Methods, devices and medium for requesting ai / ML positioning

Abstract

Embodiments of the present disclosure relate to methods, devices and computer readable storage medium for requesting artificial intelligence / machine learning (AI / ML) positioning. In a method, the terminal device receives, from a location management function (LMF), a query about whether a terminal device is configured with a valid artificial intelligence / machine learning (AI / ML) model or functionality for positioning in a target area. The terminal device transmits, based on the query and to the LMF, an indication of validity of at least one AI / ML model or functionality for positioning in the target area. The terminal device receives, from the LMF, a request to activate AI / ML-based positioning. The terminal device performs, based on the request, positioning of the terminal device using a target AI / ML model or functionality amongst the at least one AI / ML model or functionality. The terminal device transmits, to the LMF, a positioning-related result of the target AI / ML model or functionality.

Description

METHODS, DEVICES AND MEDIUM FOR REQUESTING AI / ML POSITIONINGFIELDS

[0001] Various embodiments of the present disclosure generally relate to the field of telecommunication and in particular, to methods, devices and computer readable storage medium for requesting artificial intelligence / machine learning (AI / ML) positioning.BACKGROUND

[0002] This section introduces aspects that may facilitate a better understanding of the disclosure. Accordingly, the statements of this section are to be read in this light and are not to be understood as admissions about what is in the prior art or what is not in the prior art.

[0003] In the telecommunication industry, artificial intelligence / machine learning (AI / ML) models have been employed in telecommunication systems to improve the performance of telecommunications systems. For example, supporting various positioning mechanisms to provide reliable and accurate user equipment (UE) location has always been one of the key features of in the telecommunications systems. It has been agreed to investigate the potential for AI / ML in air interface to improve comprehensive performance in fifth generation (5G)-advanced. AI / ML based positioning mechanism to improve the positioning accuracy is one of the use cases to apply AI / ML in air interface. Works are on-going regarding requesting artificial intelligence / machine learning positioning.SUMMARY

[0004] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

[0005] How a location management function (LMF) decides which positioning method to use is upon implementation. However in the highly automated data-driven network architecture, it is expected behavior of LMF to choose AI / ML positioning methods will be triggered by the nonline of slight (NLOS) condition of the environment of user equipment (UE) and / or based on positioning performance of UE using AI / ML positioning or non-AI / ML positioning methods, which is not explicitly stated. If LMF has information that UE is at NLoS rich scenario, LMF should request UE to report first if UE has stored valid models. However, such signaling are not supported yet.

[0006] To overcome or mitigate at least one of the above-mentioned problems or otherproblems or provide a useful solution, embodiments of the present disclosure propose methods, devices and storage medium for requesting AI / ML positioning.

[0007] In a first aspect of the present disclosure, there is provided a method implemented at a terminal device. In the method, the terminal device receives, from a location management function (LMF), a query about whether the terminal device is configured with a valid artificial intelligence / machine learning (AI / ML) model or functionality for positioning in a target area. The terminal device transmits, based on the query and to the LMF, an indication of validity of at least one AI / ML model or functionality for positioning in the target area. The terminal device receives, from the LMF, a request to activate AI / ML-based positioning. The terminal device performs, based on the request, positioning of the terminal device using a target AI / ML model or functionality amongst the at least one AI / ML model or functionality. The terminal device transmits, to the LMF, a positioning-related result of the target AI / ML model or functionality, the positioning-related result comprising a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

[0008] In a second aspect of the present disclosure, there is provided a method implemented at a location management function (LMF). In the method, the location management function (LMF) determines that a terminal device is configured to perform artificial intelligence / machine learning (AI / ML)-based positioning. The LMF transmits, to the terminal device, a query about whether the terminal device is configured with a valid AI / ML model or functionality for positioning in a target area. The LMF receives, from the terminal device, an indication of validity of at least one AI / ML model or functionality for positioning in the target area. The LMF transmits, based on the received indication and to the terminal device, a request to activate the AI / ML-based positioning. The LMF receives, from the terminal device, a positioning-related result of a target AI / ML model or functionality used by the terminal device, the positioning-related result comprising a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

[0009] In a third aspect of the present disclosure, there is provided a method implemented at a location management function (LMF). In the method, in accordance with a determination of performing artificial intelligence / machine learning (AI / ML)-based positioning for a terminal device, the LMF determines whether a target AI / ML model or functionality is valid for positioning of the terminal device. In accordance with a determination that the target AI / ML model or functionality is valid for positioning of the terminal device, the LMF transmits, to the terminal device, a configuration of providing measurement information as an input to the AI / ML model. The LMF receives, from the terminal device, the measurement information, the LMF determines a positioning-related result by providing the measurement information into the target AI / ML model or functionality, the positioning-related result comprising a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

[0010] In a fourth aspect of the present disclosure, there is provided a terminal device. The terminal device comprises a processor and a memory coupled to the processor, the memory containing instructions executable by the processor, whereby the terminal device is operative to perform the method according to the first aspect.

[0011] In a fifth aspect of the present disclosure, there is provided a location management function (LMF). The LMF comprises a processor and a memory coupled to the processor, the memory containing instructions executable by the processor, whereby the terminal device is operative to perform the method according to the second aspect.

[0012] In a sixth aspect of the present disclosure, there is provided a location management function (LMF). The LMF comprises a processor and a memory coupled to the processor, the memory containing instructions executable by the processor, whereby the terminal device is operative to perform the method according to the third aspect.

[0013] In a seventh aspect of the present disclosure, there is provided a computer-readable storage medium having instructions stored thereon, the instructions, which, when executed by at least one processor of a device, cause the device to perform the method according to the first or second or third aspect.

[0014] With the present disclosure, some methods are provided for a terminal device to request AI / ML positioning and some other methods are provided for a LMF to request AI / ML positioning. As such, positioning accuracy may be improved, and more energy may be saved.BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Through the more detailed description of some embodiments of the present disclosure in the accompanying drawings, the above and other objects, features and advantages of the present disclosure will become more apparent, where the same reference generally refers to the same components in the embodiments of the present disclosure.

[0016] FIG. 1 illustrates an example communication environment in which example embodiments of the present disclosure can be implemented.

[0017] FIG. 2 illustrates an example pipeline of the AI / ML model training.

[0018] FIG. 3 illustrates an example positioning architecture in new radio (NR).

[0019] FIG. 4 illustrates a signaling flow for requesting AI / ML positioning in accordance with some embodiments of the present disclosure.

[0020] FIG. 5 illustrates a signaling flow for requesting AI / ML positioning in accordance with some embodiments of the present disclosure.

[0021] FIG. 6 is a diagram showing a flowchart of an example method at a terminal device in accordance with some embodiments.

[0022] FIG. 7 is a diagram showing a flowchart of an example method at a LMF in accordance with some embodiments.

[0023] FIG. 8 is a diagram showing a flowchart of an example method at a LMF in accordance with some embodiments.

[0024] FIG. 9 is a diagram showing a communication device in accordance with some embodiments.

[0025] FIG. 10 is a diagram showing a computer readable storage medium in accordance with some embodiments.

[0026] FIG. 11 is a diagram showing an example of a communication system in accordance with some embodiments.

[0027] FIG. 12 is a block diagram showing a UE in accordance with some embodiments.

[0028] FIG. 13 is a block diagram showing a network node in accordance with some embodiments.

[0029] FIG. 14 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.DETAILED DESCRIPTION

[0030] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Other embodiments, however, are contained within the scope of the subject matter disclosed herein, the disclosed subject matter should not be construed as limited to only the embodiments set forth herein; rather, these embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0031] As used herein, the term “terminal device” refers to a device which is intended for accessing services via an access network and configured to communicate over the access network. The terminal device may be able to communicate with a network node, such as a base station, or with another terminal device by transmitting and / or receiving wireless signals. For instance, the terminal device may include, but is not limited to: a mobile phone, a smart phone, a sensor device, a meter, a vehicle, a household appliance, a medical appliance, a media player, a camera, or any type of consumer electronic, for instance, but not limited to, a television, radio, lighting arrangement, a tablet computer, a laptop, a personal computer (PC), or an Internet of Thing (loT) device. The terminal device may also include a portable, pocketstorable, hand-held, computer- comprised, or vehicle-mounted mobile device, enabled to communicate voice and / or data, via a wireless connection. In the following description, the terms “terminal device”, “user equipment” and “UE” may be used interchangeably.

[0032] As used herein, the term “network device” or “network node” refers to a device in a communication network via which a terminal device receives services from the network. The terms “network node”, “network function” may be used interchangeably. A network function can be implemented either as a network element on a dedicated hardware, as a software instance running on a dedicated hardware, or as a virtualised function instantiated on an appropriate platform, e.g., on a cloud infrastructure. The network node comprises an access network node via which a terminal device accesses an access network. Examples of access network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and newNR NodeBs (gNBs)). In thefollowing description, the terms “network device”, “network node”, “base station” and “BS” may be used interchangeably.

[0033] As used herein, the term “communication device” refers to a device capable of communications. Examples of a communication device may comprise a terminal device and a network device.

[0034] To facilitate understanding of the terminologies, some definitions of the list of terminologies used for AI / ML are provided below.

[0035] AI / ML model: A data driven algorithm that applies AI / ML techniques to generate a set of outputs based on a set of inputs.

[0036] AI / ML model delivery: A generic term referring to delivery of an AI / ML model from one entity to another entity in any manner. Note: An entity could mean a network node / function (e.g., gNB, location management function (LMF), etc.), UE, proprietary server, etc.

[0037] AI / ML model inference: A process of using a trained AI / ML model to produce a set of outputs based on a set of inputs.

[0038] AI / ML model testing: A subprocess of training, to evaluate the performance of a final AI / ML model using a dataset different from one used for model training and validation. Differently from AI / ML model validation, testing does not assume subsequent tuning of the model.

[0039] AI / ML model training: A process to train an AI / ML Model [by learning the input / output relationship] in a data driven manner and obtain the trained AI / ML Model for inference.

[0040] AI / ML model transfer: Delivery of an AI / ML model over the air interface in a manner that is not transparent to 3GPP signalling, either parameters of a model structure known at the receiving end or a new model with parameters. Delivery may contain a full model or a partial model.

[0041] AI / ML model validation: A subprocess of training, to evaluate the quality of an AI / ML model using a dataset different from one used for model training, that helps selecting model parameters that generalize beyond the dataset used for model training.

[0042] Data collection: A process of collecting data by the network nodes, management entity, or UE for the purpose of AI / ML model training, data analytics and inference.

[0043] Functionality identification: A process / method of identifying an AI / ML functionality for the common understanding between the network and the UE. Note: Information regarding the AI / ML functionality may be shared during functionality identification. Where AI / ML functionality resides depends on the specific use cases and sub use cases.

[0044] Model activation: enable an AI / ML model for a specific function.

[0045] Model deactivation: disable an AI / ML model for a specific function.

[0046] Model download: Model transfer from the network to UE.

[0047] Model identification: A process / method of identifying an AI / ML model for the common understanding between the network (NW) and the UE. Note: The process / method of model identification may or may not be applicable. Note: Information regarding the AI / ML model may be shared during model identification.

