Artificial intelligence-based device positioning with platform personalization

Abstract

A system comprises a memory configured to store a trained machine learning (ML) model; a communication unit; and one or more processors of a first network entity, the one or more processors configured to: receive a positioning personalization indicator for a second network entity, the positioning personalization indicator for the second network entity indicating information about the second network entity; obtain measurement data based on at least one measurement of at least one wireless signal; and apply the trained ML model to the measurement data to generate output data, wherein the output data indicates a physical position of a user equipment (UE) device or the output data being input data to a process that determines the physical position of the UE device, wherein the output data for the UE device is dependent on the positioning personalization indicator for the second network entity.

Description

Qualcomm Ref. No. 2402459WO 1ARTIFICIAL INTELLIGENCE-BASED DEVICE POSITIONING WITHPLATFORM PERSONALIZATION

[0001] This application claims the benefit of GR Patent Application No. 20240100698, filed 04 October 2024, the entire content of which is incorporated by reference herein.TECHNICAL FIELD

[0002] This disclosure relates to systems for device positioning.BACKGROUND

[0003] It may be important for some types of devices to be able to determine their physical positions. For example, it is frequently useful for smartphones or wearable devices to be able to accurately determine their current physical locations. Conventional positioning techniques, such as the Global Positioning System (GPS), are typically unable to provide a sufficient level of positional accuracy, especially when the devices are located inside buildings or within complex environments, such as dense urban areas. Triangulation based on direct signal strengths of wireless signals may also be used for determining positions of devices, but the accuracy may be diminished because the wireless signals may follow multiple paths.SUMMARY

[0004] In general, this disclosure describes techniques for artificial intelligence / machine learning (AI / ML)-based device positioning. In an Al-based device positioning process, a trained machine learning (ML) model is applied to measurement data in order to generate position data for a device. The measurement data may represent aspects of wireless signals received by the device. An advantage of such an AI / ML-based device positioning process is that an Al / ML model may learn a function that maps the measurement data to positioning data in a manner that is less susceptible to errors caused by wireless signals following multiple paths. It is further noted that the transmitter / receiver (Tx / Rx) hardware of different devices may have different characteristics. For example, devices from different manufacturers may have antennas with different shapes. These different characteristics may affect the accuracy of an AI / ML-based device positioning process1616-444WO01Qualcomm Ref. No. 2402459WO 2 because the measurement data generated based on wireless signals received by devices with different Tx / Rx hardware at the same position may be different. In other words, the input data provided to the trained ML model may be different for different devices despite the devices being at the same location and receiving the same wireless signals.

[0005] The techniques of this disclosure address this problem. As described herein, a first network entity may receive a positioning personalization indicator for a second network entity, the positioning personalization indicator for the second network entity indicating information about the second network entity. The first network entity may obtain measurement data based on at least one measurement of at least one wireless signal. The first network entity may apply the trained ML model to the measurement data to generate output data, wherein the output data indicates a physical position of a user equipment (UE) device or the output data being input data to a process that determines the physical position of the UE device, wherein the output data for the UE device is dependent on the positioning personalization indicator for the second network entity. By using the positioning personalization indicator as input to the ML model or to select an ML model, the position data generated by the ML model may be specific to a device group of the device. As a result, the position data may be more accurate.

[0006] In one example, this disclosure describes a system comprising: a memory configured to store a trained machine learning (ML) model: a communication unit; and one or more processors of a first network entity, the one or more processors implemented in circuitry and communicatively coupled to the memory7, the one or more processors configured to: receive a positioning personalization indicator for a second network entity, the positioning personalization indicator for the second network entity indicating information about the second network entity; obtain measurement data based on at least one measurement of at least one wireless signal; and apply the trained ML model to the measurement data to generate output data, wherein the output data indicates a physical position of a user equipment (UE) device or the output data being input data to a process that determines the physical position of the UE device, wherein the output data for the UE device is dependent on the positioning personalization indicator for the second network entity.

[0007] In another example, this disclosure describes a method comprising: receiving, by a first network entity, a positioning personalization indicator for a second network entity, the positioning personalization indicator for the second network entity7indicating information about the second network entity7; obtaining, by the first network entity7,1616-444WO01Qualcomm Ref. No. 2402459WO 3 measurement data based on at least one measurement of at least one wireless signal; and applying, by the first network entity, a trained machine learning (ML) model to the measurement data to generate output data, wherein the output data indicates a physical position of a user equipment (UE) device or the output data being input data to a process that determines the physical position of the UE device, wherein the output data for the UE device is dependent on the positioning personalization indicator for the second network entity.

[0008] The details of one or more examples are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description, drawings, and claims.BRIEF DESCRIPTION OF DRAWINGS

[0009] FIG. 1 illustrates an example wireless communications system, according to aspects of the disclosure.

[0010] FIG. 2A illustrates an example wireless network structure.

[0011] FIG. 2B illustrates another example wireless network structure.

[0012] FIG. 2C illustrates an example disaggregated base station architecture, according to aspects of the disclosure.

[0013] FIG. 3A illustrates possible locations of a quasi-model relation (QML) component.

[0014] FIG. 3B illustrates possible locations of a QML component.

[0015] FIG. 3C illustrates possible locations of a QML component.

[0016] FIG. 4 is a diagram illustrating an example frame structure.

[0017] FIG. 5A is a diagram of an example slot structure without feedback resources, according to aspects of the disclosure.

[0018] FIG. 5B is a diagram of an example slot structure with feedback resources, according to aspects of the disclosure.

[0019] FIG. 6A is a diagram illustrating an example of a resource pool for positioning configured within a sidelink resource pool for communication (i.e., a shared resource pool), according to aspects of the disclosure.

[0020] FIGS. 6B and 6C are diagrams illustrating additional examples of resource pools for positioning configured within sidelink resource pools for communication.1616-444WO01Qualcomm Ref. No. 2402459WO 4

[0021] FIG. 6D is a diagram illustrating another example of a resource pool for positioning configured within a sidelink resource pool for communication.

[0022] FIG. 7 illustrates examples of various positioning methods, according to aspects of the disclosure.

[0023] FIG. 8 A illustrates various scenarios of interest for sidelink-only or joint Uu and sidelink positioning.

[0024] FIG. 8B illustrates additional scenarios of interest for sidelink-only or joint Uu and sidelink positioning.

[0025] FIG. 9 illustrates an example neural network, according to aspects of the disclosure.

[0026] FIG. 10 is an illustrative block diagram of an example ML architecture that may be used for wireless communications in any of the various implementations, processes, environments, networks, or other use cases.

[0027] FIG. 11 is an illustrative block diagram of an example ML architecture of a first wireless device in communication with a second wireless device.

[0028] FIG. 12 A, FIG. 12B, and FIG. 12C are block diagrams illustrating example styles of implementing AI / ML-assisted positioning systems.

[0029] FIG. 13A is a block diagram illustrating an example of a direct positioning system in accordance with one or more techniques of this disclosure.

[0030] FIG. 13B is a block diagram illustrating an example of an indirect positioning system in accordance w ith one or more techniques of this disclosure.

[0031] FIG. 14A is a block diagram illustrating a first example system in accordance with one or more techniques of this disclosure.

[0032] FIG. 14B is a block diagram illustrating a second example system in accordance with one or more techniques of this disclosure.

[0033] FIG. 14C is a block diagram illustrating a third example system in accordance with one or more techniques of this disclosure.

[0034] FIG. 14D is a block diagram illustrating a fourth example system in accordance with one or more techniques of this disclosure.

[0035] FIG. 14E is a block diagram illustrating a fifth example system in accordance with one or more techniques of this disclosure.

[0036] FIG. 15 is a flowchart illustrating an example operation of a network entity in accordance with one or more techniques of this disclosure.1616-444WO01Qualcomm Ref. No. 2402459WO 5

[0037] FIG. 16A is a communication diagram illustrating a first example data exchange between a User Equipment (UE) device, a gNB / TRP device, and a location management function (LMF) device, in accordance with one or more techniques of this disclosure.

[0038] FIG. 16B is a communication diagram illustrating a second example data exchange between a UE device, a gNB / TRP device, and an LMF device, in accordance with one or more techniques of this disclosure.

[0039] FIG. 17 is a communication diagram illustrating a third example data exchange between a UE device, a gNB / TRP device, and an LMF device, in accordance with one or more techniques of this disclosure.

[0040] FIG. 18 A is a communication diagram illustrating a fourth example data exchange between a UE device, a gNB / TRP device, an LMF device, and a server 1800, in accordance with one or more techniques of this disclosure.

[0041] FIG. 18B is a communication diagram illustrating a fifth example data exchange between a UE device, a gNB / TRP device, an LMF device, and a server, in accordance with one or more techniques of this disclosure.

[0042] FIG. 19 is a communication diagram illustrating a sixth example data exchange between a UE device, a gNB / TRP device, and an LMF device, in accordance with one or more techniques of this disclosure.

[0043] FIG. 20 is a communication diagram illustrating a seventh example data exchange between a UE device, a gNB / TRP device, and an LMF device, in accordance with one or more techniques of this disclosure.

[0044] FIG. 21 is a block diagram illustrating a first example ML system in accordance with one or more techniques of this disclosure.

[0045] FIG. 22 is a block diagram illustrating a second example ML system in accordance with one or more techniques of this disclosure.

[0046] FIG. 23 is a block diagram illustrating a third example ML system in accordance with one or more techniques of this disclosure.DETAILED DESCRIPTION

[0047] Different wireless devices may generate and detect wireless signals differently. Thus, two different wireless devices at the same position may generate different measurement data based on the same wireless signal. Similarly, the wireless signals generated by two different wireless devices at the same position may have differences.1616-444WO01Qualcomm Ref. No. 2402459WO 6 resulting in differences in measurement data generated from the wireless signals. The measurement data may serve as input to a machine learning (ML) model that is trained to generate output data, such as position data or intermediate data that serves as input to a secondary' positioning process that generates position data. Thus, the differences between devices may lead to inaccuracies in the position data.

[0048] This disclosure describes techniques that address this issue. As described herein, a first network entity may receive a positioning personalization indicator for a second network entity. The positioning personalization indicator for the second network entity indicates information about the second network entity. Additionally, the first network entity may obtain measurement data based on at least one measurement of at least one wireless signal. The first network entity may apply a trained ML model to the measurement data to generate output data, wherein the output data indicates a physical position of a user equipment (UE) device or the output data being input data to a process that determines the physical position of the UE device. The output data for the UE device is dependent on the positioning personalization indicator for the second network entity. Because the output data is dependent on the positioning personalization indicator, the position data may be more accurate than if the position data were generated without respect to the information about the second network entity’.

[0049] FIG. 1 illustrates an example wireless communications system 100, according to aspects of the disclosure. The wireless communications system 100 (which may' also be referred to as a wireless wide area network (WWAN)) may include various base stations 102 (labeled “BS”) and various UEs 104. The base stations 102 may include macro cell base stations (high power cellular base stations) and / or small cell base stations (low power cellular base stations). In an aspect, the macro cell base stations may include eNBs and / or ng-eNBs where the wireless communications system 100 corresponds to an LTE network, or gNBs where the wireless communications system 100 corresponds to a New Radio (NR) network, or a combination of both, and the small cell base stations may include femtocells, picocells, microcells, etc.

[0050] The base stations 102 may collectively form a radio access network (RAN) and interface with a core network 170 (e.g., an evolved packet core (EPC) or a 5G core (5GC)) through backhaul links 122, and through the core network 170 to one or more location servers 172 (e.g., a location management function (LMF) or a secure user plane location (SUPL) location platform (SLP)). The location server(s) 172 may be part of core network 170 or may be external to core network 170. A location server 172 may be integrated1616-444WO01Qualcomm Ref. No. 2402459WO 7 with a base station 102. A UE 104 may communicate with a location server 172 directly or indirectly. For example, a UE 104 may communicate with a location server 172 via the base station 102 that is currently serving that UE 104. A UE 104 may also communicate with a location server 172 through another path, such as via an application server (not shown), via another network, such as via a wireless local area network (WLAN) access point (AP) (e.g., AP 150 described below), and so on. For signaling purposes, communication between aUE 104 and alocation server 172 may be represented as an indirect connection (e.g., through the core network 170, etc.) or a direct connection (e.g., as shown via direct connection 128), with the intervening nodes (if any) omitted from a signaling diagram for clarity.

[0051] In addition to other functions, the base stations 102 may perform functions that relate to one or more of transferring user data, radio channel ciphering and deciphering, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection setup and release, load balancing, distribution for non-access stratum (NAS) messages, NAS node selection, synchronization, RAN sharing, multimedia broadcast multicast sendee (MBMS), subscriber and equipment trace, RAN information management (RIM), paging, positioning, and delivery of warning messages. The base stations 102 may communicate with each other directly or indirectly (e.g., through the EPC / 5GC) over backhaul links 134, which may be wired or wireless.

[0052] The base stations 102 may w irelessly communicate w ith the UEs 104. Each of the base stations 102 may provide communication coverage for a respective geographic coverage area 110. In an aspect, one or more cells may be supported by a base station 102 in each geographic coverage area 110. A "‘cell” is a logical communication entity used for communication with a base station (e.g., over some frequency resource, referred to as a carrier frequency, component carrier, carrier, band, or the like), and may be associated with an identifier (e.g., a physical cell identifier (PCI), an enhanced cell identifier (ECI), a virtual cell identifier (VCI), a cell global identifier (CGI), etc.) for distinguishing cells operating via the same or a different carrier frequency. In some cases, different cells may be configured according to different protocol types (e.g., machinetype communication (MTC), narrowband loT (NB-IoT), enhanced mobile broadband (eMBB), or others) that may provide access for different types of UEs. Because a cell is supported by a specific base station, the term ‘'cell” may refer to either or both of the logical communication entity and the base station that supports it, depending on the1616-444WO01Qualcomm Ref. No. 2402459WO 8 context. In addition, because a TRP is typically the physical transmission point of a cell, the terms “cell” and “TRP” may be used interchangeably. In some cases, the term “cell” may also refer to a geographic coverage area of a base station (e.g., a sector), insofar as a carrier frequency can be detected and used for communication within some portion of geographic coverage areas 110.

[0053] While neighboring macro cell base station 102 geographic coverage areas 110 may partially overlap (e.g., in a handover region), some of the geographic coverage areas 110 may be substantially overlapped by a larger geographic coverage area 110. For example, a small cell base station 102' (labeled “SC” for “small cell”) may have a geographic coverage area 110' that substantially overlaps with the geographic coverage area 110 of one or more macro cell base stations 102. A network that includes both small cell and macro cell base stations may be known as a heterogeneous network. A heterogeneous network may also include home eNBs (HeNBs), which may provide service to a restricted group known as a closed subscriber group (CSG).

[0054] The communication links 120 betw een the base stations 102 and the UEs 104 may include uplink (also referred to as reverse link) transmissions from a UE 104 to a base station 102 and / or downlink (DL) (also referred to as forward link) transmissions from a base station 102 to a UE 104. The communication links 120 may use multiple-input multiple-output (MIMO)antenna technology, including spatial multiplexing, beamforming, and / or transmit diversity. The communication links 120 may be through one or more carrier frequencies. Allocation of carriers may be asymmetric with respect to downlink and uplink (e.g.. more or less carriers may be allocated for downlink than for uplink).

[0055] The wireless communications system 100 may further include a wireless local area network (WLAN) access point (AP) 150 in communication with WLAN stations (STAs) 152 via communication links 154 in an unlicensed frequency spectrum (e.g., 5 GHz). When communicating in an unlicensed frequency spectrum, the WLAN STAs 152 and / or the WLAN AP 150 may perform a clear channel assessment (CCA) or listen before talk (LBT) procedure prior to communicating in order to determine whether the channel is available.

[0056] The small cell base station 102' may operate in a licensed and / or an unlicensed frequency spectrum. When operating in an unlicensed frequency spectrum, the small cell base station 102' may employ LTE or NR technology and use the same 5 GHz unlicensed frequency spectrum as used by the WLAN AP 150. The small cell base station 102',1616-444WO01Qualcomm Ref. No. 2402459WO 9 employing LTE / 5G in an unlicensed frequency spectrum, may boost coverage to and / or increase capacity of the access network. NR in unlicensed spectrum may be referred to as NR-U. LTE in an unlicensed spectrum may be referred to as LTE-U, licensed assisted access (LAA), or MULTEFIRE®.

[0057] The wireless communications system 100 may further include a millimeter wave (mmW) base station 180 that may operate in mmW frequencies and / or near mmW frequencies in communication with a UE 182. Extremely high frequency (EHF) is part of the RF in the electromagnetic spectrum. EHF has a range of 30 GHz to 300 GHz and a wavelength between 1 millimeter and 10 millimeters. Radio waves in this band may be referred to as a millimeter wave. Near mmW may extend down to a frequency of 3 GHz with a wavelength of 100 millimeters. The super high frequency (SHF) band extends between 3 GHz and 30 GHz, also referred to as centimeter wave. Communications using the mmW / near mmW radio frequency band have high path loss and a relatively short range. The mmW base station 180 and the UE 182 may utilize beamforming (transmit and / or receive) over a mmW communication link 184 to compensate for the extremely high path loss and short range. Further, it will be appreciated that in alternative configurations, one or more base stations 102 may also transmit using mmW or near mmW and beamforming. Accordingly, it will be appreciated that the foregoing illustrations are merely examples and should not be construed to limit the various aspects disclosed herein.

[0058] Transmit beamforming is a technique for focusing an RF signal in a specific direction. Traditionally, when a network node (e.g., a base station) broadcasts an RF signal, it broadcasts the signal in all directions (omni-directionally). With transmit beamforming, the network node determines where a given target device (e g., a UE) is located (relative to the transmitting network node) and projects a stronger downlink RF signal in that specific direction, thereby providing a faster (in terms of data rate) and stronger RF signal for the receiving device(s). To change the directionality of the RF signal when transmitting, a network node can control the phase and relative amplitude of the RF signal at each of the one or more transmitters that are broadcasting the RF signal. For example, a network node may use an array of antennas (referred to as a “phased array’" or an “antenna array”) that creates a beam of RF waves that can be “steered” to point in different directions, without actually moving the antennas. Specifically, the RF current from the transmitter is fed to the individual antennas with the correct phase relationship1616-444WO01Qualcomm Ref. No. 2402459WO 10 so that the radio waves from the separate antennas add together to increase the radiation in a desired direction, while cancelling to suppress radiation in undesired directions.

[0059] Transmit beams may be quasi-co-located, meaning that they appear to the receiver (e.g., a UE) as having the same parameters, regardless of whether or not the transmitting antennas of the network node themselves are physically co-located. In NR, there are four types of quasi-co-location (QCL) relations. Specifically, a QCL relation of a given type means that certain parameters about a second reference RF signal on a second beam can be derived from information about a source reference RF signal on a source beam. Thus, if the source reference RF signal is QCL Type A. the receiver can use the source reference RF signal to estimate the Doppler shift, Doppler spread, average delay, and delay spread of a second reference RF signal transmitted on the same channel. If the source reference RF signal is QCL Type B, the receiver can use the source reference RF signal to estimate the Doppler shift and Doppler spread of a second reference RF signal transmitted on the same channel. If the source reference RF signal is QCL Type C, the receiver can use the source reference RF signal to estimate the Doppler shift and average delay of a second reference RF signal transmitted on the same channel. If the source reference RF signal is QCL Type D, the receiver can use the source reference RF signal to estimate the spatial receive parameter of a second reference RF signal transmitted on the same channel.

[0060] In receive beamforming, the receiver uses a receive beam to amplify RF signals detected on a given channel. For example, the receiver can increase the gain setting and / or adjust the phase setting of an array of antennas in a particular direction to amplify (e.g., to increase the gain level of) the RF signals received from that direction. Thus, when a receiver is said to beamform in a certain direction, it means the beam gam in that direction is high relative to the beam gain along other directions, or the beam gain in that direction is the highest compared to the beam gain in that direction of all other receive beams available to the receiver. This results in a stronger received signal strength (e.g., reference signal received power (RSRP), reference signal received quality (RSRQ). signal-to-interference-plus-noise ratio (SINR), etc.) of the RF signals received from that direction.

[0061] Transmit and receive beams may be spatially related. A spatial relation means that parameters for a second beam (e.g., a transmit or receive beam) for a second reference signal can be derived from information about a first beam (e.g., a receive beam or a transmit beam) for a first reference signal. For example, a UE may use a particular receive beam to receive a reference downlink reference signal (e.g., synchronization signal block1616-444WO01Qualcomm Ref. No. 2402459WO 11(SSB)) from a base station. The UE can then form a transmit beam for sending an uplink reference signal (e.g., sounding reference signal (SRS)) to that base station based on the parameters of the receive beam.

[0062] Note that a “downlink"’ beam may be either a transmit beam or a receive beam, depending on the entity forming it. For example, if a base station is forming the downlink beam to transmit a reference signal to a UE, the downlink beam is a transmit beam. If the UE is forming the downlink beam, however, it is a receive beam to receive the downlink reference signal. Similarly, an “uplink” beam may be either a transmit beam or a receive beam, depending on the entity forming it. For example, if a base station is forming the uplink beam, it is an uplink receive beam, and if a UE is forming the uplink beam, it is an uplink transmit beam.

