Methods to enable data collection at NG-ran for NG-ran node assisted positioning with gnb-side model, ai / ML assisted positioning

Abstract

Systems and methods for enabling data collection at a Radio Access Network (RAN) node for RAN node assisted positioning with a RAN node side model. In one embodiment, a method performed by a RAN node for data collection for training, retraining or evaluating an Artificial Intelligence (AI) or Machine Learning (ML) (i.e., AI / ML) model for positioning in a wireless communication system comprises sending, to a Location Management Function (LMF), a New Radio Positioning Protocol a (NRPPa) message that indicates requested positioning-related data. The method further comprises receiving, from the LMF, an NRPPa message comprising the requested positioning-related data and training, retraining, and / or evaluating a performance of an AI / ML model for positioning in the wireless communication system, based on the received positioning-related data. In this manner, the RAN node is enabled to collect positioning data information.

Description

METHODS TO ENABLE DATA COLLECTION AT NG-RAN FOR NG-RAN NODE ASSISTED POSITIONING WITH gNB-SIDE MODEL, AI / ML ASSISTED POSITIONINGRELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 700,202, filed September 27, 2024, and U.S. Provisional Patent Application No. 63 / 700,219, filed September 27, 2024, the disclosures of which are hereby incorporated herein by reference in their entireties.TECHNICAL FIELD

[0002] The present disclosure relates to a cellular or wireless communications system and, more specifically, Artificial Intelligence (AI) / Machine Learning (ML) assisted positioning in a cellular or wireless communications system.BACKGROUNDAI / ML Modeling and Associated Principles

[0003] An Artificial Intelligence (Al) or Machine Learning (ML) technique includes one or more algorithms which use a set of data as input for training one or more AI / ML models. In the context of a wireless communication system such as, for example, a 3rdGeneration Partnership Project (3GPP) 5thGeneration System (5GS), the output of an AI / ML model can be used by an entity (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 a prediction (i.e., the output of the AI / ML model), which in turn depends on the trained AI / ML model. The AI / ML model can be trained in the entity online (or on-the-fly while processing data) or offline in the background. More specifically:• Online training is an AI / ML training process where the AI / ML model being used for inference is (typically continuously) trained in (near) real-time with the arrival of new training samples or data; and• Offline training is an AI / ML training process where the AI / ML model is trained based on collected samples or data, and where the trained AI / ML model is later used or delivered for inference.

[0004] 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.

[0005] The AI / ML models can be trained in a network entity (e.g., a network node) or a device (e.g., a UE), or another node. In this respect, the AI / ML model can be broadly classified as:• Case I: UE-side (AI / ML) model. A UE-side AI / ML model is an AI / ML model whose inference is performed entirely at the UE.• Case II: Network-side (AI / ML) model. A network-side AI / ML model is an AI / ML model whose inference is performed entirely at the network.• Case III: One-sided (AI / ML) model. A one-sided AI / ML model is a UE-side (AI / ML) model or a network-side (AI / ML) model.• Case IV: Two-sided (AI / ML) model. A two-sided AI / ML model 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 a base station (e.g., a gNodeB (gNB) in the case of New Radio (NR)), or vice versa.

[0006] An AI / ML model can 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.

[0007] 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 includes several processing stages including gathering unprocessed input data from data repositories (data ingestion), finding high-quality input features (data pre-processing), finding the optimal mapping of the model input features to a desired model output target in a sense determined by a loss function (model training), and evaluating model performance on unseen data from a functional level as well as from a system level when relevant (model evaluation). The training pipeline typically ends with a model registration stage, which may include operations to make the AI / ML model runnable via compilation to a specific hardware and steps like versioning and packaging of the AI / ML model so that it can be executed. An example of the AI / ML model training pipeline illustrating the different stages is shown in Figure 1.AI / ML Model Based Positioning

[0008] A new use case addressed by 3GPP is that an AI / ML model can be used for UE positioning within a 3GPP 5G system. A UE or a gNB, depending on capability, can have a trained AI / ML model stored inside the device, or have an untrained AI / ML model that can 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 or determine the UE location by exploitingmeasurements performed by the UE or gNB on reference signals such as Positioning Reference Signal (PRS), Sounding Reference Signal (SRS), etc. within the RAN coverage area.

