Error cause report generation in positioning operations that use artificial intelligence / machine learning

Abstract

An example method for generating an error cause report associated with positioning can include detecting, by a first device, at least one error in an artificial intelligence / machine learning (AI / ML) operation performed upon one or more wireless signals as a part of a position determination procedure; identifying, by the first device, at least one error cause for the at least one error; and generating, by the first device, the error cause report that provides an indication of the at least one error cause for the at least one error in the AI / ML operation.

Description

Qualcomm Ref. No. 2403938WO -1-ERROR CAUSE REPORT GENERATION IN POSITIONING OPERATIONSTHAT USE ARTIFICIAL INTELLIGENCE / MACHINE LEARNINGRELATED APPLICATIONS

[0001] This application claims the benefit of Greek Application No. 20240100639, filed September 19, 2024, entitled “ERROR CAUSE REPORT GENERATION IN POSITIONING OPERATIONS THAT USE ARTIFICIAL INTELLIGENCE / MACHINE LEARNING,” 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 error cause reporting associated with position determination operations involving the use of artificial intelligence / machine learning (AI / ML). Description of Related Art

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

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

[0005] Some examples of non-cellular position determination procedures include satellite-based position determination procedures, sensor-based position determination procedures, and time-of-flight ranging procedures (for detecting objects).WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -2-

[0006] Cellular-based position determination procedures typically involve interaction between the UE and one or more devices that may be a part of a cellular network (such as, for example, the Long Term Evolution (LTE) network and / or the 5G New Radio (NR) network). An example cellular-based position determination procedure involves positioning measurements made by use of, what is known as a positioning reference signal (PRS) that may be provided to the LIE by an access node of a cellular network. The PRS may be used for performing measurements associated with techniques such as, for example, time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AO A), angle of departure (AOD), and received signal strength indicator (RSSI).

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

[0008] Embodiments described herein pertain to positioning and more specifically pertains to error cause reporting associated with position determination operations involving the use of an AI / ML model.

[0009] An example method for generating an error cause report associated with positioning can include detecting, by a first device, at least one error in an artificial intelligence / machine learning (AI / ML) operation performed upon one or more wireless signals as a part of a position determination procedure; identifying, by the first device, at least one error cause for the at least one error; and generating, by the first device, the error cause report that provides an indication of the at least one error cause for the at least one error in the AI / ML operation.

[0010] Another example method performed by a first device for positionWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -3- determination can include receiving a reference signal; using the reference signal to perform a position determination procedure comprising the use of an artificial intelligence / machine learning (AI / ML) operation upon one or more wireless signals; detecting at least one error in the AI / ML operation; identifying at least one error cause for the at least one error; and generating an error cause report that provides an indication of the at least one error cause for the at least one error in the AI / ML operation.

[0011] Another example method performed by a first device for position determination can include performing a position determination procedure comprising use of an artificial intelligence / machine learning (AI / ML) operation upon one or more wireless signals; detecting at least one error in the AI / ML operation; identifying at least one error cause for the at least one error; generating an error cause report that provides an indication of the at least one error cause for the at least one error in the AI / ML operation; and transmitting the error cause report to a second device.

[0012] An example apparatus for generating an error cause report associated with positioning can include means for detecting at least one error in an artificial intelligence / machine learning (AI / ML) operation performed upon one or more wireless signals as a part of a position determination procedure; means for identifying at least one error cause for the at least one error; and means for generating the error cause report that provides an indication of the at least one error cause for the at least one error in the AI / ML operation.

[0013] This summary is neither intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in isolation to determine the scope of the claimed subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification of this disclosure, any or all drawings, and each claim. The foregoing, together with other features and examples, will be described in more detail below in the following specification, claims, and accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The detailed description below pertains to a few example embodiments that are illustrated in the accompanying drawings. However, it must be understood that the description is equally relevant to various other variations of the embodiments describedWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -4- herein. Such embodiments may utilize objects and / or components other than those illustrated in the drawings. It must also be understood that like reference numerals used in the various figures indicate similar or identical objects.

[0015] FIG. l is a simplified illustration of a positioning system, according to an embodiment.

[0016] FIG. 2 is a diagram of a 5th Generation (5G) New Radio (NR) positioning system, illustrating an embodiment of a positioning system (e.g., the positioning system of FIG. 1) implemented within a 5G NR communication system.

[0017] FIG. 3 is a block diagram for describing the concept of direct AI / ML operations for generating location information.

[0018] FIG. 4 is a block diagram for describing the concept of assisted AI / ML operations for generating location information.

[0019] FIG. 5 illustrates a first example scenario where AI / ML operations may be performed for obtaining location information.

[0020] FIG. 6 illustrates a second example scenario where AI / ML operations may be performed for obtaining location information.

[0021] FIG. 7 illustrates a third example scenario where AI / ML operations may be performed for obtaining location information.

[0022] FIG. 8 illustrates a fourth example scenario where AI / ML operations may be performed for obtaining location information.

[0023] FIG. 9 illustrates a fifth example scenario where AI / ML operations may be performed for obtaining location information.

[0024] FIG. 10 illustrates a first example signal flow scenario where an error cause report is generated by a user equipment for an AI / ML operation, in accordance with the disclosure.

[0025] FIG. 11 illustrates a second example signal flow scenario where an error cause report is generated by a user equipment for an AI / ML operation, in accordance with the disclosure.WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -5-

[0026] FIG. 12 illustrates a third example signal flow scenario where an error cause report is generated by a user equipment for an AI / ML operation, in accordance with the disclosure.

[0027] FIG. 13 illustrates a fourth example signal flow scenario where an error cause report is generated by a location server that can provide a location management function for an AI / ML operation, in accordance with the disclosure.

[0028] FIG. 14 illustrates a fifth example signal flow scenario where an error cause report is generated by an access node for an AI / ML operation, in accordance with the disclosure.

[0029] FIG. 15 illustrates a sixth example signal flow scenario where an error cause report is generated by a location server for an AI / ML operation, in accordance with the disclosure.

[0030] FIG. 16 shows a flowchart of an example method to generate an error cause report for an AI / ML operation in accordance with the disclosure.

[0031] FIG. 17 shows a list of example error causes related to data collection that may be applicable to AI / ML based positioning procedures, in accordance with the disclosure.

[0032] FIG. 18 shows a list of example error causes related to model input during interference or training collection that may be applicable to AI / ML based positioning procedures, in accordance with the disclosure.

[0033] FIG. 19 shows a list of example error causes related to model output during interference or training collection that may be applicable to AI / ML based positioning procedures, in accordance with the disclosure.

[0034] FIG. 20 is a diagram showing some example functional elements of a user equipment according to an embodiment.

[0035] FIG. 21 is a diagram showing some example functional elements of an access node according to an embodiment.

[0036] FIG. 22 is a block diagram of an embodiment of a computer system which may be used, in whole or in part, to provide the functions of one or more components and / or devices in accordance with the disclosure.WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -6-DETAILED DESCRIPTION

[0037] Several illustrative examples will now be described with respect to the accompanying drawings, which form a part hereof. While particular examples, in which one or more aspects of the disclosure may be implemented, are described below, other examples may be used, and various modifications may be made without departing from the scope of the disclosure or the spirit of the appended claims.

[0038] Reference throughout this specification to “one example” or “an example” means that a particular feature, structure, or characteristic described in connection with the example is included in at least one example of claimed subject matter. Thus, the appearances of the phrase “in one example” or “an example” in various places throughout this specification are not necessarily all referring to the same example. Furthermore, particular features, structures, or characteristics described herein may be combined in one or more examples. It must be understood that words such as “position” and “positioning” are used in this disclosure interchangeably with words / phrases such as “location,” “location determination” and “position determination” and are intended to be equivalent in meaning and context.

[0039] 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.

[0040] 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 orWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -7- 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.

[0041] 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.

[0042] ML models may be deployed in one or more devices (for example, network entities and various types of user equipment (UE)) and may be configured to enhance various aspects of a wireless communication system. For example, an ML model may be trained to identify 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.

[0043] More specifically, various aspects described herein relate to systems and methods for generating an error cause report that can indicate one or more error causes for errors in an AI / ML operation performed upon wireless positioning signals as a part of a position determination procedure. Traditional error cause reports associated with various position determination schemes performed without the use of an AI / ML tool do not address error causes associated with the AI / ML tool. In at least some cases, traditional error cause reports associated with position determination schemes performed with the use of an AI / ML tool fail to produce an error cause report that addresses error causes associated with the AI / ML tool.WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -8-

[0044] The various example embodiments disclosed herein generally pertain to detecting errors that may occur upon performing an AI / ML operation as a part of a position determination procedure and generate an error cause report that identifies one or more causes for the error. An error can be caused by one or more of various types of factors. For example, some errors can be attributed to issues related to collection of data used by the AI / ML tool, issues related to model parameters provided for training the AI / ML tool, and / or issues related to generating an output by the AI / ML tool based on inference.

[0045] Accordingly, particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the potential advantages described below.

[0046] An example advantage associated with generating an error cause report that automatically identifies one or more causes for one or more errors that may occur during an AI / ML operation performed as a part of a position determination procedure eliminates the need for performing troubleshooting operations to identify the cause for these errors. More particularly, the error cause report may allow focusing upon the AI / ML tool for addressing the errors, rather than spending time and effort in looking for the error cause elsewhere in the position determination procedure. Furthermore, in some cases, the error cause report generated in the manner described herein can be used to complement, supplement, or eliminate at least some traditional error cause reports.

[0047] FIG. 1 is a simplified illustration of a positioning / sensing system 100, which may be implemented in conjunction with and / or as part of a wireless communication system (e.g., cellular communication network) and can include a mobile device 105, a location server 160, and / or other components. One or more components of the positioning / sensing system 100 can be used for implementing the techniques disclosed herein for generating an error cause report that can indicate one or more error causes for errors in an AI / ML operation performed upon wireless positioning signals as a part of a position determination procedure to determine a position of a device or a sensing procedure performed to sense one or more objects.