[0048] Model monitoring: A procedure that monitors the inference performance of the AI / ML model.

[0049] Model parameter update: Process of updating the model parameters of a model.

[0050] Model selection: The process of selecting an AI / ML model for activation among multiple models for the same AI / ML enabled feature. Note: Model selection may or may not be carried out simultaneously with model activation.

[0051] Model switching: Deactivating a currently active AI / ML model and activating a different AI / ML model for a specific function.

[0052] Model update: Process of updating the model parameters and / or model structure of a model.

[0053] Model upload: Model transfer from UE to the network.

[0054] UE-side (AI / ML) model: An AI / ML Model whose inference is performed entirely at the UE.

[0055] Network-side (AI / ML) model: An AI / ML Model whose inference is performed entirely at the network.

[0056] One-sided (AI / ML) model: A UE-side (AI / ML) model or a Network-side (AI / ML) model.

[0057] Two-sided (AI / ML) model: A paired AI / ML Model(s) over which joint inference is performed, where joint inference comprises AI / ML Inference whose inference is performed jointly across the UE and the network, i.e, the first part of inference is firstly performed by UE and then the remaining part is performed by the gNB, or vice versa.

[0058] Proprietary -format models: ML models of vendor-Zdevice-specific proprietary format, from 3 GPP perspective. They are not mutually recognizable across vendors and hide model design information from other vendors when shared. Note: An example is a device-specific binary executable format.

[0059] Open-format models: ML models of specified format that are mutually recognizable across vendors and allow interoperability, from the 3GPP perspective. They are mutually recognizable between vendors and do not hide model design information from other vendors when shared.

[0060] Principles and implementations of the present disclosure will be described in detail below with reference to the figures.

[0061] FIG. 1 illustrates a schematic diagram of an example communication environment 100 in which example embodiments of the present disclosure can be implemented. In thecommunication environment 100, a plurality of communication devices, including a terminal device 110-1, a terminal device 110-2, . . ., a terminal device 110-N and a network device 120, can communicate with each other. The terminal device 110-1, terminal device 110-2, . . ., and terminal device 110-N can be collectively referred to as “terminal device(s) 110.” The number N can be any suitable integer number.

[0062] In the example of FIG. 1, the terminal device 110 may be a UE and the network device 120 may be a base station serving the UE. The serving area of the network device 120 may be called a cell (not shown). In the communication environment 100, the network device 120 and the terminal devices 110 may communicate data and control information to each other. The terminal devices 110 may also communicate with each other.

[0063] The communication environment 100 further comprises a location management function (LMF) 130 which may be included in a core network (ON). The LMF 130 and the terminal devices 110 may communicate with each other. In some example embodiments, one or more AI / ML models may be trained and provided for use by one or more terminal devices 110. An AI / ML model may be trained to implement a certain communication related function at a terminal device 110 or at a network device 120.

[0064] As used herein, the term “AI / ML” model may be interchangeably with the term “model”. The term “AI / ML model training” may refer to a process to train an AI / ML model for example by learning the input / output relationship and obtained a trained AI / ML model for inference. The term “model monitoring” used herein may refer to a procedure that monitors the inference performance of the AI / ML model.

[0065] In some embodiments, the AI / ML models may comprise AI / ML models for positioning of a terminal device 110. In some embodiments, an AI / ML model may be a direct AI / ML positioning model. An input to the direct AI / ML positioning model may comprise information related to a channel between a terminal device 110 and a network device, such as Channel Impulse Response (CIR). The input may be collected by transmitting a reference signal, such as a positioning reference signal (PRS), a sounding reference signal (SRS), or a channel state information reference signal (CSI-RS) over the channel between the terminal device 110 and the network device. An output of the direct AI / ML positioning model may comprise a location of the terminal device 110.

[0066] In some embodiments, an AI / ML model may be an AI / ML assisted positioning model. An input to the AI / ML assisted positioning model may comprise may be the same or similar to that of the direct AI / ML positioning model. An output of the AI / ML assisted positioning model may comprise intermediate measurement results related to location information for a terminal device 110. The intermediate results of the location information may include, but are not limited to, time of arrival (TOA), time difference of arrival (TDOA), non-line of slight (NLOS) / line of sight (LOS) identification of a channel, or the like. Such intermediate results may be used to assist in determining a location of the terminal device 110.

[0067] The AI / ML model deployed at different devices may be the same or different, and may be of the same type or different types of direct AI / ML positioning model and AI / ML assisted positioning model.

[0068] In some embodiments, an AI / ML model may be trained at the network device 120 and then transferred to one or more suitable terminal devices 110 for use. In some embodiments, an AI / ML model 140 may be trained at a terminal device 110 and then applied locally or transferred to one or more other terminal devices 110 by a network device for use. It would be appreciated that the AI / ML model may be trained and / or transferred by any other entity in the communication environment 100.

[0069] It is to be understood that the number of devices and their connections shown in FIG. 1 are only for the purpose of illustration without suggesting any limitation. The communication environment 100 may include any suitable number of devices configured to implementing example embodiments of the present disclosure. Although not shown, it would be appreciated that one or more additional devices may be located in the cell, and one or more additional cells may be deployed in the communication environment 100. It is noted that although illustrated as a network device, the network device 120 may be another device than a network device. Although illustrated as a terminal device, the terminal device 110 may be other device than a terminal device.

[0070] Artificial intelligence (Al) or machine learning (ML) technique comprises one or more algorithms, which use a set of data as input for training one or more AI / ML models. The output of the AI / ML model is used by the device (e.g. user equipment (UE), base station (BS) or another node) for performing certain operations or taking certain decisions (e.g. handover etc.) fully or partially based on the prediction, which in turn depends on the trained model. The AI / ML model may be trained in the device online (or on-the-fly while processing the data) or offline in the background.

[0071] More specifically, online training is an AI / ML training process where the model being used for inference is (typically continuously) trained in (near) real-time with the arrival of new training samples or data. Offline training is an AI / ML training process where the model is trained based on collected samples or data, and where the trained model is later used or delivered for inference. AI / ML model inference refers to a process of using a trained AI / ML model to produce a set of outputs based on a set of inputs.

[0072] The AL / ML models may be trained in a device, which may be a UE, a network node, or another node. In this respect, the AI / ML modes may be broadly classified as the following cases. Case I defines a UE-side (AI / ML) model. It is an AI / ML model whose inference is performed entirely at the UE. Case II defines a network-side (AI / ML) model. It is an AI / ML model whose inference is performed entirely at the network. Case III defines a one-sided (AI / ML) model. It is a UE-side (AI / ML) model or a network-side (AI / ML) model. Case IV defines a two- sided (AI / ML) model. It is a paired AI / ML model(s) over which joint inference is performed,where joint inference comprises AI / ML Inference whose inference is performed jointly across the UE and the network, i.e., the first part of inference is firstly performed by UE and then the remaining part is performed by gNB, or vice versa.

[0073] An AI / ML model may be transferred or delivered over the air interface either in terms of one or more parameters of a model structure known at the receiving end or a new model with parameters. The model delivery may contain a full model or a partial model.

[0074] The term lifecycle management (LCM) of an AI / ML model refers to the process of developing, deploying and maintaining the AI / ML model. An AI / ML model training pipeline may be referred to FIG. 2 which illustrates an example pipeline 200 of the AI / ML model training. The pipeline 200 includes several processing stages as gathering unprocessed input data from data repositories at a data ingestion stage 210, finding high-quality input features at a data preprocessing stage 220, finding the optimal mapping of the model input features to a desired model output target in a sense determined by a loss function at a model training stage 230, evaluating model performance on unseen data from a functional level as well as from a system level when relevant at a model evaluation stage 240. The training pipeline typically ends with a model registration stage 250, which may comprise operations to make the ML model runnable via compilation to a specific hardware (HW) and of steps like versioning and packaging of the model so that it can be executed.

[0075] AI / ML model may be used for UE positioning. A UE or a gNB, depending on capability, may have a trained model stored inside the device, or have an untrained AI / ML that may be trained on-the-fly to either produce measurements that are required to localize a UE within a radio access network (RAN) coverage area or directly predict / determine the UE location by exploiting the measurements performed by the UE or gNB on reference signals such as positioning reference signal (PRS), sounding reference signal (SRS) etc. within a RAN coverage area.

[0076] Measurements predicted / determined by the UE by exploiting an AI / ML model may be defined as, but not limited to reference signal time difference (RSTD) and UE Rx-Tx time difference. Specifically, RSTD is reference signal time difference between the positioning node j and the reference positioning node i. It is measured on the DL PRS signals and always involve two cells (cell is interchangeably called as TRP). UE Rx-Tx time difference is defined as TUE-RX -TUE-TX, where TUE-RX is the UE received timing of downlink subframe #i from a positioning node, defined by the first detected path in time. It is measured on PRS signals received from the gNB. TUE-TX is the UE transmit timing of uplink subframe #J that is closest in time to the subframe #i received from the positioning node.

[0077] Measurements predicted / determined by the gNB by exploiting an AI / ML model can be defined as, but not limited to gNB Rx-Tx time difference, Timing advance (TADV) and UL Relative Time of Arrival (UL RTOA). Specifically, gNB Rx-Tx time difference is defined as TgNB-Rx - TgNB-rx, where TSNB-RX is the positioning node received timing of uplink subframe #i containing SRS associated with UE, defined by the first detected path in time. It is measured on SRS signalsreceived from the UE. TSNB-TX is the positioning node transmit timing of downlink subframe #j that is closest in time to the subframe #i received from the UE. TADV is defined as the time difference TADV = (TSNB-RX - TSNB-TX), where TSNB-RX is the transmission and reception point (TRP) received timing of uplink subframe #i containing physical random access channel (PRACH) transmitted from UE, defined by the first detected path in time. TSNB-TX is the TRP transmit timing of downlink subframe #j that is closest in time to the subframe #i received from the UE. The detected PRACH is used to determine the start of one subframe containing that PRACH. UL RTOA is defined as the beginning of subframe i containing SRS received in positioning node j, relative to the configurable reference time. For example, node 1 (e.g., base station, etc.) measures the reception time of signals transmitted by the UE with respect to a reference time.

[0078] In addition, a UE or gNB may also perform power measurements such as reference signal received power (RSRP) and / or reference signal received path power (RSRPP). These measurements may be performed on reference signals such as PRS and SRS.

[0079] Depending on the capability, a UE may also perform positioning measurements on the sidelink (SL) resources by exploiting AI / ML model, e.g. on the SL-PRS transmitted between the target UE and one or more assisting or anchor UEs. The UE performing the positioning measurement is called as a target UE and the UE(s) assisting the target UE to perform the SL positioning measurements is called as the anchor or assisting UE.

[0080] In the mode of assisted positioning, the positioning measurements are performed by UE / gNB and use as input for AI / ML model reside at UE / gNB. After completion, the output (including measurement results) is then reported to the location server. The location server upon receiving AI / ML model output (including measurement results) determines the location of the UE within the RAN coverage area. The location server depending on the need may forward the UE location to another node within the network to facilitate provisioning of UE location information to the application layer or the third party that is interested or has requested the positioning of UE within the RAN coverage area for further action to be taken. The AI / ML model outputs new measurement and / or enhancement of existing measurement, such as LOS / NLOS identification, timing and / or angle of measurement and likelihood of measurement.