[0009] Measurements predicted / determined by the UE by exploiting an AI / ML model can include, but are not limited to:• RSTD: 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: UE Rx-Tx time difference is defined as TUE-RX -TUE-TX, where: o 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. o 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.

[0010] Measurements predicted / determined by the gNB by exploiting an AI / ML model can include, but are not limited to:• gNB Rx-Tx time difference: The gNB Rx-Tx time difference is defined as TgNB-Rx - TgNB- TX where:TgNB-Rx is the positioning node received timing of uplink subframe #i containing Sounding Reference Signal (SRS) associated with UE, defined by the first detected path in time. It is measured on SRS signals received from the UE.TgNB-rx is the positioning node transmit timing of downlink subframe #j that is closest in time to the subframe #i received from the UE.• Timing advance TADV): The timing advance (TADV) is defined as the time difference TADV = (TgNB-Rx - TgNB-rx), where: o TgNB-Rx is the Transmission and Reception Point (TRP)

[0018] received timing of uplink subframe #i containing PRACH transmitted from UE, defined by the first detected path in time. o TgNB-rx is the TRP transmit timing of downlink subframe #j that is closest in time to the subframe #i received from the UE. o The detected PRACH is used to determine the start of one subframe containing that PRACH.• Uplink (UL) Relative Time of Arrival (UL RTOA): UL RTOA is defined as the beginning of subframe i containing SRS received in positioning node j, relative to aconfigurable reference time. For example, nodel (e.g., base station, etc.) measures the reception time of signals transmitted by the UE with respect to a reference time.

[0011] In addition to these, 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 can be performed on reference signals such as PRS and SRS.

[0012] 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.Assisted Positioning

[0013] In this mode, the positioning measurements are performed by the UE / gNB by using the AI / ML model. After completion, the positioning measurements are then reported to the location server. The location server, upon receiving the measurements, determines the location of the UE within the RAN coverage area. Depending on the need, 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 a third party that is interested or has requested the positioning of the UE within the RAN coverage area for further action to be taken.Representative Use Cases for AI / ML

[0014] The following are selected as representative sub-use cases:• Direct AI / ML positioning:AI / ML model output: UE location e.g., fingerprinting based on channel observation as the input of AI / ML model• AI / ML assisted positioning:AI / ML model output: new measurement and / or enhancement of existing measurement e.g., Line-Of-Sight (LOS) / Non-LOS (NLOS) identification, timing and / or angle of measurement, likelihood of measurement

[0015] More specifically, the following Cases are considered for study:• Case 1 : UE-based positioning with UE-side model, direct AI / ML or AI / ML assisted positioning• Case 2a: UE-assisted / Location Management Function (LMF)-based positioning with UE-side model, AI / ML assisted positioning• Case 2b: UE-assisted / LMF-based positioning with LMF-side model, direct AI / ML positioning• Case 3a: Next Generation RAN (NG-RAN) node assisted positioning with gNB-side model, AI / ML assisted positioning• Case 3b: NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning

[0016] One-sided model whose inference is performed entirely at the UE or at the network is prioritized in the 3 GPP Rel-18 Study Item (SI).

[0017] For all five positioning cases (Case l / 2a / 2b / 3a / 3b), RANI has not considered prioritization.

[0018] For positioning enhancement use case:• For AI / ML model training, training data can be generated by UE / Positioning Reference Unit (PRU) / gNB / LMF.• For LMF-side model inference (Case 2b, Case 3b), input data can be generated by UE / gNB and terminated at LMF.• For gNB-side model inference (Case 3a), input data is internally available at gNB.• For UE-side model inference (Case 1, Case 2a), input data is internally available at UE.• For performance monitoring at the LMF side, calculated performance metrics (if needed) or data needed for performance metric calculation (if needed) can be generated by UE / gNB and terminated at LMF.• For performance monitoring at the gNB side, calculated performance metrics (if needed) or data needed for performance metric calculation (if needed) can be generated by at least gNB.Related Aspects in Release 19 WID for AI / ML Positioning

[0019] 3GPP Work Item Description (WID) (See RP-242399) of R19 has the following Information on Work Item (emphasis added via bold text):NG-RAN Architecture

[0020] Figure 1 is a reproduction of Figure 6.1-1 of 3GPP Technical Specification (TS) 38.401 V18.2.0, which illustrates the overall architecture of the NG-RAN. The NG-RAN consists of a set of gNBs connected to the 5thGeneration Core (5GC) through the NG interface. 3GPP TS 38.401 V18.2.0 describes the overall NG-RAN architecture.