[0048] However, the techniques described herein are not limited to such components and may be implemented in other types of systems (not shown). The positioning / sensing system 100 can include: the mobile device 105 (which is oneWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -9- example of an UE); one or more satellites 110 (also referred to as space vehicles (SVs)) for a Global Navigation Satellite System (GNSS) (such as the Global Positioning System (GPS), GLONASS, Galileo or Beidou) and / or Non-Terrestrial Network (NTN) functionality; base stations 120; access points (APs) 130; location server 160; network 170; and external client 180. Generally put, the positioning / sensing system 100 can estimate a location of the mobile device 105 based on RF signals received by and / or sent from the mobile device 105 and known locations of other components (e.g., GNSS satellites 110, base stations 120, APs 130) transmitting and / or receiving the RF signals. Additionally or alternatively, wireless devices such as the mobile device 105, base stations 120, and satellites 110 (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).

[0049] 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.

[0050] 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.WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -10-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 equipment (UE). Network 170 may also include more than one network and / or more than one type of network.

[0051] 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.

[0052] 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.WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -11-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 associated with a different antenna or a different antenna array for the base station 120. As used herein, the transmission functionality of a TRP may be performed with a transmission point (TP) and / or the reception functionality of a TRP may be performed by a reception point (RP), which may be physically separate or distinct from a TP. That said, a TRP may comprise both a TP and an RP. Physical transmission points may comprise an array of antennas of a base station 120 (e.g., as in a Multiple Input-Multiple Output (MIMO) system and / or where the base station employs beamforming). According to aspects of applicable 5G cellular standards, a base station 120 (e.g., gNB) may be capable of transmitting different “beams” in different directions and performing “beam sweeping” in which a signal is transmitted in different beams, along different directions (e.g., one after the other). The term “base station” used herein may additionally refer to multiple non-co-located physical transmission points, the physical transmission points may be a Distributed Antenna System (DAS) (a network of spatially separated antennas connected to a common source via a transport medium) or a Remote Radio Head (RRH) (a remote base station connected to a serving base station).

[0053] 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.

[0054] 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,WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -12-Galileo or Beidou. Positioning using RF signals from GNSS satellites may comprise measuring multiple GNSS signals at a GNSS receiver of the mobile device 105 to perform code-based and / or carrier-based positioning, which can be highly accurate. Additionally or alternatively, satellites 110 may be utilized for NTN-based positioning, in which satellites 110 may functionally operate as TRPs (or TPs) of a network (e.g., LTE and / or NR network) and may be communicatively coupled with network 170. In particular, reference signals (e.g., PRS) transmitted by satellites 110 NTN-based positioning may be similar to those transmitted by base stations 120 and may be coordinated by a network function server that may operate as a location server. In some embodiments, satellites 110 used for NTN-based positioning may be different than those used for GNSS-based positioning. In some embodiments NTN nodes may include non-terrestrial vehicles, which may be in addition or as an alternative to NTN satellites. NTN satellites 110 and / or other NTN platforms may be further leveraged to perform RF sensing. As described in more detail hereafter, satellites may use a JCS symbol in an Orthogonal Frequency-Division Multiplexing (OFDM) waveform to allow both RF sensing and / or positioning, and communication.

[0055] 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.

[0056] 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 supportWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -13- 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) that supports location of mobile device 105 using a control plane (CP) location solution for NR or LTE radio access by mobile device 105.

[0057] 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.

[0058] 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.

[0059] 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 performWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -14-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 of transmitters and / or a plurality of receivers. To facilitate sensing (e.g., in a sensing session among one or more sensing nodes), a sensing server may provide data (e.g., “assistance data”) to the sensing nodes to facilitate RS transmission and / or measurement, object / target detection, or any combination thereof. Such data may include an RS configuration indicating which resources (e.g., time and / or frequency resources) may be used (e.g., in a sensing session) to transmit RS for RF sensing. According to some embodiments, a sensing server may comprise a Sensing Management Function (SMF or SnMF).

[0060] 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.

[0061] 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 particularWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -15- 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. Direct communication between the one or more other mobile devices 145 and mobile device 105 may comprise sidelink and / or similar Device-to-Device (D2D) communication technologies. Sidelink, which is defined by 3GPP, is a form of D2D communication under the cellular-based LTE and NR standards.

[0062] According to some embodiments, such as when the mobile device 105 comprises and / or is incorporated into a vehicle, a form of D2D communication used by the mobile device 105 may comprise vehicle-to-everything (V2X) communication. V2X is a communication standard for vehicles and related entities to exchange information regarding a traffic environment. V2X can include vehicle-to-vehicle (V2V) communication between V2X-capable vehicles, vehicle-to-infrastructure (V2I) communication between the vehicle and infrastructure-based devices (commonly termed roadside units (RSUs)), vehicle-to-person (V2P) communication between vehicles and nearby people (pedestrians, cyclists, and other road users), and the like. Further, V2X can use any of a variety of wireless RF communication technologies. Cellular V2X (CV2X), for example, is a form of V2X that uses cellular-based communication such as LTE (4G), NR (5G) and / or other cellular technologies in a direct-communication mode as defined by 3 GPP. The mobile device 105 illustrated in FIG. 1 may correspond to a component or device on a vehicle, RSU, or other V2X entity that is used to communicate V2X messages. In embodiments in which V2X is used, the static communication / positioning device 145-3 (which may correspond with an RSU) and / or the vehicle 145-2, therefore, may communicate with the mobile device 105 and may be used to determine the position of the mobile device 105 using techniques similar to those used by base stations 120 and / or APs 130 (e.g., using multi angulation and / or multilateration). It can be further noted that mobile devices 145 (which may include V2X devices), base stations 120, and / or APs 130 may be used together (e.g., in a WWAN positioning solution) to determine the position of the mobile device 105, according to some embodiments.

[0063] An estimated location of mobile device 105 can be used in a variety ofWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -16- 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 location expressed as distances north or south, east or west and possibly above or below some other known fixed location (including, e.g., the location of a base station 120 or AP 130) or some other location such as a location for mobile device 105 at some known previous time, or a location of a mobile device 145 (e.g., another UE) at some known previous time). A location may be specified as a geodetic location comprising coordinates which may be absolute (e.g. latitude, longitude and optionally altitude), relative (e.g. relative to some known absolute location) or local (e.g. X, Y and optionally Z coordinates according to a coordinate system defined relative to a local area such a factory, warehouse, college campus, shopping mall, sports stadium or convention center). A location may instead be a civic location and may then comprise one or more of a street address (e.g. including names or labels for a country, state, county, city, road and / or street, and / or a road or street number), and / or a label or name for a place, building, portion of a building, floor of a building, and / or room inside a building etc. A location may further include an uncertainty or error indication, such as a horizontal and possibly vertical distance by which the location is expected to be in error or an indication of an area or volume (e.g. a circle or ellipse) within which mobile device 105 is expected to be located with some level of confidence (e.g. 95% confidence).

[0064] 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 servicesWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -17- provider, government agency, etc.

[0065] 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.

[0066] FIG. 2 shows a diagram of a 5GNR positioning / sensing system 200, illustrating an embodiment of a positioning / sensing system (e.g., positioning / sensing system 100) implementing 5GNR. The 5G NR positioning / sensing system 200 may be configured to determine the location of a UE, such as, for example, the mobile device 105, by using access nodes, which may include NR NodeB (gNB) 210-1 and 210-2 (collectively and generically referred to herein as gNBs 210), ng-eNB 214, and / or WLAN 216 to implement one or more positioning methods. The gNBs 210 and / or the ng-eNB 214 may correspond with base stations 120 of FIG. 1, and the WLAN 216 may correspond with one or more access points 130 of FIG. 1. Optionally, the 5GNR positioning / sensing system 200 additionally may be configured to determine the location of a UE 205 (such as, for example, the mobile device 105 shown in FIG. 1) by using a location server 220 (which may correspond with location server 160) to implement the one or more positioning methods. Here, the 5G NR positioning / sensing system 200 comprises the UE 205, and components of a 5G NR network comprising a Next Generation (NG) Radio Access Network (RAN) (NG-RAN) 235 and a 5G Core Network (5G CN) 240. A 5G network may also be referred to as an NR network; NG- RAN 235 may be referred to as a 5G RAN or as an NR RAN; and 5G CN 240 may be referred to as an NG Core network. The 5G NR positioning / sensing system 200 may further utilize information from GNSS satellites 110 from a GNSS system like Global Positioning System (GPS) or similar system (e.g. GLONASS, Galileo, Beidou, Indian Regional Navigational Satellite System (IRNSS)). Additional components of the 5G NR positioning / sensing system 200 are described below. The 5G NR positioning / sensing system 200 may include additional or alternative components.

[0067] 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 5GWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -18-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 5G NR positioning / sensing system 200 include data and signaling connections which may include additional (intermediary) components, direct or indirect physical and / or wireless connections, and / or additional networks. Furthermore, components may be rearranged, combined, separated, substituted, and / or omitted, depending on desired functionality.

[0068] The UE 205 may comprise and / or be referred to as a device, a mobile device, a wireless device, a mobile terminal, a terminal, a mobile station (MS), a Secure User Plane Location (SUPL)-Enabled Terminal (SET), or by some other name. Moreover, UE 205 may correspond to a cellphone, smartphone, laptop, tablet, personal data assistant (PDA), navigation device, Internet of Things (loT) device, or some other portable or moveable device. Typically, though not necessarily, the UE 205 may support wireless communication using one or more Radio Access Technologies (RATs) such as using GSM, CDMA, W-CDMA, LTE, High Rate Packet Data (HRPD), IEEE 802.11 Wi-Fi®, Bluetooth, Worldwide Interoperability for Microwave Access (WiMAX™), 5G NR (e.g., using the NG-RAN 235 and 5G CN 240), etc. The UE 205 may also support wireless communication using a WLAN 216 which (like the one or more RATs, and as previously noted with respect to FIG. 1) may connect to other networks, such as the Internet. The use of one or more of these RATs may allow the UE 205 to communicate with an external client 230 (e.g., via elements of 5G CN 240 not shown in FIG. 2, or possibly via a Gateway Mobile Location Center (GMLC) 225) and / or allow the external client 230 to receive location information regarding the UE 205 (e.g., via the GMLC 225). The external client 230 of FIG. 2 may correspond to external client 180 of FIG. 1, as implemented in or communicatively coupled with a 5G NR network.

