User equipment location determination

Abstract

Various aspects of the present disclosure relate to a method performed by a network entity in a communication network, the method comprising: receiving a request message for a location of a user equipment (UE); obtaining, based at least in part on the request message, a machine learning (ML) model for positioning, wherein the ML model comprises a set of one or more constraints, wherein the set of one or more constraints is based at least in part on a set of one or more capabilities of the UE; determining the location of the UE according to the ML model; and transmitting a response message indicating the location of the UE. Also disclosed is a method comprising: receiving a request message for a machine learning (ML) model, the request message comprising a constraint of the ML model; obtaining a ML model comprising the constraint; and transmitting a response message, the response message comprising (i) the ML model or (ii) an indication of a network location where the ML model is accessible. Also disclosed are suitable network entities for both methods.

Description

Technical Field

[0001] This disclosure relates to wireless communication, and more specifically, to positioning methods. Background Technology

[0002] A wireless communication system may include one or more network communication devices, such as base stations, that support wireless communication for one or more user communication devices, which may also be referred to as user equipment (UE) or other suitable terms. The wireless communication system may support wireless communication with one or more user communication devices by utilizing the resources of the wireless communication system (e.g., time resources (e.g., symbols, time slots, subframes, frames, etc.) or frequency resources (e.g., subcarriers, carriers, etc.)). Furthermore, the wireless communication system may support wireless communication across various radio access technologies, including third-generation (3G), fourth-generation (4G), fifth-generation (5G), and other suitable radio access technologies beyond 5G (e.g., sixth-generation (6G)). Summary of the Invention

[0003] The article “a” preceding an element is unrestricted and should be understood to refer to “at least one” or “one or more” of those elements. The terms “a,” “at least one,” “one or more,” and “at least one of one or more” are interchangeable. As used herein, the word “or,” as used in a list of items (e.g., a list of items beginning with phrases such as “at least one,” “one or more,” or “one or two”) indicates an inclusive list, such that a list of at least one of, for example, A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Furthermore, as used herein, the phrase “based on” should not be construed as referring to a closed set of conditions. For example, without departing from the scope of this disclosure, an example step described as “based on condition A” may be based on both condition A and condition B. In other words, as used herein, the phrase “based on” should be interpreted in the same manner as the phrase “at least partially based on.” Furthermore, as used herein, the term “set” may comprise one or more elements.

[0004] Some embodiments of the methods and apparatus described herein may include a method performed by a network entity, the method comprising: receiving a request message for the location of a user equipment (UE); obtaining a machine learning (ML) model for location based at least in part on the request message, wherein the ML model includes a set of one or more constraints, wherein the set of one or more constraints is based at least in part on a set of one or more capabilities of the UE; determining the location of the UE according to the ML model; and transmitting a response message indicating the location of the UE.

[0005] The request message may include one or more of the required accuracy of the location and the duration for reporting the location of the UE, and the method may further include: transmitting a request message to the ML model, wherein the request message to the ML model indicates the required accuracy of the location of the UE, the duration for reporting the location of the UE, and one or more of the set of one or more constraints.

[0006] The method may further include receiving from a network repository entity an indication of a set of one or more network entities supporting the ML model associated with the set of one or more constraints; selecting a network entity from the set of one or more network entities; and transmitting the request message for the ML model to the selected network entity.

[0007] Sending the request message to the ML model may include sending the request message to the location management function.

[0008] Obtaining the ML model may include receiving the ML model.

[0009] Obtaining the ML model may include: receiving an indication of a network location where the ML model is stored; and retrieving the ML model from the network location.

[0010] The method may further include receiving the set of one or more capabilities, wherein the set of one or more capabilities includes one or more of a positioning process supported by the UE and conditions associated with a radio environment supported by the UE.

[0011] The method may further include receiving one or more capabilities of a radio access network (RAN) node serving the UE, and receiving one or more capabilities of a set of one or more location reference UEs (PRUs), wherein the set of one or more constraints of the ML model are based at least in part on the set of one or more capabilities of the RAN node and the set of one or more capabilities of the set of one or more PRUs.

[0012] The set of one or more constraints may include one or more of the following: the location region validity of the ML model; the time validity of the ML model; the positioning process; the positioning accuracy quality; the model inference latency; the data type as input to the ML model; and the function ID corresponding to the constraint according to a predetermined scheme.

[0013] The method may further include: obtaining positioning measurement data from one or more of the UE, a radio access network (RAN) node, and a group of one or more positioning reference UEs (PRUs), wherein the input of the ML model includes the positioning measurement data; optionally, wherein the positioning measurement data includes reference signal information.

[0014] The network entity may include a location management function (LMF).

[0015] Some embodiments of the methods and apparatus described herein may further include a network entity configured to: receive a request message for the location of a user equipment (UE); obtain a machine learning (ML) model for location based at least in part on the request message, wherein the ML model includes a set of one or more constraints, wherein the set of one or more constraints is based at least in part on a set of one or more capabilities of the UE; determine the location of the UE according to the ML model; and transmit a response message indicating the location of the UE.

[0016] Some embodiments of the methods and apparatus described herein may further include a method performed by a network entity, the method comprising: receiving a request message for a machine learning (ML) model, the request message including constraints of the ML model; obtaining an ML model including the constraints; and transmitting a response message including (i) the ML model or (ii) an indication of a network location where the ML model can be accessed.

[0017] Obtaining the ML model may include training a new ML model that includes the constraints.

[0018] Training the new ML model may include collecting training information from one or more of the following: the Analysis Data Repository Function (ADRF); the Operation, Administration, and Maintenance (OAM) process; or the User Equipment (UE).

[0019] Obtaining the ML model may include selecting an existing ML model that includes the constraints.

[0020] The method may further include assigning a model ID to the ML model.

[0021] The network entity may be a location management function (LMF) or a network data analysis function (NWDAF).

[0022] The request message can be received from the Location Management Function (LMF) of the communication network.

[0023] Some embodiments of the methods and apparatus described herein may further include a network entity configured to: receive a request message for a machine learning (ML) model, the request message including constraints of the ML model; obtain an ML model including the constraints; and transmit a response message including (i) the ML model or (ii) an indication of a network location where the ML model can be accessed. Attached Figure Description

[0024] Figure 1 Examples of wireless communication systems according to aspects of this disclosure are described.

[0025] Figure 2 This demonstrates the network architecture based on aspects of this disclosure.

[0026] Figure 3 This describes the process for locating a UE according to aspects of this disclosure.

[0027] Figure 4 Further network architecture based on this disclosure is demonstrated.

[0028] Figure 5 The following describes the further process for locating the UE according to aspects of this disclosure.

[0029] Figure 6 Examples of user equipment (UE) according to aspects of this disclosure are described.

[0030] Figure 7 Examples of processors according to aspects of this disclosure are described.

[0031] Figure 8 Examples of network equipment (NE) according to aspects of this disclosure are described.

[0032] Figure 9 This describes a method for locating a UE according to aspects of this disclosure.

[0033] Figure 10 Further methods for locating a UE according to aspects of this disclosure are described.

[0034] Figure 11 This section describes an example of direct AIML localization.

[0035] Figures 12A to 12C This section provides an example of AIML-assisted localization.

[0036] Figure 13 Explain the network architecture. Detailed Implementation

[0037] The inventors have recognized that machine learning (ML), and more generally artificial intelligence (AI) (collectively referred to as AIML), can be used to improve positioning in communication networks, especially positioning of specific user equipment (UE).

[0038] In existing communication systems, location is typically handled by a Location Management Function (LMF). This is a network entity that can be queried when a specific location needs to be determined. The LMF can determine the requested location using any of a variety of known methods and then return the location to the requester.

[0039] Some systems can utilize AIML-assisted localization. In this approach, the location returned by the LMF can be enhanced using ML models implemented elsewhere in the communication network.

[0040] However, the inventors have recognized that such schemes are still limited by the accuracy of the location initially returned by the LMF, which is typically determined using conventional methods (e.g., without the need for AIML).

[0041] Therefore, implementing AIML at an earlier stage, such as by providing the LMF with an ML model that will be used to determine the UE location, can provide the benefit of more accurate and precise positioning in the communication network.

[0042] Furthermore, using AIML can allow for more accurate location determination of UEs that cannot provide accurate measurement data. For example, if the UE can only provide non-line-of-sight measurements obtained from the RAN node, then using AIML methods may be able to improve the accuracy of these measurements.

[0043] Regarding AIML feature identification, the UE may indicate which AIML features are supported. A specific supported model can be identified using a model ID.