[0021] A disaggregated gNB may consist of a gNB-Central Unit (CU) and one or more gNB- Distributed Unit(s) (DU(s)). A gNB-CU and a gNB-DU is connected via Fl interface. As shown in Figure 2, this interface is responsible for possible information and control signaling from the Packet Data Convergence Protocol (PDCP) entity located in the CU and Radio Link Control (RLC) entity located in the DU.Positioning Architecture

[0022] Before Rel. 16, Long Term Evolution (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 Figure 3. The interactions between the gNB and the UE 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 is the location node in NR. There are also interactions between the location node and the gNB via the NR Positioning Protocol a (NRPPa) protocol.

[0023] The positioning architecture in Figure 3 will also be used to support AI / ML based positioning. Rel. 19 work on introducing AI / ML based positioning will not only exploit the legacy protocol but will also rely on already defined / exi sting reference signals that are used for positioning.

[0024] The NG-RAN positioning architecture is shown in Figure 3. The Access and Mobility Management Function (AMF) receives a request for some location service associated with a particular target UE from another entity (e.g., Gateway Mobile Location Center (GMLC) or UE), or the AMF itself decides to initiate some location service on behalf of a particular target UE (e.g., for an Internet Protocol (IP) Multimedia Subsystem (IMS) emergency call from the UE). The AMF then sends a location services request to an LMF. The LMF processes the location services request which may include transferring assistance data to the target UE to assist with UE-based and / or UE-assisted positioning and / or may include positioning of the target UE. The LMF then returns the result of the location service (e.g., a position estimate for the UE) back to the AMF. In the case of a location service requested by an entity other than the AMF (e.g., a GMLC or UE), the AMF returns the location service result to this entity. An NG-RAN node may control several Transmission and Reception Points (TRPs) or Transmission Points (TPs), such as remote radio heads, or downlink (DL)-PRS-only TPs for support of PRS-based Terrestrial Beacon System (TBS) positioning.SUMMARY

[0025] Systems and methods for enabling data collection at a Radio Access Network (RAN) node for RAN node assisted positioning with a RAN node side model. In one embodiment, a method performed by a RAN node for data collection for training, retraining or evaluating an Artificial Intelligence (Al) or Machine Learning (ML) (i.e., AI / ML) model for positioning in a wireless communication system comprises sending, to a Location Management Function (LMF), a New Radio Positioning Protocol a (NRPPa) message that indicates requested positioning-related data. The method further comprises receiving, from the LMF, an NRPPa message comprising the requested positioning-related data and training, retraining, and / or evaluating a performance of an AI / ML model for positioning in the wireless communication system, based on the received positioning-related data. In this manner, the RAN node is enabled to collect positioning data information.

[0026] In one embodiment, the sent NRPPa message is a measurement response comprising a flag that indicates that data collection is needed.

[0027] In one embodiment, the sent NRPPa message indicates the requested positioning- related data via an implicit indication.

[0028] In one embodiment, the sent NRPPa message indicates the requested positioning- related data via an explicit indication.

[0029] In one embodiment, the requested positioning-related data indicated by the sent NRPPa messages comprises one or more requested positioning related metrics indicated by the sent NRPPa message. In one embodiment, the one or more requested positioning related metrics indicated by the sent NRPPa message comprise either or both of: uplink relative time of arrival and gNodeB (gNB) Rx-Tx time difference.

[0030] In one embodiment, the sent NRPPa message further indicates a requested reporting periodicity of the requested positioning-related data.

[0031] In one embodiment, the sent NRPPa message further indicates an overall number of User Equipments (UEs) for which the requested positioning-related data is desired.

[0032] In one embodiment, the received NRPPa message comprises, for each of a plurality of UEs, a set of UE locations for that UE and corresponding time stamp.

[0033] In one embodiment, the received NRPPa message comprises a set of positioning measurements and corresponding time stamps.

