiments, if model monitoring comprises label-based training (e.g., uses a GT label (or its approximation)), a monitoring metric may comprise statistics of the difference between model output and a provided GT label (or its approximation). Examples of such a monitoring metric may comprise a mean, standard deviation, instantaneous value, threshold of ground-truth label (or its approximation), or the like. Label-free monitoring (e.g., without GT) may be based on model input (e.g., statistics of measurements) and / or actual model outputs (statistics and / or drift in model output over time). Label-free monitoring may be performed, for example, when training AIML positioning models, or in the field during inference operation. According to some embodiments, if model monitoring comprises label-free training (e.g., does not use a GT label (or its approximation)), then monitoring metrics may comprise statistics of one or more measurements compared to the statistics associated with the training data. (It can be noted that the one or more measurements may or may not be the same as model input.) Examples of such statistics may include a norm of model input, mean, min / max of some statistics related to measurement and / or model input, median or data temporal / spatial distribution, or the like. Additionally or alternatively, monitoring metrics for label-free training may comprise statistics of model output compared to the statistics associated with the training data and / or its own previous inference output. Examples of such metrics may comprise a mean, standard deviation, variance, etc. of some statistics related to model output, or the like.

[0096] It can be further noted that AIML positioning model monitoring may be performed by one or more monitoring entities, which may or may not correspond to the entities implementing the AIML positioning models. For example, a monitoring entity may comprise access node (e.g., a base station (gNB / TRP), Wi-Fi access point, etc.), UE (e.g., target UE, anchor UE / PRU), a location server (e.g., LMF, sensing management function (SnMF)), a core entity (e.g., an entity within the 5G CN 240 of FIG. 2 or network data analytic function (NWDAF)), or an operation administration and management (0AM) entity. As noted in the scenarios illustrated in FIGS. 5-9, AIML positioningWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -33- models may be implemented in a UE, access node, and / or location server. Depending on desired functionality, entity(ies) monitoring one or more AIML positioning model may be the same or different than the entity(ies) implementing the AIML positioning model(s). Moreover, the coordination of monitoring may be performed by a coordinating or configuring entity, which may be the same or different than the monitoring entity(ies) and / or entity(ies) implementing the AIML positioning model(s). According to some embodiments, a configuring entity may comprise a location server, a core entity, or 0AM entity. Additionally, or alternatively, some monitoring logic may be autonomously selected by the monitoring entity (e.g., a UE or access node). The autonomous selection may be made based on implementation, standardization, and / or an initial configuration for indication of selection logic provided by a location server or other network entity.

[0097] As noted, embodiments may employ an adaptive monitoring scheme for monitoring multiple AIML positioning models. Generally put, certain conditions observed in the monitoring of a first AIML positioning model may trigger the monitoring of one or more additional AIML positioning models. In some implementations, this adaptive monitoring scheme may follow a hierarchical / multistage or parallel / simultaneous monitoring approach. As noted above, this monitoring scheme may be managed by a configuring entity, and one or more monitoring entities may perform the monitoring. These approaches are described in more detail below with respect to FIGS. 10-12. The determination of which approach to use may be made, for example, by a configuring entity and may be based on available monitoring resources and / or other factors. For example, if only one UE is performing the monitoring, then the configuring entity may configure the UE to perform hierarchical / multistage (sequential) monitoring. However, if multiple UEs are performing the monitoring, then the configuring entity may configure each UE to monitor a separate model.

[0098] FIG. 10 is a graph of an example hierarchical / multistage approach to an adaptive monitoring scheme for monitoring multiple AIML positioning models, according to some embodiments. The AIML positioning models in FIG. 10 (and in FIGS. 11 and 12, discussed below) may correspond with AIML positioning models discussed in the embodiments above (e.g., AIML positioning model / D-AIML model 305 and / or AIML positioning model / A-AIML model 405.) In a hierarchical / multistage approach, a condition detected in the monitoring of a first model can trigger the monitoring of a second model in sequence. In the example of FIG. 10, various positioning AIMLWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -34- positioning models are monitored in sequence: AIML positioning model 1 is monitored from time T1 to time T2, after which AIML positioning model 2 is monitored from time T3 to time T4, then AIML positioning model 3 is monitored from time T5 to time T6. Prior to transitioning from monitoring one AIML positioning model to the other, a trigger condition is detected, which causes the change in which AIML positioning model to monitor. For example, a first trigger condition may be detected during the monitoring of AIML positioning model 1 (e.g., on or before time T2, but after time Tl). For example, the triggering condition may be detected at time T2, which may immediately in the monitoring of AIML positioning model 1 and start the monitoring of AIML positioning model 2. In another example, the triggering condition may be detected sometime before T2. Further, the duration of time between the end of monitoring AIML positioning model 1 (T2) and the beginning of the monitoring of AIML positioning model 2 (T3) may depend on which monitoring entities are involved. Times T2 and T3 may be substantially the same time if the monitoring is being performed at a single entity. However, there could be some delay between times T2 and T3 if different monitoring entities are used. As discussed in further detail below, trigger conditions may vary, and different trigger conditions may be used while monitoring different AIML positioning models.

[0099] FIG. 11 is a graph of an example parallel / simultaneous approach to an adaptive monitoring scheme for monitoring multiple AIML positioning models, according to some embodiments. In a parallel / simultaneous approach, a condition detected in the monitoring of a first model can trigger the monitoring of a second model in parallel. In the example of FIG. 11, a trigger condition may be detected at time T2 during the monitoring of AIML positioning model 1, which initiates the monitoring of AIML positioning model 2. Similarly, during the monitoring of AIML positioning model 2, a trigger condition is detected at time T4, which triggers the monitoring of AIML positioning model 3. The trigger condition detected at time T4 may be detected in conjunction with the monitoring of AIML positioning model 2. However, according to some embodiments, different triggering conditions detected while monitoring a first AIML positioning model may trigger the monitoring of different additional AIML positioning models. Thus, in the example of FIG. 11, the trigger condition detected at time T4 may be detected in conjunction with the monitoring of AIML positioning model 1.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -35-

[0100] Additionally or alternatively, the same conditions detected in conjunction while monitoring a first AIML positioning model may trigger the monitoring of a plurality of additional AIML positioning models. An example of this is illustrated in FIG. 12.

[0101] FIG. 12 is a graph of another example parallel / simultaneous approach to an adaptive monitoring scheme for monitoring multiple AIML positioning models, according to some embodiments. In this example, a trigger condition detected at time T2 while monitoring AIML positioning model 1 is used to initiate the monitoring of AIML positioning model 2, AIML positioning model 3, and possibly other AIML positioning models.

[0102] It can be noted that the examples illustrated in FIGS. 10-12 are nonlimiting, and variations may occur in different embodiments and different environments. For example, the times in which AIML positioning model monitoring begins and ends may vary based on when trigger conditions are detected and / or other factors. Additionally or alternatively, an adaptive monitoring scheme may be capable of monitoring a different number of AIML positioning models than illustrated in FIG. 10 (e.g., two, four, five, six, etc.) It can further be noted that an adaptive monitoring scheme may be implemented on a per-model basis or with respect to multiple models. Put differently, an adaptive monitoring scheme may be the same for all instances of a model executed by different entities (e.g., different UEs, access nodes, etc.) or may be customized to a particular entity.

[0103] The trigger conditions that can be used to trigger the monitoring of one or more additional AIML positioning models may vary, depending on desired functionality. Generally put, a trigger condition may comprise a condition detected in the monitoring of a first AIML positioning model that would be relevant to the performance of the one or more additional AIML positioning models. Not only can this include failure of the first AIML positioning model (e.g., failure of the model to provide an output or provide an output meets a threshold accuracy or reliability), but it can also include a failure in the monitoring of the first AIML positioning model (e.g., the inability of the monitoring entity to gather sufficient information to monitor model performance). Trigger conditions can also comprise radio conditions detected in the environment in which the model is operating. These radio conditions can include, for example, SINR / SNR of a reference signal (RS) (e.g., PRS) that satisfies a threshold, RSRP / RSRPP of an RS that satisfies aWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -36- threshold, delay spread of an RS that satisfies a threshold, a first arrival peak / strongest peak width of an RS that satisfies a threshold, a Rician factor of an RS that satisfies a threshold, or combination thereof. According to some embodiments, these radio conditions may be based on known issues (e.g., performance problems) with a specific AIML positioning model, a family of related AIML positioning models, or AIML positioning models in general under such radio conditions.

[0104] The determination of which additional AIML positioning models to monitor if the trigger condition for a first AIML positioning model is detected can be based on one or more of a variety of factors that define or identify relationships between models, which may be used for adaptation and trigger of multiple model monitoring. A discussion of several example factors is provided below, although embodiments are not necessarily limited to these factors. According to some embodiments, these factors and / or relationships, and the resulting adaptive multiple-model monitoring, may be determined by the configuring entity. Additionally or alternatively, factors and / or relationships may be identified manually (e.g., by a model creator) based on model information and provided to the configuring entity.

[0105] One factor that may be used for the adaptation and trigger of multiple model monitoring is the area of deployment. For example, if a trigger condition is identified for a first AIML positioning model in a first area (e.g., used to process measurements taken in the first area), then (1) the first AIML positioning model may be monitored in a second area, (2) one or more other AIML positioning models may be monitored in the first area, or (3) one or more other AIML positioning models may be monitored in a second area. According to some embodiments, any combination of (l)-(3) could be performed. According to some embodiments, an “area” for purposes of this factor may comprise a region or area defined by a wireless network. For example, area may be characterized by an area ID as listing of cell IDs (logical, physical, global, or any combination thereof) or geographic info (e.g., latitude, longitude, elevation, or any combination thereof).