[0069] 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 mayWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -19- 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 location comprising, for example, a distance and direction or relative X, Y (and Z) coordinates defined relative to some origin at a known location which may be defined geodetically, in civic terms, or by reference to a point, area, or volume indicated on a map, floor plan or building plan. In the description contained herein, the use of the term location may comprise any of these variants unless indicated otherwise. When computing the location of a UE, it is common to solve for local X, Y, and possibly Z coordinates and then, if needed, convert the local coordinates into absolute ones (e.g. for latitude, longitude and altitude above or below mean sea level).

[0070] Base stations in the NG-RAN 235 shown in FIG. 2 may correspond to base stations 120 in FIG. 1 and may include gNBs 210. Pairs of gNBs 210 in NG-RAN 235 may be connected to one another (e.g., directly as shown in FIG. 2 or indirectly via other gNBs 210). The communication interface between base stations (gNBs 210 and / or ng-eNB 214) may be referred to as an Xn interface 237. Access to the 5G network is provided to UE 205 via wireless communication between the UE 205 and one or more of the gNBs 210, which may provide wireless communications access to the 5G CN 240 on behalf of the UE 205 using 5G NR. The wireless interface between base stations (gNBs 210 and / or ng-eNB 214) and the UE 205 may be referred to as a Uu interface 239. 5G NR radio access may also be referred to as NR radio access or as 5G radio access. In FIG. 2, the serving gNB for UE 205 is assumed to be gNB 210-1, although other gNBs (e.g. gNB 210-2) may act as a serving gNB if UE 205 moves to another location or may act as a secondary gNB to provide additional throughput and bandwidth to UE 205.

[0071] 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 LTEWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -20- 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 transmit signals or data (relating to, e.g., PRS, assistance data, or other location data) to other network entities (e.g., one or more components of 5G CN 240, external client 230, or a controller) which may receive and store or use the data for positioning of at least UE 205. It is noted that while only one ng-eNB 214 is shown in FIG. 2, some embodiments may include multiple ng-eNBs 214. Base stations (e.g., gNBs 210 and / or ng-eNB 214) may communicate directly with one another via an Xn communication interface. Additionally or alternatively, base stations may communicate directly or indirectly with other components of the 5G NR positioning / sensing system 200, such as the location server 220 and AMF 215.

[0072] 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.WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -21-2) 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.

[0073] Access nodes may comprise any of a variety of network entities enabling communication between the UE 205 and the AMF 215. As noted, this can include gNBs 210, ng-eNB 214, WLAN 216, and / or other types of cellular base stations. However, access nodes providing the functionality described herein may additionally or alternatively include entities enabling communications to any of a variety of RATs not illustrated in FIG. 2, which may include non-cellular technologies. Thus, the term “access node,” as used in the embodiments described herein below, may include but is not necessarily limited to a gNB 210, ng-eNB 214 or WLAN 216.

[0074] 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 andWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -22- techniques described herein for obtaining a civic location for UE 205 may be applicable to such other networks.

[0075] 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 UE 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).

[0076] 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.,WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -23- containing the location estimate) to the external client 230.

[0077] A Network Exposure Function (NEF) 245 may be included in 5GCN 240. The NEF 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.

[0078] As further illustrated in FIG. 2, the location server 220 may communicate with the gNBs 210 and / or with the ng-eNB 214 using an NR Positioning Protocol annex (NRPPa) as defined in 3GPP Technical Specification (TS) 38.455. NRPPa messages may be transferred between a gNB 210 and the location server 220, and / or between an ng-eNB 214 and the location server 220, via the AMF 215. As further illustrated in FIG. 2, location server 220 and UE 205 may communicate using an LTE Positioning Protocol (LPP) as defined in 3GPP TS 37.355. Here, LPP messages may be transferred between the UE 205 and the location server 220 via the AMF 215 and a serving gNB 210-1 or serving ng-eNB 214 for UE 205. For example, LPP messages may be transferred between the location server 220 and the AMF 215 using messages for service-based operations (e.g., based on the Hypertext Transfer Protocol (HTTP)) and may be transferred between the AMF 215 and the UE 205 using a 5GNAS protocol. The LPP protocol may be used to support positioning of UE 205 using UE assisted and / or UE based position methods such as A-GNSS, RTK, TDOA, multi-cell RTT, AoD, and / or ECID. The NRPPa protocol may be used to support positioning of UE 205 using network based position methods such as ECID, AO A, uplink TDOA (UL-TDOA) and / or may be used by location server 220 to obtain location related information from gNBs 210 and / or ng-eNB 214, such as parameters defining DL-PRS transmission from gNBs 210 and / or ng-eNB 214.

[0079] 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 otherWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -24- 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.

[0080] In a 5G NR positioning / sensing system 200, positioning methods can be categorized as being “UE assisted” or “UE based.” This may depend on where the request for determining the position of the UE 205 originated. If, for example, the request originated at the UE (e.g., from an application, or “app,” executed by the UE), the positioning method may be categorized as being UE based. If, on the other hand, the request originates from an external client 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”).

[0081] With a UE-assisted position method, UE 205 may obtain location measurements and send the measurements to a location server (e.g., location server 220) for computation of a location estimate for UE 205. For RAT-dependent position methods location measurements may include one or more of a Received Signal Strength Indicator (RSSI), Round Trip signal propagation Time (RTT), Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Reference Signal Time Difference (RSTD), Time of Arrival (TOA), AO A, Receive Time-Transmission Time Difference (Rx-Tx), Differential AOA (DAO A), AOD, or Timing Advance (TA) for gNBs 210, ng-eNB 214, and / or one or more access points for WLAN 216. Additionally or alternatively, similar measurements may be made of sidelink signals transmitted by other UEs, which may serve as anchor points for positioning of the UE 205 if the positions of the other UEs are known. The location measurements may also or instead include measurements for RAT -independent positioning methods such as GNSS (e.g., GNSS pseudorange, GNSS code phase, and / or GNSS carrier phase for GNSS satellites 110), WLAN, etc.

[0082] With a UE-based position method, UE 205 may obtain locationWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -25- 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).

[0083] 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.

[0084] 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.

[0085] Depending on the type of positioning (e.g., UL, DL, or DL-UL based) the types of reference signals used can vary. For DL-based positioning, for example, these signals may comprise PRS (e.g., DL-PRS transmitted by base stations or SL-PRS transmitted by other UEs), which can be used for TDOA, AoD, and RTT measurements. Other reference signals that can be used for positioning (UL, DL, or DL-UL) may include Sounding Reference Signal (SRS), Channel State Information Reference Signal (CSI-RS), synchronization signals (e.g., synchronization signal block (SSB) Synchronizations Signal (SS)), Physical Uplink Control Channel (PUCCH), Physical Uplink Shared Channel (PUSCH), Physical Sidelink Shared Channel (PSSCH),WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -26-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 AO A.

[0086] FIG. 3 is a block diagram for describing the concept of direct AI / ML operations for generating location information. More particularly, direct AI / ML operations 305 represents an AI / ML operation performed in accordance with various embodiments of the disclosure. An example embodiment in accordance with the disclosure pertains to determining a location of the UE 205 (shown in FIG. 2) based on measurements of RF signals sent from, and / or received by, the UE 205 in the manner described above. In this case, the procedure that is performed for determining the location of the mobile device 105 / UE 205 includes an AI / ML operation. More particularly, the direct AI / ML operations 305 are performed upon position determination measurements obtained by the UE 205, such as, for example, measurements obtained via RF signals received by the UE 205 in response to RF signals transmitted by the UE 205 in the manner described above.

[0087] The direct AI / ML operations 305 may be useful particularly when conventional procedures for deriving location information from measurements involve uncertainties and ambiguities. The uncertainties and ambiguities may arise as a result of various conditions that may be present when the measurements are / were made. Such uncertainties and ambiguities may be minimal or absent when the measurements are made under line-of-sight (LOS) conditions that are present between the UE 205 and various devices and objects that are a part of the measurement procedure. Such uncertainties and ambiguities may be higher when the measurements are made under conditions where some or all of the devices / objects are either completely blocked from view of the UE 205 (referred to as a non-LOS (NLOS) condition) or partially obstructed from view of the UE 205 (referred to as an obstructed-LOS (OLOS) condition).

[0088] In an example scenario, the direct AI / ML operations 305 successfully produce location information generated based on the position determination measurements provided as input.

[0089] In another example scenario, the direct AI / ML operations 305 fail to produce an accurate location information generated based on the position determination measurements provided as input. The failure may be attributed to errors encountered inWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -27- the direct AI / ML operations 305 as a result of one or more causes. The error cause(s) can be included in an error cause report that is generated in accordance with the disclosure. The error cause report may be used for performing various actions such as, for example, to amend the direct AI / ML operations 305, to remedy the cause of one or more of the errors, and / or to provide an indication that the generated location information is inaccurate. Additional details pertaining to errors, error cause, and the error cause report are provided below.

[0090] FIG. 4 is a block diagram for describing the concept of assisted AI / ML operations for generating location information. More particularly, assisted AI / ML operations 405 represents another type of AI / ML operation that is performed in accordance with various embodiments of the disclosure. An example embodiment in accordance with the disclosure pertains to determining a location of the UE 205 (shown in FIG. 2) partly based on assisted AI / ML operations 405 performed upon position determination measurements. The position determination measurements are provided to the UE 205 by another device, such as, for example, by an access node (for example, gNB 210-1 that is shown in FIG. 2). The output produced by the assisted AI / ML operations 405 are AI / ML intermediate measurements that can be a refinement of the position determination measurements provided as input. The refinements may be based on taking into consideration various conditions that may have been present when the measurements were made (LOS, NLOS, and / or OLOS).

[0091] In an example scenario, the assisted AI / ML operations 405 successfully produce the AI / ML intermediate measurements based on refining the position determination measurements provided as input. Conventional non-AI / ML operations 410 may then be employed to operate upon the AI / ML intermediate measurements and produce location information.

[0092] In another example scenario, the assisted AI / ML operations 405 fail to produce accurate AI / ML intermediate measurements. The failure may be attributed to errors encountered in the assisted AI / ML operations 405 as a result of one or more causes. The error cause(s) can be included in an error cause report that is generated in accordance with the disclosure. The error cause report may be used for performing various actions such as, for example, to amend the assisted AI / ML operations 405, to remedy the cause of one or more of the errors, to refrain from using the non-AI / MLWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -28- operations 410, and / or to provide an indication that the generated AI / ML intermediate measurements are inaccurate. Additional details pertaining to errors, error cause, and the error cause report are provided below.