[0063] The electromagnetic spectrum is often subdivided, based on frequency / wav elength, into various classes, bands, channels, etc. In 5G NR two initial operating bands have been identified as frequency range designations FR1 (410 MHz - 7.125 GHz) and FR2 (24.25 GHz - 52.6 GHz). It should be understood that although a portion of FR1 is greater than 6 GHz, FR1 is often referred to (interchangeably) as a “Sub- 6 GHz” band in various documents and articles. A similar nomenclature issue sometimes occurs with regard to FR2, which is often referred to (interchangeably) as a “millimeter w ave” band in documents and articles, despite being different from the extremely high frequency (EHF) band (30 GHz - 300 GHz) which is identified by the INTERNATIONAL TELECOMMUNICATION UNION® as a “millimeter wave” band.

[0064] The frequencies between FR1 and FR2 are often referred to as mid-band frequencies. Recent 5G NR studies have identified an operating band for these mid-band frequencies as frequency range designation FR3 (7.125 GHz - 24.25 GHz). Frequency bands falling within FR3 may inherit FR1 characteristics and / or FR2 characteristics, and thus may effectively extend features of FR1 and / or FR2 into mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5GNR operation beyond 52.6 GHz. For example, three higher operating bands have been identified as frequency range designations FR4a or FR4-1 (52.6 GHz - 71 GHz), FR4 (52.6 GHz - 114.25 GHz), and FR5 (114.25 GHz - 300 GHz). Each of these higher frequency bands falls within the EHF band.

[0065] With the above aspects in mind, unless specifically stated otherwise, it should be understood that the term “sub-6 GHz” or the like if used herein may broadly represent frequencies that may be less than 6 GHz, may be within FR1 , or may include mid-band1616-444WO01Qualcomm Ref. No. 2402459WO 12 frequencies. Further, unless specifically stated otherwise, it should be understood that the term “millimeter wave” or the like if used herein may broadly represent frequencies that may include mid-band frequencies, may be within FR2, FR4, FR4-a or FR4-1, and / or FR5, or may be within the EHF band.

[0066] In a multi-carrier system, such as 5G, one of the carrier frequencies is referred to as the “primary carrier” or “anchor carrier” or “primary serving cell” or “PCell,” and the remaining carrier frequencies are referred to as “secondary carriers” or “secondary serving cells” or “SCells.” In carrier aggregation, the anchor carrier is the carrier operating on the primary frequency (e.g., FR1) utilized by a UE 104 / 182 and the cell in which the UE 104 / 182 either performs the initial radio resource control (RRC) connection establishment procedure or initiates the RRC connection re-establishment procedure. The primary carrier carries all common and UE-specific control channels, and may be a carrier in a licensed frequency (however, this is not always the case). A secondary carrier is a carrier operating on a second frequency (e.g., FR2) that may be configured once the RRC connection is established between the UE 104 and the anchor carrier and that may be used to provide additional radio resources. In some cases, the secondary carrier may be a carrier in an unlicensed frequency. The secondary carrier may contain only necessary signaling information and signals, for example, those that are UE-specific may not be present in the secondary carrier, since both primary uplink and downlink carriers are typically UE-specific. This means that different UEs 104 / 182 in a cell may have different downlink primary carriers. The same is true for the uplink primary carriers. The netw ork is able to change the primary earner of any UE 104 / 182 at any time. This is done, for example, to balance the load on different carriers. Because a “serving cell” (whether a PCell or an SCell) corresponds to a carrier frequency / component carrier over which some base station is communicating, the term “cell,” “serving cell,” “component carrier,” “carrier frequency,” and the like can be used interchangeably.

[0067] For example, still referring to FIG. 1. one of the frequencies utilized by the macro cell base stations 102 may be an anchor carrier (or “PCell”) and other frequencies utilized by the macro cell base stations 102 and / or the mmW base station 180 may be secondary carriers (“SCells”). The simultaneous transmission and / or reception of multiple carriers enables the UE 104 / 182 to significantly increase its data transmission and / or reception rates. For example, two 20 MHz aggregated carriers in a multi-carrier system would theoretically lead to a two-fold increase in data rate (i.e., 40 MHz), compared to that attained by a single 20 MHz carrier.1616-444WO01Qualcomm Ref. No. 2402459WO 13

[0068] The wireless communications system 100 may further include a UE 164 that may communicate with a macro cell base station 102 over a communication link 120 and / or the mmW base station 180 over a mmW communication link 184. For example, the macro cell base station 102 may support a PCell and one or more SCells for the UE 164 and the mmW base station 180 may support one or more SCells for the UE 164.

[0069] In some cases, the UE 164 and the UE 182 may be capable of sidelink communication. Sidelink-capable UEs (SL-UEs) may communicate with base stations 102 over communication links 120 using the Uu interface (i.e., the air interface between a UE and a base station). SL-UEs (e.g., UE 164. UE 182) may also communicate directly with each other over a wireless sidelink 160 using the PC5 interface (i.e., the air interface between sidelink-capable UEs). A wireless sidelink (or just “sidelink”) is an adaptation of the core cellular (e.g., LTE, NR) standard that allows direct communication between two or more UEs without the communication needing to go through a base station. Sidelink communication may be unicast or multicast, and may be used for device-to- device (D2D) media-sharing, vehicle-to-vehicle (V2V) communication, vehicle-to- everything (V2X) communication (e.g., cellular V2X (cV2X) communication, enhanced V2X (eV2X) communication, etc.), emergency rescue applications, etc. One or more of a group of SL-UEs utilizing sidelink communications may be within the geographic coverage area 110 of a base station 102. Other SL-UEs in such a group may be outside the geographic coverage area 110 of a base station 102 or be otherwise unable to receive transmissions from a base station 102. In some cases, groups of SL-UEs communicating via sidelink communications may utilize a one-to-many (1 :M) system in which each SLUE transmits to every other SL-UE in the group. In some cases, a base station 102 facilitates the scheduling of resources for sidelink communications. In other cases, sidelink communications are carried out between SL-UEs without the involvement of a base station 102.

[0070] In an aspect, the sidelink 160 may operate over a wireless communication medium of interest, which may be shared with other wireless communications between other vehicles and / or infrastructure access points, as well as other RATs. A “medium” may be composed of one or more time, frequency, and / or space communication resources (e.g., encompassing one or more channels across one or more carriers) associated with wireless communication between one or more transmitter / receiver pairs. In an aspect, the medium of interest may correspond to at least a portion of an unlicensed frequency band shared among various RATs. Although different licensed frequency bands have been reserved1616-444WO01Qualcomm Ref. No. 2402459WO 14 for certain communication systems (e.g.. by a government entity such as the Federal Communications Commission (FCC) in the United States), these systems, in particular those employing small cell access points, have recently extended operation into unlicensed frequency bands such as the Unlicensed National Information Infrastructure (U-NII) band used by wireless local area network (WLAN) technologies, most notably IEEE 802. 1 lx WLAN technologies generally referred to as “Wi-Fi.” Example systems of this type include different variants of CDMA systems, TDMA systems, FDMA systems, orthogonal FDMA (OFDMA) systems, single-carrier FDMA (SC-FDMA) systems, and so on.

[0071] Note that although FIG. 1 only illustrates two of the UEs as SL-UEs (i.e., UEs 164 and 182), any of the illustrated UEs may be SL-UEs. Further, although only UE 182 was described as being capable of beamforming, any of the illustrated UEs, including UE 164, may be capable of beamforming. Where SL-UEs are capable of beamforming, they may beamform towards each other (i.e., towards other SL-UEs), towards other UEs (e.g.. UEs 104), towards base stations (e.g., base stations 102, 180, small cell 102’, access point 150), etc. Thus, in some cases, UEs 164 and 182 may utilize beamforming over sidelink 160.

[0072] In the example of FIG. 1, any of the illustrated UEs (shown in FIG. 1 as a single UE 104 for simplicity) may receive signals 124 from one or more Earth orbiting space vehicles (SVs) 112 (e.g., satellites). In an aspect, the SVs 112 may be part of a satellite positioning system that a UE 104 can use as an independent source of location information. A satellite positioning system typically includes a system of transmitters (e.g.. SVs 112) positioned to enable receivers (e.g., UEs 104) to determine their location on or above the Earth based, at least in part, on positioning signals (e g., signals 124) received from the transmitters. Such a transmitter typically transmits a signal marked with a repeating pseudo-random noise (PN) code of a set number of chips. While typically located in SVs 112, transmitters may sometimes be located on ground-based control stations, base stations 102, and / or other UEs 104. A UE 104 may include one or more dedicated receivers specifically designed to receive signals 124 for deriving geo location information from the SVs 112.

[0073] In a satellite positioning system, the use of signals 124 can be augmented by various satellite-based augmentation systems (SBAS) that may be associated with or otherwise enabled for use with one or more global and / or regional navigation satellite systems. For example an SBAS may include an augmentation system(s) that provides1616-444WO01Qualcomm Ref. No. 2402459WO 15 integrity information, differential corrections, etc., such as the Wide Area Augmentation System (WAAS), the European Geostationary Navigation Overlay Service (EGNOS), the Multi-functional Satellite Augmentation System (MSAS), the Global Positioning System (GPS) Aided Geo Augmented Navigation or GPS and Geo Augmented Navigation system (GAGAN). and / or the like. Thus, as used herein, a satellite positioning system may include any combination of one or more global and / or regional navigation satellites associated with such one or more satellite positioning systems.

[0074] In an aspect, SVs 112 may additionally or alternatively be part of one or more non-terrestrial networks (NTNs). In an NTN, an SV 112 is connected to an earth station (also referred to as a ground station, NTN gateway, or gateway), which in turn is connected to an element in a 5G network, such as a modified base station 102 (without a terrestrial antenna) or anetwork node in a 5GC. This element would in turn provide access to other elements in the 5G network and ultimately to entities external to the 5G network, such as Internet web servers and other user devices. In that way. a UE 104 may receive communication signals (e.g., signals 124) from an SV 112 instead of, or in addition to, communication signals from a terrestrial base station 102.

[0075] The wireless communications system 100 may further include one or more UEs, such as UE 190, that connects indirectly to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links (referred to as “sidelinks”). In the example of FIG. 1 , UE 190 has a D2D P2P link 192 with one of the UEs 104 connected to one of the base stations 102 (e.g., through which UE 190 may indirectly obtain cellular connectivity) and a D2D P2P link 194 with WLAN STA 152 connected to the WLAN AP 150 (through which UE 190 may indirectly obtain WLAN -based Internet connectivity). In an example, the D2D P2P links 192 and 194 may be supported with any well-known D2D RAT, such as LTE Direct (LTE-D), WI-FI DIRECT®, BLUETOOTH®, and so on.

[0076] As used herein, the terms “user equipment” (UE) and “base station” are not intended to be specific or otherwise limited to any particular radio access technology (RAT), unless otherwise noted. In general, a UE may be any wireless communication device (e.g., a mobile phone, router, tablet computer, laptop computer, consumer asset locating device, wearable (e.g., smartwatch, glasses, augmented reality (AR) I virtual reality (VR) headset, etc.), vehicle (e.g., automobile, motorcycle, bicycle, etc.), Internet of Things (loT) device, etc.) used by a user to communicate over a wireless communications network. A UE may be mobile or may (e.g., at certain times) be1616-444WO01Qualcomm Ref. No. 2402459WO 16 stationary, and may communicate with a radio access network (RAN). As used herein, the term “UE” may be referred to interchangeably as an “access terminal” or “AT,” a “client device,” a “wireless device,” a “subscriber device,” a “subscriber terminal,” a “subscriber station,” a “user terminal” or “UT,” a “mobile device,” a “mobile terminal,” a “mobile station,” or variations thereof. Generally, UEs can communicate with a core network via a RAN, and through the core network the UEs can be connected with external networks such as the Internet and with other UEs. Of course, other mechanisms of connecting to the core network and / or the Internet are also possible for the UEs, such as over wired access networks, wireless local area network (WLAN) networks (e.g., based on the Institute of Electrical and Electronics Engineers (IEEE) 802. 11 specification, etc.) and so on.

[0077] A base station may operate according to one of several RATs in communication with UEs depending on the network in which it is deployed, and may be alternatively referred to as a network entity, an access point (AP), a network node. aNodeB. an evolved NodeB (eNB), a next generation eNB (ng-eNB), aNew Radio (NR) Node B (also referred to as a gNB or gNodeB), etc. A base station may be used primarily to support wireless access by UEs, including supporting data, voice, and / or signaling connections for the supported UEs. In some systems a base station may provide purely edge node signaling functions while in other systems it may provide additional control and / or network management functions. A communication link through which UEs can send signals to a base station is called an uplink (UL) channel (e.g., a reverse traffic channel, a reverse control channel, an access channel, etc.). A communication link through which the base station can send signals to UEs is called a downlink (DL) or forward link channel (e.g.. a paging channel, a control channel, a broadcast channel, a forward traffic channel, etc ). As used herein the term traffic channel (TCH) can refer to either an uplink / reverse or downlink / forward traffic channel.

[0078] The term “base station” may refer to a single physical transmission-reception point (TRP) or to multiple physical TRPs that may or may not be co-located. For example, where the term “base station” refers to a single physical TRP, the physical TRP may be an antenna of the base station corresponding to a cell (or several cell sectors) of the base station. Where the term “base station” refers to multiple co-located physical TRPs, the physical TRPs may be an array of antennas (e.g., as in a multiple-input multiple-output (MIMO) system or where the base station employs beamforming) of the base station. Where the term “base station” refers to multiple non-co-located physical1616-444WO01Qualcomm Ref. No. 2402459WO 17TRPs, the physical TRPs 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). Alternatively, the non-co-located physical TRPs may be the serving base station receiving the measurement report from the UE and a neighbor base station whose reference radio frequency (RF) signals the UE is measuring. Because a TRP is the point from which a base station transmits and receives wireless signals, as used herein, references to transmission from or reception at a base station are to be understood as referring to a particular TRP of the base station.

[0079] In some implementations that support positioning of UEs, a base station may not support wireless access by UEs (e.g., may not support data, voice, and / or signaling connections for UEs), but may instead transmit reference signals to UEs to be measured by the UEs, and / or may receive and measure signals transmitted by the UEs. Such a base station may be referred to as a positioning beacon (e.g., when transmitting signals to UEs) and / or as a location measurement unit (e g., when receiving and measuring signals from UEs).

[0080] An “RF signal” comprises an electromagnetic wave of a given frequency that transports information through the space between a transmitter and a receiver. As used herein, a transmitter may transmit a single “RF signal” or multiple “RF signals” to a receiver. However, the receiver may receive multiple “RF signals” corresponding to each transmitted RF signal due to the propagation characteristics of RF signals through multipath channels. The same transmitted RF signal on different paths between the transmitter and receiver may be referred to as a “multipath” RF signal. As used herein, an RF signal may also be referred to as a “wireless signal” or simply a “signal” where it is clear from the context that the term “signal” refers to a wireless signal or an RF signal.

[0081] FIG. 2A illustrates an example wireless network structure 200. For example, a 5GC 210 (also referred to as aNext Generation Core (NGC)) can be viewed functionally as control plane (C-plane) functions 214 (e g., UE registration, authentication, network access, gateway selection, etc.) and user plane (U-plane) functions 212, (e.g., UE gateway function, access to data networks, IP routing, etc.) which operate cooperatively to form the core network. User plane interface (NG-U) 213 and control plane interface (NG-C) 215 connect the gNB 222 to the 5GC 210 and specifically to the user plane functions 212 and control plane functions 214, respectively. In an additional configuration, an ng-eNB 224 may also be connected to the 5GC 210 via NG-C 215 to the control plane functions1616-444WO01Qualcomm Ref. No. 2402459WO 18214 and NG-U 213 to user plane functions 212. Further, ng-eNB 224 may directly communicate with gNB 222 via a backhaul connection 223. In some configurations, a Next Generation RAN (NG-RAN) 220 may have one or more gNBs 222, while other configurations include one or more of both ng-eNBs 224 and gNBs 222. Either (or both) gNB 222 or ng-eNB 224 may communicate with one or more UEs 204 (e.g., any of the UEs described herein).

[0082] Another optional aspect may include a location server 230, which may be in communication with the 5GC 210 to provide location assistance for UE(s) 204. The location server 230 can be implemented as a plurality of separate servers (e.g., physically separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc.), or alternately may each correspond to a single server. The location server 230 can be configured to support one or more location services for UEs 204 that can connect to the location server 230 via the core network, 5GC 210, and / or via the Internet (not illustrated). Further, the location server 230 may be integrated into a component of the core network, or alternatively may be external to the core network (e.g., a third part}7server, such as an original equipment manufacturer (OEM) server or service server).

[0083] FIG. 2B illustrates another example wireless network structure 240. A 5GC 260 (which may correspond to 5GC 210 in FIG. 2A) can be viewed functionally as control plane functions, provided by an access and mobility management function (AMF) 264, and user plane functions, provided by a user plane function (UPF) 262, which operate cooperatively to form the core network (i.e., 5GC 260). The functions of the AMF 264 include registration management, connection management, reachability management, mobility management, lawful interception, transport for session management (SM) messages between one or more UEs 204 (e.g., any of the UEs described herein) and a session management function (SMF) 266, transparent proxy services for routing SM messages, access authentication and access authorization, transport for short message service (SMS) messages between the UE 204 and the short message service function (SMSF) (not shown), and security anchor functionality (SEAF). The AMF 264 also interacts with an authentication server function (AUSF) (not shown) and the UE 204, and receives the intermediate key that was established as a result of the UE 204 authentication process. In the case of authentication based on a UMTS (universal mobile telecommunications system) subscriber identity module (USIM), the AMF 264 retrieves the security material from the AUSF. The functions of the AMF 264 also include security1616-444WO01Qualcomm Ref. No. 2402459WO 19 context management (SCM). The SCM receives a key from the SEAF that it uses to derive access-network specific keys. The functionality of the AMF 264 also includes location sendees management for regulatory services, transport for location sendees messages between the UE 204 and a location management function (LMF) 270 (which acts as a location server 230), transport for location services messages between the NG-RAN 220 and the LMF 270, evolved packet system (EPS) bearer identifier allocation for intenvorking with the EPS, and UE 204 mobility event notification. In addition, the AMF 264 also supports functionalities for non-3GPP® (Third Generation Partnership Project) access networks.

[0084] Functions of the UPF 262 include acting as an anchor point for intra / inter-RAT mobility (when applicable), acting as an external protocol data unit (PDU) session point of interconnect to a data netw ork (not shown), providing packet routing and forw arding, packet inspection, user plane policy rule enforcement (e.g., gating, redirection, traffic steering), lawful interception (user plane collection), traffic usage reporting, quality of service (QoS) handling for the user plane (e.g., uplink / downlink rate enforcement, reflective QoS marking in the downlink), uplink traffic verification (senice data flow7(SDF) to QoS flow mapping), transport level packet marking in the uplink and downlink, downlink packet buffering and downlink data notification triggering, and sending and forwarding of one or more "end markers’’ to the source RAN node. The UPF 262 may also support transfer of location services messages over a user plane betw een the UE 204 and a location server, such as an SLP 272.

[0085] The functions of the SMF 266 include session management, UE Internet protocol (IP) address allocation and management, selection and control of user plane functions, configuration of traffic steering at the UPF 262 to route traffic to the proper destination, control of part of policy enforcement and QoS, and downlink data notification. The interface over which the SMF 266 communicates with the AMF 264 is referred to as the N 11 interface.

[0086] Another optional aspect may include an LMF 270, which may be in communication with the 5GC 260 to provide location assistance for UEs 204. The LMF 270 can be implemented as a plurality' of separate servers (e.g., physically separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc.), or alternately may each correspond to a single server. The LMF 270 can be configured to support one or more location services for UEs 204 that can connect to the LMF 270 via the core netw ork, 5GC 260, and / or via the1616-444WO01Qualcomm Ref. No. 2402459WO 20Internet (not illustrated). The SLP 272 may support similar functions to the LMF 270. but whereas the LMF 270 may communicate with the AMF 264, NG-RAN 220, and UEs 204 over a control plane (e.g., using interfaces and protocols intended to convey signaling messages and not voice or data), the SLP 272 may communicate with UEs 204 and external clients (e.g., third-party server 274) over a user plane (e.g., using protocols intended to cany7voice and / or data like the transmission control protocol (TCP) and / or IP).

[0087] Yet another optional aspect may include a third-party server 274, which may be in communication with the LMF 270, the SLP 272, the 5GC 260 (e.g., via the AMF 264 and / or the UPF 262), the NG-RAN 220, and / or the UE 204 to obtain location information (e.g., a location estimate) for the UE 204. As such, in some cases, the third-party server 274 may be referred to as a location services (LCS) client or an external client. The third- party server 274 can be implemented as a plurality of separate servers (e.g., physically separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc.), or alternately may each correspond to a single server.

[0088] User plane interface 263 and control plane interface 265 connect the 5GC 260, and specifically the UPF 262 and AMF 264, respectively, to one or more gNBs 222 and / or ng-eNBs 224 in the NG-RAN 220. The interface between gNB(s) 222 and / or ng-eNB(s) 224 and the AMF 264 is referred to as the ‘"N2” interface, and the interface between gNB(s) 222 and / or ng-eNB(s) 224 and the UPF 262 is referred to as the “N3” interface. The gNB(s) 222 and / or ng-eNB(s) 224 of the NG-RAN 220 may communicate directly with each other via backhaul connections 223, referred to as the “Xn-C” interface. One or more of gNBs 222 and / or ng-eNBs 224 may communicate with one or more UEs 204 over a wireless interface, referred to as the “Uu” interface.