[0081] In the mode of direct positioning, the measurements performed by UE / gNB are input for positioning engine or the AI / ML model to predict or determine the UE location. AI / ML model resides within the UE node or the gNB node or the location server. The location server depending on the need may forward the UE location to another node within the network to facilitate provisioning of UE location information to the application layer or the third party that is interested or has requested the positioning of UE within the RAN coverage area for further action to be taken. The AI / ML model outputs UE location, such as fingerprinting based on channel observation may be taken as the input of AI / ML model.

[0082] The positioning measurement procedure (PMP) comprises performing one or more positioning measurements on DL RS (e.g. PRS) and / or uplink (UL) RS (e.g. SRS) transmittedbetween the UE and one or more cells. The cell may also be called as a TRP or node. Examples of the positioning measurements performed on DL and / or UL signals are RSTD, PRS-RSRP, PRS- RSRPP, UE Rx-Tx time difference, round trip time (RTT), time of arrival (TOA), channel impulse response (CIR), timing advance (TA), angle of departure (AoD), angle of arrival (AoA), power delay profile (PDP), delay profile (DP) etc.

[0083] To perform these measurements, UE may make use of a trained model acquired before being deployed or needs to train its model on-the-fly before performing AI / ML based positioning measurements to be reported to the network node such as location server. In either of the cases, UE requires assistance information / data from the network to determine or identify how and when to train its AI / ML model and what information (positioning measurements or estimated position) to report to the network node such as location server.

[0084] In some embodiments, AI / ML positioning comprises two representative sub-use cases, direct AI / ML positioning and AI / ML assisted positioning. For direct AI / ML positioning, the AI / ML model outputs UE location, for example, fingerprinting based on channel observation as the input of the AI / ML model. For the AI / ML assisted positioning, the AI / ML model outputs new measurement and / or enhancement of existing measurement, for example, LOS / NLOS identification, timing and / or angle of measurement, and likelihood of measurement.

[0085] In order to enhance positioning accuracy, AI / ML for NR Air Interface comprises direct AI / ML positioning and AI / ML assisted positioning. Furthermore, the direct AI / ML positioning may include multiple cases. For example, Case 1 defines UE -based positioning with a UE-side model for direct AI / ML positioning; Case 2b defines UE-assisted / LMF-based positioning with a LMF-side model for direct AI / ML positioning; and Case 3b defines NG-RAN node assisted positioning with a LMF-side model for direct AI / ML positioning. The AI / ML assisted positioning may also include multiple cases. For example, Case 2a defines UE-assisted / LMF-based positioning with a UE-side model for AI / ML assisted positioning; and Case 3a defines NG-RAN node assisted positioning with a gNB-side model for AI / ML assisted positioning.

[0086] In the following description, for the purpose of positioning accuracy enhancement, unless defined otherwise, necessary measurements, signalling / mechanism(s) may be specified to facilitate lifecycle management (LCM) operations specific to the positioning accuracy enhancements use cases. Further, the necessary signalling of necessary measurement enhancements may be investigated and specified. Methods may be enabled to ensure consistency between training and inference regarding network-side additional conditions (if identified) for inference at UE for relevant positioning sub-use cases.

[0087] Before Release 16, LTE based positioning was one the prevalent radio access technology (RAT) based positioning solutions available. Starting from Rel. 16 specification, positioning is also supported in New Radio (NR). Positioning in NR is supported by the architecture shown in FIG. 3 which illustrates an example positioning architecture 300 in NR. As shown in FIG. 3, the NG-RAN 310 includes an ng-eNB 312 and a gNB 314. The ng-eNB 312 mayinclude one or more transmission points (TPs), and the gNB 314 may include one or more transmission reception points (TRPs). A UE 320 may access to the NG-RAN 310, e.g., to the ng- eNB 312 or to the gNB 314. The positioning architecture 300 also includes some network entities in the core network (ON), such as a location management function (LMF) 330, an access and mobility management function (AWF) 340. The LMF 330 and AWF 340 may also interface with the ng-eNB 312 and the gNB 314 in the NG-RAN 310. The protocols between entities are shown in FIG. 3. The interactions between the gNodeB and the device is supported via the Radio Resource Control (RRC) protocol, while the location node interfaces with the UE via the LTE positioning protocol (LPP). LPP is a common protocol to both NR and LTE. LMF 330 is the location node in NR. There are also interactions between the location node and the gNodeB via the NRPPa protocol.

[0088] The positioning architecture 300 in FIG. 3 may also be used to support AI / ML based positioning. Release 19 work on introducing AI / ML based positioning will not only exploit the legacy protocol but will also rely on already defined / existing reference signals that are used for positioning.

[0089] Assistance data may be represented by information element (IE) NR-DL-PRS- ExpectedLOS-NLOS-Assistance . The IE NR-DL-PRS-ExpectedLOS-NLOS-Assistance may be used by the location server to provide the expected likelihood of a LOS propagation path from a TRP to the target device, or for all DL-PRS Resources of the TRP to the target device. The IE NR-DL-PRS-ExpectedLOS-NLOS-Assistance is shown as follows:- ASN1 STARTNR-DL-PRS-ExpectedLOS-NLOS-Assistance-rl7 ::= SEQUENCE (SIZE (L.nrMaxFreqLayers-rl6)) OFNR-DL-PRS-ExpectedLOS-NLOS-AssistancePerFreqLayer-rl7NR-DL-PRS-ExpectedLOS-NLOS-AssistancePerFreqLayer-rl7 ::=SEQUENCE (SIZE (L.nrMaxTRPsPerFreq-rl6)) OFNR-DL-PRS-ExpectedLOS-NLOS-AssistancePerTRP-rl7NR-DL-PRS-ExpectedLOS-NLOS-AssistancePerTRP-rl7 ::= SEQUENCE { dl-PRS-ID-rl7 INTEGER (0..255), nr-PhysCellID-rl7 NR-PhysCellID-rl6 OPTIONAL, - Need ON nr-CellGloballD-rl 7 NCGI-rl5 OPTIONAL, - Need ON nr-ARFCN-rl7 ARFCN-ValueNR-r 15 OPTIONAL, - Need ON nr-los-nlos-indicator-rl7 CHOICE {perTrp-rl7 LOS-NLOS-Indicator-rl7, perResource-rl7 SEQUENCE (SIZE (l..nrMaxSetsPerTrpPerFreqLayer-r!6))OFNR-DL-PRS-ExpectedLOS-NLOS-AssistancePerResource-rl7},}NR-DL-PRS-ExpectedLOS-NLOS-AssistancePerResource-rl7 ::=SEQUENCE (SIZE (L.nrMaxResourcesPerSet-rl6)) OFLOS-NLOS-Indicator-rl 7- ASN1STOP

[0090] Field descriptions of NR-DL-PRS-ExpectedLOS-NLOS-Assistance is shown in Table 1 as follows:Table 1

[0091] When LMF receives a location service (LCS) request to locate a UE, it is up to LMF to decide which positioning method to use depending on UE’s capabilities. Assisted / direct AI / ML positioning will be new positioning methods added in Release 19, and LMF will decide the positioning method(s) from AI / ML positioning and other positioning methods from previous releases e.g., DL-TDOA, DL-AoD, etc. However, AI / ML positioning and non-AI / ML positioning methods are applicable to different scenarios: AI / ML positioning are applicable to NLoS rich scenario while non-AI / ML positioning are applicable to LoS scenario.

[0092] How LMF decide which positioning method to use is upon implementation, however in the highly automated data-driven network architecture, it is expected LMF’s behavior to chooseAI / ML positioning methods will be triggered by the NLOS condition of the UE’s environment and / or based on UE’s positioning performance using AI / ML positioning or non-AI / ML positioning methods, which is not explicitly stated. If LMF has information that UE is at NLoS rich scenario, LMF should request UE to report first if UE has stored valid models. However, such signaling are not supported yet.

[0093] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Some embodiments of the present disclosure propose an improved solution for requesting AI / ML positioning. In this solution, a query about whether the terminal device is configured with a valid artificial intelligence / machine learning (AI / ML) model or functionality for positioning in a target area is received from a location management function (LMF) by a terminal device. An indication of validity of at least one AI / ML model or functionality for positioning in the target area is transmitted based on the query and to the LMF by the terminal device. A request to activate AI / ML-based positioning is received from the LMF by the terminal device. Positioning of the terminal device is performed based on the request by the terminal device using a target AI / ML model or functionality amongst the at least one AI / ML model or functionality. Further, a positioning-related result of the target AI / ML model or functionality is transmitted to the LMF by the terminal device. The positioning-related result comprising a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

[0094] In this way, the LMF to request the terminal device configured with a valid AI / ML model to perform AI / ML based positioning of the terminal device may improve positioning accuracy and save more energy.

[0095] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Some embodiments of the present disclosure propose another improved solution for requesting AI / ML positioning. In this solution, in accordance with a determination of performing artificial intelligence / machine learning (AI / ML)-based positioning for a terminal device, whether a target AI / ML model or functionality is valid for positioning of the terminal device may be determined by a LMF. In accordance with a determination that the target AI / ML model or functionality is valid for positioning of the terminal device, a configuration of providing measurement information as an input to the AI / ML model is transmitted to the terminal device by the LMF. The measurement information is received from the terminal device. Further, a positioning-related result is determined by providing the measurement information into the target AI / ML model or functionality by the LMF. The positioning-related result comprises a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

[0096] In this way, the LMF performs AI / ML based positioning of the terminal device based on measurement information received from the terminal device, thereby improving positioning accuracy and saving more energy.

[0097] In example embodiments of the present disclosure, the LMF may decide AI / ML positioning based on its knowledge of at least one of: the terminal device is in NLoS-rich environment, the terminal device is not in LoS environment, the positioning accuracy of non- AI / ML positioning methods of the terminal device to be positioned and / or other terminal devices in the same cell or nearby cell does not meet the positioning accuracy requirement, the positioning accuracy of AI / ML positioning methods of the target terminal device to be positioned and / or other terminal devices in the same cell or nearby cell works well, or capacity of the terminal device (for example, whether the terminal device is capable of performing AI / ML positioning or whether the terminal device is capable of performing AI / ML positioning measurements). In NLOS rich scenario, the LMF may request the terminal device to perform AI / ML based positioning UE is capable. The LMF may indicate LMF if it has available AI / ML model for a specific area / one or multiple cells.

[0098] When the LMF has information that UE is at AI / ML based positioning preferred scenarios, the LMF requests the terminal device only report measurements for AI / ML positioning for UE-assisted / LMF-based direct AI / ML positioning (Case 2b). Alternatively, or in addition, the LMF requests the terminal device to perform UE-based direct AI / ML positioning (Case 1) or UE assisted / LMF-based with UE-side model assisted AI / ML positioning AI / ML positioning (Case 2a) when a valid model is available.

[0099] In some embodiments, the LMF requests whether the terminal device has an available AI / ML model to an area via LPP request capability message or a new LPP message.

[0100] In some embodiments, the terminal device indicates whether it has an available AI / ML model associated via LPP provide capability message or a new LPP message.