[0044] The term "direct AIML localization" can be used to refer to the case where the UE's location is the output of an AIML model, assuming that an ML model exists at the UE or LMF to predict the location. "AIML-assisted localization" can refer to the case where the AI / ML model output can be an enhancement of new measurements and / or existing measurements, where, for example, the AI / ML model is used by the UE or gNB.

[0045] Within this framework, AIML positioning can be categorized as follows.

[0046] Scenario 1: Use UE-based positioning based on the UE side model, direct AI / ML positioning, or AI / ML assisted positioning.

[0047] Case 2a: UE-assisted / LMF-based localization using UE-side model, AI / ML-assisted localization.

[0048] Case 2b: UE-assisted / LMF-based localization using LMF-side model, direct AI / ML localization.

[0049] Case 3a: Use NG-RAN node-assisted localization with gNB side model, and AI / ML-assisted localization.

[0050] Case 3b: Use NG-RAN node-assisted localization with the LMF side model for direct AI / ML localization.

[0051] In some instances, the use of a “one-sided” model is preferred, where inference (the use of an AIML model) is performed entirely at the UE or at the network.

[0052] Specifically, Case 1 focuses on using UE-side models for direct AI / ML or AI / ML-assisted localization.

[0053] Cases 2a and 3a indicate that AI / ML-assisted localization is primarily used in conjunction with ML models on the UE side and / or gNB side, mainly to enhance the measurements provided at the LMF for deriving location estimates (e.g., more accurate measurements considering NLOS conditions). Since the ML model needs to be trained at the UE (side) or gNB (side), this requires defining data collection mechanisms for the UE and RAN, as well as procedures for model delivery / transmission and model identification / management to the UE / gNB.

[0054] Cases 2b and 3b indicate that the LMF-side model is used to derive direct AI / ML localization by collecting raw data or "AI / ML augmented data" from the UE and / or gNB. The ML model is trained at the LMF side (or generally the CN side). The LMF-side model used can be trained using raw data or AI / ML augmented data.

[0055] Figure 11 This section describes an example of direct AIML localization.

[0056] Figures 12A to 12C This section describes various implementation schemes for AIML-assisted localization.

[0057] Figure 13 This describes the network architecture that allows for data collection related to the use of Network Data Analysis Functions (NWDAF). In some cases, AnLF requests a trained ML model from MTLF by including the analysis ID corresponding to the analysis ID requested by the analysis consumer in the request. Information on available analysis IDs supported by AnLF is provided in 3GPP TS 23.288. MTLF trains the ML model corresponding to the requested analysis ID by collecting data from one or more data sources (NF, OAM, or UE).

[0058] The aspects of this disclosure are described in the context of wireless communication systems. For direct AI / ML positioning, it is assumed that an ML model exists at the UE or LMF for predicting location.

[0059] Figure 1 This describes an example of a wireless communication system 100 according to aspects of this disclosure. The wireless communication system 100 may include one or more NEs 102, one or more UEs 104, and a core network (CN) 106. The wireless communication system 100 may support various radio access technologies. In some embodiments, the wireless communication system 100 may be a 4G network, such as an LTE network or an LTE-A network. In some other embodiments, the wireless communication system 100 may be an NR network, such as a 5G network, a 5G-A network, or a 5G Ultra Wideband (5G-UWB) network. In other embodiments, the wireless communication system 100 may be a combination of 4G and 5G networks, or other suitable radio access technologies, including IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20. The wireless communication system 100 may support radio access technologies beyond 5G, such as 6G. In addition, the wireless communication system 100 can support technologies such as Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), or Code Division Multiple Access (CDMA).

[0060] One or more NEs 102 may be distributed across a geographical area to form a wireless communication system 100. One or more of the NEs 102 described herein may be, include, or be referred to as a network node, base station, network element, network function, network entity, radio access network (RAN), NodeB, eNodeB (eNB), next-generation NodeB (gNB), or other suitable terms. NEs 102 and UEs 104 may communicate via a communication link, which may be a wireless or wired connection. For example, NEs 102 and UEs 104 may perform wireless communication (e.g., receive signaling, transmit signaling) via a Uu interface.

[0061] NE 102 can provide a geographic coverage area, for which NE 102 can support services for one or more UEs 104 within the geographic coverage area. For example, NE 102 and UE 104 can support wireless communication of signals related to services (e.g., voice, video, packet data, messaging, broadcasting, etc.) according to one or more radio access technologies. In some embodiments, NE 102 can be mobile, for example, a satellite associated with a non-terrestrial network (NTN). In some embodiments, different geographic coverage areas 112 associated with the same or different radio access technologies can overlap, but different geographic coverage areas can be associated with different NEs 102.

[0062] One or more UEs 104 may be distributed across a geographical area of ​​the wireless communication system 100. UE 104 may include or be referred to as a remote unit, mobile device, wireless device, remote device, subscriber device, transmitter device, receiver device, or some other suitable term. In some embodiments, UE 104 may be referred to as a unit, station, terminal, or client, and other instances thereof. Additionally or alternatively, UE 104 may be referred to as an Internet of Things (IoT) device, an Internet of Everything (IoE) device, or a Machine Type Communication (MTC) device, and other instances thereof.

[0063] UE 104 may be able to support direct wireless communication with other UE 104 via a communication link. For example, UE 104 may support direct wireless communication with another UE 104 via a device-to-device (D2D) communication link. In some implementations, such as vehicle-to-vehicle (V2V) deployments, vehicle-to-everything (V2X) deployments, or cellular-V2X deployments, the communication link 114 may be referred to as a side link. For example, UE 104 may support direct wireless communication with another UE 104 via a PC5 interface.

[0064] NE 102 may support communication with CN 106 or with another NE 102 or both. For example, NE 102 may interface with other NE 102 or CN 106 via one or more backhaul links (e.g., S1, N2, N2, or network interfaces). In some embodiments, NE 102 may communicate directly with each other. In some other embodiments, NE 102 may communicate with each other or indirectly (e.g., via CN 106). In some embodiments, one or more NE 102 may include sub-components, such as access network entities, which may be instances of access node controllers (ANCs). The ANC may communicate with one or more UE 104s via one or more other access network transmitting entities (which may be referred to as radio headends, smart radio headends, or transmit-receive points (TRPs)).

[0065] CN 106 can support user authentication, access authorization, tracking, connectivity, and other access, routing, or mobility functions. CN 106 can be an evolved packet core (EPC) or a 5G core (5GC), which may include control plane entities that manage access and mobility (e.g., a mobility management entity (MME), access and mobility management functions (AMF)) and user plane entities that route or interconnect packets to external networks (e.g., a serving gateway (S-GW), a packet data network (PDN) gateway (P-GW), or a user plane function (UPF)). In some implementations, the control plane entities may manage non-access stratum (NAS) functions of one or more UEs 104 served by one or more NEs 102 associated with CN 106, such as mobility, authentication, and bearer management (e.g., data bearers, signaling bearers, etc.).

[0066] CN 106 can communicate with the packet data network via one or more backhaul links (e.g., via S1, N2, N2, or another network interface). The packet data network may contain an application server. In some implementations, one or more UEs 104 can communicate with the application server. UE 104 can establish a session (e.g., a Protocol Data Unit (PDU) session, etc.) with CN 106 via NE 102. CN 106 can use the established session (e.g., an established PDU session) to route services (e.g., control information, data, etc.) between UE 104 and the application server. A PDU session may be an instance of a logical connection between UE 104 and CN 106 (e.g., one or more network functions of CN 106).

[0067] In the wireless communication system 100, NE 102 and UE 104 can use the resources of the wireless communication system 100 (e.g., time resources (e.g., symbols, time slots, subframes, frames, etc.) or frequency resources (e.g., subcarriers, carriers)) to perform various operations (e.g., wireless communication). In some embodiments, NE 102 and UE 104 may support different resource structures. For example, NE 102 and UE 104 may support different frame structures. In some embodiments, such as in 4G, NE 102 and UE 104 may support a single frame structure. In some other embodiments, such as in 5G and other suitable radio access technologies, NE 102 and UE 104 may support various frame structures (i.e., multiple frame structures). NE 102 and UE 104 may support various frame structures based on one or more parameter sets.

[0068] The wireless communication system 100 may support one or more parameter sets, and the parameter sets may include subcarrier spacing and cyclic prefixes. A first parameter set (e.g., μ=0) may be associated with a first subcarrier spacing (e.g., 15 kHz) and a regular cyclic prefix. In some embodiments, the first parameter set (e.g., μ=0) associated with the first subcarrier spacing (e.g., 15 kHz) may utilize one time slot per subframe. A second parameter set (e.g., μ=1) may be associated with a second subcarrier spacing (e.g., 30 kHz) and a regular cyclic prefix. A third parameter set (e.g., μ=2) may be associated with a third subcarrier spacing (e.g., 60 kHz) and a regular cyclic prefix or an extended cyclic prefix. A fourth parameter set (e.g., μ=3) may be associated with a fourth subcarrier spacing (e.g., 120 kHz) and a regular cyclic prefix. A fifth parameter set (e.g., μ=4) may be associated with a fifth subcarrier spacing (e.g., 240 kHz) and a regular cyclic prefix.