[0106] According to some embodiments in which one or more monitoring entities are UEs, area-based triggering may be broadcast in a positioning system information block (posSIB) of a cellular (e.g., 5G) network, or sent via point-to-point communication, to indicate whether adaptive monitoring of one or more additional models should be sequential (e.g., similar to the example of FIG. 10) or simultaneous (e.g., similar toWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -37- examples of FIGS. 11 and 12). In such embodiments, monitoring UEs in an area in which monitoring of one or more additional models may be performed if a triggering condition is detected with respect to a first AIML positioning model may wait for an indication of the detected trigger condition from a configuring entity (e.g., a location server).

[0107] Another factor that may be used for the adaptation and trigger of multiple model monitoring is model complexity and / or monitoring cost. For example, a detected trigger condition in the monitoring of a first AIML positioning model may trigger the monitoring of one or more AIML positioning models in which the complexity and / or monitoring cost is higher than the first AIML positioning model. Additionally or alternatively, a detected trigger condition in the monitoring of a first AIML positioning model may trigger the monitoring of one or more AIML positioning models in which the complexity and / or monitoring cost is lower than the first AIML positioning model. Depending on model development and complexity, the monitoring of a first model can have a smaller cost than the second model or the other way around. In some instances, a model may be known to generalize well because it has a higher complexity. Thus, if it fails, other lower-complexity models are most likely to have an issue too. In some instances, if a model with a relatively low monitoring cost is failing, it may then trigger the monitoring of other models that have a higher monitoring cost. This approach can help lower overall monitoring costs.

[0108] Another factor that may be used for the adaptation and trigger of multiple model monitoring is robustness. For example, a detected trigger condition in monitoring a first AIML positioning model may trigger the monitoring of one or more AIML positioning models that are known to have lower robustness than the first AIML positioning model. In some embodiments, a detected trigger condition in monitoring a first AIML positioning model may trigger the monitoring of one or more AIML positioning models that are known to have higher robustness than the first AIML positioning model. Robustness in this context may generally mean the ability of a model to generalize to unseen changes and be robust to unexpected changes in radio characteristics and wireless environment (e.g., change in clutter settings). Robustness additionally or alternatively may apply to network changes (e.g., changes in network synchronization and timing errors, transmission power, etc.)WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -38-

[0109] Another factor that may be used for the adaptation and trigger of multiple model monitoring is model input association across models. For example, a detected trigger condition in monitoring a first AIML positioning model may trigger the monitoring of one or more AIML positioning models with input measurements that are a subset of the input measurements of the first AIML positioning model. In some embodiments, a detected trigger condition in monitoring a first AIML positioning model may trigger the monitoring of one or more AIML positioning models with input measurements that are a superset of the input measurements of the first AIML positioning model. In some embodiments, a detected trigger condition in monitoring a first AIML positioning model may trigger the monitoring of one or more AIML positioning models with input measurements that are the same as input measurements used by the first AIML positioning model. Further, in some implementations, if two or more models share the same reference signals for constructing their model input, then these models may be monitored together (e.g., simultaneously or in sequence, as described above).

[0110] Another factor that may be used for the adaptation and trigger of multiple model monitoring is model input construction (robustness). This can include, for example, which and / or how many TRPs a model uses. For example, a detected trigger condition in monitoring a first AIML positioning model may trigger the monitoring of one or more AIML positioning models that use input measurements from a larger number of TRPs than the first AIML positioning model. In some embodiments, a detected trigger condition in monitoring a first AIML positioning model may trigger the monitoring of one or more AIML positioning models that use input measurements from a smaller number of TRPs than the first AIML positioning model. It can be noted that observed evaluations have shown that models with single TRP construction are usually more robust and generalize better than those running radio frequency fingerprint (RFFP)-like model input (i.e., multi-TRP input construction) (but with loss in accuracy). This illustrates that there may be a trade-off between accuracy and robustness. With this in mind, according to some embodiments, the monitoring process can start monitoring a relatively robust model and then consider monitoring other accurate (but less robust) models when applying adaptive model monitoring. Otherwise, according to some embodiments, monitoring may start with models that have lower robustness, then moved to monitoring more robust models if there are trigger conditions in the monitoring of the models with lower robustness.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -39-[OHl] Another factor that may be used for the adaptation and trigger of multiple model monitoring is model input measurement type and size. For example, a detected trigger condition in monitoring a first AIML positioning model may trigger the monitoring of one or more AIML positioning models that use a superset of the measurement types and sizes used by the first AIML positioning model. According to some embodiments, a detected trigger condition in monitoring a first AIML positioning model may trigger the monitoring of one or more AIML positioning models that use a superset of the measurement types and sizes used by the first AIML positioning model. In some embodiments, a detected trigger condition in monitoring a first AIML positioning model may trigger the monitoring of one or more AIML positioning models that use the same measurement types and sizes used by the first AIML positioning model. According to this factor, measurement types may be related to channel time domain response and / or frequency response, such as timing information, power information, phase information, or a combination thereof (e.g., paired timing and power, paired timing and phase, paired power and phase, paired timing-power-and-phase info). Further, measurement size may be a number of measurements, such as a number of paths, a number of time-domain samples, a number of frequency-domain samples, etc., or any combination thereof. According to some embodiments, legacy measurement types may also be considered, such as RSTD, RSTD-diff, RTOA, RTOA-diff, UE tx-rx time difference, gNB tx-rx time difference, RSRP, RSRPP, RSCP, RSCPD, etc.

[0112] Another factor that may be used for adaptation and trigger of multiple model monitoring is model output type. Put differently, if two or more models share some model output types (e.g., LOS indicator) and one of them is failing or being triggered for monitoring, then it may also make sense to consider monitoring other models producing the same output type. For example, a detected trigger condition in monitoring a first AIML positioning model may trigger the monitoring of one or more AIML positioning models with a model output type that is a subset of the model output type of the first AIML positioning model. According to some embodiments, a detected trigger condition in monitoring a first AIML positioning model may trigger the monitoring of one or more AIML positioning models with a model output type that is a superset of the model output type of the first AIML positioning model. In some embodiments, a detected trigger condition in monitoring a first AIML positioning model may trigger the monitoring of one or more AIML positioning models with a model output type that is the same as theWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -40- model output type of the first AIML positioning model. Here, it may be noted that the model output can vary depending on the type of AIML positioning model being monitored (e.g., D-AIML or A-AIML). As noted elsewhere herein, for A-AIML, measurements may include, for example, LOS, timing such RSTD / ToA / RTOA / TX-RX time difference, AoA / AoD, RSRP / RSRPP, etc.

[0113] Another factor that may be used for adaptation and trigger of multiple model monitoring is the training data set. For example, a detected trigger condition in monitoring a first AIML positioning model may trigger the monitoring of one or more AIML positioning models that were trained and / or developed with at least a portion of the training dataset used to train the first AIML positioning model. This can be because, if two or more models were trained based on a (partially / fully) common dataset, then the failure of one of these models can mean the other one or more models may also fail.

[0114] Another factor that may be used for adaptation and trigger of multiple model monitoring is reference signal (RS) configuration. RS configuration can include various aspects related to the transmission of the RS, such as TX power, beam information, synchronization, group delays on the network side, etc. For example, a detected trigger condition in monitoring a first AIML positioning model may trigger the monitoring of one or more AIML positioning models that use the same RS configuration as the first AIML positioning model or a similar RS configuration (e.g., within a threshold degree of similarity). This is because, if two or more models share the same or similar RS configurations and one of them is failing or triggered for monitoring, then it may make sense to monitor the other one or more models.

[0115] In addition to the factors provided above, other considerations may be made when determining how to adapt multiple model monitoring. For example, according to some embodiments, when a trigger condition is detected at a first AIML positioning model, the monitoring of one or more additional AIML positioning models may be based on the priority of the models to be monitored. According to some embodiments, the priority may be based on one or more factors, such as the functionality of the model, monitoring cost, or the like. According to some embodiments, when a monitoring device is configured to trigger multi-model monitoring, it may follow priority and ranking assignments to models when a trigger condition is detected. For example, if a trigger condition detected in a first model would trigger the monitoring of five other models, andWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -41- conditions allow for the monitoring of only two of those models, the two models selected may be based on a configured priority or ranking.

[0116] According to some embodiments, model monitoring may have an expiration or timer. For example, when a trigger condition occurs and the monitoring of one or more additional AIML positioning models begins, the monitoring of these additional AIML positioning models may come with a validity period (e.g., timer expiration). If no errors (e.g., trigger conditions) are detected in those additional models within the validity period, then monitoring of those models may stop. According to some embodiments, the validity period or expiration timer for each model may be different (e.g., monitoring of first model can have longer periods or expiration timers than later models). Additionally or alternatively, an expiration timer may be implemented to terminate the entire multiple model monitoring scheme if no trigger conditions are detected. As part of a trigger condition, it may be possible to consider extending a timer for one or more models. Additionally or alternatively, the monitoring of some models may be cross-coupled such that the monitoring of models may revert back to a previous stage of monitoring, reverting to monitoring a previous model if the trigger condition in the monitoring of a subsequent model is not detected within a validity period.

[0117] The configuring and execution of the monitoring of multiple models described herein, which may include trigger conditions that are based on one or more of the factors above, may be implemented in of variety of ways. FIGS. 13-14, described below, provide some examples of how this may be done.

[0118] FIG. 13 is a signal flow diagram illustrating a first example of how various entities may communicate to implement the monitoring of multiple AIML positioning models as described herein, particularly between multiple monitoring entities. FIG. 13 shows communications between a configuring entity 1310, first monitoring entity 1315, and one or more additional monitoring entities 1320. As with other figures provided herein, FIG. 13 (and FIGS. 14 and 15, discussed below) is provided as a nonlimiting example. Arrows show communications between devices. However, there may be various intervening devices (not shown) configured to relay communications between the entities in FIG. 13. Further, a person of ordinary skill in the art will appreciate how various operations are communications may be altered in alternative embodiments while providing the same or similar overall functionality.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -42-

[0119] As previously noted, configuring entity 1310 and monitoring entities 1315, 1320 may vary, depending on implementation. The configuring entity 1310 entity may comprise, for example, a base station, server (e.g., LMF), core network entity, etc. of a wireless network. The monitoring entities 1315, 1320 may comprise one or more UEs / PRUs, base stations / TRPs, server (e.g., LMF), core network entity, or the like. Moreover, as also previously noted, the monitoring entities 1315, 1320 may be the same or different than the entities implementing the AIML positioning models.