[0093] FIG. 5 illustrates a first example scenario where AI / ML operations may be performed for obtaining location information in accordance with the disclosure. In this scenario, the UE 205 can perform AI / ML operations upon input provided to the UE 205 by an access node. The access node 505 can be, as indicated above, any of a variety of network entities such as, for example, gNBs 210, ng-eNB 214, WLAN 216, that are shown in FIG. 2, and / or other types of cellular base stations. In the illustrated scenario, the access node 505 provides a positioning reference signal (PRS) to the UE 205. The PRS may be used by the UE 205 for performing position measurements associated with techniques such as, for example, time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AO A), angle of departure (AOD), and received signal strength indicator (RSSI).

[0094] In one implementation, the UE 205 applies direct AI / ML operations 305 upon the position measurements obtained by use of the PRS. In an example scenario, location information obtained after successful application of the direct AI / ML operations 305 may be transmitted by the UE 205 to the location server 220 via the access node 505. In another example scenario, the direct AI / ML operations 305 may fail and the UE 205 may be unable to determine location information. In this scenario, the UE 205 may transmit an error cause report to the access node 505, the location server 220 (via the access node 505), and / or another entity (not shown) such as, for example, a user of the UE 205.

[0095] FIG. 6 illustrates a second example scenario where AI / ML operations may be performed for obtaining location information in accordance with the disclosure. In this scenario, the UE 205 can perform AI / ML operations upon input provided to the UE 205 by an access node 505. The access node 505 can be, as indicated above, any of a variety of network entities such as, for example, gNBs 210, ng-eNB 214, WLAN 216, that are shown in FIG. 2, and / or other types of cellular base stations. In the illustrated scenario, the access node 505 provides a positioning reference signal (PRS) to the UE 205. The PRS may be used by the UE 205 for performing position measurements associated with techniques such as, for example, time of arrival (TOA), time differenceWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -29- of arrival (TDOA), angle of arrival (AO A), angle of departure (AOD), and received signal strength indicator (RSSI).

[0096] In this example scenario, the UE 205 applies assisted AI / ML operations 405 upon the position measurements obtained by use of the PRS. The assisted AI / ML operations 405 may be used, for example, when the PRS received from the access node 505 is poor, such as, for example, due to RF signal fading, RF signal reflections, or NLOS / OLOS conditions between the UE 205 and a cell tower transmitting the PRS. In an example scenario, the UE 205 may perform assisted AI / ML operations 405 upon position measurements obtained by use of the PRS. The UE 205 may, upon successful application of the assisted AI / ML operations 405, transmit AI / ML intermediate measurements to the access node 505 and / or the location server 220 (via the access node 505). The access node 505 and / or the location server 220 may then perform additional operations to determine location information of the UE 205 such as, for example, by using non- AI / ML operations 410. In another example scenario, the assisted AI / ML operations 405 may fail. In this scenario, the UE 205 may transmit an error cause report to the access node 505, the location server 220 (via the access node 505), and / or another entity (not shown), such as, for example, a user of the UE 205.

[0097] FIG. 7 illustrates a third example scenario where AI / ML operations may be performed for obtaining location information in accordance with the disclosure. In this scenario, the location server 220 can perform AI / ML operations upon input provided to the location server 220 by the UE 205. The access node 505 can be, as indicated above, any of a variety of network entities such as, for example, gNBs 210, ng-eNB 214, WLAN 216, that are shown in FIG. 2, and / or other types of cellular base stations. In the illustrated scenario, the access node 505 provides a positioning reference signal (PRS) to the UE 205. The PRS may be used by the UE 205 for performing position measurements associated with techniques such as, for example, time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AO A), angle of departure (AOD), and received signal strength indicator (RSSI).

[0098] The UE 205 may determine location information of an entity (such as, for example, the UE 205 or an object sensed by the UE 205) based on techniques that may not necessarily include AI / ML and transmit the location information to the location server 220 (in the form of a PRS-based measurement). The location server 220 may thenWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -30- operate upon the PRS-based measurement by using direct AI / ML operations 305 in order to determine the location of the entity. In an example scenario, the direct AI / ML operations 305 may fail. In this scenario, the UE 205 may transmit an error cause report to the access node 505, the location server 220 (via the access node 505), and / or another entity (not shown), such as, for example, a user of the UE 205.

[0099] FIG. 8 illustrates a fourth example scenario where AI / ML operations may be performed for obtaining location information in accordance with the disclosure. In this scenario, the access node 505 can perform AI / ML operations upon input provided to the access node 505 by the UE 205. The access node 505 can be, as indicated above, any of a variety of network entities such as, for example, gNBs 210, ng-eNB 214, WLAN 216, that are shown in FIG. 2, and / or other types of cellular base stations. In the illustrated scenario, the access node 505 receives a sounding reference signal (SRS) from the UE 205. The SRS may be used by the access node 505 for performing position measurements based on assisted AI / ML operations 405.

[0100] The access node 505 may, upon successful application of the assisted AI / ML operations 405, transmit intermediate information to the location server 220. The location server 220 may then perform additional operations to determine location information of the UE 205 and / or the object, such as, for example, by using non- AI / ML operations 410.

[0101] In an example scenario, the assisted AI / ML operations 405 may fail. In this scenario, the access node 505 may transmit an error cause report to the location server 220.

[0102] FIG. 9 illustrates a fifth example scenario where AI / ML operations may be performed for obtaining location information in accordance with the disclosure. In this scenario, the location server 220 may receive a location request from a client device (not shown) and may initiate a location determination procedure based on communications with the UE 205. In the illustrated scenario, the access node 505 receives a sounding reference signal (SRS) from the UE 205 and transmits SRS-based measurements to the location server 220. The SRS-based measurements may be used by the location server 220 for performing position measurements based on direct AI / ML operations 305.

[0103] The location server 220 may, upon successful application of the direct AI / ML operations 305, transmit location information of the UE 205 and / or an objectWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -31- sensed by the UE 205, to the client device. In an example scenario, the direct AI / ML operations 305 may fail. In this scenario, the location server 220 may transmit an error cause report to the client device or to another network entity.

[0104] FIG. 10 illustrates a first example signal flow diagram where an error cause report may be generated by a user equipment upon failure of a direct AI / ML operation performed by the user equipment. The interactions between the UE 205, the access node 505, and the location server 220 may be carried out using any of various communication formats that support messaging and data transport. The first example signal flow diagram generally reflects the operations described above with reference to FIG. 5.

[0105] More particularly, in this example, the UE 205 performs a location determination procedure by using direct AI / ML operations 305 (box 1005). In an example scenario, the location determination procedure is performed successfully, and location information is transmitted by the UE 205 to the location server 220 (arrow 1010). The access node 505 conveys the location information to the location server 220 (arrow 1015). The location server 220 may then convey the location information to one or more other entities such as, for example, a client device (not shown).

[0106] In another example scenario, the direct AI / ML operations 305 may fail and the UE 205 may be unable to determine location information. In this scenario, the UE 205 generates an error cause report in accordance with the disclosure. The error cause report may be conveyed to various entities in various ways. In one case, the error cause report may be transmitted to the access node 505, the location server 220 (via the access node 505), and / or another entity such as, for example, a user of the UE 205.

[0107] FIG. 11 illustrates a second example signal flow diagram where an error cause report may be generated by a user equipment upon failure of an assisted AI / ML operation performed by the user equipment. The interactions between the UE 205, the access node 505, and the location server 220 may be carried out using any of various communication formats that support messaging and data transport. The second example signal flow diagram generally reflects the operations described above with reference to FIG. 5.

[0108] More particularly, in this example, the UE 205 performs a location determination procedure by using assisted AI / ML operations 405 (box 1120). In anWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -32- example scenario, the location determination procedure is initiated by the external client 230 via a request sent to the location server 220 (arrow 1105). In response to the request, the location server 220 transmits a location request (arrow 1110) to the access node 505. The access node 505 may transmit a position reference signal (PRS) to the user equipment 205 (arrow 1115). The UE 205 performs a location determination procedure by assisted AI / ML operations 405 (box 1120). In an example scenario, the location determination procedure is performed successfully, and AI / ML intermediate measurements are transmitted by the UE 205 to the access node 505 (arrow 1125). The access node 505 conveys the AI / ML intermediate measurements to the location server 220 (arrow 1130). The location server 220 may operate upon the AI / ML intermediate measurements to determine location information and may convey the location information to the external client 230 (arrow 1135).

[0109] In another example scenario, the assisted AI / ML operations 405 may fail and the UE 205 may be unable to determine location information. In this scenario, the UE 205 generates an error cause report in accordance with the disclosure. The error cause report may be conveyed to various entities in various ways. In one case, the error cause report may be transmitted to the access node 505, the location server 220 (via the access node 505), and / or another entity such as, for example, a user of the UE 205.

[0110] FIG. 12 illustrates a third example signal flow diagram where an error cause report may be generated by a user equipment upon failure of an assisted AI / ML operation performed by the user equipment. The interactions between the UE 205, the access node 505, and the location server 220 may be carried out using any of various communication formats that support messaging and data transport. The third example signal flow diagram generally reflects the operations described above with reference to FIG. 6.[OHl] More particularly, in this example, the UE 205 performs a location determination procedure by using assisted AI / ML operations 405 (box 1215). In an example scenario, the location determination procedure is initiated in the form of a request transmitted by the location server 220 to the access node 505 (arrow 1205). In response to the request, the access node 505 may transmit a position reference signal (PRS) to the user equipment 205 (arrow 1210). The UE 205 performs a location determination procedure by assisted AI / ML operations 405 (box 1215). In an exampleWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -33- scenario, the location determination procedure is performed successfully, and AI / ML intermediate measurements (intermediate PRS-based location measurements) are transmitted by the UE 205 to the access node 505 (arrow 1220). The access node 505 conveys the AI / ML intermediate measurements to the location server 220 (arrow 1225). The location server 220 may operate upon the AI / ML intermediate measurements to determine location information (box 1230) and may convey the location information to the access node (arrow 1235), which can be an optional action. The access node 505 may forward the location information to the UE 205, which can be another optional action (arrow 1240).