[0069] Time intervals for resources (e.g., communication resources) can be organized according to frames (also called radio frames). Each frame may have a duration, for example, 10 milliseconds (ms). In some embodiments, each frame may contain multiple subframes. For example, each frame may contain 10 subframes, and each subframe may have a duration, for example, 1 ms. In some embodiments, each frame may have the same duration. In some embodiments, each subframe of a frame may have the same duration.

[0070] Alternatively, the time intervals of resources (e.g., communication resources) can be organized according to time slots. For example, a subframe may contain a certain number (e.g., quantity) of time slots. The number of time slots in each subframe may also depend on one or more parameter sets supported in the wireless communication system 100. For example, the first, second, third, fourth, and fifth parameter sets (i.e., μ=0, μ=1, μ=2, μ=3, μ=4) associated with corresponding subcarrier intervals of 15 kHz, 30 kHz, 60 kHz, 120 kHz, and 240 kHz may utilize a single time slot per subframe, two time slots per subframe, four time slots per subframe, eight time slots per subframe, and 16 time slots per subframe, respectively. Each time slot may contain a certain number (e.g., quantity) of symbols (e.g., OFDM symbols). In some embodiments, the number (e.g., quantity) of time slots in a subframe may depend on the parameter set. For a conventional cyclic prefix, a time slot may contain 14 symbols. For an extended cyclic prefix (e.g., applicable to a 60 kHz subcarrier spacing), a time slot may contain 12 symbols. The relationship between the number of symbols per time slot, the number of time slots per subframe, and the number of time slots per frame for the regular and extended cyclic prefixes may depend on the parameter set. It should be understood that references to the first parameter set (e.g., μ=0) associated with the first subcarrier spacing (e.g., 15 kHz) may be used interchangeably between subframes and time slots.

[0071] In the wireless communication system 100, the electromagnetic (EM) spectrum can be divided into various categories, frequency bands, channels, etc., based on frequency or wavelength. For example, the wireless communication system 100 may support one or more operating frequency bands, such as frequency range names FR1 (410 MHz to 7.125 GHz), FR2 (24.25 GHz to 52.6 GHz), FR3 (7.125 GHz to 24.25 GHz), FR4 (52.6 GHz to 114.25 GHz), FR4a or FR4-1 (52.6 GHz to 71 GHz), and FR5 (114.25 GHz to 300 GHz). In some embodiments, NE 102 and UE 104 may perform wireless communication on one or more of the operating frequency bands. In some embodiments, FR1 may be used by NE 102 and UE 104, as well as other equipment or devices, for cellular communication services (e.g., control information, data). In some implementations, FR2 can be used by NE 102 and UE 104, as well as other equipment or devices, for short-range, high data rate capabilities.

[0072] FR1 may be associated with one or more parameter sets (e.g., at least three parameter sets). For example, FR1 may be associated with a first parameter set containing a 15 kHz subcarrier spacing (e.g., μ=0); a second parameter set containing a 30 kHz subcarrier spacing (e.g., μ=1); and a third parameter set containing a 60 kHz subcarrier spacing (e.g., μ=2). FR2 may be associated with one or more parameter sets (e.g., at least two parameter sets). For example, FR2 may be associated with a third parameter set containing a 60 kHz subcarrier spacing (e.g., μ=2); and a fourth parameter set containing a 120 kHz subcarrier spacing (e.g., μ=3).

[0073] Figure 2 This document describes an example of a network architecture 200 based on aspects of this disclosure. In some implementations, network architecture 200 may be implemented as described herein. Figure 1 The described aspects of the wireless communication system 100 or implemented by said aspects.

[0074] Network architecture 200 includes a first LMF 202 and a second LMF 204, also referred to herein as LMF-T (training LMF) 202 and LMF-I (inference LMF) 204. The two LMFs 202 and 204 may be able to communicate with a Network Exposure Function (NEF) 216. Each LMF 202 and 204 may also be able to access various data sources 212 and 214, such as the Analysis Data Repository Function (ADRF) and various Operations and Maintenance (OAM) processes of the communication network. Network architecture 200 may further include one or more User Equipment (UE) devices 206 (which may correspond to those referenced herein). Figure 1 The UE 104 described herein, and one or more radio access network (RAN) nodes 208 (which may correspond to, as referenced herein) Figure 1 (As described in NE 102). Each LMF 202, 204 may be able to access one or more UEs 206 and one or more RAN nodes 208. Network architecture 200 may also include Access and Mobility Management Functions (AMF) 210, which may be accessible by each of the LMFs 202, 204.

[0075] The LMF-T 202 can be configured to train ML models. The LMF-T 202 can also access several existing ML models that have already been trained. For example, these existing models can be stored in various locations accessible via the network.

[0076] LMF-T 202 can be configured to obtain training data for training ML models. For example, LMF-T 202 can access data source 214, as well as, for example, UE 206 and RAN node 208 of the network, as described above, to obtain training data. This training data can typically be location data, such as reference signal information (as typically defined in, for example, 3GPP TS 37.355 and 3GPP TS 38.355).

[0077] In some embodiments, LMF-T 202 may be as follows: Figure 2 The core network 106 shown in the diagram. Alternatively, the LMF-T 202 can be located outside of the core network 106. This reference Figure 3 This will be discussed in more detail below. In some embodiments, LMF-T may be part of the NWDAF functionality. In some embodiments, LMF-T may be part of the Model Training Logic Function (MTLF) functionality within NWDAF.

[0078] LMF-I 204 can be configured to utilize ML models when determining location (e.g., when determining the location of UE 206).

[0079] Figure 3 This describes a process 300 for locating a UE according to aspects of this disclosure. For example, process 300 may use... Figure 2 The network architecture was implemented in 200.

[0080] At step S306, a location request may be sent to AMF 210. This request may originate directly from client 304 or may arrive, for example, via Gateway Mobile Location Center (GMLC). The location request may be a request to determine and report the location of a specific UE 206.

[0081] A location request may contain a set of requirements for the location process. For example, it may require the location of the UE with a specific accuracy. Alternatively, the client may request the location within a specified time period.

[0082] At step S308, AMF 210 may select the LMF to which it forwards the location request for processing. For example, AMF 210 may select LMF-I 204 of network architecture 200, as described above.

[0083] At step S310, the location request can be forwarded to the selected LMF. Taking LMF-I 204 as an example, at step S310, AMF 210 transmits a location request to LMF-I 204.

[0084] At step S312, LMF-I 204 may establish communication with the UE 206 to be located and one or more location reference UEs (PRUs) 302. LMF-I 204 may use this connection to retrieve certain capabilities of UE 206 and PRU 302, such as, for example, which positioning methods UE 206 and / or PRU 302 can support, what the current radio ambient conditions of UE 206 and / or PRU 302 are, and whether UE 206 and / or any PRU 302 can use ML models to enhance positioning (e.g., deriving valid line-of-sight measurements from non-line-of-sight data). Example radio ambient conditions may include indications that the UE is indoors, that the UE can provide non-line-of-sight measurement data, or that the UE is mobile.

[0085] For the sake of context only, it should be noted that the positioning methods supported by UE 206 may include RAT-dependent positioning technologies, such as TDOA, multi-RTT, or side-link positioning procedures; or may include RAT-independent positioning methods, such as GNSS, Wi-Fi, or Bluetooth.

[0086] LMF-I 204 can also communicate with one or more RAN nodes 208 and retrieve the capabilities of RAN nodes 208. For example, LMF-I 204 can contact RAN nodes 208 serving UE 206 and PRU 302. If UE 206 and PRU 302 are served by more than one RAN node 208, then LMF-I 204 can obtain the capability of each RAN node 208 serving at least one PRU 302 or UE 206.

[0087] At step S314, LMF-I 204 may optionally determine whether direct AIML positioning is an appropriate method for handling location requests.