[0034] In one embodiment, the received NRPPa message comprises a list of positioning Sounding Reference Signal (SRS) configurations.

[0035] In one embodiment, the method further comprises sending, to the LMF, a further NRPPa message comprising an update to the positioning-related data requested by the RAN node.

[0036] In one embodiment, the method further comprises receiving, from the LMF, an NRPPa message that indicates that the LMF supports providing positioning-related data to the RAN node for data collection. In one embodiment, the NRPPa message that indicates that the LMF supports providing positioning-related data to the RAN node for data collection comprises information that indicates a set of available positioning-related data, and the sent NRPPa message comprises information that indicates at least as subset of the available positioning-related data as the requested positioning-related data. In one embodiment, the information that indicates the set of available positioning-related data comprises any one or more of the following: information that indicates one or more positioning related metrics that are available to be reported to the RAN node; information that indicates one or more UEs for which positioning-related data can be reported to the RAN node; information that indicates an overall maximum number of UEs for which positioning-related data can be reported to the RAN node; information that indicates one or more coverage areas where UEs for which positioning-related data can be reported to the RAN node;information that indicates one or more UEs for which one or more specific positioning-related metrics can be reported to the RAN node; information that indicates a number of UEs for which one or more specific positioning-related metrics can be reported to the RAN node. In one embodiment, the information that indicates the set of available positioning-related data comprises any one or more of the following: a list of UE IDs for which positioning-related data can be reported to the RAN node; a list of positioning measurement codepoints; a list of IDs of SRS configurations used for positioning.

[0037] Corresponding embodiments of a RAN node are also disclosed. In one embodiment, a RAN node comprises a network interface and processing circuitry associated with the network interface. The processing circuitry is configured to cause the RAN node to send, to an LMF, a NRPPa message that indicates requested positioning-related data, receive, from the LMF, an NRPPa message comprising the requested positioning-related data, and train, retrain, and / or evaluate a performance of an AI / ML model for positioning in the wireless communication system, based on the received positioning-related data.

[0038] Embodiments of a method performed by an LMF are also disclosed. In one embodiment, a method performed by an LMF for data collection for training, retraining or evaluating an AI / ML model for positioning in a wireless communication system comprises receiving, from a RAN node, a NRPPa message that indicates requested positioning-related data and sending, to the RAN node, an NRPPa message comprising the requested positioning-related data.

[0039] In one embodiment, the received NRPPa message is a measurement response comprising a flag that indicates that data collection is needed.

[0040] In one embodiment, the received NRPPa message indicates the requested positioning- related data via an implicit indication.

[0041] In one embodiment, the received NRPPa message indicates the requested positioning- related data via an explicit indication.

[0042] In one embodiment, the requested positioning-related data indicated by the received NRPPa messages comprises one or more requested positioning related metrics indicated by the received NRPPa message. In one embodiment, the one or more requested positioning related metrics indicated by the received NRPPa message comprise either or both of: uplink relative time of arrival and gNB Rx-Tx time difference.

[0043] In one embodiment, the received NRPPa message further indicates a requested reporting periodicity of the requested positioning-related data.

[0044] In one embodiment, the received NRPPa message further indicates an overall number of UEs for which the requested positioning-related data is desired.

[0045] In one embodiment, the sent NRPPa message comprises, for each of a plurality of UEs, a set of UE locations for that UE and corresponding time stamp.

[0046] In one embodiment, the sent NRPPa message comprises a set of positioning measurements and corresponding time stamps.

[0047] In one embodiment, the sent NRPPa message comprises a list of positioning SRS configurations.

[0048] In one embodiment, the method further comprises receiving, from the RAN node, an NRPPa message comprising an update to the positioning-related data requested by the RAN node.