[0101] In some embodiments, the terminal device may be capable of supporting AI / ML based positioning, i.e., performing measurement, training model, and performing inference. UE AI / ML- based positioning may be performed with UE-side model or UE-assisted / LMF-based positioning may be performed with LMF-side model.

[0102] In some embodiments, potential measurements as model input for AI / ML based positioning may include but are not limited to CIR, PDP, or DP.

[0103] In some embodiments, the validity / applicability of a AI / ML model / functionality may be defined as associated conditions of a model / functionality including a defined validity area (includes one or multiple cell IDs and / or radio conditions to signals from one or multiple TRPs), and / or a timer (timer running indicates it is applicable / valid), and / or conditions under which the available AIML model / functionality can be applicable are met, e.g. the configuration of DL reference signals are met.

[0104] References will be made to FIGS. 4 and 5 to illustrate some embodiments of requesting AI / ML positioning.

[0105] FIG. 4 illustrates a signaling flow 400 for requesting AI / ML positioning in accordance with some embodiments of the present disclosure. The signaling flow 400 involves the terminaldevice 110 and the LMF 130 in FIG. 1. The embodiments of FIG. 4 are assumed for Case 1 (i.e., UE-based positioning with a UE-side model for direct AI / ML positioning) and Case 2a (i.e., UE- assisted / LMF-based positioning with a UE-side model for AI / ML assisted positioning).

[0106] As shown in FIG. 4, at 405, the LMF 130 determines that a terminal device 110 is configured to perform artificial intelligence / machine learning (AI / ML)-based positioning.

[0107] In some embodiments, the LMF 130 may decide whether to perform positioning the terminal device 110 at LMF 130 or enable the terminal device 110 with AI / ML capability to inference its own location based on LMF’s knowledge of environment / cell information of the target terminal device (as an example of the terminal device 110). In some embodiments, the LMF 130 may determine the terminal device 110 is configured to perform AI / ML-based positioning based on the terminal device being within in a non-line-of-sight (NLoS) environment or a LoS environment. The LMF 130 may have knowledge that terminal device 110 is in a NLoS-rich environment, and the NLoS-rich environment may be defined e.g., the target terminal device has fewer than K line-of-sight links to the surrounding TRPs. For example, the probability that the number of LoS links is fewer than K and is above a threshold.

[0108] Alternatively, or in addition, the LMF 130 may determine the terminal device 110 is configured to perform AI / ML-based positioning based on a positioning accuracy of a non- AI / ML positioning mechanism of the terminal device failing to satisfy a positioning accuracy requirement. In some embodiments, the positioning accuracy of non-AI / ML positioning methods of the target terminal device and / or other terminal devices in the same cell or nearby cell does not meet the positioning accuracy requirement for the session of target terminal device which may be taken as a factor to determine the terminal device 110 is configured to perform AI / ML-based positioning.

[0109] Alternatively, or in addition, the LMF 130 may determine the terminal device 110 is configured to perform AI / ML-based positioning based on a positioning accuracy of an AI / ML positioning mechanism of the terminal device satisfying a positioning accuracy requirement. In some embodiments, the positioning accuracy of AI / ML positioning methods of the target terminal device and / or other terminal devices in the same cell or nearby cell can satisfy the positioning accuracy requirement for the session of the target terminal device which may be taken as a factor to determine the terminal device 110 is configured to perform AI / ML-based positioning.

[0110] Alternatively, or in addition, the LMF 130 may determine that the terminal device 110 is configured to perform AI / ML-based positioning based on capability of the terminal device in performing (or reporting) AI / ML-based positioning (or positioning measurements).

[0111] At 410, the LMF 130 transmits a query about whether the terminal device 110 is configured with a valid AI / ML model or functionality for positioning in a target area to terminal device 110. The terminal device 110 receives the query.

[0112] In some embodiments, the terminal device 110 may host an AI / ML model for model inference.

[0113] Alternatively, or in addition, the LMF 130 may request if the terminal device 110 hasvalid model associated with one or multiple areas besides the capability of the terminal device 110 for measurement and processing for Case 1 or Case 2a in the LPP Requestcapabilities message or a new LPP message. For example, the RequestAssistanceData message is from the LMF 130 / server to the terminal device 110 (the positioning target). The target the terminal device (as an example of the terminal device 110) may reply with an indication indicating the availability at the UE-side of one or more AIML models / functionalities in the ProvideCapabilities message or a new LPP message e.g., ProvideAssistanceData message is from the terminal device 110 to the LMF 13 (or the server). The provide capability message or new LPP message from the terminal device 110 may also indicate the conditions under which the available AIML model / functionality can be applicable, e.g. the configuration of DL reference signals such as the DL PRS. On the basis of this received message, the LMF 130 may determine whether to activate the AIML model / functionality at the terminal device or not.

[0114] In some embodiments, the target area is indicated through at least one cell identity (ID) or at least one transmission / reception point (TRP) ID. In some embodiments, a defined validity area (also referred to as the target area) includes one or multiple cell IDs and / or radio conditions to signals from one or multiple TRPs.

[0115] At 415, the terminal device 110 transmits, based on the query and to the LMF 130, an indication of validity of at least one AI / ML model or functionality for positioning in the target area. The LMF 130 receives the indication.

[0116] In some embodiments, at 420, the terminal device 110 may transmits, to the LMF 130, indication information indicating a set of conditions to associated with the at least one AI / ML model or functionality. The LMF 130 may receive the indication information. At 425, the LMF 130 may transmit a configuration of at least one condition to associated with the target AI / ML model or functionality to the terminal device 110. The terminal device 110 may receive the configuration. When the LMF 130 sends an inquiry to the target terminal device on its AI / ML positioning capability, such capability is not a static capability. Instead, it's a condition-dependent capability, where the terminal device 110 may determine that it has the capability to provide a certain information generated by AI / ML model, when the desired set of conditions associated with the AI / ML model (also referred to as the set of conditions to associated with the at least one AI / ML model or functionality) is satisfied, for example, the terminal device 110 is located within the model validity area, and the DL PRS configuration is consistent with that the model is trained for. Similarly, the terminal device 110 may determine that it does not have the capability to provide certain information generated by AI / ML model, when the desired set of conditions associated with the AI / ML model is not satisfied, for example, the terminal device 110 is located outside the model validity area, or the DL PRS configuration is not consistent with that the model is trained for.

[0117] In some embodiments, the set of conditions associated with the at least one valid AI / ML model or functionality at least comprises at least one on-demand downlink positioning referencesignal (DL-PRS). For example, whether to activate the one or more AIML models / functionalities at the target terminal device may depend on the on-demand DL-PRS requested by the target terminal device, wherein the on-demand DL-PRS requested may be needed by the AIML model / functionality available at the target terminal device to be applicable, i.e. the requested DL- PRS resources may be the resources under which the AIML model / functionality was trained, and that are needed by the AIML model / functionality for the inference. The LMF may provide the requested on-demand DL-PRS in the ProvideAssistanceData message, e.g. a subset of the on- demand DL-PRS requested by the terminal device 110.

[0118] In some embodiments, the terminal device 110 may be configured with a single AI / ML model available for a given functionality. In response to the query from the LMF, the terminal device 110 may determine whether the AI / ML model is valid by comparing a deployment condition within the target area with a set of conditions associated with the AI / ML model and transmit, based on the determining, an indication of validity of the AI / ML model for the given functionality. The following procedure may be applied when the terminal device 110 has one AI / ML model of one given functionality. After examining the deployment conditions and comparing them with the desired set of conditions associated with the AI / ML model, the terminal device 110 determines whether it can declare that it's capable of the given functionality (i.e., providing a certain information (location of the target terminal device, or intermediate measurements) generated by AI / ML model). Then the terminal device 110 reports this decision (either capable or not capable) about the condition-dependent capability to the network node (e.g., LMF 130).

[0119] If the terminal device 110 reports that it's capable of the given functionality (by transmitting the indication of validity of the AI / ML model for the given functionality), then the LMF 130 may send a message to terminal device 110 via LPP to activate the AI / ML model hosted by the terminal device 110. If the terminal device 110 reports that it's not capable of the given functionality, several options are possible. The AI / ML model hosted by the terminal device 110 cannot be activated. Alternatively, the terminal device 110 reports the set of conditions (as an example of the set of conditions to associated with the at least one AI / ML model or functionality) desired by its model and requests the LMF 130 to update the network side configurations to satisfy the conditions, so that the model can be activated. For example, the terminal device 110 may request the PRS to be reconfigured to a desired bandwidth (e.g., in terms of number of PRBs), a desired number of repetitions (by the configuration transmitted by the LMF 130). Alternatively, the terminal device 110 may make an on-demand PRS request to the LMF which includes the set of TRPs to transmit DL-PRS, and the DL-PRS configurations.

[0120] In some embodiments, the terminal device 110 may be configured with a plurality of AI / ML models available for a given functionality. In response to the query from the LMF, the terminal device 110 may determine whether the plurality of AI / ML models are valid by comparing a deployment condition within the target area with a set of conditions associated with the pluralityof AI / ML models, respectively and in accordance with a determination that at least one of the plurality of AI / ML models are valid, transmit the indication of validity of the at least one AI / ML model for the given functionality. The following procedure may be applied when the terminal device 110 has more than one AI / ML model of a given functionality. Here the functionality refers to: the terminal device 110 performs DL channel measurements and generates the desired information for reporting to the network node (e.g., LMF 130). For example, in one functionality, the desired information is location estimation, as in Case 1; in another functionality, the desired information is the intermediate measurement like timing information, as in Case 2a. For each (i- th, i=0, 1, ,... , N-l) of the N models of the given functionality, examining the deployment conditions, and comparing them with the desired set of conditions associated with the i-th AI / ML model, the terminal device 110 determines whether the i-th model is compatible with the deployment condition. If any of the N models is compatible, then the terminal device 110 can declare that it's capable of the given functionality (i.e., providing a certain information (location of the target terminal device, or intermediate measurements) generated by AI / ML model). Then the terminal device 110 reports this decision (either capable or not capable) about the conditiondependent capability to the network node (e.g., LMF 130).

[0121] If the terminal device 110 reports that it's capable (e.g., by transmitting the indication of validity of the at least one AI / ML model for the given functionality), then the LMF 130 may send a message to the terminal device 110 via LPP to activate the AI / ML functionality at the terminal device 110. When receiving the message from LMF 130, the terminal device 110 selects an AI / ML model from the M models, M<=N, where the M models are those that are found to be compatible with the deployment condition. The terminal device 110 performs model inference using the selected model. If the terminal device 110 reports that it's not capable, several options are possible. The AI / ML functionality cannot be activated, where the functionality was expected to be fulfilled by model(s) hosted by the terminal device 110. Alternatively, the terminal device 110 selects a j-th model among the N available models for the functionality. The terminal device 110 makes an on-demand PRS request to the LMF 130, where the requested on-demand PRS is according to the requirements of the j-th model.