[0088] The determination performed at step S314 may be based on the location requirements included in the location request. For example, if the location request includes an indication that only very low precision is required, then LMF-I 204 may determine that AIML positioning is unnecessary. However, if the location request includes an indication that high precision is required, then LMF-I 204 may be more likely to use AIML positioning. Additionally or alternatively, the determination performed at step S314 may be based on the capabilities of UE 206 determined at step S312, as well as any capabilities of PRU 302 and / or RAN node 208. For example, if UE 206 cannot provide location information suitable as input to an ML model, then LMF-I 204 may determine that AIML positioning is inappropriate. Additionally or alternatively, the determination performed at step S314 may be based on the radio environmental conditions of UE 206. Example radio environmental conditions may include indications that UE 206 is indoors, that UE 206 is able to provide only non-line-of-sight measurement data, or that UE 206 is mobile.

[0089] In general context, it should be noted that the location information provided by UE 206 is typically as defined in 3GPP TS 37.355 (NR Location Measurement Information) and 3GPP TS 38.355 (Sidelink Location Measurement Information). Alternatively, the location information may include a channel observation fingerprint, as described in 3GPP TR 38843, used as input data for ML models.

[0090] LMF-I 204 can determine a set of requirements for the required model (referred to herein as constraints). These requirements can be determined from any requirements included in the location request, and any capabilities of UE 206, PRU 302 or RAN node 208 that have been determined, and / or the radio environment conditions of UE 206.

[0091] For example, the model requirements may include the ability of the model to provide a location estimate with the required accuracy to meet the location request.

[0092] Alternatively, the model requirements may include a specified model inference delay; that is, the time within which the model is expected to provide a location estimate.

[0093] Alternatively, the model requirements may include the use of a specific localization method. For example, if UE 206 is only able to support a specific localization method, then the ML model may need to support the same localization method.

[0094] Additionally or alternatively, the model requirements may include validity for use with UE 206. For example, the model may need to be able to operate within a specific area of ​​interest where UE 206 is believed to be located (e.g., an area served by RAN node 208 known to serve UE 206), or it may need to be valid at a specific time of day. As another example, the model may need to be valid within a specific area ID corresponding to UE 206. As yet another example, the model may need to be able to account for UE 206 indoors and therefore cannot provide line-of-sight measurements that typically allow for better estimations of the UE's location.

[0095] Alternatively, the model requirements may include the types of inputs needed for model operation. For example, if the positioning information available from UE 206 is only a specific type of reference signal information, then the model may need to be able to use this type of reference information as input to operate.

[0096] The requirements of the model may optionally be expressed using an identifier referred to herein as a Function ID. A Function ID may be an analysis ID designed for use with NWDAF and compatible with LMF-T 202. Alternatively, a Function ID may be an ID indicating some or all of the requirements described above within a predetermined scheme.

[0097] Depending on the requirements, LMF-I 204 may determine that more than one ML model is needed. In this case, a separate list of requirements can be determined for each model.

[0098] Multiple ML models may be beneficial in allowing for higher accuracy, or if a client requests multiple UE 206 locations with a 304 error, then multiple ML models may be necessary.

[0099] At step S316, LMF-I 204 may determine that it is necessary to request a trained ML model for locating UE 206. For example, if LMF-I 204 has access to any previously obtained ML model, then LMF-I 204 may determine that none of the previously obtained models meet the requirements.

[0100] At step S318, LMF-I 204 may select another network entity from which it requests the ML model. For example, this could be LMF-T 202.

[0101] To select where to request the ML model, LMF-I 204 can contact the network repository function (not shown) and provide the model requirements to the network repository function. In some embodiments, the model requirements may include the accuracy requirements received at step S306 and / or the determined radio environmental conditions (e.g., a non-line-of-sight scenario) and / or the retrieved capabilities received in step S312. The network repository function can use these requirements to provide LMF-I 204 with a list of network entities that may be suitable for training and provide models that meet the model requirements. LMF-I 204 can then select a network entity from this list, such as LMF-T 202.

[0102] At step S320, LMF-I 204 may send a request to the selected network entity for an ML model that meets the determined model requirements. For example, LMF-I 204 may send a model request to LMF-T 202.

[0103] At step S322, LMF-T 202 can obtain a satisfactory ML model. This can be achieved by first determining whether a satisfactory ML model is already available. If the ML model is already available, then LMF-T 202 can select such an existing model. If the ML model is not already available, then LMF-T 202 can train a new model.

[0104] If multiple models have been requested by LMF-I 204, LMF-T 202 may perform step S322 for each of the requested models.

[0105] In step S324, if it is determined that no suitable model exists and a new model needs to be trained, then LMF-T 202 can be referenced as described above. Figure 2 The LMF-T 202 can train a new ML model according to the requirements received in step S320.

[0106] At step S326, LMF-T 202 can assign a model ID to any newly trained model. For example, a model ID can identify the model and distinguish it from other ML models. That is, the model ID can be a unique identifier.

[0107] Any newly trained model can optionally be stored in a data storage location accessible via the network. For example, such a model can be stored in the Analysis Data Repository Function (ADRF) of the core network 106.

[0108] At step S328, LMF-T 202 may send a response to the model request to LMF-I 204. The response may contain the model file itself, or an indication of a storage location where the model is stored and accessible. LMF-I 204 may use this response to obtain the ML model (or multiple ML models if requested).

[0109] At step S330, LMF-I 204 can use the ML model to determine the location of UE 206. This can be achieved by providing location data as input to the ML model. For example, location data can be obtained from UE 206, PRU 302 and / or RAN node 208, or any or all of these options, as model input.

[0110] At step S332, LMF-I 204 can respond to AMF 210 by providing the location determined using the model.

[0111] At step S334, AMF 210 may transmit the location provided by LMF-I 204 to client 304.

[0112] Step S336 indicates an alternative aspect of this disclosure. In some embodiments, LMF-I 204 may not request a model from LMF-T 202. Instead, LMF-T 202 may provide a trained ML model to LMF-I 204 without sending such a request. For example, LMF-T 202 may provide an ML model to LMF-T 204 at regular intervals (e.g., periodically).

[0113] In this alternative, LMF-I 204 may need to subscribe to LMF-T 202 in order to receive periodic ML models from LMF-T 202. As part of the subscription, LMF-I 204 may specify to LMF-T 202 how frequently the models should be served, and / or how the models should be trained.

[0114] It is envisioned that this aspect may be particularly useful when LMF-T 202 is not part of the core network 106 of network architecture 200, but exists outside the core network as a subscription service for ML models used for positioning.

[0115] For example, each ML model provided by LMF-T 202 may have different capabilities and functions, enabling the provided ML models to meet the requirements of various location requests over time.

[0116] Then, it is envisioned that if, upon receiving a specific location request, LMF-I 204 determines that LMF-T 202 has not yet provided a suitable model, then LMF-I 204 can then use conventional (non-ML-based) methods to determine the location of UE 206. Alternatively, if LMF-T 202 has prepared a suitable model, then LMF-I can then use said model to determine the location of UE 206, as otherwise described herein.

[0117] LMF-T 202 can provide ML models either by directly emitting model files or by indicating the storage location of the accessible models within them.

[0118] Figure 4 This describes an alternative network architecture 400 based on further aspects of this disclosure.

[0119] The network architecture 400 may include LMF 404, Network Data Analysis Function (NWDAF) 402, data sources 412, 414, UE 406, RAN node 408 and / or AMF 410.

[0120] The data sources 412 and 414, UE 406, RAN node 408, and AMF 410 of network architecture 400 can be referenced above. Figure 2 The corresponding components of the network architecture 200 described are largely the same.

[0121] LMF 404 can be configured to receive location requests from AMF 410 and estimate the location of UE 406, for example, using conventional (non-AI-based) methods. LMF 404 can be further configured to request analysis assistance from NWDAF 402, for example, to enhance the location estimation prepared by LMF.

[0122] The NWDAF 402 can be configured to train an ML model and use the trained ML model to perform inference. For example, the NWDAF 402 can be configured to train an ML model for localization and use the trained model to perform inference to generate or refine location estimates. Generally, the techniques and data sources used by the NWDAF 402 to train the ML model are envisioned to be similar to those mentioned above. Figure 2 and 3 The LMF-T 202 described uses the same technology and data source.

[0123] Figure 5 This describes a process 500 for locating a UE according to aspects of this disclosure. For example, process 500 may use... Figure 4 The network architecture is implemented in 400.

[0124] At step S506, a location request may be sent to AMF 410. This request may originate directly from client 504 or may arrive, for example, via Gateway Mobile Location Center (GMLC). The location request may be a request to determine and report the location of a specific UE 406.

[0125] A location request may include a set of requirements for the location process. For example, it may require the UE's location with a specific accuracy. Alternatively, the client may request the location within a specified time period.

[0126] At step S508, AMF 410 may choose to forward the location request to its LMF for processing.

[0127] At step S510, the location request can be forwarded to the selected LMF. For example, AMF 410 can select LMF 404 of network architecture 400, as described above.