[0089] The functionality of a gNB 222 may be divided between a gNB central unit (gNB- CU) 226. one or more gNB distributed units (gNB-DUs) 228, and one or more gNB radio units (gNB-RUs) 229. A gNB-CU 226 is a logical node that includes the base station functions of transferring user data, mobility control, radio access network sharing, positioning, session management, and the like, except for those functions allocated exclusively to the gNB-DU(s) 228. More specifically, the gNB-CU 226 generally host the radio resource control (RRC), service data adaptation protocol (SDAP), and packet data convergence protocol (PDCP) protocols of the gNB 222. A gNB-DU 228 is a logical node that generally hosts the radio link control (RLC) and medium access control (MAC)1616-444WO01Qualcomm Ref. No. 2402459WO 21 layer of the gNB 222. Its operation is controlled by the gNB-CU 226. One gNB-DU 228 can support one or more cells, and one cell is supported by only one gNB-DU 228. The interface 232 between the gNB-CU 226 and the one or more gNB-DUs 228 is referred to as the “Fl’" interface. The physical (PHY) layer functionality of a gNB 222 is generally hosted by one or more standalone gNB-RUs 229 that perform functions such as power amplification and signal transmission / reception. The interface between a gNB-DU 228 and a gNB-RU 229 is referred to as the “Fx” interface. Thus, a UE 204 communicates with the gNB-CU 226 via the RRC, SDAP. and PDCP layers, with a gNB-DU 228 via the REC and MAC layers, and with a gNB-RU 229 via the PHY layer.

[0090] Deployment of communication systems, such as 5GNR systems, may be arranged in multiple manners with various components or constituent parts. In a 5G NR system, or network, a network node, a network entity, a mobility element of a network, a RAN node, a core network node, a net ork element, or a network equipment, such as a base station, or one or more units (or one or more components) performing base station functionality, may be implemented in an aggregated or disaggregated architecture. For example, a base station (such as a Node B (NB), evolved NB (eNB), NR base station, 5G NB, AP, TRP, cell, etc.) may be implemented as an aggregated base station (also known as a standalone base station or a monolithic base station) or a disaggregated base station.

[0091] An aggregated base station may be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node. A disaggregated base station may be configured to utilize a protocol stack that is physically or logically- distributed among two or more units (such as one or more central or centralized units (CUs), one or more distributed units (DUs). or one or more radio units (RUs)). In some aspects, a CU may be implemented within a RAN node, and one or more DUs may be colocated with the CU, or alternatively, may be geographically or virtually distributed throughout one or multiple other RAN nodes. The DUs may be implemented to communicate with one or more RUs. Each of the CU, DU and RU also can be implemented as virtual units, i.e., a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU).

[0092] Base stat ion- t pe operation or network design may consider aggregation characteristics of base station functionality. For example, disaggregated base stations may be utilized in an integrated access backhaul (IAB) network, an open radio access network (O-RAN (such as the network configuration sponsored by the O-RAN ALLIANCE®)), or a virtualized radio access network (vRAN, also known as a cloud1616-444WO01Qualcomm Ref. No. 2402459WO 22 radio access network (C-RAN)). Disaggregation may include distributing functionality across two or more units at various physical locations, as well as distributing functionality for at least one unit virtually, which can enable flexibility in network design. The various units of the disaggregated base station, or disaggregated RAN architecture, can be configured for wired or wireless communication with at least one other unit.

[0093] FIG. 2C illustrates an example disaggregated base station architecture 250, according to aspects of the disclosure. The disaggregated base station architecture 250 may include one or more central units (CUs) 280 (e.g., gNB-CU 226) that can communicate directly with a core network 267 (e.g., 5GC 210, 5GC 260) via a backhaul link, or indirectly with the core network 267 through one or more disaggregated base station units (such as a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC) 259 via an E2 link, or a Non-Real Time (Non-RT) RIC 257 associated with a Sendee Management and Orchestration (SMO) Framework 255, or both). A CU 280 may communicate with one or more DUs 285 (e.g.. gNB-DUs 228) via respective midhaul links, such as an Fl interface. The DUs 285 may communicate with one or more radio units (RUs) 287 (e.g., gNB-RUs 229) via respective fronthaul links. The RUs 287 may communicate with respective UEs 204 via one or more radio frequency (RF) access links. In some implementations, the UE 204 may be simultaneously served by multiple RUs 287.

[0094] Each of the units, i.e., the CUs 280, the DUs 285, the RUs 287, as well as the Near-RT RICs 259, the Non-RT RICs 257 and the SMO Framework 255, may include one or more interfaces or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, signals) via a wired or wireless transmission medium. Each of the units, or an associated processor or controller providing instructions to the communication interfaces of the units, can be configured to communicate with one or more of the other units via the transmission medium. For example, the units can include a wired interface configured to receive or transmit signals over a wired transmission medium to one or more of the other units. Additionally, the units can include a wireless interface, which may include a receiver, a transmitter or transceiver (such as a RF transceiver), configured to receive or transmit signals, or both, over a wireless transmission medium to one or more of the other units.

[0095] In some aspects, the CU 280 may host one or more higher layer control functions. Such control functions can include RRC, PDCP, service data adaptation protocol (SDAP), or the like. Each control function can be implemented with an interface configured to1616-444WO01Qualcomm Ref. No. 2402459WO 23 communicate signals with other control functions hosted by the CU 280. The CU 280 may be configured to handle user plane functionality (i.e., Central Unit - User Plane (CU- UP)), control plane functionality (i.e., Central Unit - Control Plane (CU-CP)), or a combination thereof. In some implementations, the CU 280 can be logically split into one or more CU-UP units and one or more CU-CP units. The CU-UP unit can communicate bidirectionally with the CU-CP unit via an interface, such as the El interface when implemented in an O-RAN configuration. The CU 280 can be implemented to communicate with the DU 285, as necessary, for network control and signaling.

[0096] The DU 285 may correspond to a logical unit that includes one or more base station functions to control the operation of one or more RUs 287. In some aspects, the DU 285 may host one or more of a RLC layer, a MAC layer, and one or more high PHY layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, or the like) depending, at least in part, on a functional split, such as those defined by the 3rd Generation Partnership Project (3GPP®). In some aspects, the DU 285 may further host one or more low PHY layers. Each layer (or module) can be implemented with an interface configured to communicate signals with other layers (and modules) hosted by the DU 285. or with the control functions hosted by the CU 280.

[0097] Lower-layer functionality can be implemented by one or more RUs 287. In some deployments, an RU 287, controlled by a DU 285, may correspond to a logical node that hosts RF processing functions, or low-PHY layer functions (such as performing fast Fourier transform (FFT), inverse FFT (iFFT). digital beamforming, physical random access channel (PRACH) extraction and filtering, or the like), or both, based at least in part on the functional split, such as a lower layer functional split. In such an architecture, the RU(s) 287 can be implemented to handle over the air (OTA) communication with one or more UEs 204. In some implementations, real-time and non-real-time aspects of control and user plane communication with the RU(s) 287 can be controlled by the corresponding DU 285. In some scenarios, this configuration can enable the DU(s) 285 and the CU 280 to be implemented in a cloud-based RAN architecture, such as a vRAN architecture.

[0098] The SMO Framework 255 may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO Framework 255 may be configured to support the1616-444WO01Qualcomm Ref. No. 2402459WO 24 deployment of dedicated physical resources for RAN coverage requirements which may be managed via an operations and maintenance interface (such as an 01 interface). For virtualized network elements, the SMO Framework 255 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) 269) to perform network element life cycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface (such as an 02 interface). Such virtualized network elements can include, but are not limited to, CUs 280, DUs 285, RUs 287 and Near-RT RICs 259. In some implementations, the SMO Framework 255 can communicate with a hardware aspect of a 4G RAN. such as an open eNB (O-eNB) 261. via an 01 interface. Additionally, in some implementations, the SMO Framework 255 can communicate directly with one or more RUs 287 via an 01 interface. The SMO Framework 255 also may include aNon-RT RIC 257 configured to support functionality of the SMO Framework 255.

[0099] The Non-RT RIC 257 may be configured to include a logical function that enables non-real-time control and optimization of RAN elements and resources, artificial intelligence / machine learning (AI / ML) workflows including model training and updates, or policy-based guidance of applications / features in the Near-RT RIC 259. The Non-RT RIC 257 may be coupled to or communicate with (such as via an Al interface) the Near- RT RIC 259. The Near-RT RIC 259 may be configured to include a logical function that enables near-real-time control and optimization of RAN elements and resources via data collection and actions over an interface (such as via an E2 interface) connecting one or more CUs 280, one or more DUs 285, or both, as well as an O-eNB, with the Near-RT RIC 259.

[0100] In some implementations, to generate AI / ML models to be deployed in the Near- RT RIC 259, the Non-RT RIC 257 may receive parameters or external enrichment information from external servers. Such information may be utilized by the Near-RT RIC 259 and may be received at the SMO Framework 255 or the Non-RT RIC 257 from nonnetwork data sources or from network functions. In some examples, the Non-RT RIC 257 or the Near-RT RIC 259 may be configured to tune RAN behavior or performance. For example, the Non-RT RIC 257 may monitor long-term trends and patterns for performance and employ AI / ML models to perform corrective actions through the SMO Framework 255 (such as reconfiguration via 01) or via creation of RAN management policies (such as Al policies).1616-444WO01Qualcomm Ref. No. 2402459WO 25

[0101] FIGS. 3A, 3B. and 3C illustrate several example components (represented by corresponding blocks) that may be incorporated into a UE 302 (which may correspond to any of the UEs described herein), a base station 304 (which may correspond to any of the base stations described herein), and a network entity 306 (which may correspond to or embody any of the network functions described herein, including the location server 230 and the LMF 270, or alternatively may be independent from the NG-RAN 220 and / or 5GC 210 / 260 infrastructure depicted in FIGS. 2 A and 2B, such as a private network) to support the operations described herein. It will be appreciated that these components may be implemented in different types of apparatuses in different implementations (e.g.. in an ASIC, in a system-on-chip (SoC), etc.). The illustrated components may also be incorporated into other apparatuses in a communication system. For example, other apparatuses in a sy stem may include components similar to those described to provide similar functionality. Also, a given apparatus may contain one or more of the components. For example, an apparatus may include multiple transceiver components that enable the apparatus to operate on multiple carriers and / or communicate via different technologies.

[0102] The UE 302 and the base station 304 each include one or more wireless wide area network (WWAN) transceivers 310 and 350. respectively, providing means for communicating (e.g., means for transmitting, means for receiving, means for measuring, means for tuning, means for refraining from transmitting, etc.) via one or more wireless communication networks (not shown), such as an NR network, an LTE network, a GSM network, and / or the like. The WWAN transceivers 310 and 350 may each be connected to one or more antennas 316 and 356, respectively, for communicating with other network nodes, such as other UEs, access points, base stations (e.g., eNBs. gNBs). etc., via at least one designated RAT (e.g., NR, LTE, GSM, etc.) over a wireless communication medium of interest (e.g., some set of time / frequency resources in a particular frequency spectrum). The WWAN transceivers 310 and 350 may be variously configured for transmitting and encoding signals 318 and 358 (e.g., messages, indications, information, and so on), respectively, and, conversely, for receiving and decoding signals 318 and 358 (e g., messages, indications, information, pilots, and so on), respectively, in accordance with the designated RAT. Specifically, the WWAN transceivers 310 and 350 include one or more transmitters 314 and 354, respectively, for transmitting and encoding signals 318 and 358, respectively, and one or more receivers 312 and 352, respectively, for receiving and decoding signals 318 and 358, respectively.1616-444WO01Qualcomm Ref. No. 2402459WO 26

[0103] The UE 302 and the base station 304 each also include, at least in some cases, one or more short-range wireless transceivers 320 and 360, respectively. The short-range wireless transceivers 320 and 360 may be connected to one or more antennas 326 and 366, respectively, and provide means for communicating (e.g., means for transmitting, means for receiving, means for measuring, means for tuning, means for refraining from transmitting, etc.) with other network nodes, such as other UEs, access points, base stations, etc., via at least one designated RAT (e.g., Wi-Fi, LTE Direct, BLUETOOTH®, ZIGBEE®, Z-WAVE®, PC5, dedicated short-range communications (DSRC), wireless access for vehicular environments (WAVE), near-field communication (NFC), ultra- wideband (UWB), etc.) over a wireless communication medium of interest. The short- range wireless transceivers 320 and 360 may be variously configured for transmitting and encoding signals 328 and 368 (e.g., messages, indications, information, and so on), respectively, and, conversely, for receiving and decoding signals 328 and 368 (e.g., messages, indications, information, pilots, and so on), respectively, in accordance with the designated RAT. Specifically, the short-range wireless transceivers 320 and 360 include one or more transmitters 324 and 364, respectively, for transmitting and encoding signals 328 and 368, respectively, and one or more receivers 322 and 362, respectively, for receiving and decoding signals 328 and 368. respectively. As specific examples, the short-range wireless transceivers 320 and 360 may be Wi-Fi transceivers, BLUETOOTH® transceivers, ZIGBEE® and / or Z-WAVE® transceivers, NFC transceivers, UWB transceivers, or vehicle-to-vehicle (V2V) and / or vehicle-to- everything (V2X) transceivers.

[0104] The UE 302 and the base station 304 also include, at least in some cases, satellite signal interfaces 330 and 370, which each include one or more satellite signal receivers 332 and 372, respectively, and may optionally include one or more satellite signal transmitters 334 and 374, respectively. In some cases, the base station 304 may be a terrestrial base station that may communicate with space vehicles (e.g.. space vehicles 112) via the satellite signal interface 370. In other cases, the base station 304 may be a space vehicle (or other non-terrestrial entity) that uses the satellite signal interface 370 to communicate with terrestrial networks and / or other space vehicles.

[0105] The satellite signal receivers 332 and 372 may be connected to one or more antennas 336 and 376, respectively, and may provide means for receiving and / or measuring satellite positioning / communication signals 338 and 378, respectively. Where the satellite signal receiver(s) 332 and 372 are satellite positioning system receivers, the1616-444WO01Qualcomm Ref. No. 2402459WO 27 satellite positioning / communication signals 338 and 378 may be global positioning system (GPS) signals, global navigation satellite system (GLONASS) signals, Galileo signals, Beidou signals, Indian Regional Navigation Satellite System (NAVIC), QuasiZenith Satellite System (QZSS) signals, etc. Where the satellite signal receiver(s) 332 and 372 are non-terrestrial network (NTN) receivers, the satellite positioning / communication signals 338 and 378 may be communication signals (e.g., cartying control and / or user data) originating from a 5G network. The satellite signal receiver(s) 332 and 372 may comprise any suitable hardware and / or software for receiving and processing satellite positioning / communication signals 338 and 378, respectively. The satellite signal receiver(s) 332 and 372 may request information and operations as appropriate from the other systems, and, at least in some cases, perform calculations to determine locations of the UE 302 and the base station 304, respectively, using measurements obtained by any suitable satellite positioning system algorithm.

[0106] The optional satellite signal transmitter(s) 334 and 374, when present, may be connected to the one or more antennas 336 and 376, respectively, and may provide means for transmitting satellite positioning / communication signals 338 and 378, respectively. Where the satellite signal transmitter(s) 374 are satellite positioning system transmitters, the satellite positioning / communication signals 378 may be GPS signals, GLONASS® signals, Galileo signals, Beidou signals, NAVIC, QZSS signals, etc. Where the satellite signal transmitter(s) 334 and 374 are NTN transmitters, the satellite positioning / communication signals 338 and 378 may be communication signals (e.g., carrying control and / or user data) originating from a 5G network. The satellite signal transmitter(s) 334 and 374 may comprise any suitable hardware and / or software for transmitting satellite positioning / communication signals 338 and 378, respectively. The satellite signal transmitter(s) 334 and 374 may request information and operations as appropriate from the other systems.

[0107] The base station 304 and the network entity 306 each include one or more network transceivers 380 and 390, respectively, providing means for communicating (e.g., means for transmitting, means for receiving, etc.) with other network entities (e.g., other base stations 304, other network entities 306). For example, the base station 304 may employ the one or more network transceivers 380 to communicate with other base stations 304 or network entities 306 over one or more wired or wireless backhaul links. As another example, the network entity 306 may employ the one or more network transceivers 390 to communicate with one or more base station 304 over one or more wired or wireless1616-444WO01Qualcomm Ref. No. 2402459WO 28 backhaul links, or with other network entities 306 over one or more wired or wireless core network interfaces.

[0108] A transceiver may be configured to communicate over a wired or wireless link. A transceiver (whether a wired transceiver or a wireless transceiver) includes transmitter circuitry (e.g., transmitters 314, 324. 354, 364) and receiver circuitry (e.g., receivers 312. 322, 352, 362). A transceiver may be an integrated device (e.g., embodying transmitter circuitry and receiver circuitry in a single device) in some implementations, may comprise separate transmitter circuitry and separate receiver circuitry in some implementations, or may be embodied in other ways in other implementations. The transmitter circuitry and receiver circuitry of a wired transceiver (e.g., network transceivers 380 and 390 in some implementations) may be coupled to one or more wired network interface ports. Wireless transmitter circuitry (e.g., transmitters 314, 324, 354, 364) may include or be coupled to a plurality of antennas (e.g., antennas 316, 326, 356, 366), such as an antenna array, that permits the respective apparatus (e.g., UE 302, base station 304) to perform transmit “beamforming,” as described herein. Similarly, wireless receiver circuitry (e g., receivers 312, 322, 352, 362) may include or be coupled to a plurality of antennas (e.g., antennas 316, 326, 356, 366), such as an antenna array, that permits the respective apparatus (e.g., UE 302, base station 304) to perform receive beamforming, as described herein. In an aspect, the transmitter circuitry and receiver circuitry may share the same plurality of antennas (e.g., antennas 316, 326, 356, 366), such that the respective apparatus can only receive or transmit at a given time, not both at the same time. A wireless transceiver (e.g., WWAN transceivers 310 and 350, short-range wireless transceivers 320 and 360) may also include a network listen module (NLM) or the like for performing various measurements.

[0109] As used herein, the various wireless transceivers (e.g., transceivers 310, 320, 350, and 360, and network transceivers 380 and 390 in some implementations) and wired transceivers (e.g., network transceivers 380 and 390 in some implementations) may generally be characterized as “a transceiver,” “at least one transceiver,” or “one or more transceivers.” As such, whether a particular transceiver is a wired or wireless transceiver may be inferred from the type of communication performed. For example, backhaul communication between network devices or servers will generally relate to signaling via a wired transceiver, whereas wireless communication between a UE (e.g., UE 302) and a base station (e.g., base station 304) will generally relate to signaling via a wireless transceiver.1616-444WO01Qualcomm Ref. No. 2402459WO 29

[0110] The UE 302. the base station 304. and the network entity 306 also include other components that may be used in conjunction with the operations as disclosed herein. The UE 302, the base station 304, and the network entity 306 include one or more processors 342, 384, and 394, respectively, for providing functionality relating to, for example, wireless communication, and for providing other processing functionality. The processors 342, 384, and 394 may therefore provide means for processing, such as means for determining, means for calculating, means for receiving, means for transmitting, means for indicating, etc. In an aspect, the processors 342, 384, and 394 may include, for example, one or more general purpose processors, multi-core processors, central processing units (CPUs), ASICs, digital signal processors (DSPs), field programmable gate arrays (FPGAs), other programmable logic devices or processing circuitry, or various combinations thereof.[OHl] The UE 302, the base station 304, and the network entity 306 include memory circuitry implementing memories 340, 386, and 396 (e.g., each including a memory device), respectively, for maintaining information (e.g., information indicative of reserved resources, thresholds, parameters, and so on). The memories 340, 386, and 396 may therefore provide means for storing, means for retrieving, means for maintaining, etc. In some cases, the UE 302, the base station 304, and the network entity 306 may include quasi-model relation (QML) component 348, 388, and 398, respectively. The QML component 348, 388, and 398 may be hardware circuits that are part of or coupled to the processors 342, 384, and 394, respectively, that, when executed, cause the UE 302, the base station 304. and the network entity 306 to perform the functionality described herein. In other aspects, the QML component 348. 388, and 398 may be external to the processors 342, 384, and 394 (e g., part of a modem processing system, integrated with another processing system, etc.). Alternatively, the QML component 348, 388, and 398 may be memory modules stored in the memories 340, 386, and 396, respectively, that, when executed by the processors 342, 384, and 394 (or a modem processing system, another processing system, etc ), cause the UE 302, the base station 304, and the network entity 306 to perform the functionality described herein. FIG. 3A illustrates possible locations of the QML component 348, which may be, for example, part of the one or more WWAN transceivers 310. the memory 340, the one or more processors 342, or any combination thereof, or may be a standalone component. FIG. 3B illustrates possible locations of the QML component 388, which may be, for example, part of the one or more WWAN transceivers 350, the memory 386, the one or more processors 384, or any1616-444WO01Qualcomm Ref. No. 2402459WO 30 combination thereof, or may be a standalone component. FIG. 3C illustrates possible locations of the QML component 398, which may be, for example, part of the one or more network transceivers 390, the memory7396, the one or more processors 394, or any combination thereof, or may be a standalone component.