[0122] In some embodiments, in accordance with a determination that the plurality of AI / ML models are invalid, the terminal device 110 may select an AI / ML model from the plurality of AI / ML models and transmit, to the LMF 130, indication information indicating a set of conditions to associated with the selected AI / ML model. In some embodiments, the terminal device 110 selects a j-th model (also referred to as the AI / ML model) among the N available models (also referred to as the plurality of AI / ML models) for the functionality. The terminal device 110 reports the set of conditions (as an example of the set of conditions to associated with the at least one AI / ML model or functionality) desired by its j-th model and requests the LMF 130 to update the network side configurations to satisfy the conditions, so that the j-th model can be activated. For example, the terminal device 110 may request the PRS to be reconfigured to a desiredbandwidth (e.g., in terms of number of PRBs), a desired number of repetitions (by the configuration transmitted by the LMF 130).

[0123] In some embodiments, the terminal device 110 may be configured with a plurality of AI / ML models available for a plurality of AI / ML functionalities. For each of the plurality of AI / ML functionalities, the terminal device 110 may transmit the indication of whether at least one AI / ML model for the AI / ML functionality is valid or invalid. The following procedure may be applied when the terminal device 110 has models for more than one AI / ML positioning functionalities. For example, the terminal device 110 has one or more models for Case 1 (a first positioning functionality), and one or more models for Case 2a (a second positioning functionality). For each functionality (e.g., one functionality for Case 1, another functionality for Case 2a), the terminal device 110 may determine whether it is capable of supporting the functionality, using the procedures provided above. For each functionality (e.g., one functionality for Case 1, another functionality for Case 2a), the terminal device 110 may report (e.g., the indication of whether at least one AI / ML model for the AI / ML functionality is valid or invalid) to the network node (e.g., LMF 130) whether it is capable of supporting it (i.e., run model inference for the given functionality). When the terminal device 110 reports that it is capable of more than one functionality (e.g., both functionalities for Case 1 and Case 2a) for a given use case (e.g., positioning), then the network node (e.g., LMF 130) selects one of the supported functionalities, and sends a message to the terminal device 110 to activate an appropriate model for the selected functionality. Alternatively, the terminal device 110 may select one of the supported functionalities itself and recommends to the LMF 130 to activate the functionality selected by the terminal device 110.

[0124] It is to be noted that after a given functionality (e.g., Case 1 of UE-based AI / ML positioning) is activated, the terminal device 110 may or may not always run the AI / ML model to provide the desired information (e.g., location of the target terminal device). For example, the terminal device 110 may perform model inference of Case 1 model to generate location estimation; or use legacy positioning method(s) to generate location estimation. In this case, the terminal device 110 may use an indicator in its report to network node to differentiate whether the desired information is provided using AI / ML model or not.

[0125] In some embodiments, actions of the terminal device 110 corresponding to the request for whether the terminal device 110 has a valid AI / ML model of the LMF 130 may also be sent in an unsolicited way. For Case 1 or Case 2a, when the LMF 130 request whether the terminal device 110 has a valid model associated with one or multiple areas besides the capability of the terminal device 110 for measurement and processing for Case 1 or Case 2a in the LPP Requestcapabilities message, the terminal device 110 indicates whether it has a valid AI / ML model associated with the areas requested and optional indicates more areas it has valid model but not requested by the LMF in LPP ProvideCapabilities message.

[0126] At 430, the LMF 130 transmits, based on the received indication and to the terminaldevice 110, a request to activate the AI / ML-based positioning. The terminal device 110 receives the request.

[0127] At 435, the terminal device 110 performs, based on the request, positioning of the terminal device 110 using a target AI / ML model or functionality amongst the at least one AI / ML model or functionality.

[0128] At 440, the terminal device 110 transmits, to the LMF 130, a positioning-related result of the target AI / ML model or functionality. The LMF 130 receives the positioning-related result. The positioning-related result comprises a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

[0129] In some embodiments, the target AI / ML model or functionality may comprise an AI / ML model or functionality for direct positioning that is configured to generate the location of the terminal device, or an AI / ML model or functionality for assisted positioning that is configured to generate the intermediate measurement. In some embodiments, a certain information (target UE location, or intermediate measurements) may be generated by the AI / ML model.

[0130] In some embodiments, at 445, the terminal device 110 may transmits, to the LMF 130, a report comprising a reference location of the terminal device or a reference intermediate measurement that is determined in a non-AI / ML positioning mechanism. The LMF 130 may receive the report. At 450, the LMF 130 may determine, based on the report, whether a positioning accuracy of a non-AI / ML positioning mechanism of the terminal device fails to satisfy a positioning accuracy requirement. The terminal device 110 may be firstly configured to report measurements using non-AI / ML positioning method and the LMF 130 may detect whether the positioning accuracy of non-AI / ML positioning methods of the target terminal device and / or other terminal devices in the same cell or nearby cell meet the positioning accuracy requirement for the session of the target terminal device.

[0131] In some embodiments, the LMF 130 may request the terminal device 110 to perform AI / ML based positioning measurements as input for UE-sided AI / ML model in AI / ML based positioning preferred scenarios. In some embodiments, if the LMF 130 sends / has sent valid AI / ML model to target terminal device via LPP Provide AssistanceData message, or the LMF 130 knows target terminal device has valid AI / ML model for Casel / Case 2a, the LMF 130 requests the terminal device to only perform AI / ML based positioning measurements (e.g., CIR, PDP, DP of reference signals, e.g., DL-PRS) as input for UE-sided AI / ML model under the AI / ML based positioning preferred scenarios. In some embodiments, if the LMF 130 has no information of whether the terminal device 110 has valid AI / ML model / functionality for Case 1 / Case 2a, the LMF 130 requests the terminal device 110 to perform AI / ML based positioning measurements as input for UE-sided AI / ML model / functionality with the condition of the terminal device 110 having valid AI / ML model / functionality, otherwise, the terminal device 110 performs non-AI / ML based positioning methods.

[0132] In some embodiments, the request of the terminal device 110 to perform AI / ML basedpositioning measurements may be carried out by LPP RequestLocationlnformation.

[0133] In some embodiments, upon determining that the target terminal device has an AI / ML model / functionality valid to operate in a certain area served by the LMF 130, the LMF 130 determines what type of assistance information the target terminal device needs in order for the AIML model / functionality at the UE-side to operate. For example, in one method the terminal device 110 may transmit to the server node an indication in the RequestAssistanceData message indicating the availability at the UE-side of one or more AIML models / functionalities. The RequestAssistanceData message may include the assistance data that the terminal device 110 needs in order for the AIML model / functionality to be applicable. On the basis of the received RequestAssistanceData, the server node, LMF 130, may determine whether to activate the one or more AIML models / functionalities at the terminal device 110. For example, whether to activate the one or more AIML models / functionalities at the target terminal device may depend on the on- demand DL-PRS requested by the target terminal device, where the on-demand DL-PRS requested may be needed by the AIML model / functionality available at the terminal device 110 to be applicable, i.e. the requested DL-PRS resources may be the resources under which the AIML model / functionality was trained, and that are needed by the AIML model / functionality for the inference. The LMF 130 may provide the requested on-demand DL-PRS in the Provide AssistanceData message, e.g. a subset of the on-demand DL-PRS requested by the terminal device 110. In this case, this message also activates the one or more AIML models / functionalities available at the terminal device 110. In another example, the LMF does not provide the requested on-demand DL-PRS, thereby not activating the AIML model / functionality at the terminal device 110. In another example, the positioning results of the AIML model / functionality may be transmitted by the terminal device 110 in the ProvideLocationlnformation message. The ProvideLocationlnformation message may be transmitted in response of a RequestLocationlnformation.

[0134] FIG. 5 illustrates a signaling flow 500 for requesting AI / ML positioning in accordance with some embodiments of the present disclosure. The signaling flow 500 involves the terminal device 110 and the LMF 130 in FIG. 1. The embodiments of FIG. 5 are assumed for Case 2b (i.e., UE-assisted / LMF-based positioning with a LMF-side model for direct AI / ML positioning).

[0135] As shown in FIG.5, at 505, in accordance with a determination of performing artificial intelligence / machine learning (AI / ML)-based positioning for a terminal device, the LMF 130 determines whether a target AI / ML model or functionality is valid for positioning of the terminal device.

[0136] In some embodiments, the LMF 130 may determine whether a cell where the target device is located is within a validity area of at least one AI / ML model or functionality stored in the LMF and determine that a target AI / ML model or functionality is valid for positioning of the terminal device. The target AI / ML model or functionality may be selected from the at least one AI / ML model or functionality. When LMF has a valid AI / ML model for the target terminal device,e.g., the cell ID of the target terminal device is within the validity area of at least one of AI / ML models stored in LMF.

[0137] In some embodiments, at 510, determining to perform AI / ML-based positioning for the terminal device may be based on the terminal device being within in a non-line-of-sight (NLoS) environment or a LoS environment. The LMF 130 may have knowledge that terminal device 110 is in a NLoS-rich environment, and the NLoS-rich environment may be defined e.g., the target terminal device has fewer than K line-of-sight links to the surrounding TRPs. For example, the probability that the number of LoS links is fewer than K and is above a threshold.

[0138] Alternatively, or in addition, at 510, determining to perform AI / ML-based positioning for the terminal device may be based on a positioning accuracy of a non-AI / ML positioning mechanism of the terminal device failing to satisfy a positioning accuracy requirement. In some embodiments, the positioning accuracy of non-AI / ML positioning methods of the target terminal device and / or other terminal devices in the same cell or nearby cell does not meet the positioning accuracy requirement for the session of target terminal device which may be taken as a factor to determine to perform AI / ML-based positioning for the terminal device.

[0139] Alternatively, or in addition, at 510, determining to perform AI / ML-based positioning for the terminal device may be based on a positioning accuracy of an AI / ML positioning mechanism of the terminal device satisfying a positioning accuracy requirement. In some embodiments, the positioning accuracy of AI / ML positioning methods of the target terminal device and / or other terminal devices in the same cell or nearby cell can satisfy the positioning accuracy requirement for the session of the target terminal device which may be taken as a factor to determine to perform AI / ML-based positioning for the terminal device.

[0140] Alternatively, or in addition, at 510, determining to perform AI / ML-based positioning for the terminal device may be based on capability of the terminal device in performing (or reporting) AI / ML-based positioning (or positioning measurements).

[0141] At 515, in accordance with a determination that the target AI / ML model or functionality is valid for positioning of the terminal device, the LMF 130 transmits, to the terminal device 110, a configuration of providing measurement information as an input to the AI / ML model. The LMF 130 may request the terminal device 110 to perform and report AI / ML based positioning measurements (e.g., CIR, PDP, DP of reference signals, e.g., DL-PRS) under the AI / ML based positioning preferred scenarios. In one example, LMF 130 requests the terminal device 110 to report measurements such as PDP, DP, or CIR measured on the reference signal transmitted on a specific PFL. In this example, the terminal device 110 is provided with an indication of the recommended PFL for measurement.

[0142] At 520, the LMF 130 receives, from the terminal device 110, the measurement information. The terminal device 110 upon receiving such a request prioritizes TRPs belonging to the recommended PFL to perform measurements and report them (i.e., the measurement information) to the LMF 130. If the terminal device 110 is not able to receive PRS resources onthat PFL from a certain number of TRPs on that PFL, the terminal device 110 may also consider measuring PRS resources from the PFL other than the one that is recommended by the LMF. The terminal device 110 in this case performs measurements on TRPs that belong to the same PFL and report the resulting PDP, DP, or CIR to the LMF. The terminal device 110 in this case also indicates the measured PFL in the measurement report.