[0128] At step S512, LMF 404 may establish communication with the UE 406 to be located and one or more location reference UEs (PRUs) 502. LMF 404 may use this connection to retrieve certain capabilities of UE 406 and PRU 502, such as, for example, which location methods UE 406 and / or PRU 502 can support, and what the current radio environment conditions of UE 406 and / or PRU 502 are.

[0129] LMF 404 can also communicate with one or more RAN nodes 408 and retrieve the capabilities of RAN nodes 408. For example, LMF 404 can contact RAN nodes 408 serving UE 406 and PRU 502. If UE 406 and PRU 502 are served by more than one RAN node 408, then LMF 404 can obtain the capability of each RAN node 408 serving at least one PRU 502 or UE 406.

[0130] At step S514, LMF 404 can estimate the position of UE 406 using any known conventional positioning method. It is generally assumed that this step does not involve the use of an AIML model.

[0131] If a client requests multiple locations with a 504 error, then an LMF 404 can estimate the location for each requested location.

[0132] As part of the estimated location, it depends on the conventional positioning method used by LMF 404, which can communicate with UE 406, PRU 502 and / or one or more RAN nodes 408 to obtain measurement information.

[0133] At step S516, LMF 404 may determine that enhanced location estimation is needed. For example, if the location request includes a specified accuracy, then LMF 404 may determine that the location estimation does not meet this accuracy. Alternatively, the determination performed at step S516 may be based on the capabilities of UE 406 determined at step S512, as well as any capabilities of PRU 502 and / or RAN node 408. For example, if UE 406 cannot provide location information suitable as input to an ML model, then LMF 404 may determine that AIML positioning is inappropriate. Alternatively, the determination performed at step S516 may be based on the radio environmental conditions of UE 406. Example radio environmental conditions may include indications that UE 406 is indoors, that UE 406 is able to provide only non-line-of-sight measurement data, or that UE 406 is mobile.

[0134] At step S518, LMF 404 may send a request to NWDAF 402 to enhance the location estimation, referred to herein as an analysis request. For example, the analysis request may include the estimated location, the method used to generate the estimated location, and the capabilities reported by UE 404, PRU 502, and / or RAN node 408.

[0135] The analysis request may also include an analysis ID, which indicates the form of the analysis requested by NWDAF 402.

[0136] It should be noted that, optionally, LMF 404 can select NWDAF 402 to receive the analytics request by querying the Network Repository Function (NRF) for a list of NWDAFs capable of handling the request. For example, if the request contains an analytics ID, then LMF 404 can request a list of NWDAFs that can support the analytics ID from the NRF.

[0137] At step S520, NWDAF 402 determines whether a suitable AIML model for processing the analysis request is available. For example, NWDAF 402 may check whether an ML model capable of enhancing the location estimation prepared using the indicated method and / or compatible with the capabilities reported by UE 404, PRU 502, and / or RAN node 408 is available. Alternatively, if an analysis ID is provided, NWDAF 402 may determine whether an ML model capable of providing the analysis indicated by the analysis ID is available.

[0138] In step S522, if it is determined that no suitable model exists and a new model needs to be trained, then NWDAF 402 can be performed as described above. Figure 2 Descriptive training of new models.

[0139] At step S524, NWDAF 402 responds to the analysis request by providing LMF 404 with an enhanced location estimate, which is inferred using an ML model.

[0140] At step S526, LMF 404 can respond to AMF 410 by providing enhanced position estimation.

[0141] At step S528, AMF 410 may transmit the location provided by LMF 404 to client 504.

[0142] Figure 6 An example of a UE 600 according to aspects of this disclosure is described. UE 600 may include a processor 602, a memory 604, a controller 606, and a transceiver 608. The processor 602, memory 604, controller 606, or transceiver 608, or various combinations thereof, or various components thereof, may be examples of components for performing the various aspects of this disclosure as described herein. These components may be coupled via one or more interfaces (e.g., operatively, communicatively, functionally, electronically, electrically).

[0143] Processor 602, memory 604, controller 606, or transceiver 608, or various combinations or components thereof, may be implemented in hardware (e.g., a circuit system). The hardware may include a processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), or other programmable logic device, or any combination thereof configured to or otherwise support components for performing the functions described in this disclosure.

[0144] Processor 602 may include intelligent hardware devices (e.g., a general-purpose processor, DSP, CPU, ASIC, FPGA, or any combination thereof). In some embodiments, processor 602 may be configured to operate memory 604. In some other embodiments, memory 604 may be integrated into processor 602. Processor 602 may be configured to execute computer-readable instructions stored in memory 604 to cause UE 600 to perform various functions of this disclosure.

[0145] Memory 604 may include volatile or non-volatile memory. Memory 604 may store computer-readable, computer-executable code containing instructions that, when executed by processor 602, cause UE 600 to perform the various functions described herein. The code may be stored in a non-transitory computer-readable medium, such as memory 604 or another type of memory. Computer-readable media includes both non-transitory computer storage media and communication media, wherein the communication media includes any media that facilitates the transfer of a computer program from one place to another. Non-transitory storage media may be any available media accessible by a general-purpose or special-purpose computer.

[0146] In some implementations, processor 602 and memory 604 coupled to processor 602 may be configured to cause UE 600 to perform one or more of the functions described herein (e.g., instructions stored in memory 604 are executed by processor 602). For example, processor 602 may support wireless communication at UE 600 according to the examples disclosed herein. UE 600 may be configured to support a component for one or more positioning methods, potentially including AIML-based positioning methods.

[0147] Controller 606 manages the input and output signals of UE 600. Controller 606 can also manage peripheral devices not integrated into UE 600. In some embodiments, controller 606 may utilize an operating system such as iOS®, ANDROID®, WINDOWS®, or other operating systems. In some embodiments, controller 606 may be implemented as part of processor 602.

[0148] In some embodiments, UE 600 may include at least one transceiver 608. In other embodiments, UE 600 may have more than one transceiver 608. Transceiver 608 may represent a wireless transceiver. Transceiver 608 may include one or more receiver chains 610, one or more transmitter chains 612, or a combination thereof.

[0149] Receiver chain 610 may be configured to receive signals (e.g., control information, data, packets) via wireless media. For example, receiver chain 610 may include one or more antennas for receiving signals in the air or via wireless media. Receiver chain 610 may include at least one amplifier (e.g., a low-noise amplifier (LNA)) configured to amplify the received signal. Receiver chain 610 may include at least one demodulator configured to demodulate the received signal and obtain the transmitted data by reversing the modulation technique applied during signal transmission. Receiver chain 610 may include at least one decoder for decoding the demodulated signal to receive the transmitted data.

[0150] Transmitter chain 612 can be configured to generate and transmit signals (e.g., control information, data, packets). Transmitter chain 612 may include at least one modulator for modulating data onto a carrier signal, preparing the signal for transmission over a wireless medium. The at least one modulator may be configured to support one or more techniques, such as amplitude modulation (AM), frequency modulation (FM), or digital modulation schemes like phase shift keying (PSK) or quadrature amplitude modulation (QAM). Transmitter chain 612 may also include at least one power amplifier configured to amplify the modulated signal to an appropriate power level suitable for transmission over a wireless medium. Transmitter chain 612 may also include one or more antennas for transmitting the amplified signal into the air or wireless medium.

[0151] Figure 7 An example of a processor 700 according to aspects of this disclosure is described. Processor 700 may be an example of a processor configured to perform various operations according to the examples described herein. Processor 700 may include a controller 702 configured to perform various operations according to the examples described herein. Processor 700 may optionally include at least one memory 704, which may be, for example, an L1 / L2 / L3 cache. Additionally or alternatively, processor 700 may optionally include one or more arithmetic logic units (ALUs) 706. One or more of these components may be electronically communicated or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces (e.g., buses).

[0152] Processor 700 may be a processor chipset and includes a protocol stack (e.g., a software stack) executed by the processor chipset to perform various operations (e.g., receive, acquire, retrieve, transmit, output, forward, store, determine, identify, access, write, read) according to the examples described herein. The processor chipset may include one or more cores, one or more caches (e.g., memory native to the processor chipset (e.g., processor 700) or contained within the processor chipset) or other memory (e.g., random access memory (RAM), read-only memory (ROM), dynamic RAM (DRAM), synchronous dynamic RAM (SDRAM), static RAM (SRAM), ferroelectric RAM (FeRAM), magnetic RAM (MRAM), resistive RAM (RRAM), flash memory, phase-change memory (PCM), and others).