[0112] The UE 302 may include one or more sensors 344 coupled to the one or more processors 342 to provide means for sensing or detecting movement and / or orientation information that is independent of motion data derived from signals received by the one or more WWAN transceivers 310, the one or more short-range wireless transceivers 320, and / or the satellite signal interface 330. By way of example, the sensor(s) 344 may include an accelerometer (e.g., a micro-electrical mechanical systems (MEMS) device), a gyroscope, a geomagnetic sensor (e.g., a compass), an altimeter (e.g., a barometric pressure altimeter), and / or any other ty pe of movement detection sensor. Moreover, the sensor(s) 344 may include a plurality of different types of devices and combine their outputs in order to provide motion information. For example, the sensor(s) 344 may use a combination of a multi-axis accelerometer and orientation sensors to provide the ability to compute positions in two-dimensional (2D) and / or three-dimensional (3D) coordinate systems.

[0113] In addition, the UE 302 includes a user interface 346 providing means for providing indications (e.g., audible and / or visual indications) to a user and / or for receiving user input (e.g., upon user actuation of a sensing device such a keypad, a touch screen, a microphone, and so on). Although not shown, the base station 304 and the network entity 306 may also include user interfaces.

[0114] Referring to the one or more processors 384 in more detail, in the downlink, IP packets from the network entity 306 may be provided to the processor 384. The one or more processors 384 may implement functionality for an RRC layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, and a medium access control (MAC) layer. The one or more processors 384 may provide RRC layer functionality associated with broadcasting of system information (e g., master information block (MIB), system information blocks (SIBs)), RRC connection control (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release), inter-RAT mobility, and measurement configuration for UE measurement reporting; PDCP layer functionality associated with header compression / decompression, security (ciphering, deciphering, integrity protection, integrity verification), and handover support functions; RLC layer1616-444WO01Qualcomm Ref. No. 2402459WO 31 functionality associated with the transfer of upper layer PDUs. error correction through automatic repeat request (ARQ), concatenation, segmentation, and reassembly of RLC service data units (SDUs), re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, scheduling information reporting, error correction, priority handling, and logical channel prioritization.

[0115] The transmitter 354 and the receiver 352 may implement Layer-1 (LI) functionality associated with various signal processing functions. Layer-1, which includes a physical (PHY) layer, may include error detection on the transport channels, forward error correction (FEC) coding / decoding of the transport channels, interleaving, rate matching, mapping onto physical channels, modulation / demodulation of physical channels, and MIMO antenna processing. The transmitter 354 handles mapping to signal constellations based on various modulation schemes (e.g., binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK). M-phase-shift keying (M-PSK), M- quadrature amplitude modulation (M-QAM)). The coded and modulated symbols may then be split into parallel streams. Each stream may then be mapped to an orthogonal frequency division multiplexing (OFDM) subcarrier, multiplexed with a reference signal (e.g., pilot) in the time and / or frequency domain, and then combined together using an inverse fast Fourier transform (IFFT) to produce a physical channel carrying a time domain OFDM symbol stream. The OFDM symbol stream is spatially precoded to produce multiple spatial streams. Channel estimates from a channel estimator may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimate may be derived from a reference signal and / or channel condition feedback transmitted by the UE 302. Each spatial stream may then be provided to one or more different antennas 356. The transmitter 354 may modulate an RF carrier with a respective spatial stream for transmission.

[0116] At the UE 302, the receiver 312 receives a signal through its respective antenna(s) 316. The receiver 312 recovers information modulated onto an RF carrier and provides the information to the one or more processors 342. The transmitter 314 and the receiver 312 implement Layer-1 functionality associated with various signal processing functions. The receiver 312 may perform spatial processing on the information to recover any spatial streams destined for the UE 302. If multiple spatial streams are destined for the UE 302, they may be combined by the receiver 312 into a single OFDM symbol stream. The receiver 312 then converts the OFDM symbol stream from the time-domain to the1616-444WO01Qualcomm Ref. No. 2402459WO 32 frequency domain using a fast Fourier transform (FFT). The frequency domain signal comprises a separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols on each subcarrier, and the reference signal, are recovered and demodulated by determining the most likely signal constellation points transmitted by the base station 304. These soft decisions may be based on channel estimates computed by a channel estimator. The soft decisions are then decoded and de-interleaved to recover the data and control signals that were originally transmitted by the base station 304 on the physical channel. The data and control signals are then provided to the one or more processors 342, which implements Layer-3 (L3) and Layer-2 (L2) functionality’.

[0117] In the downlink, the one or more processors 342 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, and control signal processing to recover IP packets from the core network. The one or more processors 342 are also responsible for error detection.

[0118] Similar to the functionality described in connection with the downlink transmission by the base station 304, the one or more processors 342 provides RRC layer functionality associated with system information (e.g., MIB, SIBs) acquisition, RRC connections, and measurement reporting; PDCP layer functionality associated with header compression / decompression, and security (ciphering, deciphering, integrity protection, integrity verification); RLC layer functionality' associated with the transfer of upper layer PDUs, error correction through ARQ, concatenation, segmentation, and reassembly of RLC SDUs. re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto transport blocks (TBs), demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through hybrid automatic repeat request (HARQ), priority handling, and logical channel prioritization.

[0119] Channel estimates derived by the channel estimator from a reference signal or feedback transmitted by the base station 304 may be used by the transmitter 314 to select the appropriate coding and modulation schemes, and to facilitate spatial processing. The spatial streams generated by the transmitter 314 may be provided to different antenna(s) 316. The transmitter 314 may modulate an RF carrier with a respective spatial stream for transmission.

[0120] The uplink transmission is processed at the base station 304 in a manner similar to that described in connection with the receiver function at the UE 302. The receiver 3521616-444WO01Qualcomm Ref. No. 2402459WO 33 receives a signal through its respective antenna(s) 356. The receiver 352 recovers information modulated onto an RF carrier and provides the information to the one or more processors 384.

[0121] In the uplink, the one or more processors 384 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover IP packets from the UE 302. IP packets from the one or more processors 384 may be provided to the core network. The one or more processors 384 are also responsible for error detection.

[0122] For convenience, the UE 302, the base station 304, and / or the network entity 306 are shown in FIGS. 3A, 3B, and 3C as including various components that may be configured according to the various examples described herein. It will be appreciated, however, that the illustrated components may have different functionality in different designs. In particular, various components in FIGS. 3A to 3C are optional in alternative configurations and the various aspects include configurations that may vary due to design choice, costs, use of the device, or other considerations. For example, in case of FIG. 3A, a particular implementation of UE 302 may omit the WWAN transceiver(s) 310 (e.g., a wearable device or tablet computer or personal computer (PC) or laptop may have Wi-Fi and / or BLUETOOTH® capability’ without cellular capability), or may omit the short- range wireless transceiver(s) 320 (e.g., cellular-only, etc.), or may omit the satellite signal interface 330, or may omit the sensor(s) 344, and so on. In another example, in case of FIG. 3B, a particular implementation of the base station 304 may omit the WWAN transceiver(s) 350 (e.g., a Wi-Fi “hotspot” access point without cellular capability), or may omit the short-range wireless transceiver(s) 360 (e.g., cellular-only, etc.), or may omit the satellite signal interface 370, and so on. For brevity7, illustration of the various alternative configurations is not provided herein, but would be readily understandable to one skilled in the art.

[0123] The various components of the UE 302, the base station 304. and the network entity7306 may be communicatively coupled to each other over data buses 308, 382, and 392, respectively. In an aspect, the data buses 308, 382, and 392 may form, or be part of, a communication interface of the UE 302, the base station 304, and the network entity 306, respectively. For example, where different logical entities are embodied in the same device (e.g., gNB and location server functionality incorporated into the same base station 304), the data buses 308, 382, and 392 may provide communication between them.1616-444WO01Qualcomm Ref. No. 2402459WO 34

[0124] The components of FIGS. 3 A. 3B. and 3C may be implemented in various ways. In some implementations, the components of FIGS. 3A, 3B, and 3C may be implemented in one or more circuits such as, for example, one or more processors and / or one or more ASICs (which may include one or more processors). Here, each circuit may use and / or incorporate at least one memory component for storing information or executable code used by the circuit to provide this functionality. For example, some or all of the functionality represented by blocks 310 to 346 may be implemented by processor and memoiy component(s) of the UE 302 (e.g., by execution of appropriate code and / or by appropriate configuration of processor components). Similarly, some or all of the functionality represented by blocks 350 to 388 may be implemented by processor and memory component(s) of the base station 304 (e.g., by execution of appropriate code and / or by appropriate configuration of processor components). Also, some or all of the functionality represented by blocks 390 to 398 may be implemented by processor and memoiy component(s) of the network entity 306 (e.g., by execution of appropriate code and / or by appropriate configuration of processor components). For simplicity, various operations, acts, and / or functions are described herein as being performed “by aUE,” “by a base station,” “by a network entity. ” etc. However, as will be appreciated, such operations, acts, and / or functions may actually be performed by specific components or combinations of components of the UE 302, base station 304, network entity 306, etc., such as the processors 342, 384, 394, the transceivers 310, 320, 350, and 360, the memories 340, 386, and 396, the QML component 348, 388, and 398, etc.

[0125] In some designs, the network entity 306 may be implemented as a core network component. In other designs, the network entity 306 may be distinct from a network operator or operation of the cellular network infrastructure (e.g., NG RAN 220 and / or 5GC 210 / 260). For example, the network entity 306 may be a component of a private network that may be configured to communicate with the UE 302 via the base station 304 or independently from the base station 304 (e.g.. over anon-cellular communication link, such as Wi-Fi).

[0126] Various frame structures may be used to support downlink and uplink transmissions between network nodes (e.g., base stations and UEs). FIG. 4 is a diagram 400 illustrating an example frame structure, according to aspects of the disclosure. The frame structure may be a downlink or uplink frame structure. Other wireless communications technologies may have different frame structures and / or different channels.1616-444WO01Qualcomm Ref. No. 2402459WO 35

[0127] LTE. and in some cases NR. utilizes orthogonal frequency-division multiplexing (OFDM) on the downlink and single-carrier frequency division multiplexing (SC-FDM) on the uplink. Unlike LTE, however, NR has an option to use OFDM on the uplink as well. OFDM and SC-FDM partition the system bandwidth into multiple (K) orthogonal subcarriers, which are also commonly referred to as tones, bins. etc. Each subcarrier may be modulated with data. In general, modulation symbols are sent in the frequency domain with OFDM and in the time domain with SC-FDM. The spacing between adjacent subcarriers may be fixed, and the total number of subcarriers (K) may be dependent on the system bandwidth. For example, the spacing of the subcarriers may be 15 kilohertz (kHz) and the minimum resource allocation (resource block) may be 12 subcarriers (or 180 kHz). Consequently, the nominal fast Fourier transform (FFT) size may be equal to 128, 256, 512, 1024, or 2048 for system bandwidth of 1.25, 2.5, 5, 10, or 20 megahertz (MHz), respectively. The system bandwidth may also be partitioned into subbands. For example, a subband may cover 1.08 MHz (i.e., 6 resource blocks), and there may be 1, 2. 4, 8, or 16 subbands for system bandwidth of 1.25, 2.5, 5, 10, or 20 MHz, respectively.

[0128] LTE supports a single numerology (subcarrier spacing (SCS), symbol length, etc.). In contrast, NR may support multiple numerologies (p), for example, subcarrier spacings of 15 kHz (p=0). 30 kHz (p=l). 60 kHz (p=2). 120 kHz (p=3), and 240 kHz (p=4) or greater may be available. In each subcarrier spacing, there are 14 symbols per slot. For 15 kHz SCS (p=0), there is one slot per subframe, 10 slots per frame, the slot duration is 1 millisecond (ms), the symbol duration is 66.7 microseconds (ps), and the maximum nominal system bandwidth (in MHz) with a 4K FFT size is 50. For 30 kHz SCS (p=l). there are two slots per subframe, 20 slots per frame, the slot duration is 0.5 ms, the symbol duration is 33.3 ps, and the maximum nominal system bandwidth (in MHz) with a 4K FFT size is 100. For 60 kHz SCS (p=2), there are four slots per subframe, 40 slots per frame, the slot duration is 0.25 ms, the symbol duration is 16.7 ps, and the maximum nominal system bandwidth (in MHz) with a 4K FFT size is 200. For 120 kHz SCS (p=3), there are eight slots per subframe, 80 slots per frame, the slot duration is 0. 125 ms, the symbol duration is 8.33 ps, and the maximum nominal system bandwidth (in MHz) with a 4K FFT size is 400. For 240 kHz SCS (p=4), there are 16 slots per subframe, 160 slots per frame, the slot duration is 0.0625 ms. the symbol duration is 4.17 ps, and the maximum nominal system bandwidth (in MHz) with a 4K. FFT size is 800.

[0129] In the example of FIG. 4, a numerology of 15 kHz is used. Thus, in the time domain, a 10 ms frame is divided into 10 equally sized subframes of 1 ms each, and each1616-444WO01Qualcomm Ref. No. 2402459WO 36 subframe includes one time slot. In FIG. 4, time is represented horizontally (on the X axis) with time increasing from left to right, while frequency is represented vertically (on the Y axis) with frequency increasing (or decreasing) from bottom to top.

[0130] A resource grid may be used to represent time slots, each time slot including one or more time-concurrent resource blocks (RBs) (also referred to as physical RBs (PRBs)) in the frequency domain. The resource grid is further divided into multiple resource elements (REs). An RE may correspond to one symbol length in the time domain and one subcarrier in the frequency domain. In the numerology of FIG. 4, for a normal cyclic prefix, an RB may contain 12 consecutive subcarriers in the frequency domain and seven consecutive symbols in the time domain, for a total of 84 REs. For an extended cyclic prefix, an RB may contain 12 consecutive subcarriers in the frequency domain and six consecutive symbols in the time domain, for a total of 72 REs. The number of bits carried by each RE depends on the modulation scheme.

[0131] Some of the REs may carry reference (pilot) signals (RS). The reference signals may include positioning reference signals (PRS), tracking reference signals (TRS), phase tracking reference signals (PTRS), cell-specific reference signals (CRS), channel state information reference signals (CSI-RS), demodulation reference signals (DMRS), primary’ synchronization signals (PSS). secondary synchronization signals (SSS). synchronization signal blocks (SSBs), sounding reference signals (SRS), etc., depending on whether the illustrated frame structure is used for uplink or downlink communication. FIG. 4 illustrates example locations of REs carry ing a reference signal (labeled “R”).

[0132] Sidelink communication takes place in transmission or reception resource pools. In the frequency domain, the minimum resource allocation unit is a sub-channel (e.g.. a collection of consecutive PRBs in the frequency domain). In the time domain, resource allocation is in one slot intervals. However, some slots are not available for sidelink, and some slots contain feedback resources. In addition, sidelink resources can be (pre)configured to occupy fewer than the 14 symbols of a slot.

[0133] Sidelink resources are configured at the radio resource control (RRC) layer. The RRC configuration can be by pre-configuration (e.g., preloaded on the UE) or configuration (e.g., from a serving base station).

[0134] A collection of resource elements (REs) that are used for transmission of PRS is referred to as a “PRS resource.” The collection of resource elements can span multiple PRBs in the frequency domain and ‘N’ (such as 1 or more) consecutive symbol(s) within1616-444WO01Qualcomm Ref. No. 2402459WO 37 a slot in the time domain. In a given OFDM symbol in the time domain, a PRS resource occupies consecutive PRBs in the frequency domain.

[0135] The transmission of a PRS resource within a given PRB has a particular comb size (also referred to as the “comb density'’). A comb size ‘N’ represents the subcarrier spacing (or frequency / tone spacing) within each symbol of a PRS resource configuration. Specifically, for a comb size ‘N,’ PRS are transmitted in every Nth subcarrier of a symbol of a PRB. For example, for comb-4, for each symbol of the PRS resource configuration, REs corresponding to every fourth subcarrier (such as subcarriers 0, 4, 8) are used to transmit PRS of the PRS resource. Currently, comb sizes of comb-2, comb-4, comb-6, and comb-12 are supported for DL-PRS. FIG. 4 illustrates an example PRS resource configuration for comb-4 (which spans four symbols). That is, the locations of the shaded REs (labeled “R”) indicate a comb-4 PRS resource configuration.

[0136] Currently, a DL-PRS resource may span 2, 4, 6, or 12 consecutive symbols within a slot with a fully frequency-domain staggered pattern. A DL-PRS resource can be configured in any higher layer configured downlink or flexible (FL) symbol of a slot. There may be a constant energy per resource element (EPRE) for all REs of a given DL- PRS resource. The following are the frequency offsets from symbol to symbol for comb sizes 2. 4, 6, and 12 over 2, 4. 6, and 12 symbols. 2-symbol comb-2: {0, 1 }; 4-symbol comb-2: {0, 1, 0, 1 }; 6-symbol comb-2: {0, 1, 0, 1, 0, 1 }; 12-symbol comb-2: {0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1 }; 4-symbol comb-4: {0, 2, 1, 3} (as in the example of FIG. 4); 12- symbol comb-4: {0, 2, 1, 3, 0, 2, 1, 3, 0, 2, 1, 3} ; 6-symbol comb-6: {0, 3, 1, 4, 2, 5}; 12- symbol comb-6: {0, 3, 1, 4, 2, 5. 0, 3, 1, 4, 2, 5}; and 12-symbol comb-12: {0, 6. 3, 9, 1, 7. 4, 10, 2, 8. 5, 11}.

[0137] A “PRS resource set” is a set of PRS resources used for the transmission of PRS signals, where each PRS resource has a PRS resource ID. In addition, the PRS resources in a PRS resource set are associated with the same TRP. A PRS resource set is identified by a PRS resource set ID and is associated with a particular TRP (identified by a TRP ID). In addition, the PRS resources in a PRS resource set have the same periodicity, a common muting pattern configuration, and the same repetition factor (such as “PRS- ResourceRepetitionF actor”) across slots. The periodicity is the time from the first repetition of the first PRS resource of a first PRS instance to the same first repetition of the same first PRS resource of the next PRS instance. The periodicity may have a length selected from 2Ap*{4, 5, 8, 10, 16, 20, 32, 40, 64, 80, 160, 320, 640, 1280, 2560, 5120,1616-444WO01Qualcomm Ref. No. 2402459WO 3810240} slots, with p = 0, 1, 2. 3. The repetition factor may have a length selected from {1, 2, 4, 6, 8, 16, 32} slots.

[0138] A PRS resource ID in a PRS resource set is associated with a single beam (or beam ID) transmitted from a single TRP (where a TRP may transmit one or more beams). That is. each PRS resource of a PRS resource set may be transmitted on a different beam, and as such, a “PRS resource,” or simply “resource,” also can be referred to as a “beam.” Note that this does not have any implications on whether the TRPs and the beams on which PRS are transmitted are known to the UE.

[0139] A “PRS instance” or “PRS occasion” is one instance of a periodically repeated time window (such as a group of one or more consecutive slots) where PRS are expected to be transmitted. A PRS occasion also may be referred to as a “PRS positioning occasion,” a “PRS positioning instance, a “positioning occasion,” “a positioning instance,” a “positioning repetition,” or simply an “occasion.” an “instance,” or a “repetition.”

[0140] A “positioning frequency layer” (also referred to simply as a “frequency layer”) is a collection of one or more PRS resource sets across one or more TRPs that have the same values for certain parameters. Specifically, the collection of PRS resource sets has the same subcarrier spacing and cyclic prefix (CP) type (meaning all numerologies supported for the physical downlink shared channel (PDSCH) are also supported for PRS), the same Point A, the same value of the downlink PRS bandwidth, the same start PRB (and center frequency), and the same comb-size. The Point A parameter takes the value of the parameter “ARFCN-ValueNR” (where “ARFCN” stands for “absolute radiofrequency channel number”) and is an identifier / code that specifies a pair of physical radio channel used for transmission and reception. The downlink PRS bandwidth may have a granularity of four PRBs, with a minimum of 24 PRBs and a maximum of 272 PRBs. Currently, up to four frequency layers have been defined, and up to two PRS resource sets may be configured per TRP per frequency layer.

[0141] The concept of a frequency layer is somewhat like the concept of component carriers and bandwidth parts (BWPs), but different in that component carriers and BWPs are used by one base station (or a macro cell base station and a small cell base station) to transmit data channels, while frequency layers are used by several (usually three or more) base stations to transmit PRS. A UE may indicate the number of frequency layers it can support when it sends the netwnrk its positioning capabilities, such as during an LTE1616-444WO01Qualcomm Ref. No. 2402459WO 39 positioning protocol (LPP) session. For example, a UE may indicate whether it can support one or four positioning frequency layers.

[0142] Note that the terms “positioning reference signal” and “PRS” generally refer to specific reference signals that are used for positioning in NR and LTE systems. However, as used herein, the terms “positioning reference signal” and “PRS” may also refer to any type of reference signal that can be used for positioning, such as but not limited to, PRS as defined in LTE and NR, TRS, PTRS, CRS, CSI-RS, DMRS, PSS, SSS, SSB, SRS, UL-PRS, etc. In addition, the terms “positioning reference signal” and “PRS” may refer to downlink, uplink, or sidelink positioning reference signals, unless otherwise indicated by the context. If needed to further distinguish the type of PRS, a downlink positioning reference signal may be referred to as a “DL-PRS,” an uplink positioning reference signal (e.g., an SRS-for-positioning, PTRS) may be referred to as an “UL-PRS,” and a sidelink positioning reference signal may be referred to as an “SL-PRS.” In addition, for signals that may be transmitted in the downlink, uplink, and / or sidelink (e.g., DMRS), the signals may be prepended with “DL,” “UL,” or “SL” to distinguish the direction. For example, “UL-DMRS” is different from “DL-DMRS.”