[0143] In some embodiments, the request for measurement information may be carried out by LPP RequestLocationlnformation.

[0144] At 520, the LMF 130 determines a positioning-related result by providing the measurement information into the target AI / ML model or functionality. The positioning-related result comprises a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

[0145] In some embodiments, at 530, the LMF 130 may receive, from the terminal device 110, a report comprising a reference location of the terminal device or a reference intermediate measurement that is determined in a non-AI / ML positioning mechanism. At 535, the LMF 130 may determine, based on the report, whether a positioning accuracy of a non-AI / ML positioning mechanism of the terminal device 110 fails to satisfy a positioning accuracy requirement. The terminal device 110 may be firstly configured to report measurements using non-AI / ML positioning method and the LMF 130 may detect whether the positioning accuracy of non-AI / ML positioning methods of the target terminal device and / or other terminal devices in the same cell or nearby cell meet the positioning accuracy requirement for the session of the target terminal device.

[0146] In some embodiments, the LMF 130 may decide the positioning mode based on the status of the terminal device 110. The status of the terminal device 110 may be defined based on the channel propagation conditions, area ID, speed of the terminal device 110, channel tracking parameters. Positioning mode may be defined based on the 5 AI / ML positioning cases, legacy solutions or any combinations of those.

[0147] In some embodiment, relevant for the terminal device tracking use case, the LMF 130 may decide a change of the used positioning mode based on some changes of a given parameter which is being tracked. An example of such parameters can be any of the positioning measurements such as power, time, angle etc.

[0148] In TS 37.355 LPP, the IE RequestLocationlnformation message body in a LPP message is used by the location server to request positioning measurements or a position estimate from the target device. The IE RequestLocationlnformation is shown as follows:

[0149] Field descriptions of RequestLocationlnformation is shown in Table 2 as follows:Table 2

[0150] As shown above message body, nr-UE-assisted-AI-RequestLocationlnformation maps to Case 2b, nr-UE-based-directAI-RequestLocationlnformation maps to Case 1, and nr-UE- based-assistAI-RequestLocationlnformation maps to Case 2a.

[0151] The IE NR-UE-based-directAI-RequestCapabilities is used by the location server to request the capability of the target device to support UE-based positioning with UE-side model, direct AI / ML positioning (Case 1) and to request if target device has valid model associated with one or multiple areas. The IE NR-UE-based-directAI-RequestCapabilities is shown as follows:- ASN1 STARTNR-UE-based-directAI-RequestCapabilities-rl9 ::= SEQUENCE {Valid_area_List-rl9 AreaID-CellList-rl7 OPTIONAL,- ASN1STOP

[0152] Field descriptions of NR-UE-based-directAI-RequestCapabilities is shown in Table 3 as follows:Table 3

[0153] The IE NR-UE-based-directAI-ProvideCapabilities is used by the target device to indicate its capability to support UE-based positioning with UE-side model, direct AI / ML positioning (Case 1) and to indicate if target device has valid model associated with one or multiple areas. The IE NR-UE-based-directAI-ProvideCapabilities is shown as follows:

[0154] Field descriptions of NR-UE-based-directAI-RequestCapabilities is shown in Table 4 as follows:Table 4

[0155] The IE NR-UE-based-assistAI-RequestCapabilities is used by the location server to request the capability of the target device to support UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning (Case 2a) and to request if target device has valid model associated with one or multiple areas. The IE NR-UE-based-assistAI-RequestCapabilities is shown as follows:

[0156] Field descriptions of NR-UE-based-assistAI-RequestCapabilities is shown in Table 5 as follows:Table 5

[0157] The IE NR-UE-based-assistAI-ProvideCapabilities is used by the target device to indicate its capability to support UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning (Case 2a) and to indicate if target device has valid model associated with one or multiple areas. The IE NR-UE-based-assistAI-ProvideCapabilities is shown as follows:

[0158] Field descriptions of NR-UE-based-assistAI-RequestCapabilities is shown in Table 6 as follows:Table 6

[0159] In another embodiment, the terminal device 110 indicates the availability of one or more AIML model / functionality in the RequestAssistanceData which may include the requested data that the AIML model / functionality needs in order to be applicable.

[0160] The RequestAssistanceData message body in a LPP message is used by the target device to request assistance data from the location server. The IE RequestAssistanceData is shown as follows:

[0161] In addtion, the server or LMF may provide the requested assistance data for AIML on the ProvideAssistanceData.

[0162] The ProvideAssistanceData message body in a LPP message is used by the location server to provide assistance data to the target device either in response to a request from the target device or in an unsolicited manner. The ProvideAssistanceData is shown as follows:

[0163] FIG. 6 is a diagram showing a flowchart of an example method 600 at a terminal device in accordance with some embodiments. The method 600 may be implemented by the terminal device 110 as shown in FIG. 1. For the purpose of discussion, the method 500 will be describedfrom the perspective of the terminal device 110 with reference to FIG. 1.

[0164] As shown in FIG. 6, at block 610, the terminal device 110 receives, from a location management function (LMF), a query about whether the terminal device is configured with a valid artificial intelligence / machine learning (AI / ML) model or functionality for positioning in a target area.

[0165] At block 620, the terminal device 110 transmits, based on the query and to the LMF, an indication of validity of at least one AI / ML model or functionality for positioning in the target area.

[0166] At block 630, the terminal device 110 receive, from the LMF, a request to activate AI / ML-based positioning.

[0167] At block 640, the terminal device 110 performs, based on the request, positioning of the terminal device using a target AI / ML model or functionality amongst the at least one AI / ML model or functionality.

[0168] At block 650, the terminal device 110 transmits, to the LMF, a positioning-related result of the target AI / ML model or functionality, the positioning-related result comprising a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

[0169] In some embodiments, the terminal device 110 may transmits, to the LMF, a report comprising a reference location of the terminal device or a reference intermediate measurement that may be determined in a non-AI / ML positioning mechanism.

[0170] In some embodiments, the target area may be indicated through at least one cell identity (ID) or at least one transmission / reception point (TRP) ID.

[0171] In some embodiments, the terminal device 110 may transmit, to the LMF, indication information indicating a set of conditions to associated with the at least one AI / ML model or functionality and receives, from the LMF, a configuration of at least one condition to associated with the target AI / ML model or functionality.

[0172] In some embodiments, the set of conditions associated with the at least one valid AI / ML model or functionality at least comprises at least one on-demand downlink positioning reference signal (DL-PRS).

[0173] In some embodiments, in response to the query from the LMF, the terminal device 110 may determine whether the AI / ML model may be valid by comparing a deployment condition within the target area with a set of conditions associated with the AI / ML model; the terminal device may transmit, based on the determining, an indication of validity of the AI / ML model for the given functionality.

[0174] In some embodiments, in response to the query from the LMF, the terminal device 110 may determine whether the plurality of AI / ML models may be valid by comparing a deployment condition within the target area with a set of conditions associated with the plurality of AI / ML models, respectively; in accordance with a determination that at least one of the plurality ofAI / ML models may be valid, the terminal device may transmit the indication of validity of the at least one AI / ML model for the given functionality.

[0175] In some embodiments, in response to the request to activate AI / ML-based positioning, the terminal device 110 may select the target AI / ML model from the at least one valid AI / ML model; the terminal device performs positioning of the terminal device using the selected target AI / ML model.

[0176] In some embodiments, in accordance with a determination that the plurality of AI / ML models may be invalid, the terminal device 110 may select an AI / ML model from the plurality of AI / ML models; the terminal device may transmit, to the LMF, indication information indicating a set of conditions to associated with the selected AI / ML model.

[0177] In some embodiments, for each of the plurality of AI / ML functionalities, the terminal device may transmit the indication of whether at least one AI / ML model for the AI / ML functionality may be valid or invalid.

[0178] In some embodiments, the target AI / ML model or functionality comprises: an AI / ML model or functionality for direct positioning that may be configured to generate the location of the terminal device, or an AI / ML model or functionality for assisted positioning that may be configured to generate the intermediate measurement.

[0179] FIG. 7 is a diagram showing a flowchart of an example method 700 at a location management function (LMF) in accordance with some embodiments. The method 700 may be implemented by the LMF 130 as shown in FIG. 1. For the purpose of discussion, the method 700 will be described from the perspective of the LMF 130 with reference to FIG. 1.

[0180] As shown in FIG. 7, at block 710, the LMF 130 determines that a terminal device is configured to perform artificial intelligence / machine learning (AI / ML)-based positioning.

[0181] At block 720, the LMF 130 transmits, to the terminal device, a query about whether the terminal device is configured with a valid AI / ML model or functionality for positioning in a target area.

[0182] At block 730, the LMF 130 receives, from the terminal device, an indication of validity of at least one AI / ML model or functionality for positioning in the target area.

[0183] At block 740, the LMF 130 transmits, based on the received indication and to the terminal device, a request to activate the AI / ML-based positioning.

[0184] At block 750, the LMF 130 receives, from the terminal device, a positioning-related result of a target AI / ML model or functionality used by the terminal device, the positioning- related result comprising a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

[0185] In some embodiments, the LMF 130 may determine that the terminal device may be configured to perform AI / ML-based positioning based on at least one of the following: the terminal device may be within in a non-line-of-sight (NLoS) environment or a LoS environment, a positioning accuracy of a non-AI / ML positioning mechanism of the terminal device failing tosatisfy a positioning accuracy requirement, a positioning accuracy of an AI / ML positioning mechanism of the terminal device satisfying a positioning accuracy requirement, or capability of the terminal device in performing AI / ML-based positioning.

[0186] In some embodiments, the LMF 130 may receive, from the terminal device, a report comprising a reference location of the terminal device or a reference intermediate measurement that may be determined in a non-AI / ML positioning mechanism; determining, based on the report, whether a positioning accuracy of a non-AI / ML positioning mechanism of the terminal device fails to satisfy a positioning accuracy requirement.

[0187] In some embodiments, the target area may be indicated through at least one cell identity (ID) or at least one transmission / reception point (TRP) ID.

[0188] In some embodiments, the LMF 130 may receive, from the terminal device, indication information indicating a set of conditions to associated with the at least one AI / ML model or functionality; transmitting, to the terminal device, a configuration of at least one condition to associated with the target AI / ML model or functionality.

[0189] In some embodiments, the set of conditions associated with the at least one valid AI / ML model or functionality at least comprises at least one on-demand downlink positioning reference signal (DL-PRS).

[0190] In some embodiments, the target AI / ML model or functionality comprises: an AI / ML model or functionality for direct positioning that may be configured to generate the location of the terminal device, or an AI / ML model or functionality for assisted positioning that may be configured to generate the intermediate measurement.

[0191] FIG. 8 is a diagram showing a flowchart of an example method 800 at a location management function (LMF) in accordance with some embodiments. The method 800 may be implemented by the LMF as shown in FIG. 1. For the purpose of discussion, the method 800 will be described from the perspective of the LMF 130 with reference to FIG. 1.