[0112] In another example scenario, the assisted AI / ML operations 405 may fail and the UE 205 may be unable to determine location information. In this scenario, the UE 205 generates an error cause report in accordance with the disclosure. The error cause report may be conveyed to various entities in various ways. In one case, the error cause report may be transmitted to the access node 505, the location server 220 (via the access node 505), and / or another entity such as, for example, a user of the UE 205.

[0113] FIG. 13 illustrates a fourth example signal flow diagram where an error cause report may be generated by a location server upon failure of a direct AI / ML operation performed by the location server. The interactions between the UE 205, the access node 505, and the location server 220 may be carried out using any of various communication formats that support messaging and data transport. The fourth example signal flow diagram generally reflects the operations described above with reference to FIG. 7.

[0114] More particularly, in this example, the location server 220 performs a location determination procedure by using direct AI / ML operations 305 (box 1330). In an example scenario, the location determination procedure is initiated in the form of a request transmitted by the location server 220 to the access node 505 (arrow 1305). In response to the request, the access node 505 may transmit a position reference signal (PRS) to the user equipment 205 (arrow 1310). The UE 205 performs a location determination procedure based on a procedure that does not involve AI / ML. (box 1315). In an example scenario, the location determination procedure is performed successfully, and PRS-based location measurements are transmitted by the UE 205 to the access node 505 (arrow 1320). The access node 505 conveys the PRS-based location measurementsWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -34- to the location server 220 (arrow 1325). The location server 220 may operate upon the PRS-based location measurements by using direct AI / ML operations 305 to determine location information (box 1330). The location server 220 may, in an optional implementation, convey the location information to the access node (arrow 1335) and the access node 505 may forward the location information to the UE 205 (arrow 1340).

[0115] In another example scenario, the direct AI / ML operations 305 may fail and the location server 220 may be unable to determine location information. In this scenario, the location server 220 generates an error cause report in accordance with the disclosure. The error cause report may be conveyed to various entities in various ways.

[0116] FIG. 14 illustrates a fifth example signal flow diagram where an error cause report may be generated by an access node upon failure of an assisted AI / ML operation performed by the access node. The interactions between the UE 205, the access node 505, and the location server 220 may be carried out using any of various communication formats that support messaging and data transport. The fifth example signal flow diagram generally reflects the operations described above with reference to FIG. 8.

[0117] More particularly, in this example, the access node 505 may receive a Sounding Reference Signal (SRS) from the UE 205 (arrow 1405) and perform a location determination procedure by using assisted AI / ML operations 405 (box 1410). In an example scenario, the location determination procedure is performed successfully, and AI / ML intermediate measurements (intermediate SRS-based location measurements) are transmitted by the access node 505 to the location server 220 (arrow 1415). The location server 220 may use the intermediate SRS-based location measurements to perform additional operations to determine location information of the UE 205 such as, for example, by using non-AI / ML operations (or additional AI / ML operations).

[0118] In another example scenario, the assisted AI / ML operations 405 may fail and the UE 205 may be unable to determine location information. In this scenario, the UE 205 generates an error cause report in accordance with the disclosure. The error cause report may be conveyed to various entities in various ways. In one case, the error cause report may be transmitted to the access node 505, the location server 220 (via the access node 505), and / or another entity such as, for example, a user of the UE 205.

[0119] FIG. 15 illustrates a sixth example signal flow diagram where an error causeWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -35- report may be generated by a location server upon failure of a direct AI / ML operation performed by the location server. The interactions between the UE 205, the access node 505, and the location server 220 may be carried out using any of various communication formats that support messaging and data transport. The fifth example signal flow diagram generally reflects the operations described above with reference to FIG. 9.

[0120] More particularly, in this example, the access node 505 may receive a Sounding Reference Signal (SRS) from the UE 205 (Arrow 1505) and perform a location determination procedure by using non- AI / ML operations box 1510). In an example scenario, the location determination procedure is performed successfully, and SRS-based location measurements are transmitted by the access node 505 to the location server 220 (arrow 1520). The location server 220 may operate upon the SRS- based location measurements by using direct AI / ML operations 305 to determine location information (box 1525). In an example scenario, the assisted AI / ML operations 405 may fail and the location server 220 may be unable to determine location information. In this scenario, the location server 220 generates an error cause report in accordance with the disclosure.

[0121] FIG. 16 shows a flowchart of an example method to generate an error cause report for an AI / ML operation in accordance with the disclosure. Means for performing the functionality illustrated in one or more of the blocks of the flowchart 1600 may be performed by hardware and / or software components of a UE such as described herein with reference to the UE 205 or a network entity that is a part of a cellular network (for example, the base station 120 or the LMF 220).

[0122] At block 1605, the functionality can include detecting, by a first device, at least one error in an AI / ML operation performed upon one or more wireless signals as a part of a position determination procedure. Some aspects pertaining to this functionality are described above with reference to devices such as, for example, the mobile device 105, the UE 205, the location server 160, and the LMF 220. Additional aspects are described above with reference to FIG. 5 through FIG. 15. In an example implementation, the wireless signals are cellular signals that may be acquired by a UE or provided to the UE by an access node (AN). The AI / ML operation can either be a direct AI / ML operation or an assisted AI / ML operation.

[0123] At block 1610, the functionality can include identifying, by the first device,WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -36- at least one error cause for the at least one error. In an example implementation, this functionality may include identifying a first error cause in a data collection operation, identifying a second error cause in an inference operation, or identifying a third error cause in a training operation. Examples of various types of error causes are described below with reference to FIG. 17 through FIG. 19.

[0124] At block 1615, the functionality can include generating, by the first device, the error cause report that provides an indication of the at least one error cause for the at least one error in the AI / ML operation. Some aspects pertaining to this functionality are described above with reference to FIG. 5 through FIG. 15.

[0125] Some other example functions that may be performed in addition to the ones described above with reference to block 1605, block 1610, and block 1615, can include modifying the AI / ML operation based on a solution obtained by evaluating the error cause report and performing the position determination procedure based on the modified AI / ML operation.

[0126] Furthermore, in an example implementation where the first device is a UE, the method described above with reference to block 1605, block 1610, and block 1615 can further include receiving priority information applicable to a plurality of error causes, determining a priority of one or more error causes based on the priority information and including at least one error cause in the error cause report based on determining the priority of the error cause. The error cause report may be transmitted by the UE to an AN. In some cases, at least a subset of error causes included in the plurality of error causes is arranged in a hierarchical structure that provides error details based on a hierarchy scheme.

[0127] FIG. 17 shows a list 1700 of example error causes related to data collection that may be applicable to AI / ML based positioning procedures, in accordance with the disclosure. Error causes, which are not necessarily limited to data collection, can be attributable to various factors. For example, some error causes may be attributable to network conditions and / or other conditions that can cause a discrepancy between training and interference in AI / ML based positioning procedures. Some of such network conditions may be related to timing issues, for example, a synchronization error, relative time differences, and timing errors. Some other example error causes can include changes in received power in a device, changes in transmitted power from aWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -37- device, changes in a device location, changes in an orientation of a device, changes in transmitted signals (beam characteristics), changes in mapping between resources and identification parameters, changes in mapping between resources and device location, changes in mapping of a beam angle, and changes to TRP LOS / NLOS state.

[0128] The list 1700 includes an error cause 1705 due to ground truth labels being unavailable for performing an AI / ML based positioning procedure. An error cause 1710 pertains to ground truth labels that are supported but currently unavailable. An error cause 1715 pertains to a quality indication for one or more ground truth labels being unavailable. An error cause 1720 pertains to lack of support for one or more ground truth labels. An error cause 1725 pertains to lack of availability of one or more ground truth labels. An error cause 1730 pertains to lack of availability of requested training data. An error cause 1735 pertains to lack of support for requested assistance signaling data that may be used for generating training data. An error cause 1740 pertains to lack of availability of requested assistance signaling data that may be used for generating training data. An error cause 1745 pertains to lack of support for requested assistance signaling that may be used for generating ground truth labels. An error cause 1750 pertains to lack of availability of requested assistance signaling that may be used for generating ground truth labels. An error cause 1755 pertains to data collection with consistent network conditions not being feasible. An error cause 1760 pertains to an inability to determine network conditions in data collection. An error cause 1765 pertains to an insufficient amount of data collected for model training. An error cause 1770 pertains to an insufficient amount of requested data being collected. An error cause 1775 pertains to a reference signal configuration change / request for data collection being supported but unavailable currently. An error cause 1780 pertains to a reference signal configuration change / request for data collection not being supported.

[0129] FIG. 18 shows a list 1800 of example error causes relates to model input during inference or training. An error cause 1805 pertains to infeasibility of training with consistent network conditions. An error cause 1810 pertains to an inability to determine network conditions associated with training data. An error cause 1815 pertains to an inability to perform inference with consistent network conditions. An error cause 1820 pertains to an attempt at model inference or training having failed. An error cause 1825 pertains to insufficient assistance data being available for performing model inference or training. An error cause 1830 pertains to insufficient amount ofWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -38- signals being available for performing model inference or training. An error cause 1835 pertains to insufficient processing capacity for performing model inference or training. An error cause 1840 pertains to insufficient amount of memory being available for performing model inference or training. An error cause 1845 pertains to AI / ML fallback conditions being satisfied. An error cause 1850 pertains to a failure of a model inference or training attempt due to being outside a validity area. An error cause 1855 pertains to a failure of a model inference or training attempt due to timer validity expiry. An error cause 1860 pertains to an infeasibility to accommodate all requested model inferences or training.

[0130] FIG. 19 shows a list 1900 of example error causes relates to model output during inference or training. An error cause 1905 pertains to a failure of a model inference attempt or a training attempt having failed due to an invalid model being used. An error cause 1910 pertains to a failure of a model inference attempt or a training attempt having failed due to an inactive model being used. An error cause 1915 pertains to a failure of a model inference to satisfy minimum threshold for monitoring metrics. An error cause 1920 pertains to a failure of a model output monitoring attempt. An error cause 1925 pertains to a failure of a model output monitoring attempt due to lack of adequate monitoring occasions. An error cause 1930 pertains to a failure of a model output monitoring attempt due to lack of adequate signals / TRPs / measurements within monitoring occasions. An error cause 1935 pertains to a failure of a model output monitoring attempt due to lack of ground truth labels.