[0143] In an aspect, the reference signal carried on the REs labeled “R” in FIG. 4 may be SRS. SRS transmitted by a UE may be used by a base station to obtain the channel state information (CSI) for the transmitting UE. CSI describes how an RF signal propagates from the UE to the base station and represents the combined effect of scattering, fading, and power decay with distance. The system uses the SRS for resource scheduling, link adaptation, massive MIMO, beam management, etc.

[0144] A collection of REs that are used for transmission of SRS is referred to as an “SRS resource,” and may be identified by the parameter “SRS-Resourceld.” The collection of resource elements can span multiple PRBs in the frequency domain and ‘N’ (e.g., one or more) consecutive symbol(s) w ithin a slot in the time domain. In a given OFDM symbol, an SRS resource occupies one or more consecutive PRBs. An “SRS resource set” is a set of SRS resources used for the transmission of SRS signals, and is identified by an SRS resource set ID (“SRS-ResourceSetld”).

[0145] The transmission of SRS resources within a given PRB has a particular comb size (also referred to as the “comb density”). A comb sizeLN’ represents the subcarrier spacing (or frequency / tone spacing) within each symbol of an SRS resource configuration. Specifically, for a comb size ’N,’ SRS are transmitted in every Nth subcarrier of a sy mbol of a PRB. For example, for comb-4, for each symbol of the SRS1616-444WO01Qualcomm Ref. No. 2402459WO 40 resource configuration. REs corresponding to every fourth subcarrier (such as subcarriers 0, 4, 8) are used to transmit SRS of the SRS resource. In the example of FIG. 4, the illustrated SRS is comb-4 over four symbols. That is, the locations of the shaded SRS REs indicate a comb-4 SRS resource configuration.

[0146] Currently, an SRS resource may span 1, 2, 4, 8, or 12 consecutive symbols within a slot w ith a comb size of comb-2, comb-4, or comb-8. The following are the frequency offsets from symbol to symbol for the SRS comb patterns that are currently supported. 1 - symbol comb-2: {0}; 2-symbol comb-2: {0, 1}; 2-symbol comb-4: {0, 2}; 4-symbol comb-2: {0, 1, 0, 1 }; 4-symbol comb-4: {0, 2, 1, 3} (as in the example of FIG. 4); 8- symbol comb-4: {0, 2, 1, 3, 0, 2, 1, 3}; 12-symbol comb-4: {0, 2, 1, 3, 0, 2, 1, 3, 0, 2, 1, 3}; 4-symbol comb-8: {0, 4, 2, 6}; 8-symbol comb-8: {0, 4, 2, 6, 1, 5, 3, 7); and 12- symbol comb-8: {0, 4, 2, 6, 1, 5, 3, 7, 0, 4, 2, 6}.

[0147] Generally, as noted above, a UE transmits SRS to enable the receiving base station (either the serving base station or a neighboring base station) to measure the channel quality (i.e., CSI) between the UE and the base station. However, SRS can also be specifically configured as uplink positioning reference signals for uplink-based positioning procedures, such as uplink time difference of arrival (UL-TDOA), round-triptime (RTT), uplink angle-of-arrival (UL-AoA), etc. As used herein, the term ■’SRS ' may refer to SRS configured for channel quality measurements or SRS configured for positioning purposes. The former may be referred to herein as “SRS-for-communication” and / or the latter may be referred to as ‘‘SRS -for-positioning” or “positioning SRS” when needed to distinguish the two types of SRS.

[0148] Several enhancements over the previous definition of SRS may be available for SRS-for-positioning (also referred to as “UL-PRS”), such as a new staggered pattern within an SRS resource (except for single-symbol / comb-2), a new comb type for SRS, new sequences for SRS, a higher number of SRS resource sets per component carrier, and a higher number of SRS resources per component carrier. In addition, the parameters “SpatialRelationlnfo” and “PathLossReference” are to be configured based on a downlink reference signal or SSB from a neighboring TRP. Further still, one SRS resource may be transmitted outside the active BWP, and one SRS resource may span across multiple component carriers. Also, SRS may be configured in RRC connected state and only transmitted within an active BWP. Further, there may be no frequency hopping, no repetition factor, a single antenna port, and new lengths for SRS (e.g., 8 and 12 symbols). There also may be open-loop power control and not closed-loop power control, and comb-1616-444WO01Qualcomm Ref. No. 2402459WO 418 (i.e., an SRS transmited every eighth subcarrier in the same symbol) may be used. Lastly, the UE may transmit through the same transmit beam from multiple SRS resources for UL-AoA. These features may be configured through RRC higher layer signaling (and potentially triggered or activated through a MAC control element (MAC-CE) or downlink control information (DCI)).

[0149] NR sidelinks support hybrid automatic repeat request (HARQ) retransmission. FIG. 5A is a diagram 500 of an example slot structure without feedback resources, according to aspects of the disclosure. In the example of FIG. 5A, time is represented horizontally and frequency is represented vertically. In the time domain, the length of each block is one orthogonal frequency division multiplexing (OFDM) symbol, and the 14 symbols make up a slot. In the frequency domain, the height of each block is one subchannel. Currently, the (pre)configured sub-channel size can be selected from the set of { 10, 15, 20, 25, 50, 75. 100} physical resource blocks (PRBs).

[0150] For a sidelink slot, the first symbol is a repetition of the preceding symbol and is used for automatic gain control (AGC) seting. This is illustrated in FIG. 5 A by the vertical and horizontal hashing. As shown in FIG. 5A, for sidelink, the physical sidelink control channel (PSCCH) and the physical sidelink shared channel (PSSCH) are transmited in the same slot. Similar to the physical downlink control channel (PDCCH). the PSCCH carries control information about sidelink resource allocation and descriptions about sidelink data transmited to the UE. Likewise, similar to the physical downlink shared channel (PDSCH), the PSSCH carries user data for the UE. In the example of FIG. 5A, the PSCCH occupies half the bandwidth of the sub-channel and only three symbols. Finally, a gap symbol is present after the PSSCH.

[0151] FIG. 5B is a diagram 550 of an example slot structure with feedback resources, according to aspects of the disclosure. In the example of FIG. 5B, time is represented horizontally and frequency is represented vertically. In the time domain, the length of each block is one OFDM symbol, and the 14 symbols make up a slot. In the frequency domain, the height of each block is one sub-channel.

[0152] The slot structure illustrated in FIG. 5B is similar to the slot structure illustrated in FIG. 5A, except that the slot structure illustrated in FIG. 5B includes feedback resources. Specifically, two symbols at the end of the slot have been dedicated to the physical sidelink feedback channel (PSFCH). The first PSFCH symbol is a repetition of the second PSFCH symbol for AGC seting. In addition to the gap symbol after the1616-444WO01Qualcomm Ref. No. 2402459WO 42PSSCH, there is a gap symbol after the two PSFCH symbols. Currently, resources for the PSFCH can be configured with a periodicity selected from the set of {0, 1 , 2, 4} slots.

[0153] FIG. 6A is a diagram 600 illustrating an example of a resource pool for positioning configured within a sidelink resource pool for communication (i.e., a shared resource pool), according to aspects of the disclosure. In the example of FIG. 6A, time is represented horizontally and frequency is represented vertically. In the time domain, the length of each block is an orthogonal frequency division multiplexing (OFDM) symbol, and the 14 symbols make up a slot. In the frequency domain, the height of each block is a sub-channel.

[0154] In the example of FIG. 6A, the entire slot (except for the first and last symbols) can be a resource pool for sidelink communication. That is, any of the symbols other than the first and last can be allocated for sidelink communication. However, a resource pool for positioning (RP-P) is allocated in the last four pre-gap symbols of the slot. As such, non-sidelink positioning data, such as user data (PSSCH). channel state information reference signal (CSI-RS), and control information, can only be transmitted in the first eight post-automatic gain control (AGC) symbols and not in the last four pre-gap symbols to prevent a collision with the configured RP-P. The non-sidelink positioning data that would otherwise be transmitted in the last four pre-gap symbols can be punctured or muted, or the non-sidelink data that would normally span more than the eight post-AGC symbols can be rate matched to fit into the eight post-AGC symbols.

[0155] Sidelink positioning reference signals (SL-PRS) have been defined to enable sidelink positioning procedures among UEs. Like a downlink PRS (DL-PRS), a SL-PRS resource is composed of one or more resource elements (i.e.. one OFDM symbol in the time domain and one subcarrier in the frequency domain). SL-PRS resources have been designed with a comb-based pattern to enable fast Fourier transform (FFT)-based processing at the receiver. SL-PRS resources are composed of unstaggered, or only partially staggered, resource elements in the frequency domain to provide small time of arrival (TOA) uncertainty and reduced overhead of each SL-PRS resource. SL-PRS may also be associated with specific RP-Ps (e.g., certain SL-PRS may be allocated in certain RP-Ps). SL-PRS have also been defined with intra-slot repetition (not shown in FIG. 6A) to allow for combining gains (if needed). There may also be inter-UE coordination of RP- Ps to provide for dynamic SL-PRS and data multiplexing while minimizing SL-PRS collisions.1616-444WO01Qualcomm Ref. No. 2402459WO 43

[0156] FIGS. 6B and 6C are diagrams 630 and 650, respectively, illustrating additional examples of resource pools for positioning configured within sidelink resource pools for communication. Similar to FIG. 6A, the examples of FIGS. 6B and 6C illustrate shared resource pool structures. With respect to FIGS. 6B and 6C. in some designs, the following parameters may be defined, for example: physical sidelink control channel (PSCCH) and SL-PRS are only time-division multiplexed, PSSCH and SL-PRS are only time-division multiplexed (e.g., the maximum comb size is 4), PSSCH carries both type 2 sidelink control information (SCI-2) and a sidelink shared channel (SL-SCH) (e.g., a new SCI-2 format is introduced), SL-PRS is mapped on consecutive symbols. SL-PRS is not mapped on symbols with PSSCH demodulation reference signals (DMRS), and / or SL-PRS transmit power is the same as the transmit power of the PSSCH (e.g., this implies perresource element power boosting will be applied for comb-2 and comb-4).

[0157] FIG. 6D is a diagram 670 illustrating another example of a resource pool for positioning configured within a sidelink resource pool for communication. In the example of FIG. 6D, a dedicated resource pool structure is depicted. With respect to FIG. 6D, in some designs, the following parameters may be defined, for example: SL-PRS is immediately preceded by an AGC symbol, SL-PRS is immediately followed by a gap symbol (at least when the gap symbol is the last sidelink symbol in a slot), PSCCH and SL-PRS can only be time-division multiplexed, different comb sizes (N) and SL-PRS durations (M) can be supported in the same resource pool (e.g., one set of SL-PRS resources can only have a single (M, N) combination), PSSCH is mapped to the first sidelink symbols in a slot, the number of PSCCH symbols is (pre-)configured to 1, 2, or 3. the number of physical resource blocks is (pre-)configured using sidelink communications values, and / or there is a one-to-one implicit mapping between PSCCH and SL-PRS.

[0158] In some designs, in a shared resource pool, with regards to the fields in SCI format 2-D, the following fields may be included, for example: a SL-PRS resource information indication of the current slot (ceiling(log2(#SL-PRS resources (pre-)configured in the resource pool) bits)), SL-PRS request (0 or 1 bit), and / or embedded SCI format ([X] bit(s)). If the “embedded SCI format” field is set to [0], the SCI 2-A fields are included with necessary padding. If the “embedded SCI format” field is set to [1]. the SCI 2-B fields are included.

[0159] In some designs, for a shared resource pool, there may be an explicit (pre- )configuration of SL-PRS resources in a slot, applicable for an indicated frequency1616-444WO01Qualcomm Ref. No. 2402459WO 44 domain allocation, which includes, for example: SL-PRS Resource ID, (M. N) pattern, and / or comb offset. In some designs, for a given value of ‘M,’ a SL-PRS resource is mapped to the last consecutive ‘M’ sidelink symbol(s) in the slot that can be used for SL- PRS, taking into consideration multiplexing with PSSCH DMRS, phase tracking reference signals (PT-RS), CSI-RS, PSFCH. gap symbols. AGC symbols, and / or PSCCH in the slot. In some designs, the maximum number of SL-PRS resources in a slot of a shared resource pool may be (pre-)configured.

[0160] In some designs, in dedicated resource pools, with regards to the procedure for determining the subset of resources to be reported to higher layers, when triggering the resource (re-)selection procedure, the higher layers provide the following parameters for candidate SL-PRS transmission(s), for example: resource pool from which to report SL- PRS resources, priority, delay budget, reservation period, list of resources for pre-emption and re-evaluation, and / or the set of SL-PRS resource identifiers that can include all (pre- )configured SL-PRS resource identifiers.

[0161] NR supports a number of cellular network-based positioning technologies, including downlink-based, uplink-based, and downlink-and-uplink-based positioning methods. Downlink-based positioning methods include observed time difference of arrival (OTDOA) in LTE, downlink time difference of arrival (DL-TDOA) in NR, and downlink angle-of-departure (DL-AoD) in NR. FIG. 7 illustrates examples of various positioning methods, according to aspects of the disclosure. In an OTDOA or DL-TDOA positioning procedure, illustrated by scenario 710, a UE measures the differences between the times of arrival (ToAs) of reference signals (e.g.. positioning reference signals (PRS)) received from pairs of base stations, referred to as reference signal time difference (RSTD) or time difference of arrival (TDOA) measurements, and reports them to a positioning entity. More specifically, the UE receives the identifiers (IDs) of a reference base station (e.g., a serving base station) and multiple non-reference base stations in assistance data. The UE then measures the RSTD between the reference base station and each of the non-reference base stations. Based on the known locations of the involved base stations and the RSTD measurements, the positioning entity (e.g., the UE for UE- based positioning or a location server for UE-assisted positioning) can estimate the UE’s location.

[0162] For DL-AoD positioning, illustrated by scenario 720, the positioning entity uses a measurement report from the UE of received signal strength measurements of multiple downlink transmit beams to determine the angle(s) betw een the UE and the transmitting1616-444WO01Qualcomm Ref. No. 2402459WO 45 base station(s). The positioning entity can then estimate the location of the UE based on the determined angle(s) and the known location(s) of the transmitting base station(s).

[0163] Uplink-based positioning methods include uplink time difference of arrival (UL- TDOA) and uplink angle-of-arrival (UL-AoA). UL-TDOA is similar to DL-TDOA, but is based on uplink reference signals (e.g., sounding reference signals (SRS)) transmitted by the UE to multiple base stations. Specifically, a UE transmits one or more uplink reference signals that are measured by a reference base station and a plurality of nonreference base stations. Each base station then reports the reception time (referred to as the relative time of arrival (RTOA)) of the reference signal(s) to a positioning entity (e.g., a location server) that knows the locations and relative timing of the involved base stations. Based on the reception-to-reception (Rx-Rx) time difference between the reported RTOA of the reference base station and the reported RTOA of each nonreference base station, the known locations of the base stations, and their known timing offsets, the positioning entity can estimate the location of the UE using TDOA.

[0164] For UL-AoA positioning, one or more base stations measure the received signal strength of one or more uplink reference signals (e.g., SRS) received from a UE on one or more uplink receive beams. The positioning entity uses the signal strength measurements and the angle(s) of the receive beam(s) to determine the angle(s) between the UE and the base station(s). Based on the determined angle(s) and the known location(s) of the base station(s), the positioning entity can then estimate the location of the UE.

[0165] Downlink-and-uplink-based positioning methods include enhanced cell-ID (E- CID) positioning and multi-round-trip-time (RTT) positioning (also referred to as '‘multicell RTT” and '‘multi-RTT”). In an RTT procedure, a first entity (e.g., a base station or a UE) transmits a first RTT-related signal (e.g., a PRS or SRS) to a second entity (e.g., a UE or base station), which transmits a second RTT-related signal (e.g., an SRS or PRS) back to the first entity. Each entity measures the time difference between the time of arrival (ToA) of the received RTT-related signal and the transmission time of the transmitted RTT-related signal. This time difference is referred to as a reception-to- transmission (Rx-Tx) time difference. The Rx-Tx time difference measurement may be made, or may be adjusted, to include only a time difference between nearest slot boundaries for the received and transmitted signals. Both entities may then send their Rx- Tx time difference measurement to a location server (e.g., an LMF 270), which calculates the round trip propagation time (i. e. , RTT) between the two entities from the two Rx-Tx1616-444WO01Qualcomm Ref. No. 2402459WO 46 time difference measurements (e.g.. as the sum of the two Rx-Tx time difference measurements). Alternatively, one entity may send its Rx-Tx time difference measurement to the other entity, which then calculates the RTT. The distance between the two entities can be determined from the RTT and the known signal speed (e.g., the speed of light). For multi-RTT positioning, illustrated by scenario 730, a first entity (e.g.. a UE or base station) performs an RTT positioning procedure with multiple second entities (e.g., multiple base stations or UEs) to enable the location of the first entity to be determined (e.g., using multilateration) based on distances to, and the known locations of, the second entities. RTT and multi-RTT methods can be combined with other positioning techniques, such as UL-AoA and DL-AoD, to improve location accuracy, as illustrated by scenario 740.

[0166] The E-CID positioning method is based on radio resource management (RRM) measurements. In E-CID, the UE reports the serving cell ID, the timing advance (TA), and the identifiers, estimated timing, and signal strength of detected neighbor base stations. The location of the UE is then estimated based on this information and the known locations of the base station(s).

[0167] To assist positioning operations, a location server (e.g., location server 230, LMF 270, SLP 272) may provide assistance data to the UE. For example, the assistance data may include identifiers of the base stations (or the cells / TRPs of the base stations) from which to measure reference signals, the reference signal configuration parameters (e.g., the number of consecutive slots including PRS, periodicity of the consecutive slots including PRS, muting sequence, frequency hopping sequence, reference signal identifier, reference signal bandwidth, etc.), and / or other parameters applicable to the particular positioning method. Alternatively, the assistance data may originate directly from the base stations themselves (e.g., in periodically broadcasted overhead messages, etc.). In some cases, the UE may be able to detect neighbor network nodes itself without the use of assistance data.

[0168] In the case of an OTDOA or DL-TDOA positioning procedure, the assistance data may further include an expected RSTD value and an associated uncertainty, or search window, around the expected RSTD. In some cases, the value range of the expected RSTD may be + / - 500 microseconds (ps). In some cases, when any of the resources used for the positioning measurement are in FR1, the value range for the uncertainty of the expected RSTD may be + / - 32 ps. In other cases, when all of the resources used for the1616-444WO01Qualcomm Ref. No. 2402459WO 47 positioning measurement(s) are in FR2, the value range for the uncertainty of the expected RSTD may be + / - 8 ps.

[0169] A location estimate may be referred to by other names, such as a position estimate, location, position, position fix, fix, or the like. A location estimate may be geodetic and comprise coordinates (e.g.. latitude, longitude, and possibly altitude) or may be civic and comprise a street address, postal address, or some other verbal description of a location. A location estimate may further be defined relative to some other known location or defined in absolute terms (e.g., using latitude, longitude, and possibly altitude). A location estimate may include an expected error or uncertainty (e.g., by including an area or volume within which the location is expected to be included with some specified or default level of confidence).

[0170] NR supports, or enables, various sidelink positioning techniques. FIG. 8A illustrates various scenarios of interest for sidelink-only or joint Uu and sidelink positioning, according to aspects of the disclosure. In scenario 810, at least one peer UE with a known location can improve the Uu-based positioning (e.g., multi-cell round-triptime (RTT), downlink time difference of arrival (DL-TDOA), etc.) of a target UE by providing an additional anchor (e.g., using sidelink RTT (SL-RTT)). In scenario 820, a low-end (e.g., reduced capacity, or “RedCap’?) target UE may obtain the assistance of premium UEs to determine its location using, e.g., sidelink positioning and ranging procedures with the premium UEs. Compared to the low-end UE, the premium UEs may have more capabilities, such as more sensors, a faster processor, more memon'. more antenna elements, higher transmit power capability, access to additional frequency’ bands, or any combination thereof. In scenario 830. a relay UE (e.g., with a known location) participates in the positioning estimation of a remote UE without performing uplink positioning reference signal (PRS) transmission over the Uu interface. Scenario 840 illustrates the joint positioning of multiple UEs. Specifically, in scenario 840, two UEs with unknown positions can be jointly located in non-line-of-sight (NLOS) conditions by utilizing constraints from nearby UEs.

[0171] FIG. 8B illustrates additional scenarios of interest for sidelink-only or joint Uu and sidelink positioning, according to aspects of the disclosure. In scenario 850, UEs used for public safety (e.g., by police, firefighters, and / or the like) may perform peer-to- peer (P2P) positioning and ranging for public safety and other uses. For example, in scenario 850, the public safety7UEs may be out of coverage of a network and determine a location or a relative distance and a relative position among the public safety7UEs using1616-444WO01Qualcomm Ref. No. 2402459WO 48 sidelink positioning techniques. Similarly, scenario 860 shows multiple UEs that are out of coverage and determine a location or a relative distance and a relative position using sidelink positioning techniques, such as SL-RTT.

[0172] Machine learning may be used to generate models that may be used to facilitate various aspects associated with processing of data. One specific application of machine learning relates to generation of measurement models for processing of reference signals for positioning (e.g., positioning reference signal (PRS)), such as feature extraction, reporting of reference signal measurements (e.g.. selecting which extracted features to report), and so on.