[0192] As shown in FIG. 8, at block 810, in accordance with a determination of performing artificial intelligence / machine learning (AI / ML)-based positioning for a terminal device, the LMF130 determines whether a target AI / ML model or functionality is valid for positioning of the terminal device.

[0193] At block 820, in accordance with a determination that the target AI / ML model or functionality is valid for positioning of the terminal device, the LMF 130 transmits, to the terminal device, a configuration of providing measurement information as an input to the AI / ML model.

[0194] At block 830, the LMF 130 receives, from the terminal device, the measurement information.

[0195] At block 840, the LMF 130 determines a positioning-related result by providing the measurement information into the target AI / ML model or functionality, the positioning-related result comprising a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

[0196] In some embodiments, the LMF 130 may determine to perform AI / ML-based positioning for the terminal device based on at least one of the following: the terminal device may be within in a non-line-of-sight (NLoS) environment or a LoS environment, a positioning accuracy of a non-AI / ML positioning mechanism of the terminal device failing to satisfy a positioning accuracy requirement, a positioning accuracy of an AI / ML positioning mechanism of the terminal device satisfying a positioning accuracy requirement, or capability of the terminal device in performing AI / ML-based positioning.

[0197] In some embodiments, the LMF 130 may receive, from the terminal device, a report comprising a reference location of the terminal device or a reference intermediate measurement that may be determined in a non-AI / ML positioning mechanism; determining, based on the report, whether a positioning accuracy of a non-AI / ML positioning mechanism of the terminal device fails to satisfy a positioning accuracy requirement.

[0198] In some embodiments, the LMF 130 may determine whether a cell where the target device may be located may be within a validity area of at least one AI / ML model or functionality stored in the LMF; determining that a target AI / ML model or functionality may be valid for positioning of the terminal device, the target AI / ML model or functionality may be selected from the at least one AI / ML model or functionality.

[0199] All operations and features related to the terminal device 110 and the LMF 130 as described above with reference to FIGS. 1 to 8 are likewise applicable to the methods 600, 700 and 800 and have similar effects.

[0200] FIG. 9 is a diagram showing a communication device 900 in accordance with some embodiments.

[0201] As shown in FIG. 9, the communication device 900 may comprise a processor 905 and a memory 910. The memory 910 may contain instructions 915 executable by the processor 905, whereby the communication device 900 may be operative to implement actions or operations according to any of the above-mentioned embodiments described with reference to FIGS. 1 to 8.

[0202] In some embodiments, the communication device 900 may operate as a terminal device. In these embodiments, the communication device 900 may be operative to: receive, from a location management function (LMF), a query about whether the terminal device is configured with a valid artificial intelligence / machine learning (AI / ML) model or functionality for positioning in a target area; transmit, based on the query and to the LMF, an indication of validity of at least one AI / ML model or functionality for positioning in the target area; receive, from the LMF, a request to activate AI / ML-based positioning; perform, based on the request, positioning of the terminal device using a target AI / ML model or functionality amongst the at least one AI / ML model or functionality; transmit, to the LMF, a positioning-related result of the target AI / ML model or functionality, the positioning-related result comprising a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

[0203] In some embodiments, the communication device 900 may operate as a locationmanagement function (LMF). In these embodiments, the communication device 900 may be operative to: determine that a terminal device is configured to perform artificial intelligence / machine learning (AI / ML)-based positioning; transmit, to the terminal device, a query about whether the terminal device is configured with a valid AI / ML model or functionality for positioning in a target area; receive, from the terminal device, an indication of validity of at least one AI / ML model or functionality for positioning in the target area; transmit, based on the received indication and to the terminal device, a request to activate the AI / ML-based positioning; receive, from the terminal device, a positioning-related result of a target AI / ML model or functionality used by the terminal device, the positioning-related result comprising a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

[0204] In some embodiments, the communication device 900 may operate as a location management function (LMF). In these embodiments, the communication device 900 may be operative to: in accordance with a determination of performing artificial intelligence / machine learning (AI / ML)-based positioning for a terminal device, determine whether a target AI / ML model or functionality is valid for positioning of the terminal device; in accordance with a determination that the target AI / ML model or functionality is valid for positioning of the terminal device, transmit, to the terminal device, a configuration of providing measurement information as an input to the AI / ML model; receive, from the terminal device, the measurement information; determine a positioning-related result by providing the measurement information into the target AI / ML model or functionality, the positioning-related result comprising a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

[0205] The processor 905 may be any kind of processing component, such as one or more microprocessor or microcontrollers, as well as other digital hardware, which may include digital signal processors (DSPs), special-purpose digital logic, and the like. The memory 910 may be any kind of storage component, such as read-only memory (ROM), random -access memory, cache memory, flash memory devices, optical storage devices, etc.

[0206] FIG. 10 is a diagram showing a computer readable storage medium 1000 in accordance with some embodiments.

[0207] As shown in FIG. 10, the computer readable storage medium 1000 comprising instructions 915 which when executed by a processor of a device, cause the device to perform any above-mentioned embodiments described with reference to FIGS. 1 to 9.

[0208] The computer readable storage medium 1000 may be configured to include memory such as RAM, ROM, programmable read-only memory (PROM), erasable programmable readonly memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, floppy disks, hard disks, removable cartridges, or flash drives.

[0209] In some embodiments, an apparatus capable of performing the method 600 or 700 maycomprise means for performing the respective operations of the method 600 or 700. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module.

[0210] FIG. 11 shows a UE 1200 in accordance with some embodiments. The UE 1200 presents additional details of some embodiments of the UE 1112 of FIG. 11. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage / playback device, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), an Augmented Reality (AR) or Virtual Reality (VR) device, wireless customer-premise equipment (CPE), vehicle, vehicle -mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0211] A UE may support device-to-device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle- to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0212] The UE 1200 includes processing circuitry 1202 that is operatively coupled via a bus 1204 to an input / output interface 1206, a power source 1208, a memory 1210, a communication interface 1212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in FIG. 12. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0213] The processing circuitry 1202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine -readable computer programs in the memory 1210. The processing circuitry 1202 may be implemented as one or more hardware -implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.);programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 1202 may include multiple central processing units (CPUs).

[0214] In the example, the input / output interface 1206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 1200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0215] In some embodiments, the power source 1208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 1208 may further include power circuitry for delivering power from the power source 1208 itself, and / or an external power source, to the various parts of the UE 1200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 1208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 1208 to make the power suitable for the respective components of the UE 1200 to which power is supplied.

[0216] The memory 1210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 1210 includes one or more application programs 1214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1216. The memory 1210 may store, for use by the UE 1200, any of a variety of various operating systems or combinations of operating systems.

[0217] The memory 1210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital datastorage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 1210 may allow the UE 1200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 1210, which may be or comprise a device-readable storage medium.

[0218] The processing circuitry 1202 may be configured to communicate with an access network or other network using the communication interface 1212. The communication interface 1212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1222. The communication interface 1212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 1218 and / or a receiver 1220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1218 and receiver 1220 may be coupled to one or more antennas (e.g., antenna 1222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0219] In the illustrated embodiment, communication functions of the communication interface 1212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short- range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0220] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response toa request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0221] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0222] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 1200 shown in FIG. 11.

[0223] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0224] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0225] FIG. 12 shows a network node 1300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0226] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0227] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0228] The network node 1300 includes a processing circuitry 1302, a memory 1304, a communication interface 1306, and a power source 1308. The network node 1300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 1300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 1300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 1304 for different RATs) and some components may be reused (e.g., a same antenna 1310 may be shared by different RATs). The network node 1300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1300, for example GSM, WCDMA,LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 1300.

[0229] The processing circuitry 1302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 1300 components, such as the memory 1304, to provide network node 1300 functionality.

[0230] In some embodiments, the processing circuitry 1302 includes a system on a chip (SOC). In some embodiments, the processing circuitry 1302 includes one or more of radio frequency (RF) transceiver circuitry 1312 and baseband processing circuitry 1314. In some embodiments, the radio frequency (RF) transceiver circuitry 1312 and the baseband processing circuitry 1314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 1312 and baseband processing circuitry 1314 may be on the same chip or set of chips, boards, or units.

[0231] The memory 1304 may comprise any form of volatile or non-volatile computer- readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 1302. The memory 1304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 1302 and utilized by the network node 1300. The memory 1304 may be used to store any calculations made by the processing circuitry 1302 and / or any data received via the communication interface 1306. In some embodiments, the processing circuitry 1302 and memory 1304 is integrated.

[0232] The communication interface 1306 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 1306 comprises port(s) / terminal(s) 1316 to send and receive data, for example to and from a network over a wired connection. The communication interface 1306 also includes radio front-end circuitry 1318 that may be coupled to, or in certain embodiments a part of, the antenna 1310. Radio front-end circuitry 1318 comprises filters 1320 and amplifiers 1322. The radio front-end circuitry 1318 may be connected to an antenna 1310 and processing circuitry 1302. The radio front-end circuitry may be configured to condition signals communicated between antenna 1310 and processing circuitry 1302. The radio front-end circuitry 1318 mayreceive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 1318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1320 and / or amplifiers 1322. The radio signal may then be transmitted via the antenna 1310. Similarly, when receiving data, the antenna 1310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1318. The digital data may be passed to the processing circuitry 1302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0233] In certain alternative embodiments, the network node 1300 does not include separate radio front-end circuitry 1318, instead, the processing circuitry 1302 includes radio front-end circuitry and is connected to the antenna 1310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1312 is part of the communication interface 1306. In still other embodiments, the communication interface 1306 includes one or more ports or terminals 1316, the radio front-end circuitry 1318, and the RF transceiver circuitry 1312, as part of a radio unit (not shown), and the communication interface 1306 communicates with the baseband processing circuitry 1314, which is part of a digital unit (not shown).

[0234] The antenna 1310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1310 may be coupled to the radio front-end circuitry 1318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1310 is separate from the network node 1300 and connectable to the network node 1300 through an interface or port.

[0235] The antenna 1310, communication interface 1306, and / or the processing circuitry 1302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 1310, the communication interface 1306, and / or the processing circuitry 1302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0236] The power source 1308 provides power to the various components of network node 1300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1300 with power for performing the functionality described herein. For example, the network node 1300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 1308. As a further example, the power source 1308 may comprise a source of power in the form of a battery or battery pack which is connected to,or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0237] Embodiments of the network node 1300 may include additional components beyond those shown in FIG. 12 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 1300 may include user interface equipment to allow input of information into the network node 1300 and to allow output of information from the network node 1300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1300. In some embodiments providing a core network node, such as core network node 108 of FIG. 11, some components, such as the radio front-end circuitry 1318 and the RF transceiver circuitry 1312 may be omitted.

[0238] FIG. 13 is a block diagram illustrating a virtualization environment 1400 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 1400 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1400 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface. Virtualization may facilitate distributed implementations of a network node, UE, core network node, or host.

[0239] Applications 1402 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0240] Hardware 1404 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1406 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 1408a and 1408b (one or more of which may be generally referred to as VMs 1408), and / or perform any of thefunctions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 1406 may present a virtual operating platform that appears like networking hardware to the VMs 1408.