[0120] As illustrated by block 1325, the configuring entity 1310 may it will initially determine monitoring configurations for the first monitoring entity 1315 and additional monitoring entity(ies) 1320. As noted above, the configuring entity 1310 may determine a monitoring scheme, which may take into account various factors to determine how models might be related and / or obtain information regarding such relationships. As noted above, a configuring entity 310 may be provided information regarding such relationships from a model creator and / or may deduce such relationships via information it gathers from additional or alternative data sources. Once these configurations are determined, they may be sent to the first monitoring entity 1315, and additional monitoring entities 1320, as indicated by arrows 1330.

[0121] The configurations may include information for the monitoring entities 1315, 1320 to perform the monitoring of the models, to implement an adaptive AIML positioning model monitoring scheme. This can include information regarding the models (which can include factors and / or relationships of models, as described herein), trigger conditions, etc. Additionally or alternatively, monitoring configurations may include an indication of which device(s) to alert if a trigger condition is detected. For first monitoring entity 1315 this may include, for example, notifying other monitoring entity(ies) 1320 and / or the configuring entity 310. According to some embodiments, the monitoring configurations sent at arrows 1330 may be sent as discrete messages or included in other communications (e.g., included in assistance data, other positioning configuration data, etc.). Atblock 1335, the first monitoring entity 1315 can begin monitoring the first AIML positioning model in accordance with the monitoring configuration received at arrows 1330.

[0122] The subsequent operations shown in FIG. 13 show actions that can be taken to implement adaptive monitoring of multiple models. Any or all of these operations canWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -43- be performed in accordance with monitoring configurations received from the configuring entity 1310. At block 1340, the first monitoring entity 1315 detects the trigger condition. In response, the first monitoring entity 1315 can then notify the one or more additional monitoring entities 1320 that a trigger condition was detected, as shown by arrow 1345. According to some embodiments, the determination of which monitoring entity(ies) 1320 to notify may be made at the first monitoring entity 1315 in view of the monitoring configuration received by the configuring entity 1310. Moreover, according to some embodiments, the detection of different trigger conditions may results in different entities being notified and different additional AIML positioning models being monitored. Optionally (as indicated by dashed arrow 1350) the first monitoring entity 1315 may also send notification that the trigger condition was detected to the configuring entity 1310, which may inform the configuring entity 1310 of the status of the monitoring. Responsive to the notification, the monitoring entity(ies) 1320 can begin monitoring one or more additional AIML positioning models, as shown at block 1360. Further, depending on whether parallel or sequential monitoring is being performed, the first monitoring entity 1315 may and the monitoring of the first AIML positioning model, as indicated at block 1355.

[0123] FIG. 14 is a signal flow diagram illustrating a second example of how various entities may communicate to implement the monitoring of multiple AIML positioning models as described herein. FIG. 14 shows a variation to FIG. 13, in which entities 1410- 1420 and operations 1425-1440 respectively correspond to counterpart entities 1310-1320 and operations 1325-1340 in FIG. 13, described above. Here, however, the configuring entity 1410 may play a larger role in the operations performed after the trigger condition is detected at block 1440. Specifically, after receiving a notification from the first monitoring entity 1415 that the trigger condition was detected (shown by arrow 1445), the configuring entity may then notify the monitoring entity(ies) 1320, as shown by arrow 1450. This can allow the first monitoring entity 1415 to send a single notification to the configuring entity 1410, which can then determine which monitoring entities to notify. Similar to FIG. 13, the first monitoring entity 1415 may (optionally) end monitoring the first AIML positioning model (as shown by block 1455), and the monitoring entity(ies) 1420 may begin monitoring the additional AIML positioning models, as shown by block 1460.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -44-

[0124] FIG. 15 is a signal flow diagram illustrating a third example of how entities may communicate to implement the monitoring of multiple AIML positioning models as described herein. FIG. 15 is generally similar to FIGS. 13 and 14, in which items 1510- 1540 may be generally similar to counterpart items 1410-1440 in FIG. 14 and 1310-1340 in FIG. 13, described above. This example, however, indicates how the first monitoring entity 1515 may be the sole monitoring entity used to implement a monitoring scheme. As such, after (optionally) notifying the configuring entity 1510 that a trigger condition was detected (shown by arrow 1550) and / or (optionally) ending the monitoring of the first AIML positioning model (shown by block 1560), the first monitoring entity 1515 may begin monitoring one or more additional AIML positioning models.

[0125] It can be noted that alternative embodiments may implement variations to the examples shown in FIGS. 13-15, and / or may include additional functionality. For example, the monitoring entities may detect triggering conditions when monitoring the additional one or more AIML positioning models, which can trigger further model monitoring (not shown), as described in the embodiments above. According to some embodiments, the configuring entity may play a more active role in which, each time a trigger condition is detected, the configuring entity may be notified of the detection and may further send one or more new monitoring configurations to corresponding one or more monitoring entities involved in monitoring the next set of one or more AIML positioning models. Additionally or alternatively, the configuring entity 1310 may determine the monitoring configurations based on monitoring capability information received from the one or more monitoring entities. According to some embodiments, this may be provided by the monitoring entity(ies) in response to a request from the configuring entity for the capability information.

[0126] Although embodiments herein are primarily directed toward the implementation of adaptive monitoring of AIML positioning models upon detecting a trigger condition, it can be noted that other actions may be performed upon the detection of such a trigger condition. Not only may one or more additional AIML positioning models be monitored, as described herein, but model management may be performed to determine how positioning may be modified in view of the detection of the trigger condition. When a trigger condition is detected with respect to a first AIML positioning model, a device implementing the first AIML positioning model may perform any of a variety of actions, including, for example, activating a second AIML positioning modelWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -45-(in which case, according to some embodiments, the selection of the new AIML positioning model may be based at least in part on the trigger condition detected), deactivating the first AIML model, implementing traditional (non- AIML) positioning, or the like.

[0127] As noted above, the configuring entity and monitoring entity(ies) may include different combinations of devices in a wireless network system. As such, different protocols may be used to communicate between these devices. Further, such protocols may be modified to convey monitoring information to allow for the functionality described herein.

[0128] For example, LPP may be used to communicate messages between an LMF (e.g., a configuring entity) and one or more UEs (e.g., monitoring entity(ies)). According to some embodiments, configurations from the LMF may be broadcast LMF configurations to be broadcast in the posSIB to communicate monitoring configuration information, such as whether multimodel monitoring is sequential / simultaneous. If a UE needs this information, then may wait for a response from the LMF with additional results. Additionally or alternatively, a UE may also be provided with point-to-point assistance data (which can be used along with any measurements the UE makes for detecting a trigger condition and / or implementing additional monitoring). gNB / edge server could use this assistance data accordingly. This can also come from 3rd party server, so UE can proactively request LMF to spin a fallback AIML model sequentially or simultaneously. Similarly, NRPPa may be used to communicate messages between an LMF (e.g., a configuring entity) and one or more base stations (e.g., monitoring entity(ies)).

[0129] As previously noted, capability information may be provided to a configuring entity by one or more monitoring entities, to enable the configuring entity to determine how monitoring may be performed. Capability information may include, for example, a monitoring entity's capabilities regarding monitoring models in parallel and / or in sequence (e.g., whether the monitoring entity is capable of monitoring in parallel or sequence, a number of models the monitoring entity is capable of monitoring in each mode, whether monitoring can be done in a time division multiplex (TDM) fashion, or the like). This capability information may be relayed via LPP, for example, when a monitoring entity is a UE and the configuring entity is an LMF. This capabilityWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -46- information may be relayed via NRPPa, for example, when a monitoring entity is a base station and the configuring entity is an LMF.

[0130] FIG. 16 is a block diagram of an embodiment of a UE 1600, which can be utilized as described herein above (e.g., in association with FIGS. 1-15, e.g., mobile device 105, UE 205, PRU, monitoring entity, and / or configuring entity as described herein). It should be noted that FIG. 16 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. 16 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. 16.

[0131] The UE 1600 is shown comprising hardware elements that can be electrically coupled via a bus 1605 (or may otherwise be in communication, as appropriate). The hardware elements may include a processor(s) 1610 which can include without limitation one or more general -purpose processors (e.g., an application processor), one or more special-purpose processors (such as digital signal processor (DSP) chips, graphics acceleration processors, application specific integrated circuits (ASICs), and / or the like), and / or other processing structures or means. Processor(s) 1610 may comprise one or more processing units, which may be housed in a single integrated circuit (IC) or multiple ICs. As shown in FIG. 16, some embodiments may have a separate DSP 1620, depending on desired functionality. Location determination and / or other determinations based on wireless communication may be provided in the processor(s) 1610 and / or wireless communication interface 1630 (discussed below). The UE 1600 also can include one or more input devices 1670, 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 1615, which can include without limitation one or more displays (e.g., touch screens), light emitting diodes (LEDs), speakers, and / or the like.

[0132] The UE 1600 may also include a wireless communication interface 1630, which may comprise without limitation a modem, a network card, an infrared communication device, a wireless communication device, and / or a chipset (such as aWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -47-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 1600 to communicate with other devices as described in the embodiments above. The wireless communication interface 1630 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) 1632 that send and / or receive wireless signals 1634. According to some embodiments, the wireless communication antenna(s) 1632 may comprise a plurality of discrete antennas, antenna arrays, or any combination thereof. The antenna(s) 1632 may be capable of transmitting and receiving wireless signals using beams (e.g., Tx beams and Rx beams). Beam formation may be performed using digital and / or analog beam formation techniques, with respective digital and / or analog circuitry. The wireless communication interface 1630 may include such circuitry.