[0173] Machine learning models are generally categorized as either supervised or unsupervised. A supervised model may further be sub-categorized as either a regression or classification model. Supervised learning involves learning a function that maps an input to an output based on example input-output pairs. For example, given a training dataset with two variables of age (input) and height (output), a supervised learning model could be generated to predict the height of a person based on their age. In regression models, the output is continuous. One example of a regression model is a linear regression, which simply attempts to find a line that best fits the data. Extensions of linear regression include multiple linear regression (e.g., finding a plane of best fit) and polynomial regression (e.g., finding a curve of best fit).

[0174] Another example of a machine learning model is a decision tree model. In a decision tree model, a tree structure is defined with a plurality of nodes. Decisions are used to move from a root node at the top of the decision tree to a leaf node at the bottom of the decision tree (i.e., a node with no further child nodes). Generally, a higher number of nodes in the decision tree model is correlated with higher decision accuracy.

[0175] Another example of a machine learning model is a decision forest. Random forests are an ensemble learning technique that builds off of decision trees. Random forests involve creating multiple decision trees using bootstrapped datasets of the original data and randomly selecting a subset of variables at each step of the decision tree. The model then selects the mode of all of the predictions of each decision tree. By relying on a “majority wins'’ model, the risk of error from an individual tree is reduced.

[0176] Another example of a machine learning model is a neural network (NN). A neural network is essentially a network of mathematical equations. Neural networks accept one or more input variables, and by going through a network of equations, result in one or1616-444WO01Qualcomm Ref. No. 2402459WO 49 more output variables. Put another way. a neural network takes in a vector of inputs and returns a vector of outputs.

[0177] FIG. 9 illustrates an example neural network 900, according to aspects of the disclosure. The neural network 900 includes an input layer ‘i’ that receives ‘n’ (one or more) inputs (illustrated as “Input 1 ,” “Input 2,” and “Input n”), one or more hidden layers (illustrated as hidden layers ‘hl,’ ‘h2,’ and ‘h3’) for processing the inputs from the input layer, and an output layer ‘o’ that provides ‘m’ (one or more) outputs (labeled “Output 1” and “Output m”). The number of inputs ‘n,’ hidden layers ‘h,’ and outputs ‘m’ may be the same or different. In some designs, the hidden layers ‘h’ may include linear function(s) and / or activation function(s) that the nodes (illustrated as circles) of each successive hidden layer process from the nodes of the previous hidden layer.

[0178] In classification models, the output is discrete. One example of a classification model is logistic regression. Logistic regression is similar to linear regression but is used to model the probability of a finite number of outcomes, typically two. In essence, a logistic equation is created in such a way that the output values can only be between ‘0’ and ‘1.’ Another example of a classification model is a support vector machine. For example, for two classes of data, a support vector machine will find a hyperplane or a boundary between the two classes of data that maximizes the margin between the two classes. There are many planes that can separate the two classes, but only one plane can maximize the margin or distance between the classes. Another example of a classification model is Naive Bayes, which is based on Bayes Theorem. Other examples of classification models include decision tree, random forest, and neural network, similar to the examples described above except that the output is discrete rather than continuous.

[0179] Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without references to labeled outcomes. Two examples of unsupervised learning models include clustering and dimensionality reduction.

[0180] Clustering is an unsupervised technique that involves the grouping, or clustering, of data points. Clustering is frequently used for customer segmentation, fraud detection, and document classification. Common clustering techniques include k-means clustering, hierarchical clustering, mean shift clustering, and density-based clustering. Dimensionality reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables. In simpler terms, dimensionality reduction is the process of reducing the dimension of a feature set (in even simpler terms, reducing the number of features). Most dimensionality reduction techniques can be1616-444WO01Qualcomm Ref. No. 2402459WO 50 categorized as either feature elimination or feature extraction. One example of dimensionality reduction is called principal component analysis (PCA). In the simplest sense, PCA involves project higher dimensional data (e.g., three dimensions) to a smaller space (e.g., two dimensions). This results in a lower dimension of data (e.g., two dimensions instead of three dimensions) while keeping all original variables in the model.

[0181] Regardless of which machine learning model is used, at a high-level, a machine learning module (e.g., implemented by a processing system) may be configured to iteratively analyze training input data (e.g., measurements of reference signals to / from various target UEs) and to associate this training input data with an output data set (e.g.. a set of possible or likely candidate locations of the various target UEs), thereby enabling later determination of the same output data set when presented with similar input data (e.g., from other target UEs at the same or similar location).

[0182] In some designs, a position estimation entity (e.g., UE, gNB, LMF, etc.) may utilize a direct AI / ML position estimation technique. In this case, a direct (D)-AI / ML model is trained so as to accept input data (e g., DL-PRS measurements, UL-PRS measurements, SL-PRS measurements, etc.) that is processed to provide a position estimate of a UE as an output (i.e., a direct label).

[0183] In some designs, an assisted (or indirect) AI / ML position estimation technique is utilized. In this case, an assisted (A)-AI / ML model is trained so as to accept input data (e.g., DL-PRS measurements, UL-PRS measurements, SL-PRS measurements, etc.) that is processed to provide intermediate data as an output (i.e., or intermediate label, sometimes referred to as positioning feature extraction, such as timing / angle information, LOS identification, etc.), with the intermediate data in turn provided as an input to another position estimation model. Note that the other position estimation model may be another AI / ML model or anon-AI model (e.g., Chan’s algorithm, a Kalman Filter (KF) algorithm, etc.). Also the A-AI / ML model and the another model may be implemented at the same entity (e.g.. UE, LMF, etc.) or at different entities (e.g., for network-assisted positioning. UE applies the A-AI / ML model to compress the measurement data, which is then reported to the LMF, which then applies the other position estimation model; for UE-based positioning, network component such as gNB or LMF or another UE for sidelink applies the A-AI / ML model to compress the measurement data, which is then reported to the UE. which then applies the other position estimation model).1616-444WO01Qualcomm Ref. No. 2402459WO 51

[0184] Note that, as used herein, an Al / ML model (e.g.. A-A1 / ML model or D-AI / ML model) may be alternatively referred to as an ‘'ML model” or an ‘'Al model” or an ‘ML- based model” or an “Al-based model,” and so on.

[0185] In example aspects, an ML model may be trained prior to, or at some point following, operation of the ML model, such as neural network 900. on input data. When training the ML model, information in the form of applicable training data may be gathered or otherwise created for use in training an ANN accordingly. For example, training data may be gathered or otherwise created regarding information associated with received / transmitted signal strengths, interference, and resource usage data, as well as any other relevant data that might be useful for training a model to address one or more problems or issues in a communication system. In certain instances, all or part of the training data may originate in a user equipment (UE) or other device in a wireless communication system, or one or more network entities, or aggregated from multiple sources (such as a UE and a network entity / entities. one or more other UEs, the Internet, or the like). For example, wireless network architectures, such as self-organizing networks (SON) or mobile drive test (MDT) networks, may be adapted to support collection of data for ML model applications. In another example, training data may be generated or collected online, offline, or both online and offline by a UE, network entity, or other device(s), and all or part of such training data may be transferred or shared (in real or near-real time), such as through store and forw ard functions or the like.

[0186] Offline training may refer to creating and using a static training dataset, such as, in a batched manner, whereas online training may refer to a real-time collection and use of training data. For example, an ML model at a network device (such as, a UE) may be trained or fine-tuned using online or offline training. For offline training, data collection and training can occur in an offline manner at the network side (such as, at a base station or other network entity ) or at the UE side. For online training, the training of a UE-side ML model may be performed locally at the UE or by a server device (such as, a server hosted by a UE vendor) in a real-time or near-real-time manner based on data provided to the server device from the UE. In certain instances, all or part of the training data may be shared within in a wireless communication system, or even shared (or obtained from) outside of the wireless communication system.

[0187] Once an ANN has been configured by setting parameters, including weights and biases, from training data, the ANN’s performance may be evaluated. In some scenarios, evaluation / verification tests may use a validation dataset, which may include data not in1616-444WO01Qualcomm Ref. No. 2402459WO 52 the training data, to compare the model’s performance to baseline or other benchmark information. The ANN configuration may be further refined, for example, by changing its architecture, retraining it on the data, or using different optimization techniques, etc.

[0188] As part of a training process, parameters affecting the functioning of the artificial neurons and layers may be adjusted. For example, backpropagation techniques may be used to train an ANN by iteratively adjusting weights or biases of certain artificial neurons associated with errors between a predicted output of the model and a desired output that may be known or otherwise deemed acceptable. Backpropagation may include a forw ard pass, a loss function, a backward pass, and a parameter update that may be performed in training iteration. The process may be repeated for a certain number of iterations for each set of training data until the weights of the artificial neurons / layers are adequately tuned.

[0189] Backpropagation techniques associated with a loss function may measure how well a model is able to predict a desired output for a given input. An optimization algorithm may be used during a training process to adjust weights and biases as needed to reduce or minimize the loss function which should improve the performance of the model. There are a variety of optimization algorithms that may be used along with backpropagation techniques or other training techniques. Some initial examples include a gradient descent-based optimization algorithm and a stochastic gradient descent-based optimization algorithm. A stochastic gradient descent technique may be used to adjust weights / biases in order to minimize or otherwise reduce a loss function. A mini-batch gradient descent technique, which is a variant of gradient descent, may involve updating weights / biases using a small batch of training data rather than the entire dataset. A momentum technique may accelerate an optimization process by adding a momentum term to update or otherw ise affect certain weights / biases.

[0190] An adaptive learning rate technique may adjust a learning rate of an optimization algorithm associated with one or more characteristics of the training data. A batch normalization technique may be used to normalize inputs to a model in order to stabilize a training process and potentially improve the performance of the model. A ‘'dropout” technique may be used to randomly drop out some of the artificial neurons from a model during a training process, for example, in order to reduce overfitting and potentially improve the generalization of the model. An “early stopping” technique may be used to stop an on-going training process early, such as when a performance of the model using a validation dataset starts to degrade.1616-444WO01Qualcomm Ref. No. 2402459WO 53

[0191] Another example technique includes data augmentation to generate additional training data by applying transformations to all or part of the training information. A transfer learning technique may be used which involves using a pre-trained model as a starting point for training a new model, which may be useful when training data is limited or when there are multiple tasks that are related to each other. A multi-task learning technique may be used which involves training a model to perform multiple tasks simultaneously to potentially improve the performance of the model on one or more of the tasks. Hyperparameters or the like may be input and applied during a training process in certain instances.

[0192] Another example technique that may be useful with regard to an ANN is a “pruning” technique. A pruning technique, which may be performed during a training process or after a model has been trained, involves the removal of unnecessary or less necessary , or possibly redundant features from a model. In certain instances, a pruning technique may reduce the complexity of a model or improve efficiency of a model without undermining the intended performance of the model.

[0193] Pruning techniques may be particularly useful in the context of wireless communication, where the available resources (such as power and bandwidth) may be limited. Some example pruning techniques include a weight pruning technique, a neuron pruning technique, a layer pruning technique, a structural pruning technique, and a dynamic pruning technique. Pruning techniques may, for example, reduce the amount of data corresponding to a model that may need to be transmitted or stored. Weight pruning techniques may involve removing some of the weights from a model. Neuron pruning techniques may involve removing some neurons from a model. Layer pruning techniques may involve removing some layers from a model. Structural pruning techniques may involve removing some connections between neurons in a model. Dynamic pruning techniques may involve adapting a pruning strategy of a model associated with one or more characteristics of the data or the environment. For example, in certain wireless communication devices, a dynamic pruning technique may more aggressively prune a model for use in a low-power or low-bandwidth environment, and less aggressively prune the model for use in a high-power or high-bandwidth environment. In certain example implementations, pruning techniques also may be applied to training data, for example, to remove outliers. In some implementations, pre-processing techniques directed to all or part of a training dataset may improve model performance or promote faster convergence of a model. For example, training data may be pre-processed to change or remove1616-444WO01Qualcomm Ref. No. 2402459WO 54 unnecessary data, extraneous data, incorrect data, or otherwise identifiable data. Such pre- processed training data may, for example, lead to a reduction in potential overfitting, or otherwise improve the performance of the trained model.

[0194] One or more of the example training techniques presented above may be employed as part of a training process. Some example training processes that may be used to train an ANN include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning technique. With supervised learning, a model is trained on a labeled training dataset, wherein the input data is accompanied by a correct or otherwise acceptable output. With unsupervised learning, a model is trained on an unlabeled training dataset, such that the model will need to leam to identify patterns and relationships in the data without the explicit guidance of a labeled training dataset. With semi -su ervised learning, a model is trained using some combination of supervised and unsupervised learning processes, for example, when the amount of labeled data is somewhat limited. With reinforcement learning, a model may leam from interactions with its operation / environment, such as in the form of feedback akin to rewards or penalties. Reinforcement learning may be particularly beneficial when used to improve or attempt to optimize a behavior of a model deployed in a dynamically changing environment, such as a wireless communication network.

[0195] Distributed, shared, or collaborative learning techniques may be used for the training process. For example, techniques such as federated learning may be used to decentralize the training process and rely on multiple devices, network entities, or organizations for training various versions or copies of a ML model, without relying on a centralized training mechanism. Federated learning may be particularly useful in scenarios where data is sensitive or subject to privacy constraints, or where it is impractical, inefficient, or expensive to centralize data. In the context of wireless communication, for example, federated learning may be used to improve performance by allowing an ANN to be trained on data collected from a wide range of devices and environments. For example, an ANN may be trained on data collected from a large number of wireless devices in a network, such as distributed wireless communication nodes, smartphones, or intemet-of-things (loT) devices, to improve the network's performance and efficiency. With federated learning, a user equipment (UE) or other device may receive a copy of all or part of a global or shared model and perform local training on the local model using locally available training data. The UE may provide update information regarding the locally trained model to one or more other devices (such1616-444WO01Qualcomm Ref. No. 2402459WO 55 as a network entity or a server) where the updates from other-like devices (such as other UEs) may be aggregated and used to provide an update to global or shared model. A federated learning process may be repeated iteratively until all or part of a model obtains a satisfactory level of performance. Federated learning may enable devices to protect the privacy and security of local data, while supporting collaboration regarding training and updating of all or part of a shared model.

[0196] In some implementations, one or more devices or services may support processes relating to a ML model’s usage, maintenance, activation, reporting, or the like. In certain instances, all or part of a dataset or model may be shared across multiple devices, to provide or otherwise augment or improve processing. In some examples, signaling mechanisms may be utilized at various nodes of wireless network to signal the capabilities for performing specific functions related to ML model, support for specific ML models, capabilities for gathering, creating, transmitting training data, or other ML related capabilities. ML models in wireless communication systems may, for example, be employed to support decisions or improve performance relating to wireless resource allocation or selection, wireless channel condition estimation, interference mitigation, beam management, positioning accuracy, energy savings, or modulation or coding schemes, etc. In some implementations, model deployment may occur jointly or separately at various network levels, such as, a UE, a network entity such as a base station, or a disaggregated network entity such as a central unit (CU), a distributed unit (DU), a radio unit (RU), or the like.

[0197] FIG. 10 is an illustrative block diagram of an example ML architecture 1000 that may be used for wireless communications in any of the various implementations, processes, environments, networks, or other use cases. As illustrated, architecture 1000 includes multiple logical entities, such as model training host 1002, model inference host 1004. data source(s) 1006, and agent 1008. Model inference host 1004 is configured to run an ML model based on inference data 1012 provided by data source(s) 1006. Model inference host 1004 may produce output 1014, which may include a prediction or inference, such as a discrete or continuous value based on inference data 1012, which may then be provided as input to the agent 1008.

[0198] Agent 1008 may represent an element or an entity of a wireless communication system including, for example, a radio access network (RAN), a wireless local area network, a device-to-device (D2D) communications system, etc. As an example, agent B08 may be a user equipment such as UE 104, a base station such as BS 102, or a1616-444WO01Qualcomm Ref. No. 2402459WO 56 disaggregated network entity (such as a centralized unit (CU). a distributed unit (DU), or a radio unit (RU), an access point, a wireless station, a RAN intelligent controller (RIC) in a cloud-based RAN, among some examples. Additionally, agent 1008 also may be a type of agent that depends on the type of tasks performed by model inference host 1004, the type of inference data 1012 provided to model inference host 1004, or the type of output 1014 produced by model inference host 1004.

[0199] Agent 1008 may perform one or more actions associated with receiving output 1014 from model inference host 1004. For instance, in examples where output 1014 indicates positioning data for a UE device, agent 1008 may provide positioning data to an application or another service. In examples where output 1014 is intermediate data, agent 1008 may perform a positioning process that uses the intermediate data to generate positioning data. Agent 1008 may provide the positioning data to an application or other service. Agent B08 may indicate the one or more actions performed to at least one subject of action B10. In some cases, agent 1008 and the subject of action 1010 are the same entity.

[0200] Data can be collected from data sources 1006 and may be used as training data 1016 for training an ML model, or as inference data 1012 for feeding an ML model inference operation. Data sources 1006 may collect data from various subject of action 1010 entities (such as, a UE or a network entity), and provide the collected data to a model training host 1002 for ML model training. In some examples, if output 1014 provided to agent 1008 is inaccurate (or the accuracy is below an accuracy threshold), model training host 1002 may provide feedback to model inference host 1004 to modify or retrain the ML model used by model inference host 1004, such as via an ML model deployment update.

[0201] Model training host 1002 may be deployed at the same or a different entity than that in which model inference host 1004 is deployed. For example, in order to offload model training processing, which can impact the performance of model inference host 1004, model training host 1002 may be deployed at a model server.

[0202] In some aspects, an ML model is deployed at or on a network entity (such as BS 102). More specifically, a model interference host, such as model inference host 1004 in FIG. 10, may be deployed at or on the network entity for such a gNB / TRP.

[0203] In some other aspects, an ML model is deployed at or on a UE (such as UEs 104, 190, 164, 182). More specifically, a model inference host, such as model inference host1616-444WO01Qualcomm Ref. No. 2402459WO 571004 in FIG. 10, may be deployed at or on the UE for generating output data (e.g.. positioning data or intermediate data).

[0204] FIG. 11 is an illustrative block diagram of an example ML architecture of first wireless device 1100 in communication with second wireless device 1104. First wireless device 1100 may be configured for hosting a ML system for positioning of UE devices. Note that the example ML architecture of first wireless device 1100 may be applied to second wireless device 1102, and vice versa.

[0205] First wireless device 1100 may be, or may include, a chip, system on chip (SoC), chipset, package or device that includes one or more processors, processing blocks or processing elements (collectively ‘'processor 1110”) and one or more memory blocks or elements (collectively “memory 1120”). Processor 1110 may be coupled to transceiver 1140, which includes radio frequency (RF) circuitry 1142 coupled to antennas 1146 via an interface for transmitting or receiving signals.

[0206] One or more ML models 1130 (collectively “ML system 1130”) may be stored in memory 1120 and accessible to processor(s) 1 110. Individual or groups of ML models in ML system 1130 may be associated with respective model identifiers. In some aspects, different ML models of ML system 1130, which may optionally be associated with different model identifiers, may have different characteristics. One or more ML models of ML system 1 130 may be selected based on respective features, characteristics, or applications, as well as characteristics or conditions of first wireless device 1100 (such as, a power state, a mobility state, a battery reserve, a temperature, etc.). For example, ML models 1130 may have different inference data and output pairings (such as, different types of inference data produce different types of output), different levels of accuracies associated with the predictions, different latencies associated with producing the predictions, different ML model sizes, different coefficients, different parameters, etc.

[0207] Processor 1110 may deploy ML system 1130 to produce respective output data based on input data. As an example, the ML models of ML system 1130 may take measurements of a reference signal (such as, corresponding to a wide beam) as input to predict a channel characteristic associated with a different reference signal (such as, corresponding to a narrow beam within the wide beam, another wide beam, a narrow beam outside the wide beam, etc.). The input data may include, for example, measurements of one or more reference or pilot signals, such as a channel quality indicator (CQI), a signal-to-noise ratio (SNR), a signal-to-interference plus noise ratio (SINR), a signal-to-noise-plus-distortion ratio (SNDR), a received signal strength1616-444WO01Qualcomm Ref. No. 2402459WO 58 indicator (RSSI). a reference signal received power (RSRP), a reference signal received quality (RSRQ), and / or a block error rate (BLER). The output data may include, for example, positioning data or intermediate data used by a positioning process to generate positioning data. In some aspects, a model server 1150 may perform various ML management tasks for first wireless device 1100 and / or second wireless device 1102.

[0208] FIG. 12A, FIG. 12B, and FIG. 12C are block diagrams illustrating example styles of implementing AI / ML-assisted positioning systems. In the examples of FIG. 12A, FIG. 12B, and FIG. 12C, a network entity, such as a UE device or LMF device, obtains data associated a set of TRPs (i.e., TRP 1. TRP 2. ... , TRPN). For example, the network entity may obtain carrier-to-interference ratio (CIR) data for signals generated by the TRPs. In the example of FIG. 12A, the network entity implements different copies of the same AI / ML model (AI / ML model A 1210A, 1210B, ... , 1210N) for different TRPs. Thus, when the network entity obtains data associated with a specific TRP. the network entity applies the AI / ML model associated with the specific TRP to the data associated with the specific TRP to generate output data associated with the specific TRP. The output data associated with the TRPs may include data that is used as input data to a process that determines the physical position of a UE device. For example, the output data may include Time of Arrival (ToA) data associated with the signal generated by the specific TRP.