[0049] In one embodiment, the method further comprises sending, to the RAN node, an NRPPa message that indicates that the LMF supports providing positioning-related data to the RAN node for data collection. In one embodiment, the NRPPa message that indicates that the LMF supports providing positioning-related data to the RAN node for data collection comprises information that indicates a set of available positioning-related data, and the received NRPPa message comprises information that indicates at least as subset of the available positioning-related data as the requested positioning-related data. In one embodiment, the information that indicates the set of available positioning-related data comprises any one or more of the following: information that indicates one or more positioning related metrics that are available to be reported to the RAN node; information that indicates one or more UEs for which positioning-related data can be reported to the RAN node; information that indicates an overall maximum number of UEs for which positioning-related data can be reported to the RAN node; information that indicates one or more coverage areas where UEs for which positioning-related data can be reported to the RAN node; information that indicates one or more UEs for which one or more specific positioning- related metrics can be reported to the RAN node; information that indicates a number of UEs for which one or more specific positioning-related metrics can be reported to the RAN node. In one embodiment, the information that indicates the set of available positioning-related data comprises any one or more of the following: a list of UE IDs for which positioning-related data can be reported to the RAN node; a list of positioning measurement codepoints; a list of IDs of SRS configurations used for positioning.

[0050] Corresponding embodiments of an LMF are also disclosed. In one embodiment, an LMF comprises a network interface and processing circuitry associated with the network interface. The processing circuitry is configured to cause the LMF to receive, from a RAN node, a NRPPamessage that indicates requested positioning-related data and send, to the RAN node, an NRPPa message comprising the requested positioning-related data.BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.

[0052] Figure 1 illustrates an example of an Artificial Intelligence (AI) / Machine Learning (ML) model training pipeline.

[0053] Figure 2 illustrates the 5thGeneration (5G) Next Generation Radio Access Network (NG-RAN) architecture.

[0054] Figure 3 illustrates the NG-RAN positioning architecture.

[0055] Figure 4 illustrates the operation of a first network node and a second network node of a wireless communication system, in accordance with embodiments of the present disclosure.

[0056] Figure 5 illustrates the operation of an NG-RAN node and a Location Management Function (LMF), in accordance with an example embodiment of the present disclosure.

[0057] Figure 6 illustrates an alternative embodiment in which existing New Radio (NR) Positioning Protocol a (NRPPa) message(s) can be reused with additional enhancements / extensions and with limited number of new messages, in accordance with another embodiment of the present disclosure.

[0058] Figure 7 illustrates the operation of a Radio Access Network (RAN) node and an LMF 702 for handling of failure cases at the RAN for AI / ML inference output requests, in accordance with an example embodiment of the present disclosure.

[0059] Figure 8 illustrates the operation of a RAN node and an LMF to enable exit conditions for the reporting of inferred measurements from the RAN node, in accordance with an example embodiment of the present disclosure.

[0060] Figure 9 illustrates the operation of a RAN node and an LMF that enables indication of Sounding Reference Signal (SRS) configuration purpose from the LMF to the RAN node, in accordance with another embodiment of the present disclosure.

[0061] Figure 10 illustrates the operation of a RAN node, LMF, and User Equipment (UE), in accordance with another example embodiment of the present disclosure.

[0062] Figure 11 shows an example of a communication system in accordance with some embodiments.

[0063] Figure 12 shows a UE in accordance with some embodiments.

[0064] Figure 13 shows a network node in accordance with some embodiments.

[0065] Figure 14 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.DETAILED DESCRIPTION

[0066] The embodiments set forth below represent information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.

[0067] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0068] There currently exist certain challenge(s). For case 3a: “NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioning” highlighted in the Work Item Description (WID) extract included in the Background above, the Artificial Intelligence (AI) / Machine Learning (ML) (AI / ML) model is on the Radio Access Network (RAN) side (e.g., New Radio (NR) base station, i.e., gNodeB (gNB)), and the AI / ML model outputs are inferred measurements, which are sent to the Location Management Function (LMF) to then calculate the estimated User Equipment (UE) location.

[0069] Methods have been proposed for enabling the Next Generation RAN (NG-RAN) node (e.g., gNB) hosting the AI / ML model to request model performance measurements from the LMF so that the NG-RAN node can assess the accuracy of the inferred positioning measurements. This information is crucial for the AI / ML model hosting NG-RAN node, so that it can perform model retraining or weighting factor adjustments, to improve the accuracy of model prediction based on the feedback received.

[0070] However, the NG-RAN node does not always perform AI / ML model training online, or may not even have an AI / ML model implemented when it requests ground truth measurements. In fact, an NG-RAN node may still request information from the LMF for collecting data to be used for different purposes. The system should therefore allow the NG-RAN node to request the collection of positioning data for any purpose, be it for information, data collection for later model training, online model inference, performance feedback, or any other purpose.