[0131] Some or all of the example error causes described above with reference to FIGs. 17-19 can be included in an error cause report that can be generated in accordance with the disclosure upon failure of an AI / ML based positioning procedure. Various aspects related to performing AI / ML based positioning procedures and generation of error cause reports have been described above with reference to other figures.

[0132] In an example embodiment, an error cause report can include a prioritized list of error causes. A priority of each of the error causes can be specified in any of various ways. In an example implementation, an error cause report may include a first error cause related to data collection such as, for example, error cause 1750 described above (lack of availability of requested assistance signaling that may be used for generating ground truth labels) and a second error cause related to model input duringWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -39- inference / training, such as, for example, error cause 1810 described above (inability to determine network conditions associated with training data).

[0133] In one case, error cause 1750 may be assigned a higher priority level in comparison to error cause 1810. In this case, the error cause 1750 can be included in all, or most, error cause reports and the error cause 1810 may be omitted in some or all of the error cause reports. In another case, error cause 1810 may be assigned a higher priority level in comparison to error cause 1750. In this case, the error cause 1810 can be included in all, or most, error cause reports and the error cause 1750 may be omitted in some or all of the error cause reports.

[0134] In another example embodiment, an error cause report can include a collective representation of a set of error causes such as, for example, a bitmap comprising a set of bits, where each bit indicates a presence or an absence of an error cause. Thus, for example, a bitmap 001111 may indicate an absence of a first error case (for example, due to an undefined error), an absence of a second error case (for example, due to missing downlink assistance data), presence of a third error cause (for example, an inability to measure TRP), presence of a fourth error case (for example, due to an inability to measure TRP in spite of an attempt being made), presence of a fifth error case (for example, due to uplink SRS missing configuration), and presence of a sixth error case (for example, due to an inability to transmit an uplink SRS).

[0135] The scenario described above with reference to the example bitmap 001111 may be applicable, for example, to a multi-cell RTT position procedure in which multiple error causes may be present. A traditional approach wherein an enumerated list of error causes is used to provide error cause reports, limits each error cause report to be limited to one error cause among the multiple error causes.

[0136] In another example embodiment, an error cause report can include a hierarchical representation of error causes. For example, one or more error causes in a set of error causes can include a subset of error causes.

[0137] In another example embodiment, an error cause report can include a numeric or alpha-numeric representation of an error cause. Thus, for example, an error cause report may include a list of numeric or alpha-numeric labels that represent various error causes.WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -40-

[0138] FIG. 20 illustrates an embodiment of the UE 205 such as, for example, the mobile device 105, which can be utilized as described herein with reference to FIGS. 1- 16 for example. It should be noted that FIG. 20 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. 20 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. 20.

[0139] The mobile device 105 is shown comprising hardware elements that can be electrically coupled via a bus 2005 (or may otherwise be in communication, as appropriate). The hardware elements may include a processing unit(s) 2010 which can include without limitation one or more general-purpose processors, one or more specialpurpose processors (such as DSP chips, graphics acceleration processors, application specific integrated circuits (ASICs), and / or the like), and / or other processing structures or means. As shown in FIG. 20, some embodiments may have a separate DSP 2020, depending on desired functionality. Location determination authentication, spoofing detection, and / or other operations that may be based on wireless communication can be provided in the processing unit(s) 2010 and / or wireless communication interface 2030 (discussed below). The mobile device 105 can also include one or more input devices 2070, 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 2015, which can include without limitation one or more displays (e.g., touch screens), light emitting diodes (LEDs), speakers, and / or the like.

[0140] The mobile device 105 may also include a wireless communication interface 2030, which may comprise without limitation a modem, a network card, an infrared communication device, a wireless communication device, and / or a chipset (such as a Bluetooth® device, an IEEE 802.11 device, an IEEE 802.15.4 device, a Wi-Fi device, a WiMAX device, a WAN device, and / or various cellular devices, etc.), and / or the like, which may enable the mobile device 105 to communicate with other devices such as, for example, the base stations 120 described in the embodiments above. The wireless communication interface 2030 may permit data and signaling to be communicated (e.g.,WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -41- 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) 2032 that send and / or receive wireless signals 2034. According to some embodiments, the wireless communication antenna(s) 2032 may comprise a plurality of discrete antennas, antenna arrays, or a combination thereof.

[0141] Depending on desired functionality, the wireless communication interface 2030 may comprise a separate receiver and transmitter, or a combination of transceivers, transmitters, and / or receivers to communicate with base stations (e.g., ng- eNBs and gNBs) and other terrestrial transceivers, such as wireless devices and access points. The mobile device 105 may communicate with different data networks that may comprise various network types. For example, a Wireless Wide Area Network (WWAN) may be a CDMA network, a Time Division Multiple Access (TDMA) network, a Frequency Division Multiple Access (FDMA) network, an Orthogonal Frequency Division Multiple Access (OFDMA) network, a Single-Carrier Frequency Division Multiple Access (SC-FDMA) network, a WiMAX (IEEE 802.16) network, and so on. A CDMA network may implement one or more RATs such as CDMA2000, WCDMA, and so on. CDMA2000 includes IS-95, IS-2000 and / or IS-856 standards. A TDMA network may implement GSM, Digital Advanced Mobile Phone System (D- AMPS), or some other RAT. An OFDMA network may employ LTE, LTE Advanced, 5G NR, and so on. 5G NR, LTE, LTE Advanced, GSM, and WCDMA are described in documents from 3GPP. Cdma2000 is described in documents from a consortium named “3rd Generation Partnership Project 3” (3GPP2). 3GPP and 3GPP2 documents are publicly available. A WLAN may also be an IEEE 802.1 lx network, and a wireless personal area network (WPAN) may be a Bluetooth network, an IEEE 802.15x, or some other type of network. The techniques described herein may also be used for a combination of WWAN, WLAN and / or WPAN.

[0142] The mobile device 105 can further include sensor(s) 2040. Sensors 2040 may comprise, without limitation, one or more inertial sensors and / or other sensors (e.g., accelerometer(s), gyroscope(s), camera(s), magnetometer(s), altimeter(s), microphone(s), proximity sensor(s), light sensor(s), barometer(s), and the like), some ofWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -42- which may be used to authenticate location determination and to detect position spoofing, for example.

[0143] Embodiments of the mobile device 105 may also include a GlobalNavigation Satellite System (GNSS) receiver 2080 capable of receiving signals 2084 from one or more GNSS satellites using an antenna 2082 (which could be the same as antenna 2032). Positioning based on GNSS signal measurement can be utilized to complement and / or incorporate the techniques described herein. The GNSS receiver 2080 can extract a position of the mobile device 105, using conventional techniques, from GNSS satellites 110 of a GNSS system, such as Global Positioning System (GPS), Galileo, GLONASS, Quasi-Zenith Satellite System (QZSS) over Japan, Indian Regional Navigational Satellite System (IRNSS) over India, BeiDou Navigation Satellite System (BDS) over China, and / or the like. Moreover, the GNSS receiver 2080 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), Multifunctional Satellite Augmentation System (MSAS), and Geo Augmented Navigation system (GAGAN), and / or the like.

[0144] It can be noted that, although GNSS receiver 2080 is illustrated in FIG. 20 as a distinct component, embodiments are not so limited. As used herein, the term “GNSS receiver” may comprise hardware and / or software components configured to obtain GNSS measurements (measurements from GNSS satellites). In some embodiments, therefore, the GNSS receiver may comprise a measurement engine executed (as software) by one or more processing units, such as processing unit(s) 2010, DSP 2020, and / or a processing unit within the wireless communication interface 2030 (e.g., in a modem). A GNSS receiver may optionally also include a positioning engine, which can use GNSS measurements from the measurement engine to determine a position of the GNSS receiver using an Extended Kalman Filter (EKF), Weighted Least Squares (WLS), a hatch filter, particle filter, or the like. The positioning engine may also be executed by one or more processing units, such as processing unit(s) 2010 or DSP 2020.

[0145] The mobile device 105 may further include and / or be in communication with a memory 2060. The memory 2060 can include, without limitation, local and / orWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -43- 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.

[0146] The memory 2060 of the mobile device 105 also can comprise software elements (not shown in FIG. 20), 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 2060 that are executable by the mobile device 105 (and / or processing unit(s) 2010 or DSP 2020 within mobile device 105). 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.

[0147] FIG. 21 illustrates an embodiment of an access node 2100. As described above with reference to FIG. 2, an 5GNR positioning / sensing system may be configured to determine the location of a UE, such as, for example, the mobile device 105, by using access nodes, which may include NR NodeB (gNB) 210-1 and 210-2 (collectively and generically referred to herein as gNBs 210), ng-eNB 214, and / or WLAN 216 to implement one or more positioning methods. The gNBs 210 and / or the ng-eNB 214 may correspond with base stations 120 of FIG. 1, and the WLAN 216 may correspond with one or more access points 130 of FIG. 1.

[0148] The access node 2100 can be utilized in various ways as described above (e.g., in association with FIGS. 1-16). It should be noted that FIG. 21 is meant only to provide a generalized illustration of various components, any or all of which may be utilized as appropriate. The access node 2100 is shown comprising hardware elements that can be electrically coupled via a bus 2105 (or may otherwise be in communication, as appropriate). The hardware elements may include a processing unit(s) 2110 which can include without limitation one or more general-purpose processors, one or moreWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -44- 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. 21, some embodiments may have a separate DSP 2120, depending on desired functionality. Operations such as authenticating location determination and detecting position spoofing, for example, that may be based on wireless communication can be provided in the processing unit(s) 2110 and / or wireless communication interface 2130 according to some embodiments.

[0149] The wireless communication interface 2130 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 2100 to communicate as described herein. The wireless communication interface 2130 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) 2132 that send and / or receive wireless signals 2134.

[0150] The access node 2100 may also include a network interface 2180, which can include support of wireline communication technologies. The network interface 2180 may include a modem, network card, chipset, and / or the like. The network interface 2180 may include one or more input and / or output communication interfaces to permit data to be exchanged with a network, communication network servers, computer systems, and / or any other electronic devices described herein.