[0153] Controller 702 can be configured to manage and coordinate various operations of processor 700 (e.g., signaling, receiving, acquiring, retrieving, transmitting, outputting, forwarding, storing, determining, identifying, accessing, writing, and reading) to enable processor 700 to support various operations according to the examples described herein. For example, controller 702 can operate as a control unit of processor 700, generating control signals that manage the operation of various components of processor 700. These control signals include enabling or disabling functional units, selecting data paths, initiating memory accesses, and coordinating operation timing.

[0154] Controller 702 may be configured to fetch (e.g., fetch, retrieve, receive) instructions from memory 704 and determine subsequent instructions to be executed to enable processor 700 to support various operations according to the examples described herein. Controller 702 may be configured to track the memory addresses of instructions associated with memory 704. Controller 702 may be configured to decode instructions to determine the operations to be performed and the operands involved. For example, controller 702 may be configured to interpret instructions and determine control signals to be output to other components of processor 700 to enable processor 700 to support various operations according to the examples described herein. Alternatively or additionally, controller 702 may be configured to manage data flow within processor 700. Controller 702 may be configured to control data transfers between registers, ALU 706, and other functional units of processor 700.

[0155] Memory 704 may include one or more caches (e.g., memory local to processor 700 or included in processor 700) or other memories, such as RAM, ROM, DRAM, SDRAM, SRAM, MRAM, flash memory, etc. In some embodiments, memory 704 may reside within or on the processor chipset (e.g., local to processor 700). In some other embodiments, memory 704 may reside outside the processor chipset (e.g., remote from processor 700).

[0156] Memory 704 may store computer-readable, computer-executable code containing instructions that, when executed by processor 700, cause processor 700 to perform the various functions described herein. The code may be stored in a non-transitory computer-readable medium, such as system memory or another type of memory. Controller 702 and / or processor 700 may be configured to execute the computer-readable instructions stored in memory 704 to cause processor 700 to perform various functions. For example, processor 700 and / or controller 702 may be coupled to or coupled to memory 704, and processor 700 and controller 702 may be configured to perform the various functions described herein. In some instances, processor 700 may include multiple processors, and memory 704 may include multiple memories. One or more of the multiple processors may be coupled to one or more of the multiple memories, which may be individually or collectively configured to perform the various functions described herein.

[0157] One or more ALU 706s can be configured to support various operations according to the examples described herein. In some embodiments, one or more ALU 706s may reside within or on a processor chipset (e.g., processor 700). In some other embodiments, one or more ALU 706s may reside outside the processor chipset (e.g., processor 700). One or more ALU 706s can perform one or more calculations on data, such as addition, subtraction, multiplication, and division. For example, one or more ALU 706s can receive input operands and an opcode that determines the operation to be performed. One or more ALU 706s can be configured with various logic and arithmetic circuitry, including adders, subtractors, shifters, and logic gates, to process and manipulate data according to the operation. Alternatively, one or more ALU 706s may support logical operations such as AND, OR, XOR, NOR, and NAND, enabling one or more ALU 706s to handle conditional operations, comparisons, and bitwise operations.

[0158] Processor 700 may support wireless communication according to examples disclosed herein. Processor 700 may be configured or operable to support implementations as described above. Figures 2 to 5The described location management function (LMF) or network data analysis function (NWDAF) component. For example, processor 700 may be configured or operable to support a component for: receiving a request message for the location of a user equipment (UE); obtaining a machine learning (ML) model for location based at least in part on the request message, wherein the ML model includes a set of one or more constraints, wherein the set of one or more constraints is based at least in part on a set of one or more capabilities of the UE; determining the location of the UE according to the ML model; and outputting a response message indicating the location of the UE. In another example, processor 700 may be configured or operable to support a component for: receiving a request message for a machine learning (ML) model, the request message including constraints of the ML model; obtaining the ML model including the constraints; and outputting a response message, the response message including (i) the ML model or (ii) an indication of a network location in which the ML model is accessible. In another example, processor 700 may be configured or operable to support a component for: receiving a location request for determining the location of a user equipment (UE); receiving the capabilities of the UE; receiving location information from the UE; using the location information to determine a location estimate of the UE; outputting an analysis request to enhance the location estimate, the analysis request including the location estimate and capabilities; receiving the enhanced location estimate in response to the analysis request; and outputting a response to the location request, the response including the enhanced location estimate. In another example, processor 700 may be configured or operable to support a component for: receiving an analysis request, the analysis request including: a location estimate of a user equipment (UE) of a communication system; a request to enhance the location estimate; and the capabilities of the UE; obtaining an ML model configured to enhance the location estimate; using the ML model to enhance the location estimate based on capabilities, thereby obtaining the enhanced location estimate; and outputting a response to the analysis request, the response including the enhanced location estimate. In another instance, processor 700 may be configured or operable to support a component for: acquiring an ML model trained to determine the location of a user equipment (UE); receiving a location request for determining the location of the UE; receiving the UE and one or more location reference UEs (PRUs); determining, based on the capability, that the acquired ML model is suitable for determining the location of the UE; using the ML model to determine the location of the UE; and outputting a response to the location request, the response including the location of the UE.

[0159] Figure 8Examples of network equipment (NE) 800 according to aspects of this disclosure are described below. For example, an NE may correspond to LMF-I 204, LMF-T 202, LMF 404, or NWDAF 402 as described above. NE 800 may include a processor 802, a memory 804, a controller 806, and a transceiver 808. The processor 802, memory 804, controller 806, or transceiver 808, or various combinations thereof, or various components thereof, may be examples of components for performing the various aspects of this disclosure as described herein. These components may be coupled via one or more interfaces (e.g., operative ground, communicative ground, functional ground, electronic ground, electrical ground).

[0160] Processor 802, memory 804, controller 806, or transceiver 808, or various combinations or components thereof, may be implemented in hardware (e.g., a circuit system). The hardware may include a processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), or other programmable logic device, or any combination thereof configured or otherwise supporting components for performing the functions described in this disclosure.

[0161] Processor 802 may include intelligent hardware devices (e.g., a general-purpose processor, DSP, CPU, ASIC, FPGA, or any combination thereof). In some embodiments, processor 802 may be configured to operate memory 804. In some other embodiments, memory 804 may be integrated into processor 802. Processor 802 may be configured to execute computer-readable instructions stored in memory 804 to cause NE 800 to perform various functions of this disclosure.

[0162] Memory 804 may include volatile or non-volatile memory. Memory 804 may store computer-readable, computer-executable code containing instructions that, when executed by processor 802, cause NE 800 to perform the various functions described herein. The code may be stored in a non-transitory computer-readable medium, such as memory 804 or another type of memory. Computer-readable media includes both non-transitory computer storage media and communication media, wherein the communication media includes any media that facilitates the transfer of a computer program from one place to another. Non-transitory storage media may be any available media accessible by a general-purpose or special-purpose computer.

[0163] In some implementations, processor 802 and memory 804 coupled to processor 802 may be configured to cause NE 800 to perform one or more of the functions described herein (e.g., instructions stored in memory 804 are executed by processor 802). For example, processor 802 may support wireless communication at NE 800 according to the examples disclosed herein. NE 800 may be configured to support implementations as referenced above. Figures 2 to 5The described location management function (LMF) or network data analysis function (NWDAF) component. For example, the NE 800 may be configured to support a component for: receiving a request message for the location of a user equipment (UE); obtaining, at least in part, a machine learning (ML) model for location based on the request message, wherein the ML model includes a set of one or more constraints, wherein the set of one or more constraints is at least in part based on a set of one or more capabilities of the UE; determining the location of the UE based on the ML model; and transmitting a response message indicating the location of the UE. In another example, the NE 800 may be configured to support a component for: receiving a request message for a machine learning (ML) model, the request message including constraints of the ML model; obtaining the ML model including the constraints; and transmitting a response message including (i) the ML model or (ii) an indication of a network location in which the ML model is accessible. In another example, the NE 800 may be configured to support a component for: receiving a location request for determining the location of a user equipment (UE); receiving the UE's capabilities; receiving location information from the UE; using the location information to determine a location estimate of the UE; transmitting an analysis request to enhance the location estimate, the analysis request including the location estimate and capabilities; receiving the enhanced location estimate in response to the analysis request; and transmitting a response to the location request, the response including the enhanced location estimate. In another example, the NE 800 may be configured to support a component for: receiving an analysis request, the analysis request including: a location estimate of a user equipment (UE) of a communication system; a request to enhance the location estimate; and the UE's capabilities; obtaining an ML model configured to enhance the location estimate; using the ML model to enhance the location estimate based on capabilities, thereby obtaining the enhanced location estimate; and transmitting a response to the analysis request, the response including the enhanced location estimate. In another instance, the NE 800 may be configured to support a component for: acquiring an ML model trained to determine the location of a user equipment (UE); receiving a location request for determining the location of the UE; receiving the UE and one or more location reference UEs (PRUs); determining, based on the capability, that the acquired ML model is suitable for determining the location of the UE; using the ML model to determine the location of the UE; and transmitting a response to the location request, the response including the location of the UE.