[0209] In the example of FIG. 12B, the network entity implements different AI / ML models (AI / ML models 1220A, 1220B, ... ,1220N) for different TRPs. The AI / ML models are not copies of one another. The AI / ML models may have the same inputs and outputs as the AI / ML models of FIG. 12A. In the example of FIG. 12C, the network entity implements a single AI / ML model 1230 that is trained to accept data associated with multiple TRPs as input.

[0210] FIG. 13A is a block diagram illustrating an example of a direct positioning system 1300 in accordance with one or more techniques of this disclosure. In the example of FIG. 13 A, a ML model 1302 receives PRS / SRS measurements and outputs a target location. ML model 1302 may be a ML model of ML system 1130 (FIG. 11). The target location is a position of a device whose position is being determined (i.e., the target device). In different examples, ML model 1302 may be implemented at a UE device, a gNB / TRP device, or a LMF device.

[0211] FIG. 13B is a block diagram illustrating an example of an indirect positioning system 1320 in accordance with one or more techniques of this disclosure. In the example of FIG. 13B, a primary ML model 1322 receives measurement data (e.g., PRS1616-444WO01Qualcomm Ref. No. 2402459WO 59 measurement data. SRS measurement data, etc.) and outputs intermediate data. The intermediate data include information that a secondary positioning system 1324 uses to determine a target position (i.e., the position of the target device). Example intermediate measurements may include information on the timing and angle of signals received by the target device, line of sight identification, and so on. Secondary positioning system 1324 may be implemented in one of a variety of ways. For example, secondary positioning system 1324 may be implemented using Chan’s algorithm, KF, an Al positioning model, or implemented in another way. Primary' ML model 1322 is implemented at a target device (e.g., a UE device) or gNB / TRP device. Secondary positioning system 1324 may be implemented at the target device, a gNB / TRP, or LMF.

[0212] FIG. 14A is a block diagram illustrating a first example system 1400A in accordance with one or more techniques of this disclosure. In the example of FIG. 14A, a UE device 1402 includes a ML system 1408. UE device 1402 receives a PRS generated by gNB / TRP device 1404. UE device 1402 measures the PRS to generate measurement data. UE device 1402 applies ML system 1408 to the measurement data to generate target position data. UE device 1402 may send the target position data to LMF device 1406. System 1400A may be an example of a direct Al positioning system as described with respect to FIG. 13 A or an example of an Al-assisted positioning system as described with respect to FIG. 13B.

[0213] FIG. 14B is a block diagram illustrating a second example system 1400B in accordance with one or more techniques of this disclosure. In the example of FIG. 14B, UE device 1402 includes a ML system 1408. UE device 1402 receives a PRS generated by gNB / TRP device 1404. UE device 1402 measures the PRS to generate measurement data. UE device 1402 applies ML system 1408 to the measurement data to generate PRS- based measurement data. UE device 1402 sends the PRS-based measurement data to LMF device 1406. LMF device 1406 may use the PRS-based measurement data to generate position data. Thus, system 1400B is an example of an Al-assisted positioning system as described with respect to FIG. 13B.

[0214] FIG. 14C is a block diagram illustrating a third example system 1400C in accordance with one or more techniques of this disclosure. In the example of FIG. 14C, UE device 1402 receives a PRS generated by gNB / TRP device 1404. UE device 1402 measures the PRS to generate PRS-based measurement data. UE device 1402 sends the PRS-based measurement data to LMF device 1406. LMF device 1406 implements a ML system 1408. LMF device 1406 may apply ML model 1902C to the PRS-based1616-444WO01Qualcomm Ref. No. 2402459WO 60 measurement data to generate position data. Thus, system 1400C is an example of a direct Al positioning system as described with respect to FIG. 13A.

[0215] FIG. 14D is a block diagram illustrating a fourth example system MOOD in accordance with one or more techniques of this disclosure. In the example of FIG. 14D, UE device 1402 generates an SRS. gNB / TRP device 1404 measures the SRS to generate measurement data. gNB / TRP device 1404 implements a ML system 1408. gNB / TRP device 1404 applies ML system 1408 to the measurement data to generate SRS-based intermediate data. gNB / TRP device 1404 sends the SRS-based intermediate data to LMF device 1406. LMF device 1406 may then use the SRS-based intermediate data to generate position data. Thus, system MOOD is an example of an Al-assisted positioning system as described with respect to FIG. 13B.

[0216] FIG. 14E is a block diagram illustrating a fifth example system 1400E in accordance with one or more techniques of this disclosure. In the example of FIG. 14D, UE device 1402 generates an SRS. gNB / TRP device 1404 measures the SRS to generate SRS-based measurement data. gNB / TRP device 1404 sends the SRS-based measurement data to LMF device 1406. LMF device 1406 implements ML system 1408. LMF device 1406 applies ML system 1408 to the SRS-based measurement data to generate position data.

[0217] In an AI / ML-based device positioning process, such as those described above with respect to FIG. 14A - 14E, one or more trained ML models are applied to measurement data in order to generate a position data for UE device 1402. The input data may represent aspects of wireless signals received by UE device 1402 or another network entity. An advantage of such an Al / ML-based device positioning process is that the one or more AI / ML models of ML system 1408 may leam a function that maps the measurement data to positioning data in a manner that is less susceptible to errors caused by wireless signals following multiple paths. It is further noted that the transmi tter / receiver (Tx / Rx) hardw are of different devices, such as UE devices and gNB / TRP devices, may have different characteristics. For example, devices from different manufacturers may have antennas with different shapes. These different characteristics may affect the accuracy of an AI / ML-based device positioning process because the measurement data generated based on wireless signals received by devices with different Tx / Rx hardware at the same position may be different. In other words, the input data provided to the trained ML model may be different for different devices despite the devices being at the same location and receiving the same wireless signals.1616-444WO01Qualcomm Ref. No. 2402459WO 61

[0218] The techniques of this disclosure address this issue. As described herein, a system may comprise a memory (e.g., memory 1120) configured to store an ML system 1130 that includes one or more trained ML models. Processors (e.g., processors 1110) of a first network entity (e.g., first wireless device 1100) may receive a positioning personalization indicator for a second network entity (e.g., second wireless device 1102 or another network entity). The positioning personalization indicator for the second network entity indicates information about the second network entity. Furthermore, the processors may obtain measurement data based on at least one measurement of at least one wireless signal. The processors may apply the trained ML model to the measurement data to generate output data. In some examples, such as examples in accordance with FIG. 13 A, the output data indicates a physical position of a UE device. In some examples, such as examples in accordance with FIG. 13B, the output data is input data to a process (e.g., secondary positioning system 1324) that determines the physical position of the UE device. The position data for the UE device is dependent on the positioning personalization indicator for the second network entity. Because the position data for the UE device is dependent on the positioning personalization indicator, the output data may be more accurate than if the output data were not dependent on the positioning personalization indicator.

[0219] FIG. 15 is a flowchart illustrating an example operation of a network entity in accordance with one or more techniques of this disclosure. In the example of FIG. 15, a first network entity may receive a positioning personalization indicator for a second network entity (1500). The positioning personalization indicator for the second network entity indicates information about the second network entity. In the examples of FIG. 14A and FIG. 14B, the first network entity is UE device 1402 and the second network entity is gNB / TRP device 1404. In the example of FIG. 14C, the first network entity is LMF device 1406 and the second network entity is UE device 1402. The example of FIG. 14D, the first network entity is gNB / TRP device 1404 and the second network entity is UE device 1402. In the example of FIG. 14E, the first network entity' is LMF device 1406 and the second network entity may be UE device 1402 or gNB / TRP device 1404. The first network entity may receive the positioning personalization indicator from the second network entity or another network entity. The positioning personalization indicator may indicate one or more of: a manufacturer of the second network entity, a vendor of a chipset of the second network entity, a platform model of the second network entity, a serial number of the second network entity, or other data about the second network entity.1616-444WO01Qualcomm Ref. No. 2402459WO 62

[0220] The first network entity obtains measurement data based on at least one measurement of at least one wireless signal (1502). In some examples, the first network entity obtains the measurement data by receiving the measurement data from another network entity. In some examples, the first network entity obtains the measurement data by generating the measurement data. In some examples, the measurement data includes one or more of: data based on a PRS or data based on an SRS.

[0221] The first network entity applies a trained ML model (e.g., a ML model of ML system 1408) to the measurement data to generate output data (1504). In examples that implement direct AI / ML positioning, the output data indicates a physical position of a UE device, such as UE device 1402. In examples that implement AI / ML-assisted positioning, the output data may be input data to a process that determines the physical position of the UE device.

[0222] The output data for the UE device is dependent on the positioning personalization indicator for the second network entity. For example, the first network entity may select a trained ML model from a plurality of trained ML models based on the positioning personalization indicator. In some examples, the first network entity7applies the trained ML model to the measurement data and the positioning personalization indicator to generate the output data for the UE device. In some examples, the first network entity may determine, based on the positioning personalization indicator, a layer set from among a plurality of layer sets, apply the determined layer set to the measurement data to generate an intermediate feature vector, and apply a shared ML model to the intermediate feature vector to generate the output data. In some such examples, two or more layer sets of the plurality of layer sets correspond to different vendors.

[0223] FIG. 16A is a communication diagram illustrating a first example data exchange between UE device 1402, gNB / TRP device 1404, and LMF device 1406, in accordance with one or more techniques of this disclosure. The data exchange of FIG. 16A is consistent with system 1400C of FIG. 14C. In the example of FIG. 16A, UE device 1402 sends a positioning personalization indicator to LMF device 1406 (1600). Additionally, gNB / TRP device 1404 may generate one or more wireless signals, such as one or more of a PRS, TRS, or a SSB (1602). UE device 1402 may measure the one or more wireless signals to generate measurement data. The measurement data can include timing, power, and / or phase information related to channel time-domain impulse response (e.g., channel impulse response (CIR), power delay profile (PDP), delay profile (DP)), intermediate measurements for positioning, e.g., reference signal RS time difference (RSTD), RSTD1616-444WO01Qualcomm Ref. No. 2402459WO 63 diff, RS received power (RSRP), RSRP per path (RSRPP), DL-RTOA. DL-AoA, UL- AoD, UE tx-rx time difference, RS carrier phase (RSCP), RSCP difference (RSCPD). UE device 1402 may then send the measurement data to LMF device 1406 (1604). LMF device 1406 may apply ML system 1408 to generate target positioning data for UE device 1402 based on the positioning personalization indicator and the measurement data.

[0224] FIG. 16B is a communication diagram illustrating a second example data exchange between UE device 1402, gNB / TRP device 1404, and LMF device 1406, in accordance with one or more techniques of this disclosure. The data exchange in FIG. 16B is similar to that of FIG. 16 A, except that instead of UE device 1402 directly sending the positioning personalization indicator to LMF device 1406, LMF device 1406 may first send a request for the positioning personalization indicator to UE device 1402 (1650) and UE device 1402 may send the positioning personalization indicator to LMF device 1406 in response to the request for the positioning personalization indicator (1652).

[0225] Thus, in the example of FIG. 16A and FIG. 16B. LMF device 1406 may be a first network entity and UE device 1402 may be a second network entity. LMF device 1406 may receive measurement data (e.g., PRS -based measurement data generated by UE device 1402 by measuring a PRS, TRS, or SSB generated by gNB / TRP device 1404) from UE device 1402. LMF device 1406 may apply trained ML model 1908 to the measurement data to generate output data. In some examples, the output data indicates a physical position of UE device 1402. In some examples, the output data is input data to a process that determines the physical position of UE device 1402. The position data for the UE device is dependent on the positioning personalization indicator for the second network entity. As shown in FIG. 16B, LMF device 1406 sends a request to UE device 1402 for the positioning personalization indicator. In some examples, LMF device 1406 sends the request and receives the positioning personalization indicator as part of LTE positioning protocol (LPP) signaling. In some examples, LMF device 1406 sends the request and receives the positioning personalization indicator as part of a capability exchange.

[0226] FIG. 17 is a communication diagram illustrating a third example data exchange between UE device 1402, gNB / TRP device 1404. and LMF device 1406, in accordance with one or more techniques of this disclosure. The data exchange of FIG. 17 is consistent with system MOOD of FIG. 14D. In the example of FIG. 17, gNB / TRP device 1404 sends a request for a positioning personalization indicator to LMF device 1406 (1700). LMF1616-444WO01Qualcomm Ref. No. 2402459WO 64 device 1406 responds to the request by providing the positioning personalization indicator to gNB / TRP device 1404 (1702).

[0227] The positioning personalization indicator may indicate information related to a platform that UE device 1402 uses for positioning operations. In some examples, the positioning personalization indicator includes a set of indicators that correspond to components of UE device 1402, such as antennas, radio frequency hardware, etc., for processing reference signals. In some examples, the positioning personalization indicator includes a finite length code, such as an 8-bit code or a 16-bit code.

[0228] In some examples, the positioning personalization indicator provides information about a domain level of the positioning personalization indicator. Positioning personalization indicators having different domain levels may include different information. For example, a positioning personalization indicator having a first domain level may include a device vendor identifier (or device-chip vendor identifier) for UE device 1402. a positioning personalization indicator having a second domain level may include the device vendor identifier (or device-chip vendor identifier) for UE device 1402 and a device model identifier for UE device 1402, a positioning personalization indicator having a third domain level may include the device vendor identifier (or device-chip vendor identifier), the device model identifier for UE device 1402, and a device serial number for UE device 1402.

[0229] In some examples, the positioning personalization indicator includes a hashed value that is generated based on one or more pieces of information about UE device 1402. For example, a hash function may be applied to one or more of a device vendor identifier (or device-chip vendor identifier) for UE device 1402, a name of a platform used by UE device 1402 for positioning operations, an identifier of a version of the platform used by UE device 1402 for positioning operations, a name of a chip of UE device 1402, a version identifier of the chip of UE device 1402. a device model identifier for UE device 1402, a serial number for UE device 1402, and / or other information. In some examples, the positioning personalization indicator is scrambled to protect security and / or privacy of UE device 1402.

[0230] In some examples, the format and content of the positioning personalization indicator is predefined or standardized. In some examples, the format and content of the positioning personalization indicator is independently selected by individual device or chipset vendors. In such examples, applying a hash function to information about UE devices may be considered to ensure collision-free indicator assignment among vendors1616-444WO01Qualcomm Ref. No. 2402459WO 65 and platforms without requiring direct coordination among such vendors. In some such examples, the hashing function may be standardized among such vendors.

[0231] Additionally, in the example of FIG. 17, UE device 1402 generates an SRS (1704). gNB / TRP device 1404 may obtain measurement data based on at least one measurement of the SRS generated by UE device 1402. GNB / TRP device 1404 may apply ML system 1408 to generate output data. In some examples, the output data indicates a physical position of UE device 1402 (1706). In some examples, the output data serves as input data to a process that determines the physical position of UE device 1402. GNB / TRP device 1404 may send the output data to LMF device 1406.

[0232] In this way, gNB / TRP device 1404 is a first network entity and UE device 1402 is a second network entity. gNB / TRP device 1404 receives the positioning personalization indicator from LMF device 1406. As part of obtaining the measurement data, gNB / TRP device 1404 may measure an SRS generated by UE device 1402.

[0233] FIG. 18 A is a communication diagram illustrating a fourth example data exchange between UE device 1402, gNB / TRP device 1404, LMF device 1406, and a server 1800, in accordance with one or more techniques of this disclosure. The data exchange in FIG. 23A is a variation of system 1400C of FIG. 14C. In the example of FIG. 18A, UE device 1402 sends a positioning personalization indicator to LMF device 1406 (1800). LMF device 1406 reports the positioning personalization indicator to a server 1800 (1802). In response, server 1800 sends information related to AI / ML life cycle management (LCM) to LMF device 1406 (1804). The information related to AI / ML LCM may include instructions to activate or deactivate positioning systems, instructions related to training ML system 1408, and so on.

[0234] The format and / or content of positioning personalization indicators may vary among different device vendors. LMF device 1406 may report the positioning personalization indicator to server 1800 to obtain information regarding how to interpret a positioning personalization indicator received from UE device 1402. For example, the information regarding how to interpret the positioning personalization indicator may include an indication of how to de-hash, or otherwise interpret the positioning personalization indicator.

[0235] Additionally, in the example of FIG. 18A, gNB / TRP device 1404 generates a PRS, TRS, and / or SSB (1806). UE device 1402 receives and measures the PRS, TRS, and / or SSB. The timing of gNB / TRP device 1404 sending the PRS / TRS / SSB to UE device 1402 may be independent of the timing of the data exchange between LMF device1616-444WO01Qualcomm Ref. No. 2402459WO 661406 and server 1800. UE device 1402 may generate PRS-based measurement data based on the PRS / TRS / SSB. UE device 1402 may send the PRS-based measurement data to LMF device 1406 (1808). LMF device 1406 may then apply ML system 1408 to generate target position data for UE device 1402 based on the PRS-based measurement data and the information related to AI / ML LCM information. For example. LMF device 1402 may interpret the positioning personalization indicator according to the AI / ML LMC information. In some examples, LMF device 1402 selects an ML model of ML system 1808 based on the interpreted positioning personalization indicator. In some examples, LMF device 1402 reformatted the data of the positioning personalization indicator based on the AI / ML LMC information and provides the reformatted data as input to a ML model of ML system 1408.

[0236] FIG. 18B is a communication diagram illustrating a fifth example data exchange between UE device 1402, gNB / TRP device 1404, LMF device 1406, and server 1800, in accordance with one or more techniques of this disclosure. The data exchange of FIG. 18B is a variation of the data exchange of FIG. 18A. The data exchange of FIG. 18B may be the same as the data exchange of FIG. 18A except that LMF device 1406 sends a request for the positioning personalization indicator to UE device 1402 (1850) and UE device 1402 responds to the request by sending the positioning personalization indicator to LMF device 1406 (1800).

[0237] FIG. 19 is a communication diagram illustrating a sixth example data exchange between UE device 1402, gNB / TRP device 1404, and LMF device 1406, in accordance with one or more techniques of this disclosure. The data exchange of FIG. 19 is consistent with system 1400E of FIG. 14E. In the example of FIG. 19. LMF device 1406 may send a request for a positioning personalization indicator to gNB / TRP device 1404 (1900). In response to the request, gNB / TRP device 1404 sends the positioning personalization indicator to LMF device 1406 (1902). LMF device 1406 may send the request for the positioning personalization indicator and gNB / TRP device 1404 may send the positioning personalization indicator as part of a TRP information exchange (i.e., an exchange of capability information). In some examples, LMF device 1406 may send the request for the positioning personalization indicator and gNB / TRP device 1404 may send the positioning personalization indicator as part of New Radio Positioning Protocol A (NRPPa) signaling. The positioning personalization indicator provides information about related to a platform that gNB / TRP device 1404 uses for positioning operations. For example, the positioning personalization indicator may include the same types of data1616-444WO01Qualcomm Ref. No. 2402459WO 67 described above with respect to the positioning personalization indicator for HE device 1402.

[0238] Additionally, UE device 1402 may generate an SRS (1904). gNB / TRP device 1404 may measure the SRS generated by UE device 1402 to generate SRS -based measurement data. gNB / TRP device 1404 may send the SRS-based measurement data to LMF device 1406 (1906). LMF device 1406 may apply ML system 1408 to generate target position data for UE device 1402 based on the SRS-based measurement data and the positioning personalization indicator.

[0239] In some examples, LMF device 1406 uses the positioning personalization indicator of gNB / TRP device 1404 to take an input indexing or an LCM action. Example LCM actions include activation / deactivation, selection, switching, fallback, or other actions for an LMF-side ML system 1408. For example, LMF device 1406 may active or deactivate a positioning system of gNB / TRP device 1404 or UE device 1402. In some examples, LMF device 1406 may select a ML model of ML system 1408, switch ML models of ML system 1408, or fall back to a simpler ML model of ML system 1408, cause gNB / TRP device 1404 or UE device 1402 to do so.

[0240] Thus, in the example of FIG. 19, LMF device 1406 may be a first network entity and gNB / TRP device 1404 may be a second network entity. LMF device 1406 may receive a positioning personalization indicator for gNB / TRP device 1404. The positioning personalization indicator for the second network entity indicates information about the second network entity7. LMF device 1406 may obtain measurement data from gNB / TRP device 1404. The measurement data is based on measurements of wireless signals, such as an SRS generated by UE device 1402. LMF device 1406 may then apply a trained ML model of ML system 1408 to the measurement data to generate output data. In some examples, the output data indicates a physical position of UE device 1402. In some examples, the output data is input data to a process that determines the physical position of UE device 1402. The position data for the UE device is dependent on the positioning personalization indicator for UE device 1402. LMF device 1406 may be configured to send a request for positioning personalization indicator to gNB / TRP device 1404.

[0241] FIG. 20 is a communication diagram illustrating a seventh example data exchange between UE device 1402, a gNB / TRP device 1404, and LMF device 1406, in accordance with one or more techniques of this disclosure. The data exchange of FIG. 20 may be consistent with system 1400A of FIG. 14A or system 1400B of FIG. 14B. In the example of FIG. 20, UE device 1402 may request a positioning personalization indicator for1616-444WO01Qualcomm Ref. No. 2402459WO 68 gNB / TRP device 1404 from LMF device 1406 (2000). The positioning personalizing indicator for gNB / TRP device 1404 provides information regarding gNB / TRP device 1404. In response to the request, LMF device 1406 may send the positioning personalization indicator for gNB / TRP device 1404 to UE device 1402 (2002). LMF device 1406 may obtain the positioning personalization indicator for gNB / TRP device 1404 from gNB / TRP device 1404, e.g., as described with respect to FIG. 17. In some examples, UE device 1402 may request the positioning personalization indicator and LMF device 1406 may send the positioning personalization indicator as part of assistance data. The assistance information may include one or more types of information, such as one or more of correlation of the downlink / uplink (DL / UL) measurements or sidelink measurements, a previous history of UE locations, information indicating data collection capabilities of the UE devices, label generation capabilities, information regarding vendors of the UE devices and / or chip vendors of the UE devices, and so on.