[0133] Depending on desired functionality, the wireless communication interface 1630 may comprise a separate receiver and transmitter, or any 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 access points. The UE 1600 may communicate with different data networks that may comprise various network types. For example, a 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 2” (3GPP2). 3 GPP and 3GPP2 documents are publicly available. A wireless local area networkWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -48-(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 any combination of WWAN, WLAN and / or WPAN.

[0134] The UE 1600 can further include sensor(s) 1640. Sensor(s) 1640 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) (e.g., lidar), infrared sensor(s), RF sensor(s) (e.g., radar), barometer(s), and the like), some of which may be used to obtain position-related measurements and / or other information. In some configurations, the sensor(s) 1640 may not be co-located with the UE 1600, e.g., communicatively coupled (wired or wirelessly) but not disposed at the UE 1600.

[0135] Embodiments of the UE 1600 may also include a Global Navigation Satellite System (GNSS) receiver 1680 capable of receiving signals 1684 from one or more GNSS satellites using an antenna 1682 (which could be the same as antenna 1632). Positioning based on GNSS signal measurement can be utilized to complement and / or incorporate the techniques described herein. The GNSS receiver 1680 can extract a position of the UE 1600, using conventional techniques, from GNSS satellites of a GNSS system, such as Global Positioning System (GPS), Galileo, GLONASS, Quasi-Zenith Satellite System (QZSS) over Japan, IRNSS over India, BeiDou Navigation Satellite System (BDS) over China, and / or the like. Moreover, the GNSS receiver 1680 can be used with various augmentation systems (e.g., a Satellite Based Augmentation System (SB AS)) 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.

[0136] It can be noted that, although GNSS receiver 1680 is illustrated in FIG. 16 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)WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -49- by one or more processors, such as processor(s) 1610, DSP 1620, and / or a processor within the wireless communication interface 1630 (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), particle filter, or the like. The positioning engine may also be executed by one or more processors, such as processor(s) 1610 or DSP 1620.

[0137] The UE 1600 may further include and / or be in communication with a memory 1660. The memory 1660 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.

[0138] The memory 1660 of the UE 1600 also can comprise software elements (not shown in FIG. 16), 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 1660 that are executable by the UE 1600 (and / or processor(s) 1610 or DSP 1620 within UE 1600). In some embodiments, 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. Additionally or alternatively, such code and / or instructions can be used by the UE 1600 to implement one or more Al models, including one or more AIML positioning models as described herein.

[0139] FIG. 17 is a block diagram of an embodiment of an access node 1700, which can be utilized as described herein above (e.g., in association with FIGS. 1-16, e.g., base station 120, access point 130, access node 210 / 214 / 216 / 505, TRP, monitoring entity, and / or configuring entity as described herein). It should be noted that FIG. 17 is meant only to provide a generalized illustration of various components, any or all of which mayWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -50- be utilized as appropriate. In some embodiments, the access node 1700 may correspond to a gNB, an ng-eNB, and / or (more generally) a TRP or base station.

[0140] The access node 1700 is shown comprising hardware elements that can be electrically coupled via a bus 1705 (or may otherwise be in communication, as appropriate). The hardware elements may include a processor(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, ASICs, and / or the like), and / or other processing structure or means. As shown in FIG. 17, some embodiments may have a separate DSP 1720, depending on desired functionality. Location determination and / or other determinations based on wireless communication may be provided in the processor(s) 1710 and / or wireless communication interface 1730 (discussed below), according to some embodiments. The access node 1700 also can include one or more input devices, which can include without limitation a keyboard, display, mouse, microphone, button(s), dial(s), switch(es), and / or the like; and one or more output devices, which can include without limitation a display, light emitting diode (LED), speakers, and / or the like.

[0141] The access node 1700 might 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, cellular communication facilities, etc.), and / or the like, which may enable the access node 1700 to communicate as described herein. The wireless communication interface 1730 may permit data and 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) 1732 that send and / or receive wireless signals 1734.

[0142] The access node 1700 may also include a network interface 1780, which can include support of wireline communication technologies. The network interface 1780 may include a modem, network card, chipset, and / or the like. The network interface 1780 may include one or more input and / or output communication interfaces to permit data toWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -51- be exchanged with a network, communication network servers, computer systems, and / or any other electronic devices described herein.

[0143] In many embodiments, the access node 1700 may further comprise 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 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.

[0144] The memory 1760 of the access node 1700 also may 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 / or instructions in memory 1760 that are executable by the access node 1700 (and / or processor(s) 1710 or DSP 1720 within access node 1700). In some embodiments, 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. Additionally or alternatively, such code and / or instructions can be used by the UE 1600 to implement one or more Al models, including one or more AIML positioning models as described herein.

[0145] FIG. 18 is a block diagram of an embodiment of a computer system 1800, which may be used, in whole or in part, to provide the functions of one or more network components, servers, and / or similar computing devices as described in the embodiments herein (e.g., location server 160 / 220, core entity (e.g., LMF, SnMF, NWDAF), 0AM entity, monitoring entity, and / or configuring entity as described herein). 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. FIG. 18, 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 illustratedWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -52- by FIG. 18 can be localized to a single device and / or distributed among various networked devices, which may be disposed at different geographical locations.

[0146] The computer system 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 processor(s) 1810, which may comprise without limitation one or more general-purpose processors, one or more specialpurpose 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 1800 also may comprise one or more input devices 1815, which may comprise without limitation a mouse, a keyboard, a camera, a microphone, and / or the like; and one or more output devices 1820, which may comprise without limitation a display device, a printer, and / or the like.

[0147] The computer system 1800 may further include (and / or be in communication with) one or more non-transitory storage devices 1825, 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 RAM and / or 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.

[0148] The computer system 1800 may also include a communications subsystem 1830, which may comprise wireless communication technologies managed and controlled by a wireless communication interface 1833, as well as wired technologies (such as Ethernet, coaxial communications, universal serial bus (USB), and the like). The wireless communication interface 1833 may comprise one or more wireless transceivers that may send and receive wireless signals 1855 (e.g., signals according to 5G NR or LTE) via wireless antenna(s) 1850. Thus the communications subsystem 1830 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 computerWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -53- system 1800 to communicate on any or all of the communication networks described herein to any device on the respective network, including a User Equipment (UE), base stations and / or other TRPs, and / or any other electronic devices described herein. Hence, the communications subsystem 1830 may be used to receive and send data as described in the embodiments herein.

[0149] In many embodiments, the computer system 1800 will further comprise a working memory 1835, which may comprise a RAM or ROM device, as described above. Software elements, shown as being located within the working memory 1835, may comprise an operating system 1840, device drivers, executable libraries, and / or other code, such as one or more applications 1845, 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. Additionally or alternatively, such code and / or instructions can be used by the UE 1600 to implement one or more Al models, including one or more AIML positioning models as described herein.

[0150] 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) 1825 described above. In some cases, the storage medium might be incorporated within a computer system, such as computer system 1800. 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 / code stored thereon. These instructions might take the form of executable code, which is executable by the computer system 1800 and / or might take the form of source and / or installable code, which, upon compilation and / or installation on the computer system 1800 (e.g., using any of a variety of generally available compilers, installation programs, compression / decompression utilities, etc.), then takes the form of executable code.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -54-

[0151] FIG. 19 is a flowchart of an example method 1900 of enabling monitoring of multiple artificial intelligence / machine learning (AIML) positioning models in a wireless network. In some implementations, one or more method blocks of FIG. 19 may be performed by a monitoring entity. As previously noted, a monitoring entity may comprise a UE, a base station of the wireless network, a core entity of the wireless network, a location management function (LMF) of the wireless network, or an operation administration and management (0AM) entity of the wireless network. As such, example means and / or structure for performing the functionality of the one or more method blocks of FIG. 19 may comprise software and / or hardware components of a UE 1600, access node 1700, or computer system 1800, as discussed previously with respect to FIGS. 16, 17, and 18, respectively.

[0152] As shown in FIG. 19, method 1900 may include monitoring, with a monitoring entity, a first AIML positioning model in one or more operations used to determine a position estimate of a user equipment (UE) or an object based on one or more wireless reference signals (block 1910). This functionality may, for example, correspond to the functionality of block 1335 of FIG. 13, block 1435 of FIG. 14, or block 1535 of FIG. 15, described above. As discussed herein, operations used to determine a position estimate of a UE may comprise positioning operations that include measuring signals sent to and / or received from the UE. As also discussed herein, operations used to determine a position estimate of an object may comprise sensing operations that include measuring RF signals that reflect off of the object.

[0153] As noted above, means and / or structure for performing the functionality of block 1910 may comprise one or more components of a UE 1600, access node 1700, or computer system 1800, as discussed previously with respect to FIGS. 16, 17, and 18, respectively. For example, according to some embodiments, means and / or structure for performing the functionality of block 1910 may comprise bus 1605, one or more processors 1610, a digital signal processor 1620, wireless communication interface 1630 (e.g., one or more transceivers), one or more memories 1660, a GNSS receiver 1680, and / or other components of the UE 1600 of FIG. 16. According to some embodiments, means and / or structure for performing the functionality of block 1910 may comprise bus 1705, one or more processors 1710, a digital signal processor 1720, wireless communication interface 1730 (e.g., one or more transceivers), one or more memories 1760, a GNSS receiver 1780, and / or other components of access node 1700 of FIG. 17.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -55-According to some embodiments, means and / or structure for performing the functionality of block 1910 may comprise bus 1805, one or more processors 1810, communications subsystem 1830 (e.g., one or more transceivers), one or more memories 1835, and / or other components of computer system 1800 of FIG. 18.