[0151] In many embodiments, the access node 2100 may further comprise a memory 2160. The memory 2160 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.WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -45-

[0152] The memory 2160 of the access node 2100 also may comprise software elements (not shown in FIG. 21), 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 2160 that are executable by the access node 2100 (and / or processing unit(s) 2110 or DSP 2120 within access node 2100). 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.

[0153] FIG. 22 is a block diagram of an embodiment of a computer system 2200, which may be used, in whole or in part, to provide the functions of one or more components and / or devices as described in the embodiments herein. The computer system 2200, for example, may be utilized within and / or executed by a server (e.g., location server 220). It should be noted that FIG. 22 is meant only to provide a generalized illustration of various components, any or all of which may be utilized as appropriate. FIG. 22, therefore, broadly illustrates how individual system elements may be implemented in a relatively separated or relatively more integrated manner. In addition, it can be noted that components illustrated by FIG. 22 can be localized to a single device and / or distributed among various networked devices, which may be disposed at different geographical locations.

[0154] The computer system 2200 is shown comprising hardware elements that can be electrically coupled via a bus 2205 (or may otherwise be in communication, as appropriate). The hardware elements may include processor(s) 2210, which may comprise without limitation one or more general-purpose processors, one or more special-purpose processors (such as digital signal processing chips, graphics acceleration processors, and / or the like), and / or other processing structure, which can be configured to perform one or more of the methods described herein. The computer system 2200 also may comprise one or more input devices 2220, which may comprise without limitation a mouse, a keyboard, a camera, a microphone, and / or the like; and one or more output devices 2225, which may comprise without limitation a displayWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -46- device, a printer, and / or the like.

[0155] The computer system 2200 may further include (and / or be in communication with) one or more non-transitory storage devices 2215, which can comprise, without limitation, local and / or network accessible storage, and / or may comprise, without limitation, a disk drive, a drive array, an optical storage device, a solid-state storage device, such as a random-access memory (RAM) and / or read-only memory (ROM), which can be programmable, flash-updateable, and / or the like. Such storage devices may be configured to implement any appropriate data stores, including without limitation, various file systems, database structures, and / or the like. Such data stores may include database(s) and / or other data structures used store and administer messages and / or other information to be sent to one or more devices via hubs, as described herein.

[0156] The computer system 2200 may also include a communications subsystem 2230, which may comprise wireless communication technologies managed and controlled by a wireless communication interface 2235, as well as wired technologies (such as Ethernet, coaxial communications, universal serial bus (USB), and the like). The wireless communication interface 2235 may comprise one or more wireless transceivers that may send and receive wireless signals 2237 (e.g., signals according to 5G NR or LTE) via wireless antenna(s) 2236. Thus the communications subsystem 2230 may comprise a modem, a network card (wireless or wired), an infrared communication device, a wireless communication device, and / or a chipset, and / or the like, which may enable the computer system 2200 to communicate on any or all of the communication networks described herein to any device on the respective network, including UE, base stations and / or other transmission reception points (TRPs), satellites, and / or any other electronic devices described herein. Hence, the communications subsystem 2230 may be used to receive and send data as described in the embodiments herein.

[0157] In many embodiments, the computer system 2200 will further comprise a working memory 2240, which may comprise a RAM or ROM device, as described above. Software elements, shown as being located within the working memory 2240, may comprise an operating system 2245, device drivers, executable libraries, and / or other code, such as one or more applications 2250, which may comprise computer programs provided by various embodiments, and / or may be designed to implementWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -47- 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.

[0158] 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) 2215 described above. In some cases, the storage medium might be incorporated within a computer system, such as computer system 2200. 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 2200 and / or might take the form of source and / or installable code, which, upon compilation and / or installation on the computer system 2200 (e.g., using any of a variety of generally available compilers, installation programs, compression / decompression utilities, etc.), then takes the form of executable code.

[0159] 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.

[0160] 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 toWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -48- 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.

[0161] 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.

[0162] It has proven convenient at times, principally for reasons of common usage, to refer to such signals as bits, information, values, elements, symbols, characters, variables, terms, numbers, numerals, or the like. It should be understood, however, that all of these or similar terms are to be associated with appropriate physical quantities and are merely convenient labels. Unless specifically stated otherwise, as is apparent from the discussion above, it is appreciated that throughout this Specification discussion utilizing terms such as “processing,” “computing,” “calculating,” “determining,” “ascertaining,” “identifying,” “associating,” “measuring,” “performing,” or the like refer to actions or processes of a specific apparatus, such as a special purpose computer or a similar special purpose electronic computing device. In the context of this Specification, therefore, a special purpose computer or a similar special purpose electronic computing device is capable of manipulating or transforming signals, typically represented as physical electronic, electrical, or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the special purpose computer or similar special purpose electronic computing device.WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -49-

[0163] 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.

[0164] 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.

[0165] In view of this description embodiments may include different combinations of features. Implementation examples are described in the following numbered clauses:

[0166] Clause 1 A method for generating an error cause report associated with positioning, the method comprising detecting, by a first device, at least one error in an artificial intelligence / machine learning (AI / ML) operation performed upon one or more wireless signals as a part of a position determination procedure; identifying, by the first device, at least one error cause for the at least one error; and generating, by the first device, the error cause report that provides an indication of the at least one error cause for the at least one error in the AI / ML operation.

[0167] Clause 2 The method of clause 1, wherein identifying the at least one error cause comprises at least one of identifying a first error cause in a data collection operation, identifying a second error cause in an inference operation, or identifying a third error cause in a training operation.WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -50-

[0168] Clause 3 The method of clause 2, wherein identifying the first error cause in the data collection operation comprises at least one of identifying an unavailability of a ground truth label, identifying a change in a network condition, identifying a first factor that prevented performing of a training operation, or identifying a second factor that prevented performing of an inference operation.

[0169] Clause 4 The method of clause 2, wherein identifying the second error cause in the inference operation comprises identifying a lack of a consistent operating condition in a cellular network for performing an inference operation.

[0170] Clause 5 The method of clause 2, wherein identifying the third error cause in the training operation comprises identifying a lack of a consistent operating condition in a cellular network for performing a training operation.

[0171] Clause 6 The method of clause 1, wherein the first device is one of a user equipment (UE) that is communicatively coupled to a cellular network or a first network entity that is a part of the cellular network, the method further comprising modifying the AI / ML operation based on a solution obtained by evaluating the error cause report; and performing the position determination procedure based on the modified AI / ML operation.

[0172] Clause 7 The method of clause 1, wherein the first device is a user equipment (UE) communicatively coupled to a cellular network, and wherein the AI / ML operation is one of a direct AI / ML operation that provides location information of the UE based on evaluating the one or more wireless signals or an assisted AI / ML operation that provides location information of the UE based on refining the one or more wireless signals.

[0173] Clause 8 The method of clause 1, wherein the first device is a user equipment (UE) that is communicatively coupled to an access node (AN) of a cellular network, wherein the detecting of the at least one error in the AI / ML operation is performed by the UE, and wherein the one or more wireless signals are one of acquired by the UE or provided to the UE by the AN.

[0174] Clause 9 The method of clause 8, further comprising receiving, by the UE, priority information applicable to a plurality of error causes; determining, by the UE, a priority of the at least one error cause based on the priority information; including, byWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -51- the UE, the at least one error cause in the error cause report based on determining the priority of the at least one error cause; and transmitting, by the UE, to the AN, the error cause report.

[0175] Clause 10 The method of clause 9, wherein at least a subset of error causes included in the plurality of error causes is arranged in a hierarchical structure that provides error details based on a hierarchy scheme.

[0176] Clause 11 The method of clause 1, wherein the first device is a user equipment (UE) communicatively coupled to an access node (AN) of a cellular network, wherein the detecting of the at least one error in the AI / ML operation is performed by the AN, and wherein the AI / ML operation is an assisted AI / ML operation that provides location information of the UE based on refining the one or more wireless signals provided to the AN by the UE.

[0177] Clause 12 The method of clause 1, wherein the first device is a location server, wherein the detecting of the at least one error in the AI / ML operation is performed by the location server, and wherein the AI / ML operation is a direct AI / ML operation that provides location information of a user equipment (UE) based on evaluating the one or more wireless signals provided to the location server by the UE.

[0178] Clause 13 The method of clause 1, wherein the first device is a location server, wherein the detecting of the at least one error in the AI / ML operation is performed by the location server, and wherein the AI / ML operation is a direct AI / ML operation that provides location information of a user equipment (UE) based on evaluating the one or more wireless signals provided to the location server by one of the UE or an access node (AN).

[0179] Clause 14 A method performed by a first device for position determination, the method comprising receiving a reference signal; using the reference signal to perform a position determination procedure comprising the use of an artificial intelligence / machine learning (AI / ML) operation upon one or more wireless signals; detecting at least one error in the AI / ML operation; identifying at least one error cause for the at least one error; and generating an error cause report that provides an indication of the at least one error cause for the at least one error in the AI / ML operation.WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -52-

[0180] Clause 15 The method of clause 14, wherein identifying the at least one error cause comprises at least one of identifying a first error cause in a data collection operation, identifying a second error cause in an inference operation, or identifying a third error cause in a training operation.

[0181] Clause 16 The method of clause 14, wherein the first device is one of a user equipment (UE) that is communicatively coupled to a cellular network or a first network entity that is a part of the cellular network, the method further comprising modifying the AI / ML operation based on a solution obtained by evaluating the error cause report; and performing the position determination procedure based on the modified AI / ML operation.

[0182] Clause 17 The method of clause 14, wherein the first device is a user equipment (UE) that is communicatively coupled to an access node (AN) of a cellular network, and wherein the reference signal is a positioning reference signal (PRS) received by the UE from the AN.

[0183] Clause 18 The method of clause 17, wherein the AI / ML operation is one of a direct AI / ML operation that provides location information of the UE based on evaluating the one or more wireless signals, or an assisted AI / ML operation that provides intermediate location information of the UE based on refining the one or more wireless signals.

[0184] Clause 19 The method of clause 18, further comprising receiving priority information applicable to a plurality of error causes; determining a priority of the at least one error cause based on the priority information; including the at least one error cause in the error cause report based on determining the priority of the at least one error cause; and transmitting to the AN, the error cause report.