[0164] Controller 806 manages the input and output signals of NE 800. Controller 806 can also manage peripheral devices not integrated into NE 800. In some embodiments, controller 806 may utilize an operating system such as iOS®, ANDROID®, WINDOWS®, or other operating systems. In some embodiments, controller 806 may be implemented as part of processor 802.

[0165] In some embodiments, NE 800 may include at least one transceiver 808. In other embodiments, NE 800 may have more than one transceiver 808. Transceiver 808 may represent a wireless transceiver. Transceiver 808 may include one or more receiver chains 810, one or more transmitter chains 812, or a combination thereof.

[0166] Receiver chain 810 may be configured to receive signals (e.g., control information, data, packets) via wireless media. For example, receiver chain 810 may include one or more antennas for receiving signals in the air or via wireless media. Receiver chain 810 may include at least one amplifier (e.g., a low-noise amplifier (LNA)) configured to amplify the received signal. Receiver chain 810 may include at least one demodulator configured to demodulate the received signal and obtain the transmitted data by reversing the modulation technique applied during signal transmission. Receiver chain 810 may include at least one decoder for decoding the demodulated signal to receive the transmitted data.

[0167] Transmitter chain 812 can be configured to generate and transmit signals (e.g., control information, data, packets). Transmitter chain 812 may include at least one modulator for modulating data onto a carrier signal, preparing the signal for transmission over a wireless medium. The at least one modulator may be configured to support one or more techniques, such as amplitude modulation (AM), frequency modulation (FM), or digital modulation schemes like phase shift keying (PSK) or quadrature amplitude modulation (QAM). Transmitter chain 812 may also include at least one power amplifier configured to amplify the modulated signal to an appropriate power level suitable for transmission over a wireless medium. Transmitter chain 812 may also include one or more antennas for transmitting the amplified signal into the air or wireless medium.

[0168] Figure 9 A flowchart illustrating method 900 according to an aspect of this disclosure is provided. The operation of method 900 can be implemented by a network entity (e.g., an LMF) as described herein. For example, method 900 can be implemented by [the entity described above]. Figure 2 and 3 The LMF-I 204 is described. In some implementations, the network entity can execute a set of instructions to control the functional elements of the network entity to perform the described functions.

[0169] At 902, the method may include receiving a request message for the location of the user equipment (UE).

[0170] The operation of 902 can be performed according to the examples described herein. In some implementations, aspects of the operation of 902 may be as described in the references. Figure 2 and 3 The LMF-I 204 described is executed.

[0171] At 904, the method may include obtaining a machine learning (ML) model for positioning based at least in part on a request message, wherein the ML model includes a set of one or more constraints, wherein the set of one or more constraints is based at least in part on a set of one or more capabilities of the UE.

[0172] The operation of 904 can be performed according to the examples described herein. In some implementations, aspects of the operation of 904 may be as described in the references. Figure 2 and 3 The LMF-I 204 described is executed.

[0173] At 906, the method may include determining the location of the UE based on the ML model.

[0174] The operation of 906 can be performed according to the examples described herein. In some implementations, aspects of the operation of 906 may be provided by reference to [reference needed]. Figure 2 and 3 The LMF-I 204 described is executed.

[0175] At 908, the method may include transmitting a response message indicating the location of the UE.

[0176] The operation of 908 can be performed according to the examples described herein. In some implementations, aspects of the operation of 908 may be as described in the references. Figure 2 and 3 The LMF-I 204 described is executed.

[0177] It should be noted that the method described herein describes one possible implementation, and the operation and steps may be rearranged or modified in other ways, and other implementations are possible.

[0178] Figure 10 A flowchart illustrating method 1000 according to an aspect of this disclosure is provided. The operation of method 1000 can be implemented by a network entity (e.g., an LMF) as described herein. For example, method 1000 can be implemented by [the entity described above]. Figure 2 and 3 The LMF-T202 described herein is executed. In some implementations, the LMF may execute a set of instructions to control the functional elements of the LMF to perform the described functions.

[0179] Alternatively, method 1000 can be performed by a different network entity (e.g., the network's NWDAF).

[0180] At 1002, the method may include receiving a request message for a machine learning (ML) model, the request message including constraints of the ML model.

[0181] The operation of 1002 can be performed according to the examples described herein. In some implementations, aspects of the operation of 1002 may be as described in the references. Figure 2 and 3 The LMF-T 202 described is executed.

[0182] At 1004, the method may include obtaining an ML model that includes constraints.

[0183] The operation of 1004 can be performed according to the examples described herein. In some implementations, aspects of the operation of 1004 may be as described in the references. Figure 2 and 3 The LMF-T 202 described is executed.

[0184] At 1006, the method may include transmitting a response message, the response message including (i) an ML model or (ii) an indication of a network location where the ML model can be accessed.

[0185] The operation of 1006 can be performed according to the examples described herein. In some implementations, aspects of the operation of 1006 may be as described in the references. Figure 2 and 3 The LMF LMF-T 202 described is executed.

[0186] It should be noted that the method described herein describes one possible implementation, and the operation and steps may be rearranged or modified in other ways, and other implementations are possible.

[0187] The description herein is provided to enable those skilled in the art to make or use this disclosure. Various modifications to this disclosure will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the scope of this disclosure. Therefore, this disclosure is not limited to the examples and designs described herein, but should be given the widest scope consistent with the principles and novel features disclosed herein.

[0188] Further aspects of this disclosure can be found by referring to the following terms.

[0189] A1. A method performed by a network entity in a communication system, the method comprising:

[0190] Receive a location request to determine the location of the user equipment (UE);

[0191] The ability to receive the UE;

[0192] Receive positioning information from the UE;

[0193] The location information is used to determine the estimated location of the UE;

[0194] A request to analyze the location estimate is sent to enhance the location estimate, the request including the location estimate and the capability;

[0195] In response to the analysis request, receive enhanced location estimation; and

[0196] A response to the location request is transmitted, the response including the enhanced location estimation.

[0197] A2. The method according to Clause A1, wherein the location request includes a location requirement, the location requirement including at least one of a required accuracy of the location and a deadline for providing the location, and the analysis request includes the location requirement.

[0198] A3. The method described in accordance with clause A1 or A2, wherein the capability includes a positioning method supported by the UE and radio environmental conditions of the UE.

[0199] A4. The method described in any of the clauses A1 to A3, wherein the network entity is the location management function (LMF) of the communication system.

[0200] A5. The method described in any of the clauses A1 to A4, wherein the analysis request is sent to the Network Data Analysis Function (NWDAF) of the communication system.

[0201] A6. The method described in any of the clauses A1 to A5, wherein the analysis request is sent to a further network entity, which is selected in the following manner:

[0202] Establish communication with the Network Repository Function (NRF);

[0203] Receive from the NRF a list of network entities capable of providing enhanced location estimation; and

[0204] Select the further network entity from the list.

[0205] A7. The method described in any of the clauses A1 to A6, wherein the analysis request includes an analysis ID that indicates the type of analysis to be performed by the NWDAF.

[0206] A8. The method described under any of the provisions A1 to A7 further includes the ability to receive one or more location reference UEs (PRUs), wherein the analysis request further includes the capability of the PRU.

[0207] A9. The method according to any one of the provisions A1 to A8, further comprising receiving PRU positioning information from one or more location reference UEs (PRUs), wherein the location estimation of the UE is determined using the PRU positioning information.

[0208] A10. The method according to any one of the clauses A1 to A9 further includes the ability to receive one or more radio access network (RAN) nodes of the communication system, wherein the analysis request further includes the capability of the RAN nodes.

[0209] A11. The method according to any one of the provisions A1 to A10, further comprising receiving RAN positioning information from one or more radio access network (RAN) nodes of the communication system, wherein the location estimation of the UE is determined using the RAN positioning information.

[0210] A12. A network entity in a communication system, the network entity being configured to:

[0211] Receive a location request to determine the location of the user equipment (UE);

[0212] The ability to receive the UE;

[0213] Receive positioning information from the UE;

[0214] The location information is used to determine the estimated location of the UE;

[0215] A request to analyze the location estimate is sent to enhance the location estimate, the request including the location estimate and the capability;

[0216] In response to the analysis request, receive enhanced location estimation; and

[0217] A response to the location request is transmitted, the response including the enhanced location estimation.