[0071] In addition, one problem with the aforementioned proposed method is that it stipulates that the NG-RAN node can spontaneously request ground truth information from the LMF. However, the NR Positioning Protocol a (NRPPa) protocol between the LMF and the NG-RAN node is limited by the fact that an NRPPa transaction can only be initiated by the server, as stated in 3rdGeneration Partnership Project (3GPP) Technical Specification (TS) 38.305 VI 8.2.0 and shown in the excerpt from Section 7.2.1 of 3GPP TS 38.305 below (emphasis added via bold, italicized text):

[0072] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges.

[0073] Note that the embodiments of the solution(s) described herein can be applicable to 3GPP NR, but are not limited to 3GPP NR. Hence, a base station (BS), can be a NR gNB, or 6thGeneration (6G) Radio Access Technology (6G-RAT) base station, or any other device with similar function.

[0074] In the present disclosure, the communication between the positioning function (e.g., LMF) and the RAN is described. However, the methods described herein apply equally to the associated communication between a gNB-Central Unit (CU) and a gNB -Distributed Unit (DU) within the same gNB.

[0075] In present disclosure, embodiments of systems and methods for a LMF to notify a NG- RAN node that data collection may be requested. The LMF may specify that data is available for one or more UEs (e.g., one or more UEs indicated in a list of UEs) and / or for one or more specific metrics (e.g., one or more specific metrics indicated in a specific list of metrics) (e.g., positioning information) and / or for a specific coverage area. Such notification from the LMF enables the NG- RAN node to initiate a data collection request to the LMF, indicating details about the data theNG-RAN node would like to be reported, such as the metrics to be reported, the periodicity of the data collection updates, the UEs for which data should be reported and more.

[0076] Embodiments of systems and methods are also disclosed for an LMF to update an NG- RAN node on metrics that may be requested, or are no longer possible to request, for data provisioning, and for the NG-RAN node to update the LMF if other metrics are required or are no longer needed.

[0077] In the preferred embodiments, the above methods involve signaling between two nodes (e.g., LMF and gNB) over NRPPa protocol. However, the solution(s) disclosed herein are not limited thereto. For example, the methods may additionally or alternatively involve signaling between CU and DU over Fl AP protocol, in case of split gNB and the DU hosting the model.

[0078] Certain embodiments may provide one or more of the following technical advantage(s). Embodiments of the solution(s) disclosed herein may allow an NG-RAN node to collect positioning data information in a way that is aligned with NRPPa protocol design, i.e., LMF -initiated. Embodiments of the solution(s) disclosed herein may enable flexibility at the protocol level for the LMF to update the NG-RAN on the list of positioning information metrics that could be requested for data collection purpose, and for the LMF to indicate to NG-RAN that data collection is to be terminated (or no longer possible for certain metrics). This will enable the NG-RAN to adapt for when it should activate or start training the AI / ML model.

[0079] Figure 4 illustrates the operation of a first network node 400 and a second network node 402 of a wireless communication system, in accordance with embodiments of the present disclosure. In one embodiment, the first network node 400 is a RAN node (e.g., a NG-RAN node such as, e.g., a gNB) in a RAN of the wireless communications system (e.g., 3GPP 5GS or a 6G system), and the second network node 402 is a positioning node (e.g., an LMF). Note that optional steps are represented in Figure 4 by dashed lines / boxes. As illustrated, the first network node 400 receives a first message from the second network node 402, where the first message indicates that the second network node 402 is able to provide positioning related data (e.g., positioning information, positioning related measurements, etc.) to the first network node 400 where this data may be used for the purpose of data collection (step 404). In one embodiment, the first message sent by the second network node 402 may indicate to the first network node 400 any one or more of the following:• One or more positioning related metrics (e.g., positioning information, measurements, or the like) that are available to be reported to the first network node 400. The one or more metrics may be indicated via a list of metrics (or a list of metric indicators or IDs);• One or more particular UEs (e.g., a list of UEs) for which such positioning related data can be reported. Such list may, for example, be derived on the basis of the UEs for which user consent has been provided by the respective users;• An indication of an overall maximum number of UEs for which data can be reported.• An indication of one or more coverage areas where the UEs for which data can be reported are located, where such coverage areas can be described in the form of e.g. a list of cells, a list of tracking areas, a list of Transmission and / or Reception Points (TRPs).• One or more UEs (e.g., indicated by one or more lists of UEs) or one or more numbers of UEs for which specific metrics can be reported. As an example, such UEs might include or consist of those UEs whose users provided consent to the collection of sensitive information. For these UEs, all metrics requested could be provided. Alternatively, such UEs could include or consist of those UEs whose users did not provide consent to the collection of sensitive information. In this case, the data that the second network node 402 may report to the first network node 400 may consist of nonsensitive data for which consent is not needed.