[0242] In some examples, UE device 1402 requests the positioning personalization indicator and LMF device 1406 sends the positioning personalization indicator as part of LTE positioning protocol (LPP) signaling. Additionally, gNB / TRP device 1404 may generate PRS / TRS / SSB that is received by UE device 1402 (2004). Since different gNB / TRP devices may generate wireless signals differently, use of the positioning personalization indicator for gNB / TRP device 1404 may increase the accurate of output data that UE device 1402 generates by applying an ML model of ML system 1408 to measurement data based on the wireless signals.

[0243] UE device 1402 may apply ML system 1408 to generate output data for UE device 1402 based on measurement data generated by measuring the PRS / TRS / SSB and based on the positioning personalization indicator. For instance, UE device 1402 may use the positioning personalization indicator for input indexing or an LCM action (e.g., activation / deactivation, selection, switching, fallback, etc.) for UE-side ML system 1408.

[0244] Thus, in the example of FIG. 20, UE device 1092 is a first network entity and gNB / TRP device 1404 is the second network entity. UE device 1402 is configured to receive the positioning personalization indicator for gNB / TRP device 1404 from LMF device 1406. as part of obtaining the measurement data. UE device 1402 may measure a PRS generated by gNB / TRP device 1404. UE device 1402 may apply atained ML model of ML system 1408 to the measurement data to generate output data. The output data indicates a physical position of UE device 1402 or the output data being input data to a process that determines the physical position of UE device 1402. The position data for1616-444WO01Qualcomm Ref. No. 2402459WO 69UE device 1402 is dependent on the positioning personalization indicator for UE device 1402.

[0245] FIG. 21 is a block diagram illustrating a first example ML system 2100 in accordance with one or more techniques of this disclosure. ML system 2100 may be an instance of ML system 1408. In the example of FIG. 21, ML system 2100 includes a plurality of ML models 2102A, 2102B, through 2102N (collectively, '‘ML models 2102”). Each of ML models 2102 may be trained to generate output data based on measurement data generated from a PRS, a SRS, or another Wpe of signal. In other examples, ML models 2102 may output intermediate data, e.g.. as described above with respect to FIG. 13B. A ML host system (i.e., a system of one or more devices hosting ML system 2100, such as UE device 1402, gNB / TRP device 1404, or LMF device 1406) may select which of ML models 2102 to use based on a positioning personalization indicator, such as a positioning personalization indicator for UE device 1402 or gNB / TRP device 1404. For example, different ML models 2102 may correspond to different device vendors. In this example, the ML host system may determine a device vendor of a UE device based on the positioning personalization indicator and provide PRS / SRS measurement data of the UE device as input to the ML model 2102 corresponding to the device vendor. ML models 2102 may be implemented as neural network models, e g., as described above with respect to FIG. 9 or implemented in another way.

[0246] FIG. 22 is a block diagram illustrating a second example ML system 2200 in accordance with one or more techniques of this disclosure. ML system 2200 may be an instance of ML system 1408. In the example of FIG. 22, an ML model 2202 may receive PRS / SRS measurement data and personalization data as input. The personalization data may include data included in or derived from a positioning personalization indicator of UE device 1402 or gNB / TRP device 1404. ML model 2202 is trained to generate output data (e.g., position data or intermediate data) based on the PRS / SRS measurement data and the personalization data. ML model 2202 may be implemented as a neural network model, e.g., as described above with respect to FIG. 9 or implemented in another way.

[0247] FIG. 23 is a block diagram illustrating a third example ML system 2300 in accordance with one or more techniques of this disclosure. ML system 2300 may be an instance of ML system 1408. In the example of FIG. 23. ML system 2300 includes a core ML model 2302 and a plurality of layer sets 2304 A, 2304B, through 2304N (collectively, “layer sets 2304”). Core ML model 2302 may be trained to generate output data (e.g.,1616-444WO01Qualcomm Ref. No. 2402459WO 70 position data or intermediate data) based on feature data generated by one of layer sets 2804.

[0248] A model inference host, such as model inference host 1004 may select which of layer sets 2304 to use based on a positioning personalization indicator, such as a positioning personalization indicator for UE device 1402 or gNB / TRP device 1404. The model inference host may be one or more devices that host ML system 2300, such as UE device 1402, gNB / TRP device 1404, or LMF device 1406. Different layer sets 2804 may correspond to different device vendors. In this example, the model inference host may determine a device vendor of a UE device based on the positioning personalization indicator and provide PRS / SRS measurement data of the UE device as input to the layer set 2304 corresponding to the device vendor. In this way, core ML model 2302 may be the same for all positioning personalization indicators, but layer sets 2304 may be customized for different positioning personalization indicators.

[0249] In some examples, each of layer sets 2304 includes one or more layers of artificial neurons. In some such examples, different layer sets 2304 may include different numbers of layers and / or artificial neurons. Core ML model 2302 may be implemented using one or more layers of artificial neurons, e.g., as described with respect to FIG. 9.

[0250] In some examples, the model inference host may host a first ML system that is dependent on position personalization indicators and a second ML system that is not dependent on position personalization indicators. The model inference host may switch between the two ML systems based on target positioning accuracy or performance and associated complexity. That is. in some examples, systems hosting ML system 1408 may switch between a personalized mode and a common mode. When operating in the personalized mode, the systems may use personalization indictors as part of the process for determining a target position of a UE device. When operating in the common mode, the systems do not use positioning personalization indicators as part of the process for determining a target position of a UE device. Thus, when operating in the common mode, the positioning process is not personalized to a UE device or a gNB / TRP device. In some examples, the systems may receive data from other devices to indicate whether to use the personalized mode or the common mode. For example, if UE device 1402 is the system hosting ML system 1408, UE device 1402 may receive data from gNB / TRP device 1404 or LMF device 1406 indicating whether UE device 1402 is to use the personalized mode or the common mode.1616-444WO01Qualcomm Ref. No. 2402459WO 71

[0251] Various examples of the techniques of this disclosure are summarized in the following clauses.

[0252] Clause 1. A system comprising: a memory configured to store a trained machine learning (ML) model: a communication unit; and one or more processors of a first network entity, the one or more processors implemented in circuitry and communicatively coupled to the memory, the one or more processors configured to: receive a positioning personalization indicator for a second network entity, the positioning personalization indicator for the second network entity indicates information about the second network entity; obtain measurement data based on at least one measurement of at least one wireless signal; and apply the trained ML model to the measurement data to generate output data, wherein the output data indicates a physical position of a user equipment (UE) device or the output data being input data to a process that determines the physical position of the UE device, wherein the output data for the UE device is dependent on the positioning personalization indicator for the second network entity.

[0253] Clause 2. The system of clause 1, wherein the one or more processors are configured to select the trained ML model from a plurality of trained ML models based on the positioning personalization indicator.

[0254] Clause 3. The system of clause 1, wherein the one or more processors are configured to, as part of applying the trained ML model, apply the trained ML model to the measurement data and the positioning personalization indicator to generate the output data for the UE device.

[0255] Clause 4. The system of clause 1. wherein the one or more processors are configured to, as part of applying the trained ML model: determine, based on the positioning personalization indicator, a layer set from among a plurality' of layer sets; apply the determined layer set to the measurement data to generate an intermediate feature vector; and apply a shared ML model to the intermediate feature vector to generate the output data.

[0256] Clause 5. The system of clause 4, wherein two or more layer sets of the plurality of layer sets correspond to different vendors.

[0257] Clause 6. The system of any of clauses 1-5. wherein the measurement data includes one or more of: data based on a positioning reference signal (PRS) or data based on a sounding reference signal (SRS).1616-444WO01Qualcomm Ref. No. 2402459WO 72

[0258] Clause 7. The system of any of clauses 1-6. wherein the first network entity receives the positioning personalization indicator from the second network entity.

[0259] Clause 8. The system of any of clauses 1-7, wherein the positioning personalization indicator indicates one or more of: a manufacturer of the second network entity, a vendor of a chipset of the second network entity, a platform model of the second network entity, or a serial number of the second network entity.

[0260] Clause 9. The system of any of clauses 1-8, wherein: the first network entity is a location management function (LMF) device, the second network entity is a gNB device, the LMF device receives the positioning personalization indicator and the measurement data from the gNB device, the gNB device is configured to generate the measurement data based on a sounding reference signal (SRS) generated by the UE device.

[0261] Clause 10. The system of any of clauses 1-8. wherein: the first network entity is a location management function (LMF) device, the second network entity is the UE device, the LMF device receives the measurement data from the UE device, and the LMF device is configured to send a request to the UE device for the positioning personalization indicator.

[0262] Clause 11. The system of clause 10, wherein: the LMF device sends the request and receives the positioning personalization indicator as part of Long-Term Evolution Positioning Protocol (LPP) signaling, or the LMF device sends the request and receives the positioning personalization indicator as part of a capability exchange.

[0263] Clause 12. The system of any of clauses 1-8. wherein: a gNB device is the first network entity, the UE device is the second network entity, the gNB device receives the positioning personalization indicator from a LMF device, the one or more processors are configured to, as part of obtaining the measurement data, measure a sounding reference signal generated by the UE device.

[0264] Clause 13. The system of any of clauses 1-8. wherein: the UE device is the first network entity, a gNB device is the second network entity, and the one or more processors are configured to receive the positioning personalization indicator from a location management function device, and the one or more processors are configured to, as part of obtaining the measurement data, measure a positioning reference signal generated by the gNB device.

[0265] Clause 14. A method comprising: receiving, by a first network entity, a positioning personalization indicator for a second network entity, the positioning1616-444WO01Qualcomm Ref. No. 2402459WO 73 personalization indicator for the second network entity indicates information about the second network entity; obtaining, by the first network entity, measurement data based on at least one measurement of at least one w ireless signal; and applying, by the first network entity, a trained machine learning (ML) model to the measurement data to generate output data, wherein the output data indicates a physical position of a user equipment (UE) device or the output data being input data to a process that determines the physical position of the UE device, wherein the output data for the UE device is dependent on the positioning personalization indicator for the second network entity.

[0266] Clause 15. The method of clause 14, wherein the method further comprises selecting, by the first network entity, the trained ML model from a plurality of trained ML models based on the positioning personalization indicator.

[0267] Clause 16. The method of clause 14, wherein applying the trained ML model comprises applying the trained ML model to the measurement data and the positioning personalization indicator to generate the output data for the UE device.

[0268] Clause 17. The method of clause 14, wherein applying the trained ML model comprises: determining, by the first network entity, based on the positioning personalization indicator, a layer set from among a plurality of layer sets; applying, by the first network entity, the determined layer set to the measurement data to generate an intermediate feature vector; and applying, by the first network entity, a shared ML model to the intermediate feature vector to generate the output data.

[0269] Clause 18. The method of clause 17, wherein two or more layer sets of the plurality of layer sets correspond to different vendors.

[0270] Clause 19. The method of any of clauses 14-18, wherein the measurement data includes one or more of: data based on a positioning reference signal or data based on a sounding reference signal.

[0271] Clause 20. The method of any of clauses 14-19, wherein the first netw ork entity receives the positioning personalization indicator from the second network entity.

[0272] Clause 21. The method of any of clauses 14-20, wherein the positioning personalization indicator indicates one or more of: a manufacturer of the second network entity, a vendor of a chipset of the second network entity, a platform model of the second network entity, or a serial number of the second network entity.

[0273] Clause 22. The method of any of clauses 14-21, wherein: the first network entity' is a location management function (LMF) device, the second network entity is a gNB device, the LMF device receives the positioning personalization indicator and the1616-444WO01Qualcomm Ref. No. 2402459WO 74 measurement data from the gNB device, the gNB device is configured to generate the measurement data based on a sounding reference signal (SRS) generated by the UE device.

[0274] Clause 23. The method of any of clauses 14-21, wherein: the first network entity is a LMF device, the second network entity is the UE device, the LMF device receives the measurement data from the UE device, and the LMF device is configured to send a request to the UE device for the positioning personalization indicator.

[0275] Clause 24. The method of clause 23, wherein: the LMF device sends the request and receives the positioning personalization indicator as part of Long Term Evolution Positioning Protocol signaling, or the LMF device sends the request and receives the positioning personalization indicator as part of a capability exchange.

[0276] Clause 25. The method of any of clauses 14-21, wherein: a gNB device is the first network entity, the UE device is the second network entity, the gNB device receives the positioning personalization indicator from a LMF device, obtaining the measurement data comprises measuring, by the first network entity, a SRS generated by the UE device.

[0277] Clause 26. The method of any of clauses 14-21, wherein: the UE device is the first network entity, a gNB device is the second network entity, and the method comprises receiving, by the first network entity, the positioning personalization indicator from a LMF device, and obtaining the measurement data comprises measuring, by the first network entity a positioning reference signal generated by the gNB device.

[0278] It is to be recognized that depending on the example, certain acts or events of any of the techniques described herein can be performed in a different sequence, may be added, merged, or left out altogether (e.g., not all described acts or events are necessary' for the practice of the techniques). Moreover, in certain examples, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially.

[0279] In one or more examples, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media, which corresponds to a tangible medium such as data storage media, or communication media1616-444WO01Qualcomm Ref. No. 2402459WO 75 including any medium that facilitates transfer of a computer program from one place to another, e g., according to a communication protocol. In this manner, computer-readable media generally may correspond to (1) tangible computer-readable storage media which is non-transitory or (2) a communication medium such as a signal or carrier wave. Data storage media may be any available media that can be accessed by one or more computers or one or more processors to retrieve instructions, code and / or data structures for implementation of the techniques described in this disclosure. A computer program product may include a computer-readable medium.

[0280] By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage, or other magnetic storage devices, flash memory. or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer- readable medium. For example, if instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. It should be understood, however, that computer-readable storage media and data storage media do not include connections, carrier waves, signals, or other transitory media, but are instead directed to non-transitory, tangible storage media. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0281] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the terms “processor” and “processing circuitry',” as used herein may refer to any of the foregoing structures or any other structure suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and / or software modules. Also, the techniques could be fully implemented in one or more circuits or logic elements.1616-444WO01Qualcomm Ref. No. 2402459WO 76

[0282] The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including a wireless handset, an integrated circuit (IC) or a set of ICs (e g., a chip set). Various components, modules, or units are described in this disclosure to emphasize functional aspects of devices configured to perform the disclosed techniques, but do not necessarily require realization by different hardware units. Rather, as described above, various units may be combined in a hardware unit or provided by a collection of interoperative hardware units, including one or more processors as described above, in conjunction with suitable software and / or firmware.

[0283] Various examples have been described. These and other examples are within the scope of the following claims.1616-444WO01

Claims

Qualcomm Ref. No. 2402459WO 77WHAT IS CLAIMED IS:

1. A system comprising: a memory’ configured to store a trained machine learning (ML) model; a communication unit; and one or more processors of a first network entity, the one or more processors implemented in circuitry and communicatively coupled to the memory, the one or more processors configured to: receive a positioning personalization indicator for a second network entity, the positioning personalization indicator for the second network entity indicating information about the second network entity; obtain measurement data based on at least one measurement of at least one wireless signal; and apply the trained ML model to the measurement data to generate output data, wherein the output data indicates a physical position of a user equipment (UE) device or the output data being input data to a process that determines the physical position of the UE device, wherein the output data for the UE device is dependent on the positioning personalization indicator for the second network entity.

2. The system of claim 1, wherein the one or more processors are configured to select the trained ML model from a plurality of trained ML models based on the positioning personalization indicator.

3. The system of claim 1, wherein the one or more processors are configured to, as part of applying the trained ML model, apply the trained ML model to the measurement data and the positioning personalization indicator to generate the output data for the UE device.

4. The system of claim 1, wherein the one or more processors are configured to. as part of applying the trained ML model: determine, based on the positioning personalization indicator, a layer set from among a plurality of layer sets;1616-444WO01Qualcomm Ref. No. 2402459WO 78 apply the determined layer set to the measurement data to generate an intermediate feature vector; and apply a shared ML model to the intermediate feature vector to generate the output data.

5. The system of claim 4, wherein two or more layer sets of the plurality of layer sets correspond to different vendors.

6. The system of claim 1, wherein the measurement data includes one or more of data based on a positioning reference signal (PRS) or data based on a sounding reference signal (SRS).

7. The system of claim 1, wherein the first network entity receives the positioning personalization indicator from the second network entity.

8. The system of claim 1, wherein the positioning personalization indicator indicates one or more of: a manufacturer of the second netw ork entity, a vendor of a chipset of the second network entity, a platform model of the second network entity, or a serial number of the second netw ork entity.

9. The system of claim 1, wherein: the first network entity is a location management function (LMF) device, the second network entity is a gNB device, the LMF device receives the positioning personalization indicator and the measurement data from the gNB device, the gNB device is configured to generate the measurement data based on a sounding reference signal (SRS) generated by the UE device.

10. The system of claim 1, wherein: the first network entity is a location management function (LMF) device, the second network entity’ is the UE device, the LMF device receives the measurement data from the UE device, and the LMF device is configured to send a request to the UE device for the positioning personalization indicator.1616-444WO01Qualcomm Ref. No. 2402459WO 7911. The system of claim 10, wherein: the LMF device sends the request and receives the positioning personalization indicator as part of Long-Term Evolution Positioning Protocol (LPP) signaling, or the LMF device sends the request and receives the positioning personalization indicator as part of a capability exchange.

12. The system of claim 1, wherein: a gNB device is the first network entity, the UE device is the second network entity, the gNB device receives the positioning personalization indicator from a LMF device, the one or more processors are configured to, as part of obtaining the measurement data, measure a sounding reference signal generated by the UE device.

13. The system of claim 1, wherein: the UE device is the first network entity, a gNB device is the second network entity, and the one or more processors are configured to receive the positioning personalization indicator from a location management function device, and the one or more processors are configured to, as part of obtaining the measurement data, measure a positioning reference signal generated by the gNB device.

14. A method comprising: receiving, by a first network entity, a positioning personalization indicator for a second network entity, the positioning personalization indicator for the second network entity indicating information about the second network entity; obtaining, by the first network entity, measurement data based on at least one measurement of at least one wireless signal; and applying, by the first network entity, a trained machine learning (ML) model to the measurement data to generate output data, wherein the output data indicates a physical position of a user equipment (UE) device or the output data being input data to a process that determines the physical position of the UE device, wherein the output1616-444WO01Qualcomm Ref. No. 2402459WO 80 data for the UE device is dependent on the positioning personalization indicator for the second network entity.

15. The method of claim 14, wherein the method further comprises selecting, by the first network entity, the trained ML model from a plurality of trained ML models based on the positioning personalization indicator.

16. The method of claim 14, wherein applying the trained ML model comprises applying the trained ML model to the measurement data and the positioning personalization indicator to generate the output data for the UE device.

17. The method of claim 14, wherein applying the trained ML model comprises: determining, by the first network entity, based on the positioning personalization indicator, a layer set from among a plurality of layer sets; applying, by the first network entity, the determined layer set to the measurement data to generate an intermediate feature vector; and applying, by the first network entity, a shared ML model to the intermediate feature vector to generate the output data.

18. The method of claim 17, wherein two or more layer sets of the plurality of layer sets correspond to different vendors.

19. The method of claim 14. wherein the measurement data includes one or more of: data based on a positioning reference signal or data based on a sounding reference signal.

20. The method of claim 14, wherein the first network entity receives the positioning personalization indicator from the second network entity.

21. The method of claim 14, wherein the positioning personalization indicator indicates one or more of: a manufacturer of the second network entity, a vendor of a chipset of the second network entity, a platform model of the second network entity, or a serial number of the second network entity'.1616-444WO01Qualcomm Ref. No. 2402459WO 8122. The method of claim 14. wherein: the first network entity is a location management function (LMF) device, the second network entity is a gNB device, the LMF device receives the positioning personalization indicator and the measurement data from the gNB device. the gNB device is configured to generate the measurement data based on a sounding reference signal (SRS) generated by the UE device.

23. The method of claim 14, wherein: the first network entity is a LMF device, the second network entity is the UE device, the LMF device receives the measurement data from the UE device, and the LMF device is configured to send a request to the UE device for the positioning personalization indicator.

24. The method of claim 23, wherein: the LMF device sends the request and receives the positioning personalization indicator as part of Long Term Evolution Positioning Protocol signaling, or the LMF device sends the request and receives the positioning personalization indicator as part of a capability7exchange.

25. The method of claim 14, wherein: a gNB device is the first network entity. the UE device is the second network entity', the gNB device receives the positioning personalization indicator from a LMF device, obtaining the measurement data comprises measuring, by the first network entity, a SRS generated by the UE device.

26. The method of claim 14, wherein: the UE device is the first network entity. a gNB device is the second network entity, and the method comprises receiving, by the first network entity, the positioning personalization indicator from a LMF device, and1616-444WO01Qualcomm Ref. No. 2402459WO 82 obtaining the measurement data comprises measuring, by the first network entity a positioning reference signal generated by the gNB device.1616-444WO01

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