[0218] A13. A method performed by a network entity in a communication system, the method comprising:

[0219] Receive an analysis request, the analysis request including:

[0220] Location estimation of the user equipment (UE) of the communication system;

[0221] A request to enhance the location estimation; and

[0222] The capabilities of the UE;

[0223] Obtain an ML model configured to enhance the location estimation;

[0224] The ML model is used to enhance the location estimate based on the capability, thereby obtaining an enhanced location estimate; and

[0225] A response to the analysis request is sent, the response including the enhanced location estimation.

[0226] A14. The method according to clause A13, wherein obtaining the ML model includes training a new ML model.

[0227] A15. The method according to clause A14, wherein training the new ML model includes collecting training information from one or more of the following: the analytical data repository function (ADRF) of the communication system; the operation, management, and maintenance (OAM) process of the communication system; or the user equipment (UE) of the communication system.

[0228] A16. The method according to clause A13, wherein obtaining the ML model includes selecting an existing ML model.

[0229] A17. The method described in any of the clauses A13 to A16, wherein the network entity is the Network Data Analysis Function (NWDAF) of the communication system.

[0230] A18. The method according to any one of the clauses A13 to A17, wherein the analysis request is received from the location management function (LMF) of the communication system.

[0231] A19. The method according to any one of the provisions A13 to A18, wherein the analysis request further includes further capabilities of one or more location reference UEs (PRUs) and / or one or more radio access network (RAN) nodes of the communication system, and wherein the enhanced location estimation is further based on the further capabilities.

[0232] A20. A network entity in a communication system, the network entity being configured to:

[0233] Receive an analysis request, the analysis request including:

[0234] Location estimation of the user equipment (UE) of the communication system;

[0235] A request to enhance the location estimation; and

[0236] The capabilities of the UE;

[0237] Obtain an ML model configured to enhance the location estimation;

[0238] The ML model is used to enhance the location estimate based on the capability, thereby obtaining an enhanced location estimate; and

[0239] A response to the analysis request is sent, the response including the enhanced location estimation.

[0240] B1. A method performed by a network entity in a communication system, the method comprising:

[0241] Obtain a trained ML model to determine the location of the user equipment (UE);

[0242] Receive a location request to determine the location of user equipment;

[0243] The ability to receive the UE and one or more Positioning Reference UEs (PRUs);

[0244] Based on the aforementioned capabilities, it is determined that the obtained ML model is suitable for determining the location of the UE;

[0245] The location of the UE is determined using the ML model; and

[0246] A response to the location request is transmitted, the response including the location of the UE.

[0247] B2. The method according to Clause B1, wherein the ML model is obtained from a further network entity, the method further comprising subscribing to receive the ML model from the further network entity.

[0248] B3. The method according to Clause B2, wherein subscribing to receive the ML model from the further network entity includes specifying at least one of the frequency at which the ML model should be received and the manner in which the ML model should be trained.

[0249] B4. The method according to any one of clauses B1 to B3, wherein the location request includes a location requirement, the location requirement including at least one of a required accuracy of the location and a deadline for providing the location, and wherein determining that the received ML model is suitable for determining the location of the UE is further based on the location requirement.

[0250] B5. The method according to any one of clauses B1 to B4, further comprising receiving the capability of a radio access network (RAN) node serving the UE and the PRU, wherein determining that the received ML model is suitable for determining the location of the UE is further based on the capability of the RAN node.

[0251] B6. The method according to any one of clauses B1 to B5, wherein determining the location of the UE using the ML model includes using location data as input data to the ML model.

[0252] B7. The method according to clause B6 further includes obtaining the positioning data from one or more of the UE, the PRU, and the RAN nodes serving the UE and the PRU, wherein the positioning data optionally includes reference signal information.

[0253] B8. The method described in any of the clauses B1 to B7, wherein the network entity is the location management function (LMF) of the communication system.

[0254] B9. The method described in any of the clauses B1 to B8, wherein the ML model is obtained periodically.

[0255] B10. A network entity in a communication system, the network entity being configured to:

[0256] Obtain a trained ML model to determine the location of the user equipment (UE);

[0257] Receive a location request to determine the location of the UE;

[0258] The ability to receive the UE and one or more Positioning Reference UEs (PRUs);

[0259] Based on the aforementioned capabilities, it is determined that the obtained ML model is suitable for determining the location of the UE;

[0260] The location of the UE is determined using the ML model; and

[0261] A response to the location request is transmitted, the response including the location of the UE.

Claims

1. A network entity configured to: Receive a request message for the location of the user equipment (UE); A machine learning (ML) model for localization is obtained at least in part based on the request message, wherein the ML model includes a set of one or more constraints, wherein the set of one or more constraints is at least in part based on a set of one or more capabilities of the UE. The location of the UE is determined based on the ML model; and Transmit a response message indicating the location of the UE.

2. The network entity of claim 1, wherein the request message includes one or more of the desired accuracy of the location and the duration for reporting the location of the UE, and the network entity is further configured to: A request message for the ML model is transmitted, wherein the request message for the ML model indicates the required accuracy of the UE's location, the duration for reporting the UE's location, and one or more of the set of one or more constraints.

3. The network entity according to claim 2, wherein the network entity is further configured to: Receive instructions from the network repository entity to support the ML model associated with the set of one or more constraints. Select a network entity from the set of one or more network entities; and The request message for the ML model is sent to the selected network entity.

4. The network entity according to claim 2 or 3, wherein transmitting the request message to the ML model includes transmitting the request message to the location management function.

5. The network entity according to any of the preceding claims, wherein obtaining the ML model includes receiving the ML model.

6. The network entity according to any one of claims 1 to 4, wherein obtaining the ML model may include: Receive an indication of the network location where the ML model is stored; And retrieve the ML model from the network location.

7. The network entity according to any of the preceding claims, wherein the network entity is further configured to: Receive the set of one or more capabilities, wherein the set of one or more capabilities includes one or more of a positioning process supported by the UE and conditions associated with a radio environment supported by the UE.

8. The network entity according to any of the preceding claims, wherein the network entity is further configured to: Receive one or more capabilities from a radio access network (RAN) node serving the UE, and receive one or more capabilities from a location reference UE (PRU) set. The set of one or more constraints of the ML model are at least partially based on the set of one or more capabilities of the RAN node and the set of one or more capabilities of the set of one or more PRUs.

9. The network entity according to any of the preceding claims, wherein the set of one or more constraints includes one or more of the following: The location region validity of the ML model; The ML model's time-of-day validity; Positioning process; Accuracy of positioning; quality. Model inference latency; The data type used as input to the ML model; and The function ID corresponding to the constraint condition is determined according to the predetermined scheme.

10. The network entity according to any of the preceding claims, wherein the network entity is further configured to: Positioning measurement data is obtained from one or more of the UE, the radio access network RAN ​​node, and a group of one or more positioning reference UE PRUs. The input to the ML model includes the positioning measurement data; Optionally, the positioning measurement data may include reference signal information.

11. The network entity according to any of the preceding claims, wherein the network entity includes a location management function (LMF).

12. A method performed by a network entity, the method comprising: Receive a request message for the location of the user equipment (UE); A machine learning (ML) model for localization is obtained at least in part based on the request message, wherein the ML model includes a set of one or more constraints, wherein the set of one or more constraints is at least in part based on a set of one or more capabilities of the UE. The location of the UE is determined based on the ML model; and Transmit a response message indicating the location of the UE.

13. A network entity configured to: Receive a request message for a machine learning (ML) model, the request message including the constraints of the ML model; Obtain an ML model including the constraints; and Transmit a response message, the response message including (i) the ML model or (ii) an indication of a network location where the ML model can be accessed.

14. The network entity of claim 13, wherein obtaining the ML model comprises training a new ML model including the constraints.

15. The network entity of claim 14, wherein training the new ML model includes collecting training information from one or more of the following: analyzing the Data Repository Function (ADRF); operating, managing, and maintaining the OAM process; or user equipment (UE).

16. The network entity of claim 13, wherein obtaining the ML model includes selecting an existing ML model that includes the constraints.

17. The network entity according to any one of claims 13 to 16, wherein the network entity is further configured to assign a model ID to the ML model.

18. The network entity according to any one of claims 13 to 17, wherein the network entity is a location management function (LMF) or a network data analysis function (NWDAF).

19. The network entity according to any one of claims 13 to 18, wherein the request message is received from the location management function (LMF).

20. A method performed by a network entity, the method comprising: Receive a request message for a machine learning (ML) model, the request message including the constraints of the ML model; Obtain an ML model that includes the aforementioned constraints; and Transmit a response message, the response message including (i) the ML model or (ii) an indication of a network location where the ML model can be accessed.

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