[0154] As also shown in FIG. 19, method 1900 may include detecting, based on the monitoring of the first AIML positioning model and with the monitoring entity, a trigger condition for monitoring a second AIML positioning model (block 1920). This functionality may, for example, correspond to the functionality of block 1340 of FIG. 13, block 1440 of FIG. 14, or block 1540 of FIG. 15, described above. As noted, according to some embodiments, the trigger condition may include at least one of a predetermined radio condition of the one or more wireless reference signals, an input of the first AIML positioning model failing to meet one or more predetermined input criteria, or an output of the first AIML positioning model failing to meet one or more predetermined output criteria. In such embodiments, the trigger condition may include the output of the first AIML positioning model failing to meet the one or more predetermined output criteria, and where the one or more predetermined output criteria may include an expected output in view of ground-truth information of the position of the UE or object. Additionally or alternatively, the predetermined radio condition may include at least one of: a signal-to- interference-plus-noise ratio (SINK) or signal-to-noise ratio (SNR) of a RS (reference signal) meeting a threshold, exceeding a threshold, or falling below a threshold; reference signal received power reference signal received power (RSRP) or RSRP per path (RSRPP) meeting a threshold, exceeding a threshold, or falling below a threshold; a delay spread meeting a threshold, exceeding a threshold, or falling below a threshold; a first arrival peak or strongest peak width meeting a threshold, exceeding a threshold, or falling below a threshold; or a Rician factor of a RS meeting a threshold, exceeding a threshold, or falling below a threshold.

[0155] As noted above, means and / or structure for performing the functionality of block 1920 may comprise one or more components of a UE 1600, access node 1700, or computer system 1800, as discussed previously with respect to FIGS. 16, 17, and 18, respectively. For example, according to some embodiments, means and / or structure for performing the functionality of block 1920 may comprise bus 1605, one or more processors 1610, a digital signal processor 1620, wireless communication interface 1630 (e.g., one or more transceivers), one or more memories 1660, a GNSS receiver 1680,WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -56- and / or other components of the UE 1600 of FIG. 16. According to some embodiments, means and / or structure for performing the functionality of block 1920 may comprise bus 1705, one or more processors 1710, a digital signal processor 1720, wireless communication interface 1730 (e.g., one or more transceivers), one or more memories 1760, a GNSS receiver 1780, and / or other components of access node 1700 of FIG. 17. According to some embodiments, means and / or structure for performing the functionality of block 1920 may comprise bus 1805, one or more processors 1810, communications subsystem 1830 (e.g., one or more transceivers), one or more memories 1835, and / or other components of computer system 1800 of FIG. 18.

[0156] As further shown in Fig. 19, method 1900 may include responsive to the detection of the trigger condition, performing at least one of: monitoring the second AIML positioning model with the monitoring entity, or sending, from the monitoring entity, a message indicative of the detection of the trigger condition (block 1930). This functionality may, for example, correspond to the functionality of arrows 1345 and / or 1350 of FIG. 13, arrow 1445 of FIG. 14, or arrow 1550 and / or block 1570 of FIG. 15, described above.

[0157] As noted above, means and / or structure for performing the functionality of block 1930 may comprise one or more components of a UE 1600, access node 1700, or computer system 1800, as discussed previously with respect to FIGS. 16, 17, and 18, respectively. For example, according to some embodiments, means and / or structure for performing the functionality of block 1930 may comprise bus 1605, one or more processors 1610, a digital signal processor 1620, wireless communication interface 1630 (e.g., one or more transceivers), one or more memories 1660, and / or other components of the UE 1600 of FIG. 16. According to some embodiments, means and / or structure for performing the functionality of block 1930 may comprise bus 1705, one or more processors 1710, a digital signal processor 1720, wireless communication interface 1730 (e.g., one or more transceivers), one or more memories 1760, and / or other components of access node 1700 of FIG. 17. According to some embodiments, means and / or structure for performing the functionality of block 1930 may comprise bus 1805, one or more processors 1810, communications subsystem 1830 (e.g., one or more transceivers), one or more memories 1835, and / or other components of computer system 1800 of FIG. 18.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -57-

[0158] Process 1900 may include additional implementations, such as any single implementation or any combination of implementations described below and / or in connection with one or more other processes described elsewhere herein. For example, according to some embodiments, method 1900 further includes receiving a monitoring configuration from a configuration entity; and responsive to receipt of the monitoring configuration, monitoring the first AIML positioning model. In such embodiments, the monitoring configuration may include at least one of: an identity of the second AIML positioning model, a monitoring priority of the second AIML positioning model, an identity of another entity to which the message is to be sent, or a validity period for monitoring the first AIML positioning model. According to some embodiments, method 1900 may include sending, from the monitoring entity and prior to monitoring the first AIML positioning model, model monitoring capability information of the monitoring entity, the model monitoring capability information having at least one of: a number of models the monitoring entity is capable of monitoring, or a capability of the monitoring entity to monitor models in parallel or in sequence. Additionally or alternatively, method 1900 may include sending, from the monitoring entity and responsive to the detection of the trigger condition, the message via long-term evolution (LTE) positioning protocol (LPP) or new radio positioning protocol a (NRPPa).

[0159] Although FIG. 19 shows example blocks of method 1900, in some implementations, method 1900 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Fig. 19. Additionally, or alternatively, two or more of the blocks of method 1900 may be performed in parallel.

[0160] FIG. 20 is a flowchart of an example method 2000 of enabling monitoring of multiple artificial intelligence / machine learning (AIML) positioning models in a wireless network. In some implementations, one or more method blocks of FIG. 20 may be performed by a configuring entity. As previously noted, a configuring entity may comprise a location management function (LMF) of the wireless network, or an operation administration and management (0AM) entity of the wireless network. As such, example means and / or structure for performing the functionality of the one or more method blocks of FIG. 19 may comprise software and / or hardware components of a computer system 1800, as discussed previously with respect to FIG. 18.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -58-

[0161] As shown in FIG. 20, method 2000 may include obtaining, with a configuring entity, relationship information indicative of a relationship between a first AIML positioning model and a second AIML positioning model (block 2010). As noted above, the relationship information may comprise a variety of information, depending on desired functionality. This relationship information may be determined and provided by a AIML positioning model provider, for example. According to some embodiments, the relationship between the first AIML positioning model and the second AIML positioning model may include at least one of the first AIML positioning model having a lower or a higher complexity than the second AIML positioning model, the first AIML positioning model having a lower or a higher robustness than the second AIML positioning model, the first AIML positioning model having input measurements having a subset or a superset of input measurements of the second AIML positioning model, the first AIML positioning model having the same input measurements as the second AIML positioning model, the first AIML positioning model using measurements from a larger or a smaller number of transmission reception points (TRPs) than the second AIML positioning model, the first AIML positioning model having output types having a subset or a superset of output types of the second AIML positioning model, the first AIML positioning model having the same output types as the second AIML positioning model, the first AIML positioning model being trained on at least a portion of a training dataset used to train the second AIML positioning model, or the first AIML positioning model being implemented at a first device having the same reference signal (RS) configuration as a second device at which the second AIML positioning model is implemented. According to some embodiments, the configuring entity may obtain relationship information at least in part by determining the relationship information. For example, some embodiments of the method 2000 may include determining the relationship information based on: the second AIML positioning model and the first AIML positioning model being implemented in a common area, or the second AIML positioning model being implemented in a different area than the first AIML positioning model.

[0162] As noted above, means and / or structure for performing the functionality of block 2010 may comprise one or more components of a computer system 1800, as discussed previously with respect to FIG. 18. For example, according to some embodiments, means and / or structure for performing the functionality of block 2010 may comprise bus 1805, one or more processors 1810, communications subsystem 1830 (e.g.,WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -59- one or more transceivers), one or more memories 1835, and / or other components of computer system 1800 of FIG. 18.

[0163] As also shown in FIG. 20, method 2000 may include determining, with the configuring entity, a monitoring configuration for monitoring the first AIML positioning model in one or more operations used to determine a position estimate of a user equipment (UE) or object based on one or more wireless reference signals, where the monitoring configuration includes a trigger condition, detectable by a monitoring entity, for monitoring a second AIML positioning model based at least in part on the relationship information (block 2020). This functionality may, for example, correspond to the functionality of block 1325 of FIG. 13, block 1425 of FIG. 14, or block 1525 of FIG. 15, described above. As noted, according to some embodiments, the trigger condition may include at least one of a predetermined radio condition of the one or more wireless reference signals, an input of the first AIML positioning model failing to meet one or more predetermined input criteria, or an output of the first AIML positioning model failing to meet one or more predetermined output criteria. In such embodiments, the trigger condition may include the output of the first AIML positioning model failing to meet the one or more predetermined output criteria, and the one or more predetermined output criteria may include an expected output in view of ground-truth information of the position of the UE or object. Additionally or alternatively, the predetermined radio condition may include at least one of: a signal-to-interference-plus-noise ratio (SINK) or signal-to-noise ratio (SNR) of a RS (reference signal) meeting a threshold, exceeding a threshold, or falling below a threshold; reference signal received power reference signal received power (RSRP) or RSRP per path (RSRPP) meeting a threshold, exceeding a threshold, or falling below a threshold; a delay spread meeting a threshold, exceeding a threshold, or falling below a threshold; a first arrival peak or strongest peak width meeting a threshold, exceeding a threshold, or falling below a threshold; or a Rician factor of a RS meeting a threshold, exceeding a threshold, or falling below a threshold. As noted, according to some embodiments, the monitoring configuration may include at least one of: an identity of the second AIML positioning model, a monitoring priority of the second AIML positioning model, an identity of another entity to which a message indicative of detection of the trigger condition is to be sent, or a validity period for monitoring the first AIML positioning model.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -60-

[0164] As noted above, means and / or structure for performing the functionality of block 2020 may comprise one or more components of a computer system 1800, as discussed previously with respect to FIG. 18. For example, according to some embodiments, means and / or structure for performing the functionality of block 2020 may comprise bus 1805, one or more processors 1810, communications subsystem 1830 (e.g., one or more transceivers), one or more memories 1835, and / or other components of computer system 1800 of FIG. 18.

[0165] As further shown in FIG. 20, method 2000 may include sending the monitoring configuration from the configuring entity (block 2030). For example, configuring entity may send the monitoring configuration from the configuring entity, as described above. ). This functionality may, for example, correspond to the functionality of arrows 1330 of FIG. 13, arrows 1430 of FIG. 14, or arrow 1530 of FIG. 15, described above. According to some embodiments, method 2000 may include sending the monitoring configuration via long-term evolution (LTE) positioning protocol (LPP) or new radio positioning protocol a (NRPPa).