[0241] The VMs 1408 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 1406. Different embodiments of the instance of a virtual appliance 1402 may be implemented on one or more of VMs 1408, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0242] In the context of NFV, a VM 1408 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 1408, and that part of hardware 1404 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 1408 on top of the hardware 1404 and corresponds to the application 1402.

[0243] Hardware 1404 may be implemented in a standalone network node with generic or specific components. Hardware 1404 may implement some functions via virtualization. Alternatively, hardware 1404 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1410, which, among others, oversees lifecycle management of applications 1402. In some embodiments, hardware 1404 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1412 which may alternatively be used for communication between hardware nodes and radio units.

[0244] Although the computing devices described herein (e.g., UEs, network nodes) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information storedin the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0245] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer- readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

Claims

WHAT IS CLAIMED IS:

1. A method (600) implemented at a terminal device (110-1, 110-2, ... , 110-N), comprising: receiving (610), from a location management function (LMF) (130), a query about whether the terminal device is configured with a valid artificial intelligence / machine learning (AI / ML) model or functionality for positioning in a target area; transmitting (620), based on the query and to the LMF, an indication of validity of at least one AI / ML model or functionality for positioning in the target area; receiving (630), from the LMF, a request to activate AI / ML-based positioning; performing (640), based on the request, positioning of the terminal device using a target AI / ML model or functionality amongst the at least one AI / ML model or functionality; and transmitting (650), to the LMF, a positioning-related result of the target AI / ML model or functionality, the positioning-related result comprising a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

2. The method (600) of claim 1, further comprising: transmitting, to the LMF, a report comprising a reference location of the terminal device or a reference intermediate measurement that is determined in a non-AI / ML positioning mechanism.

3. The method (600) of claim 1 or 2, wherein the target area is indicated through at least one cell identity (ID) or at least one transmission / reception point (TRP) ID.

4. The method (600) of any of claims 1 to 3, further comprising: transmitting, to the LMF, indication information indicating a set of conditions to associated with the at least one AI / ML model or functionality; and receiving, from the LMF, a configuration of at least one condition to associated with the target AI / ML model or functionality.

5. The method (600) of clam 4, wherein the set of conditions associated with the at least one valid AI / ML model or functionality at least comprises at least one on-demand downlink positioning reference signal (DL-PRS).

6. The method (600) of any of claims 1 to 5, wherein the terminal device is configured with a single AI / ML model available for a given functionality, and wherein transmitting the indication of validity of at least one AI / ML model or functionality for positioning in the target area comprises: in response to the query from the LMF, determining whether the AI / ML model is valid by comparing a deployment condition within the target area with a set of conditions associated with the AI / ML model; and transmitting, based on the determining, an indication of validity of the AI / ML model for the given functionality.

7. The method (600) of any of claims 1 to 5, wherein the terminal device is configured with a plurality of AI / ML models available for a given functionality, and wherein transmitting the indication of validity of at least one AI / ML model or functionality for positioning in the target area comprises: in response to the query from the LMF, determining whether the plurality of AI / ML models arevalid by comparing a deployment condition within the target area with a set of conditions associated with the plurality of AI / ML models, respectively; in accordance with a determination that at least one of the plurality of AI / ML models are valid, transmitting the indication of validity of the at least one AI / ML model for the given functionality.

8. The method (600) of claim 7, wherein performing, based on the request, positioning of the terminal device using a target AI / ML model or functionality amongst the at least one valid AI / ML model comprises: in response to the request to activate AI / ML-based positioning, selecting the target AI / ML model from the at least one valid AI / ML model; and performing positioning of the terminal device using the selected target AI / ML model.

9. The method (600) of claim 7, further comprising: in accordance with a determination that the plurality of AI / ML models are invalid, selecting an AI / ML model from the plurality of AI / ML models; and transmitting, to the LMF, indication information indicating a set of conditions to associated with the selected AI / ML model.

10. The method (600) of any of claims 1 to 7, wherein the terminal device is configured with a plurality of AI / ML models available for a plurality of AI / ML functionalities, and wherein transmitting the indication of validity of at least one AI / ML model or functionality for positioning in the target area comprises: for each of the plurality of AI / ML functionalities, transmitting the indication of whether at least one AI / ML model for the AI / ML functionality is valid or invalid.

11. The method (600) of any of claims 1 to 10, wherein the target AI / ML model or functionality comprises: an AI / ML model or functionality for direct positioning that is configured to generate the location of the terminal device, or an AI / ML model or functionality for assisted positioning that is configured to generate the intermediate measurement.

12. A method (700) implemented at a location management function (LMF) (130), comprising: determining (710) that a terminal device (110-1, 110-2, ..., 110-N) is configured to perform artificial intelligence / machine learning (AI / ML)-based positioning; transmitting (720), to the terminal device, a query about whether the terminal device is configured with a valid AI / ML model or functionality for positioning in a target area; receiving (730), from the terminal device, an indication of validity of at least one AI / ML model or functionality for positioning in the target area; transmitting (740), based on the received indication and to the terminal device, a request to activate the AI / ML-based positioning; receiving (750), from the terminal device, a positioning-related result of a target AI / ML model or functionality used by the terminal device, the positioning-related result comprising a location of theterminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

13. The method (700) of claim 12, wherein determining that the terminal device is configured to perform AI / ML-based positioning comprises: determining that the terminal device is configured to perform AI / ML-based positioning based on at least one of the following: the terminal device being within in a non-line-of-sight (NLoS) environment or a LoS environment, a positioning accuracy of a non-AI / ML positioning mechanism of the terminal device failing to satisfy a positioning accuracy requirement, a positioning accuracy of an AI / ML positioning mechanism of the terminal device satisfying a positioning accuracy requirement, or capability of the terminal device in performing AI / ML-based positioning.

14. The method (700) of claim 13, further comprising: receiving, from the terminal device, a report comprising a reference location of the terminal device or a reference intermediate measurement that is determined in a non-AI / ML positioning mechanism; and determining, based on the report, whether a positioning accuracy of a non-AI / ML positioning mechanism of the terminal device fails to satisfy a positioning accuracy requirement.

15. The method (700) of any of claims 12 to 14, wherein the target area is indicated through at least one cell identity (ID) or at least one transmission / reception point (TRP) ID.

16. The method (700) of any of claims 12 to 15, further comprising: receiving, from the terminal device, indication information indicating a set of conditions to associated with the at least one AI / ML model or functionality; and transmitting, to the terminal device, a configuration of at least one condition to associated with the target AI / ML model or functionality.

17. The method (700) of clam 16, wherein the set of conditions associated with the at least one valid AI / ML model or functionality at least comprises at least one on-demand downlink positioning reference signal (DL-PRS).

18. The method (700) of any of claims 12 to 17, wherein the target AI / ML model or functionality comprises: an AI / ML model or functionality for direct positioning that is configured to generate the location of the terminal device, or an AI / ML model or functionality for assisted positioning that is configured to generate the intermediate measurement.

19. A method (800) implemented at a location management function (LMF) (130), comprising: in accordance with a determination of performing artificial intelligence / machine learning(AI / ML)-based positioning for a terminal device (110-1, 110-2, ... , 110-N), determining (810) whether a target AI / ML model or functionality is valid for positioning of the terminal device; in accordance with a determination that the target AI / ML model or functionality is valid forpositioning of the terminal device, transmitting (820), to the terminal device, a configuration of providing measurement information as an input to the AI / ML model; receiving (830), from the terminal device, the measurement information; and determining (840) a positioning-related result by providing the measurement information into the target AI / ML model or functionality, the positioning-related result comprising a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

20. The method (800) of claim 19, further comprising determining to perform AI / ML-based positioning for the terminal device based on at least one of the following: the terminal device being within in a non-line-of-sight (NLoS) environment or a LoS environment, a positioning accuracy of a non-AI / ML positioning mechanism of the terminal device failing to satisfy a positioning accuracy requirement, a positioning accuracy of an AI / ML positioning mechanism of the terminal device satisfying a positioning accuracy requirement, or capability of the terminal device in performing AI / ML-based positioning.

21. The method (800) of claim 20, further comprising: receiving, from the terminal device, a report comprising a reference location of the terminal device or a reference intermediate measurement that is determined in a non-AI / ML positioning mechanism; and determining, based on the report, whether a positioning accuracy of a non-AI / ML positioning mechanism of the terminal device fails to satisfy a positioning accuracy requirement.

22. The method (800) of any of claims 19 to 21, wherein determining whether a target AI / ML model or functionality is valid for positioning of the terminal device comprises: determining whether a cell where the target device is located is within a validity area of at least one AI / ML model or functionality stored in the LMF; and determining that a target AI / ML model or functionality is valid for positioning of the terminal device, the target AI / ML model or functionality being selected from the at least one AI / ML model or functionality.

23. A terminal device (110-1, 110-2, ... , 110-N, 900), comprising: a processor (905); and a memory (910), the memory (910) containing instructions executable by the processor (905), whereby the terminal device (110-1, 110-2, ... , 110-N, 900) is operative to: receive, from a location management function (LMF), a query about whether the terminal device is configured with a valid artificial intelligence / machine learning (AI / ML) model or functionality for positioning in a target area; transmit, based on the query and to the LMF, an indication of validity of at least one AI / MLmodel or functionality for positioning in the target area; receive, from the LMF, a request to activate AI / ML-based positioning; perform, based on the request, positioning of the terminal device using a target AI / ML model or functionality amongst the at least one AI / ML model or functionality; and transmit, to the LMF, a positioning-related result of the target AI / ML model or functionality, the positioning-related result comprising a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

24. A location management function (LMF) (130, 900), comprising: a processor (905); and a memory (910), the memory (910) containing instructions executable by the processor (905), whereby the LMF (130, 900) is operative to: determine that a terminal device is configured to perform artificial intelligence / machine learning (AI / ML)-based positioning; transmit, to the terminal device, a query about whether the terminal device is configured with a valid AI / ML model or functionality for positioning in a target area; receive, from the terminal device, an indication of validity of at least one AI / ML model or functionality for positioning in the target area; transmit, based on the received indication and to the terminal device, a request to activate the AI / ML-based positioning; receive, from the terminal device, a positioning-related result of a target AI / ML model or functionality used by the terminal device, the positioning-related result comprising a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

25. A location management function (LMF) (130, 900), comprising: a processor (905); and a memory (910), the memory (910) containing instructions executable by the processor (905), whereby the LMF (130, 900) is operative to: in accordance with a determination of performing artificial intelligence / machine learning (AI / ML)-based positioning for a terminal device, determine whether a target AI / ML model or functionality is valid for positioning of the terminal device; in accordance with a determination that the target AI / ML model or functionality is valid for positioning of the terminal device, transmit, to the terminal device, a configuration of providing measurement information as an input to the AI / ML model; receive, from the terminal device, the measurement information; and determine a positioning-related result by providing the measurement information into the target AI / ML model or functionality, the positioning-related result comprising a location of the terminal device or an intermediate measurement that is assisted in determining the location of the terminal device.

26. A computer-readable storage medium (1000) having instructions (915) stored thereon, theinstructions (915), which, when executed by at least one processor of a device, causes the device to perform the method (600) according to any of claims 1 to 11, the method (700) according to any of claims 12 to 18, or the method (800) according to any of claims 19 to 22.

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