[0185] Clause 20 The method of clause 14, wherein the first device is an access node (AN) of a cellular network, and wherein the reference signal is a sounding reference signal (PRS) received by the AN from a user equipment (UE).

[0186] Clause 21 The method of clause 20, wherein the AI / ML operation is an assisted AI / ML operation that provides intermediate location information of the UE based on refining the one or more wireless signals.WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -53-

[0187] Clause 22 The method of clause 21, further comprising transmitting, to a location server, the intermediate location information of the UE.

[0188] Clause 23 The method of clause 22, further comprising determining, by the location server, the location of the UE based on the intermediate location information received from the AN.

[0189] Clause 24 A method performed by a first device for position determination, the method comprising performing a position determination procedure comprising use of an artificial intelligence / machine learning (AI / ML) operation upon one or more wireless signals; detecting at least one error in the AI / ML operation; identifying at least one error cause for the at least one error; generating an error cause report that provides an indication of the at least one error cause for the at least one error in the AI / ML operation; and transmitting the error cause report to a second device.

[0190] Clause 25 The method of clause 24, wherein identifying the at least one error cause comprises at least one of identifying a first error cause in a data collection operation, identifying a second error cause in an inference operation, or identifying a third error cause in a training operation.

[0191] Clause 26 The method of clause 24, wherein the first device is one of a user equipment (UE) that is communicatively coupled to a cellular network or a first network entity that is a part of the cellular network, the method further comprising modifying the AI / ML operation based on a solution obtained by evaluating the error cause report; and performing the position determination procedure based on the modified AI / ML operation.

[0192] Clause 27 The method of clause 24, wherein the first device is a user equipment (UE) that is communicatively coupled to a cellular network, and wherein the second device is an access node (AN) that is part of the cellular network.

[0193] Clause 28 The method of clause 27, further comprising receiving, from the AN, a positioning reference signal (PRS); and using the PRS to perform the position determination procedure comprising the use of the AI / ML operation.

[0194] Clause 29 The method of clause 28, wherein the AI / ML operation is one of a direct AI / ML operation or an assisted AI / ML operation.WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -54-

[0195] Clause 30 The method of clause 24, wherein the first device is an access node (AN) that is a part of a cellular network, and wherein the second device is a user equipment (UE) that is communicatively coupled to the cellular network.

[0196] Clause 31 The method of clause 30, further comprising receiving, from the UE, a sounding reference signal (SRS); and using the SRS to perform the position determination procedure comprising the use of the AI / ML operation.

[0197] Clause 32 The method of clause 31, wherein the AI / ML operation is an assisted AI / ML operation.

[0198] Clause 33 An apparatus for generating an error cause report associated with positioning, the apparatus comprising means for detecting at least one error in an artificial intelligence / machine learning (AI / ML) operation performed upon one or more wireless signals as a part of a position determination procedure; means for identifying at least one error cause for the at least one error; and means for generating the error cause report that provides an indication of the at least one error cause for the at least one error in the AI / ML operation.WAVS Ref. No. QLCM464WO

Claims

1. Qualcomm Ref. No. 2403938WO -55-CLAIMSWhat is claimed is:

1. A method for generating an error cause report associated with positioning, the method comprising: detecting, by a first device, at least one error in an artificial intelligence / machine learning (AI / ML) operation performed upon one or more wireless signals as a part of a position determination procedure; identifying, by the first device, at least one error cause for the at least one error; and generating, by the first device, the error cause report that provides an indication of the at least one error cause for the at least one error in the AI / ML operation.

2. The method of claim 1, wherein identifying the at least one error cause comprises at least one of identifying a first error cause in a data collection operation, identifying a second error cause in an inference operation, or identifying a third error cause in a training operation.

3. The method of claim 1, wherein the first device is one of a user equipment (UE) that is communicatively coupled to a cellular network or a first network entity that is a part of the cellular network, the method further comprising: modifying the AI / ML operation based on a solution obtained by evaluating the error cause report; and performing the position determination procedure based on the modified AI / ML operation.

4. The method of claim 1, wherein the first device is a user equipment (UE) communicatively coupled to a cellular network, and wherein the AI / ML operation is one of a direct AI / ML operation that provides location information of the UE based on evaluating the one or more wireless signals or an assisted AI / ML operation that provides location information of the UE based on refining the one or more wireless signals.

5. The method of claim 1, wherein the first device is a user equipment (UE) that isWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -56- communicatively coupled to an access node (AN) of a cellular network, wherein the detecting of the at least one error in the AI / ML operation is performed by the UE, and wherein the one or more wireless signals are one of acquired by the UE or provided to the UE by the AN.

6. The method of claim 5, further comprising: receiving, by the UE, priority information applicable to a plurality of error causes; determining, by the UE, a priority of the at least one error cause based on the priority information; including, by the UE, the at least one error cause in the error cause report based on determining the priority of the at least one error cause; and transmitting, by the UE, to the AN, the error cause report.

7. The method of claim 6, wherein at least a subset of error causes included in the plurality of error causes is arranged in a hierarchical structure that provides error details based on a hierarchy scheme.

8. The method of claim 1, wherein the first device is a user equipment (UE) communicatively coupled to an access node (AN) of a cellular network, wherein the detecting of the at least one error in the AI / ML operation is performed by the AN, and wherein the AI / ML operation is an assisted AI / ML operation that provides location information of the UE based on refining the one or more wireless signals provided to the AN by the UE.

9. The method of claim 1, wherein the first device is a location server, wherein the detecting of the at least one error in the AI / ML operation is performed by the location server, and wherein the AI / ML operation is a direct AI / ML operation that provides location information of a user equipment (UE) based on evaluating the one or more wireless signals provided to the location server by the UE.

10. The method of claim 1, wherein the first device is a location server, wherein the detecting of the at least one error in the AI / ML operation is performed by the location server, and wherein the AI / ML operation is a direct AI / ML operation that provides location information of a user equipment (UE) based on evaluating the one or more wireless signals provided to the location server by one of the UEWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -57- or an access node (AN).

11. A method performed by a first device for position determination, the method comprising: receiving a reference signal; using the reference signal to perform a position determination procedure comprising the use of an artificial intelligence / machine learning (AI / ML) operation upon one or more wireless signals; detecting at least one error in the AI / ML operation; identifying at least one error cause for the at least one error; and generating an error cause report that provides an indication of the at least one error cause for the at least one error in the AI / ML operation.

12. The method of claim 11, wherein identifying the at least one error cause comprises at least one of identifying a first error cause in a data collection operation, identifying a second error cause in an inference operation, or identifying a third error cause in a training operation.

13. The method of claim 11, wherein the first device is one of a user equipment (UE) that is communicatively coupled to a cellular network or a first network entity that is a part of the cellular network, the method further comprising: modifying the AI / ML operation based on a solution obtained by evaluating the error cause report; and performing the position determination procedure based on the modified AI / ML operation.

14. The method of claim 11, wherein the first device is a user equipment (UE) that is communicatively coupled to an access node (AN) of a cellular network, and wherein the reference signal is a positioning reference signal (PRS) received by the UE from the AN.

15. The method of claim 14, wherein the AI / ML operation is one of a direct AI / ML operation that provides location information of the UE based on evaluating the one or more wireless signals, or an assisted AI / ML operation that provides intermediate location information of the UE based on refining the one or more wireless signals.WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -58-16. The method of claim 15, further comprising: receiving priority information applicable to a plurality of error causes; determining a priority of the at least one error cause based on the priority information; including the at least one error cause in the error cause report based on determining the priority of the at least one error cause; and transmitting, to the AN, the error cause report.

17. The method of claim 11, wherein the first device is an access node (AN) of a cellular network, and wherein the reference signal is a sounding reference signal (PRS) received by the AN from a user equipment (UE).

18. The method of claim 17, wherein the AI / ML operation is an assisted AI / ML operation that provides intermediate location information of the UE based on refining the one or more wireless signals.

19. The method of claim 18, further comprising: transmitting, to a location server, the intermediate location information of the UE.

20. The method of claim 19, further comprising: determining, by the location server, the location of the UE based on the intermediate location information received from the AN.

21. A method performed by a first device for position determination, the method comprising: performing a position determination procedure comprising use of an artificial intelligence / machine learning (AI / ML) operation upon one or more wireless signals; detecting at least one error in the AI / ML operation; identifying at least one error cause for the at least one error; generating an error cause report that provides an indication of the at least one error cause for the at least one error in the AI / ML operation; and transmitting the error cause report to a second device.

22. The method of claim 21, wherein identifying the at least one error causeWAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -59- comprises at least one of identifying a first error cause in a data collection operation, identifying a second error cause in an inference operation, or identifying a third error cause in a training operation.

23. The method of claim 21, wherein the first device is one of a user equipment (UE) that is communicatively coupled to a cellular network or a first network entity that is a part of the cellular network, the method further comprising: modifying the AI / ML operation based on a solution obtained by evaluating the error cause report; and performing the position determination procedure based on the modified AI / ML operation.

24. The method of claim 21, wherein the first device is a user equipment (UE) that is communicatively coupled to a cellular network, and wherein the second device is an access node (AN) that is part of the cellular network.

25. The method of claim 24, further comprising: receiving, from the AN, a positioning reference signal (PRS); and using the PRS to perform the position determination procedure comprising the use of the AI / ML operation.

26. The method of claim 25, wherein the AI / ML operation is one of a direct AI / ML operation or an assisted AI / ML operation.

27. The method of claim 21, wherein the first device is an access node (AN) that is a part of a cellular network, and wherein the second device is a user equipment (UE) that is communicatively coupled to the cellular network.

28. The method of claim 27, further comprising: receiving, from the UE, a sounding reference signal (SRS); and using the SRS to perform the position determination procedure comprising the use of the AI / ML operation.

29. The method of claim 28, wherein the AI / ML operation is an assisted AI / ML operation.

30. An apparatus for generating an error cause report associated with positioning,WAVS Ref. No. QLCM464WOQualcomm Ref. No. 2403938WO -60- the apparatus comprising: means for detecting at least one error in an artificial intelligence / machine learning (AI / ML) operation performed upon one or more wireless signals as a part of a position determination procedure; means for identifying at least one error cause for the at least one error; and means for generating the error cause report that provides an indication of the at least one error cause for the at least one error in the AI / ML operation.WAVS Ref. No. QLCM464WO

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