[0166] As noted above, means and / or structure for performing the functionality of block 2030 may comprise one or more components of a computer system 1800, as discussed previously with respect to FIG. 18. For example, according to some embodiments, means and / or structure for performing the functionality of block 2030 may comprise bus 1805, one or more processors 1810, communications subsystem 1830 (e.g., one or more transceivers), one or more memories 1835, and / or other components of computer system 1800 of FIG. 18.

[0167] Process 2000 may include additional implementations, such as any single implementation or any combination of implementations described below and / or in connection with one or more other processes described elsewhere herein. For example, according to some embodiments, method 2000 further includes receiving, from the monitoring entity and prior to sending the monitoring configuration, model monitoring capability information of the monitoring entity, the model monitoring capability information having at least one of: a number of models the monitoring entity is capable of monitoring, or a capability of the monitoring entity to monitor models in parallel or in sequence; where determining the monitoring configuration is based at least in part on the model monitoring capability information.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -61-

[0168] Although FIG. 20 shows example blocks of method 2000, in some implementations, method 2000 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in FIG. 20. Additionally, or alternatively, two or more of the blocks of method 2000 may be performed in parallel.

[0169] 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.

[0170] Certain aspects and techniques as described herein may be implemented, at least in part, using an artificial intelligence (Al) program, such as a program that includes a machine learning (ML) or artificial neural network (ANN) model. An example ML model may include mathematical representations or define computing capabilities for making inferences from input data based on patterns or relationships identified in the input data. As used herein, the term “inferences” can include one or more of decisions, predictions, determinations, or values, which may represent outputs of the ML model. The computing capabilities may be defined in terms of certain parameters of the ML model, such as weights and biases. Weights may indicate relationships between certain input data and certain outputs of the ML model, and biases are offsets that may indicate a starting point for outputs of the ML model. An example ML model operating on input data may start at an initial output based on the biases and then update its output based on a combination of the input data and the weights.

[0171] In some aspects, an ML model may be configured to provide computing capabilities for wireless communications. Such an ML model may be configured with weights and biases to perform denoising of RF sensing data. Thus, during the operation of a device, the ML model may receive input data (such as a range-Doppler image (RDI), reference noise variance, reference signal-to-noise ratio (SNR), number of targets, channel condition, etc.) and make inferences (such as object detection with reduced noise) based on the weights and biases.

[0172] ML models may be deployed in one or more devices (for example, network entities and user equipments (UEs)) and may be configured to enhance various aspects of a wireless communication system. For example, an ML model may be trained to identifyWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -62- patterns or relationships in data corresponding to a network, a device, an air interface, or the like. An ML model may support operational decisions relating to one or more aspects associated with wireless communications devices, networks, or services. For example, an ML model may be utilized for supporting or improving aspects such as RF sensing, signal coding / decoding, network routing, energy conservation, etc.

[0173] 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.

[0174] 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, technology evolves and, thus many of the elements are examples that do not limit the scope of the disclosure to those specific examples.

[0175] 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 theWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -63- 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.

[0176] 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.

[0177] 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 the 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.

[0178] In view of this description embodiments may include different combinations of features. Implementation examples are described in the following numbered clauses:

[0179] Clause 1 : A method of enabling monitoring of multiple artificial intelligence / machine learning (AIML) positioning models in a wireless network, theWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -64- method comprising: monitoring, with a monitoring entity, a first AIML positioning model in one or more operations used to determine a position estimate of a user equipment (UE) or object based on one or more wireless reference signals; detecting, based on the monitoring of the first AIML positioning model and with the monitoring entity, a trigger condition for monitoring a second AIML positioning model; and responsive to the detection of the trigger condition, performing at least one of monitoring the second AIML positioning model with the monitoring entity, or sending, from the monitoring entity, a message indicative of the detection of the trigger condition.

[0180] Clause 2: The method of clause 1, wherein the trigger condition comprises at least one of a predetermined radio condition of the one or more wireless reference signals, an input of the first AIML positioning model failing to meet one or more predetermined input criteria, or an output of the first AIML positioning model failing to meet one or more predetermined output criteria.

[0181] Clause 3 : The method of clause 2, wherein the trigger condition comprises the output of the first AIML positioning model failing to meet the one or more predetermined output criteria, and wherein the one or more predetermined output criteria comprises an expected output in view of ground-truth information of the position of the UE or object.

[0182] Clause 4: The method of any one of clauses 2-3, wherein the predetermined radio condition comprises at least one of a signal-to-interference-plus-noise ratio (SINK) or signal-to-noise ratio (SNR) of a RS (reference signal) meeting a threshold, exceeding a threshold, or falling below a threshold; reference signal received power reference signal received power (RSRP) or RSRP per path (RSRPP) meeting a threshold, exceeding a threshold, or falling below a threshold; a delay spread meeting a threshold, exceeding a threshold, or falling below a threshold; a first arrival peak or strongest peak width meeting a threshold, exceeding a threshold, or falling below a threshold; or a Rician factor of a RS meeting a threshold, exceeding a threshold, or falling below a threshold.

[0183] Clause 5: The method of any one of clauses 1-4, wherein the monitoring entity comprises: the UE, a base station of the wireless network, a core entity of the wireless network, a location management function (LMF) of the wireless network, or an operation administration and management (0AM) entity of the wireless network.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -65-

[0184] Clause 6: The method of any one of clauses 1-5, further comprising: receiving a monitoring configuration from a configuration entity; and responsive to receipt of the monitoring configuration, monitoring the first AIML positioning model.

[0185] Clause 7: The method of clause 6, wherein the monitoring configuration comprises at least one of: an identity of the second AIML positioning model, a monitoring priority of the second AIML positioning model, an identity of another entity to which the message is to be sent, or a validity period for monitoring the first AIML positioning model.

[0186] Clause 8: The method of any one of clauses 6-7, further comprising sending, from the monitoring entity and prior to monitoring the first AIML positioning model, model monitoring capability information of the monitoring entity, the model monitoring capability information comprising at least one of: a number of models the monitoring entity is capable of monitoring, or a capability of the monitoring entity to monitor models in parallel or in sequence.

[0187] Clause 9: The method of any one of clauses 1-8, further comprising sending, from the monitoring entity and responsive to the detection of the trigger condition, the message via long-term evolution (LTE) positioning protocol (LPP) or new radio positioning protocol a (NRPPa).

[0188] Clause 10: A method of enabling monitoring of multiple artificial intelligence / machine learning (AIML) positioning models in a wireless network, the method comprising: obtaining, with a configuring entity, relationship information indicative of a relationship between a first AIML positioning model and a second AIML positioning model; determining, with the configuring entity, a monitoring configuration for monitoring the first AIML positioning model in one or more operations used to determine a position estimate of a user equipment (UE) or object based on one or more wireless reference signals, wherein the monitoring configuration includes a trigger condition, detectable by a monitoring entity, for monitoring a second AIML positioning model based at least in part on the relationship information; and sending the monitoring configuration from the configuring entity.

[0189] Clause 11 : The method of clause 10, wherein the trigger condition comprises at least one of a predetermined radio condition of the one or more wireless reference signals, an input of the first AIML positioning model failing to meet one or moreWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -66- predetermined input criteria, or an output of the first AIML positioning model failing to meet one or more predetermined output criteria.

[0190] Clause 12: The method of clause 11, wherein the trigger condition comprises the output of the first AIML positioning model failing to meet the one or more predetermined output criteria, and wherein the one or more predetermined output criteria comprises an expected output in view of ground-truth information of the position of the UE or object.

[0191] Clause 13: The method of any one of clauses 11-12, wherein the predetermined radio condition comprises at least one of: a signal-to-interference-plus- noise ratio (SINK) or signal-to-noise ratio (SNR) of a RS (reference signal) meeting a threshold, exceeding a threshold, or falling below a threshold; reference signal received power reference signal received power (RSRP) or RSRP per path (RSRPP) meeting a threshold, exceeding a threshold, or falling below a threshold; a delay spread meeting a threshold, exceeding a threshold, or falling below a threshold; a first arrival peak or strongest peak width meeting a threshold, exceeding a threshold, or falling below a threshold; or a Rician factor of a RS meeting a threshold, exceeding a threshold, or falling below a threshold.

[0192] Clause 14: The method of any one of clauses 10-13, wherein the configuring entity comprises: a location management function (LMF) of the wireless network, or an operation administration and management (0AM) entity of the wireless network.

[0193] Clause 15: The method of any one of clauses 10-14, wherein the monitoring configuration comprises at least one of: an identity of the second AIML positioning model, a monitoring priority of the second AIML positioning model, an identity of another entity to which a message indicative of detection of the trigger condition is to be sent, or a validity period for monitoring the first AIML positioning model.

[0194] Clause 16: The method of any one of clauses 10-15, further comprising: receiving, from the monitoring entity and prior to sending the monitoring configuration, model monitoring capability information of the monitoring entity, the model monitoring capability information comprising at least one of: a number of models the monitoring entity is capable of monitoring, or a capability of the monitoring entity to monitor models in parallel or in sequence; and determining the monitoring configuration based at least in part on the model monitoring capability information.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -67-

[0195] Clause 17: The method of any one of clauses 10-16, further comprising sending the monitoring configuration via long-term evolution (LTE) positioning protocol (LPP) or new radio positioning protocol a (NRPPa).

[0196] Clause 18: The method of any one of clauses 10-17, wherein the relationship between the first AIML positioning model and the second AIML positioning model comprises at least one of: the first AIML positioning model having a lower or a higher complexity than the second AIML positioning model, the first AIML positioning model having a lower or a higher robustness than the second AIML positioning model, the first AIML positioning model having input measurements comprising a subset or a superset of input measurements of the second AIML positioning model, the first AIML positioning model having the same input measurements as the second AIML positioning model, the first AIML positioning model using measurements from a larger or a smaller number of transmission reception points (TRPs) than the second AIML positioning model, the first AIML positioning model having output types comprising a subset or a superset of output types of the second AIML positioning model, the first AIML positioning model having the same output types as the second AIML positioning model, the first AIML positioning model being trained on at least a portion of a training dataset used to train the second AIML positioning model, or the first AIML positioning model being implemented at a first device having the same reference signal (RS) configuration as a second device at which the second AIML positioning model is implemented.

[0197] Clause 19: The method of any one of clauses 10-18, further comprising determining the relationship information based on: the second AIML positioning model and the first AIML positioning model being implemented in a common area, or the second AIML positioning model being implemented in a different area than the first AIML positioning model.

[0198] Clause 20: A monitoring entity comprising: at least one transceiver; at least one memory; and at least one processor communicatively coupled with the at least one transceiver and at least one memory, the at least one processor configured to: monitor a first artificial intelligence / machine learning (AIML) positioning model in one or more operations used to determine a position estimate of a user equipment (UE) or object based on one or more wireless reference signals; detect, based on the monitoring of the first AIML positioning model, a trigger condition for monitoring a second AIML positioningWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -68- model; and responsive to the detection of the trigger condition, perform at least one of the following operations: monitor the second AIML positioning model, or send, via the at least one transceiver, a message indicative of the detection of the trigger condition.

[0199] Clause 21: The monitoring entity of clause 20, wherein, to detect the trigger condition, the at least one processor is configured to detect at least one of a predetermined radio condition of the one or more wireless reference signals, an input of the first AIML positioning model failing to meet one or more predetermined input criteria, or an output of the first AIML positioning model failing to meet one or more predetermined output criteria.

[0200] Clause 22: The monitoring entity of clause 21, wherein the one or more predetermined output criteria comprises an expected output in view of ground-truth information of a position of the UE or object.

[0201] Clause 23: The monitoring entity of any one of clauses 21-22, wherein, to detect at least one of a predetermined radio condition of the one or more wireless reference signals, the at least one processor is configured to detect at least one of: a signal- to-interference-plus-noise ratio (SINK) or signal-to-noise ratio (SNR) of a RS (reference signal) meeting a threshold, exceeding a threshold, or falling below a threshold; reference signal received power reference signal received power (RSRP) or RSRP per path (RSRPP) meeting a threshold, exceeding a threshold, or falling below a threshold; a delay spread meeting a threshold, exceeding a threshold, or falling below a threshold; a first arrival peak or strongest peak width meeting a threshold, exceeding a threshold, or falling below a threshold; or a Rician factor of a RS meeting a threshold, exceeding a threshold, or falling below a threshold.

[0202] Clause 24: The monitoring entity of any one of clauses 20-23, wherein the monitoring entity comprises: the UE, a base station of a wireless network, a core entity of a wireless network, a location management function (LMF) of a wireless network, or an operation administration and management (0AM) entity of a wireless network.

[0203] Clause 25: The monitoring entity of any one of clauses 20-24, wherein the at least one processor is further configured to: receive a monitoring configuration from a configuration entity; and responsive to receipt of the monitoring configuration, monitor the first AIML positioning model.WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -69-

[0204] Clause 26: The monitoring entity of clause 25, wherein, to receive the monitoring configuration, the at least one processor is configured to receive at least one of: an identity of the second AIML position model, a monitoring priority of the second AIML position model, an identity of another entity to which the message is to be sent, or a validity period for monitoring the first AIML position model.

[0205] Clause 27: The monitoring entity of any one of clauses 25-26, wherein the at least one processor is further configured to send, via the at least one transceiver and prior to monitoring the first AIML positioning model, model monitoring capability information of the monitoring entity, the model monitoring capability information comprising at least one of: a number of models the monitoring entity is capable of monitor, or a capability of the monitoring entity to monitor models in parallel or in sequence.

[0206] Clause 28: The monitoring entity of any one of clauses 20-27, wherein the at least one processor is further configured to send, via the at least one transceiver and responsive to the detection of the trigger condition, the message via long-term evolution (LTE) positioning protocol (LPP) or new radio positioning protocol a (NRPPa).

[0207] Clause 29: A configuring entity comprising: at least one transceiver; at least one memory; and at least one processor communicatively coupled with the at least one transceiver and at least one memory, the at least one processor configured to: obtain relationship information indicative of a relationship between a first artificial intelligence / machine learning (AIML) positioning model and a second AIML positioning model; determine a monitoring configuration for monitoring the first AIML positioning model in one or more operations used to determine a position estimate of a user equipment (UE) or object based on one or more wireless reference signals, wherein the monitoring configuration includes a trigger condition, detectable by a monitoring entity, for monitoring a second AIML positioning model based at least in part on the relationship information; and send, via the at least one transceiver, the monitoring configuration from the configuring entity.

[0208] Clause 30: The configuring entity of clause 29, wherein the at least one processor is configured to indicate, in the monitoring configuration, the trigger condition comprises at least one of a predetermined radio condition of the one or more wireless reference signals, an input of the first AIML positioning model failing to meet one orWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -70- more predetermined input criteria, or an output of the first AIML positioning model failing to meet one or more predetermined output criteria.

[0209] Clause 31 : The configuring entity of clause 30, wherein the at least one processor is configured to indicate, in the monitoring configuration, the trigger condition comprises the output of the first AIML positioning model failing to meet the one or more predetermined output criteria, and wherein the at least one processor is configured to indicate, in the monitoring configuration, the one or more predetermined output criteria comprises an expected output in view of ground-truth information of a position of the UE or object.

[0210] Clause 32: The configuring entity of any one of clauses 30-31, wherein the at least one processor is configured to indicate, in the monitoring configuration, the one or more predetermined output criteria comprises at least one of: a signal-to-interference- plus-noise ratio (SINK) or signal-to-noise ratio (SNR) of a RS (reference signal) meeting a threshold, exceeding a threshold, or falling below a threshold; reference signal received power reference signal received power (RSRP) or RSRP per path (RSRPP) meeting a threshold, exceeding a threshold, or falling below a threshold; a delay spread meeting a threshold, exceeding a threshold, or falling below a threshold; a first arrival peak or strongest peak width meeting a threshold, exceeding a threshold, or falling below a threshold; or a Rician factor of a RS meeting a threshold, exceeding a threshold, or falling below a threshold.

[0211] Clause 33: The configuring entity of any one of clauses 29-32, wherein the configuring entity comprises: a location management function (LMF) of a wireless network, or an operation administration and management (0AM) entity of a wireless network.

[0212] Clause 34: The configuring entity of any one of clauses 29-33, wherein the at least one processor is configured to include, in the monitoring configuration: an identity of the second AIML position model, a monitoring priority of the second AIML position model, an identity of another entity to which a message indicative of detection of the trigger condition is to be sent, or a validity period for monitoring the first AIML position model.

[0213] Clause 35: The configuring entity of any one of clauses 29-34, wherein the at least one processor is further configured to: receive, from the monitoring entity and priorWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -71- to sending the monitoring configuration, model monitoring capability information of the monitoring entity, the model monitoring capability information comprising at least one of a number of models the monitoring entity is capable of monitor, or; a capability of the monitoring entity to monitor models in parallel or in sequence; and determine the monitoring configuration based at least in part on the model monitoring capability information.

[0214] Clause 36: The configuring entity of any one of clauses 29-35, wherein the at least one processor is further configured to send the monitoring configuration via longterm evolution (LTE) positioning protocol (LPP) or new radio positioning protocol a (NRPPa).

[0215] Clause 37: The configuring entity of any one of clauses 29-36, wherein, to obtain the relationship information between the first AIML positioning model and the second AIML positioning model, the at least one processor is configured to obtain information indicative of at least one of: the first AIML position model having a lower or a higher complexity than the second AIML positioning model, the first AIML position model having a lower or a higher robustness than the second AIML positioning model, the first AIML position model having input measurements comprising a subset or a superset of input measurements of the second AIML positioning model, the first AIML position model having the same input measurements as the second AIML positioning model, the first AIML position model using measurements from a larger or a smaller number of transmission reception points (TRPs) than the second AIML positioning model, the first AIML position model having output types comprising a subset or a superset of output types of the second AIML positioning model, the first AIML position model having the same output types as the second AIML positioning model, the first AIML position model being trained on at least a portion of a training dataset used to train the second AIML positioning model, or the first AIML position model being implemented at a first device having the same reference signal (RS) configuration as a second device at which the second AIML positioning model is implemented.

[0216] Clause 38: The configuring entity of any one of clauses 29-37, wherein the at least one processor is further configured to determine the relationship information based on: the second AIML position model and the first AIML positioning model beingWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -72- implemented in a common area, or the second AIML position model being implemented in a different area than the first AIML positioning model.

[0217] Clause 39: An apparatus having means for performing the method of any one of clauses 1-19.

[0218] Clause 40: A non-transitory computer-readable medium storing instructions, the instructions comprising code for performing the method of any one of clauses 1-19.WAVS Ref. No. QLCMP476WO

Claims

Qualcomm Ref. No. 2404046 WO -73-WHAT IS CLAIMED IS:

1. A method of enabling monitoring of multiple artificial intelligence / machine learning (AIML) positioning models in a wireless network, the method comprising: monitoring, with a monitoring entity, a first AIML positioning model in one or more operations used to determine a position estimate of a user equipment (UE) or object based on one or more wireless reference signals; detecting, based on the monitoring of the first AIML positioning model and with the monitoring entity, a trigger condition for monitoring a second AIML positioning model; and responsive to the detection of the trigger condition, performing at least one of: monitoring the second AIML positioning model with the monitoring entity, or sending, from the monitoring entity, a message indicative of the detection of the trigger condition.

2. The method of claim 1, wherein the trigger condition comprises at least one of a predetermined radio condition of the one or more wireless reference signals, an input of the first AIML positioning model failing to meet one or more predetermined input criteria, or an output of the first AIML positioning model failing to meet one or more predetermined output criteria.

3. The method of claim 2, wherein the trigger condition comprises the output of the first AIML positioning model failing to meet the one or more predetermined output criteria, and wherein the one or more predetermined output criteria comprises an expected output in view of ground-truth information of a position of the UE or object.

4. The method of claim 2, wherein the predetermined radio condition comprises at least one of: a signal-to-interference-plus-noise ratio (SINK) or signal-to-noise ratio (SNR) of a RS (reference signal) meeting a threshold, exceeding a threshold, or falling below a threshold;WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -74- reference signal received power reference signal received power (RSRP) or RSRP per path (RSRPP) meeting a threshold, exceeding a threshold, or falling below a threshold; a delay spread meeting a threshold, exceeding a threshold, or falling below a threshold; a first arrival peak or strongest peak width meeting a threshold, exceeding a threshold, or falling below a threshold; or a Rician factor of a RS meeting a threshold, exceeding a threshold, or falling below a threshold.

5. The method of claim 1, wherein the monitoring entity comprises: the UE, a base station of the wireless network, a core entity of the wireless network, a location management function (LMF) of the wireless network, or an operation administration and management (0AM) entity of the wireless network.

6. The method of claim 1, further comprising: receiving a monitoring configuration from a configuration entity; and responsive to receipt of the monitoring configuration, monitoring the first AIML positioning model.

7. The method of claim 6, wherein the monitoring configuration comprises at least one of: an identity of the second AIML positioning model, a monitoring priority of the second AIML positioning model, an identity of another entity to which the message is to be sent, or a validity period for monitoring the first AIML positioning model.

8. The method of claim 6, further comprising sending, from the monitoring entity and prior to monitoring the first AIML positioning model, model monitoring capability information of the monitoring entity, the model monitoring capability information comprising at least one of: a number of models the monitoring entity is capable of monitoring, orWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -75- a capability of the monitoring entity to monitor models in parallel or in sequence.

9. The method of claim 1, further comprising sending, from the monitoring entity and responsive to the detection of the trigger condition, the message via long-term evolution (LTE) positioning protocol (LPP) or new radio positioning protocol a (NRPPa).

10. A method of enabling monitoring of multiple artificial intelligence / machine learning (AIML) positioning models in a wireless network, the method comprising: obtaining, with a configuring entity, relationship information indicative of a relationship between a first AIML positioning model and a second AIML positioning model; determining, with the configuring entity, a monitoring configuration for monitoring the first AIML positioning model in one or more operations used to determine a position estimate of a user equipment (UE) or object based on one or more wireless reference signals, wherein the monitoring configuration includes a trigger condition, detectable by a monitoring entity, for monitoring a second AIML positioning model based at least in part on the relationship information; and sending the monitoring configuration from the configuring entity.

11. The method of claim 10, wherein the trigger condition comprises at least one of a predetermined radio condition of the one or more wireless reference signals, an input of the first AIML positioning model failing to meet one or more predetermined input criteria, or an output of the first AIML positioning model failing to meet one or more predetermined output criteria.

12. The method of claim 11, wherein the trigger condition comprises the output of the first AIML positioning model failing to meet the one or more predetermined output criteria, and wherein the one or more predetermined output criteria comprises an expected output in view of ground-truth information of a position of the UE or object.

13. The method of claim 11, wherein the predetermined radio condition comprises at least one of:WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -76- a signal-to-interference-plus-noise ratio (SINR.) or signal-to-noise ratio (SNR) of a RS (reference signal) meeting a threshold, exceeding a threshold, or falling below a threshold; reference signal received power reference signal received power (RSRP) or RSRP per path (RSRPP) meeting a threshold, exceeding a threshold, or falling below a threshold; a delay spread meeting a threshold, exceeding a threshold, or falling below a threshold; a first arrival peak or strongest peak width meeting a threshold, exceeding a threshold, or falling below a threshold; or a Rician factor of a RS meeting a threshold, exceeding a threshold, or falling below a threshold.

14. The method of claim 10, wherein the configuring entity comprises: a location management function (LMF) of the wireless network, or an operation administration and management (0AM) entity of the wireless network.

15. The method of claim 10, wherein the monitoring configuration comprises at least one of: an identity of the second AIML positioning model, a monitoring priority of the second AIML positioning model, an identity of another entity to which a message indicative of detection of the trigger condition is to be sent, or a validity period for monitoring the first AIML positioning model.

16. The method of claim 10, further comprising: receiving, from the monitoring entity and prior to sending the monitoring configuration, model monitoring capability information of the monitoring entity, the model monitoring capability information comprising at least one of: a number of models the monitoring entity is capable of monitoring, or a capability of the monitoring entity to monitor models in parallel or in sequence; andWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -77- determining the monitoring configuration based at least in part on the model monitoring capability information.

17. The method of claim 10, further comprising sending the monitoring configuration via long-term evolution (LTE) positioning protocol (LPP) or new radio positioning protocol a (NRPPa).

18. The method of claim 10, wherein the relationship between the first AIML positioning model and the second AIML positioning model comprises at least one of the first AIML positioning model having a lower or a higher complexity than the second AIML positioning model, the first AIML positioning model having a lower or a higher robustness than the second AIML positioning model, the first AIML positioning model having input measurements comprising a subset or a superset of input measurements of the second AIML positioning model, the first AIML positioning model having the same input measurements as the second AIML positioning model, the first AIML positioning model using measurements from a larger or a smaller number of transmission reception points (TRPs) than the second AIML positioning model, the first AIML positioning model having output types comprising a subset or a superset of output types of the second AIML positioning model, the first AIML positioning model having the same output types as the second AIML positioning model, the first AIML positioning model being trained on at least a portion of a training dataset used to train the second AIML positioning model, or the first AIML positioning model being implemented at a first device having the same reference signal (RS) configuration as a second device at which the second AIML positioning model is implemented.

19. The method of claim 10, further comprising determining the relationship information based on: the second AIML positioning model and the first AIML positioning model being implemented in a common area, orWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -78- the second AIML positioning model being implemented in a different area than the first AIML positioning model.

20. A monitoring entity comprising: at least one transceiver; at least one memory; and at least one processor communicatively coupled with the at least one transceiver and at least one memory, the at least one processor configured to: monitor a first artificial intelligence / machine learning (AIML) positioning model in one or more operations used to determine a position estimate of a user equipment (UE) or object based on one or more wireless reference signals; detect, based on the monitoring of the first AIML positioning model, a trigger condition for monitoring a second AIML positioning model; and responsive to the detection of the trigger condition, perform at least one of the following operations: monitor the second AIML positioning model, or send, via the at least one transceiver, a message indicative of the detection of the trigger condition.

21. The monitoring entity of claim 20, wherein, to detect the trigger condition, the at least one processor is configured to detect at least one of a predetermined radio condition of the one or more wireless reference signals, an input of the first AIML positioning model failing to meet one or more predetermined input criteria, or an output of the first AIML positioning model failing to meet one or more predetermined output criteria.

22. The monitoring entity of claim 20, wherein the monitoring entity comprises: the UE, a base station of a wireless network, a core entity of a wireless network, a location management function (LMF) of a wireless network, orWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -79- an operation administration and management (OAM) entity of a wireless network.

23. The monitoring entity of claim 20, wherein the at least one processor is further configured to: receive a monitoring configuration from a configuration entity; and responsive to receipt of the monitoring configuration, monitor the first AIML positioning model.

24. The monitoring entity of claim 23, wherein, the at least one processor is configured to receive, in the monitoring configuration, at least one of: an identity of the second AIML position model, a monitoring priority of the second AIML position model, an identity of another entity to which the message is to be sent, or a validity period for monitoring the first AIML position model.

25. The monitoring entity of claim 23, wherein the at least one processor is further configured to send, via the at least one transceiver and prior to monitoring the first AIML positioning model, model monitoring capability information of the monitoring entity, the model monitoring capability information comprising at least one of: a number of models the monitoring entity is capable of monitor, or a capability of the monitoring entity to monitor models in parallel or in sequence.

26. A configuring entity comprising: at least one transceiver; at least one memory; and at least one processor communicatively coupled with the at least one transceiver and at least one memory, the at least one processor configured to: obtain relationship information indicative of a relationship between a first artificial intelligence / machine learning (AIML) positioning model and a second AIML positioning model; determine a monitoring configuration for monitoring the first AIML positioning model in one or more operations used to determine a positionWAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -80- estimate of a user equipment (UE) or object based on one or more wireless reference signals, wherein the monitoring configuration includes a trigger condition, detectable by a monitoring entity, for monitoring a second AIML positioning model based at least in part on the relationship information; and send, via the at least one transceiver, the monitoring configuration from the configuring entity.

27. The configuring entity of claim 26, wherein the at least one processor is configured to indicate, in the monitoring configuration, the trigger condition comprises at least one of a predetermined radio condition of the one or more wireless reference signals, an input of the first AIML positioning model failing to meet one or more predetermined input criteria, or an output of the first AIML positioning model failing to meet one or more predetermined output criteria.

28. The configuring entity of claim 26, wherein the configuring entity comprises: a location management function (LMF) of a wireless network, or an operation administration and management (0AM) entity of a wireless network.

29. The configuring entity of claim 26, wherein the at least one processor is configured to include, in the monitoring configuration: an identity of the second AIML position model, a monitoring priority of the second AIML position model, an identity of another entity to which a message indicative of detection of the trigger condition is to be sent, or a validity period for monitoring the first AIML position model.

30. The configuring entity of claim 26, wherein the at least one processor is further configured to: receive, from the monitoring entity and prior to sending the monitoring configuration, model monitoring capability information of the monitoring entity, the model monitoring capability information comprising at least one of: a number of models the monitoring entity is capable of monitor, or;WAVS Ref. No. QLCMP476WOQualcomm Ref. No. 2404046 WO -81- a capability of the monitoring entity to monitor models in parallel or in sequence; and determine the monitoring configuration based at least in part on the model monitoring capability information.WAVS Ref. No. QLCMP476WO

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