[0080] In one embodiment, the first message sent by the second network node 402 consists of indices (e.g., one or more lists of indices) for which data collection is possible. For example, this information may include:• A list of UEs IDs for which collection of positioning related data (e.g., positioning information, positioning related measurements, etc.) is available;• A list of positioning measurements codepoints (e.g., UL-RTOA, gNB Rx-Tx time difference) obtained from previous or on-going positioning sessions to locate the UE; and / or• A list of IDs of the SRS configurations used for positioning, with the IDs of the SRS resources and positioning SRS resources in the SRS configuration.

[0081] In one variant of this embodiment, an inclusion of any of the parameters above implies that user consent for data collection was given for the UE associated to the parameter.

[0082] In one embodiment, after receiving the first message from the second network node 402, the first network node 400 sends a second message to the second network node 402 indicating the positioning information requested by the first network node 400 (step 406). The second message includes information that indicates a subset of the available positioning-related data (as indicated by the first message) that is requested by the first network node 402. Note subset requested may be less than all available or may be all available data. In one embodiment, the second message contains any one or more of the following:• Information that indicates a subset of the indices indicated by the second network node 402 in the first message, or the full list of indices previously indicated in the first message, where such indices map to a subset of the UEs for which data reporting from the second network node 402 is available;• An implicit or explicit indication of the measurements requested by the first network node 400. As an example, if the first network node 400 does not specify any requested message, then the second network node 402 would report all the data reportable for the available UEs (implicit indication). Alternatively, the first network node 400 may indicate a specific list of metrics needed from the second network node 402 (explicit indication).• A periodicity for the data reporting from the second network node 402 to the first network node 400. In one embodiment, such periodicity may be indicated, for example, in milliseconds or in seconds.• An indication of the overall number of UEs for which data is required. Namely in this case the first network node 400 may not list indices pointing at specific UEs, but it may only indicate the overall number of UEs for which data is needed.

[0083] In one embodiment, after receiving the second message, the second network node 402 signals a third message to the first network node 400 (step 408). In one embodiment, the third message includes the requested data (e.g., requested positioning data and / or requested measurements) indicated in the second message. Such message may be signaled either once, or periodically according to the required periodicity indicated in the second message. Alternatively, the third message may be signaled only once and followed by further periodic messages sent from the second network node 402 including the requested data. The requested data may include or alternatively consist of data which can be a choice of one or more of the following:• List of UE locations per index, where an index maps to a specific UE and where each UE location is complemented with a time stamp for which the location was determined and, optionally, quality information (e.g. an accuracy score for the UE location reported).• List of positioning measurements, each with an associated time stamp. The measurements may be complemented by an indication of the measurement quality.• List of Positioning SRS configurations.

[0084] In one embodiment, the presence of the information above indicates that the second network node 402 verified that user consent for collection of such information was given for the UE associated to the parameter.

[0085] In one embodiment, the first network node 400 sends a fourth message to the second network node 402, where the fourth message contains an update of requested positioning-related data (step 410). In other words, the fourth message includes an update to the request of step 406. In one embodiment, the update may contain details on the information that is requested, which constitute one or more of the information signaled by the first network node 400 in the second message of step 406. In one embodiment, the first network node 400 may, for example, delist some of the indices that were listed in the second message, or it may add some new indices with respect to the second message. In these cases, the second network node 402 stops (for delisting) or starts (new listing) reporting the requested data to the first network node 400. In other words, the second network node 402 may send, to the first network node 400, a fifth message including the requested positioning-related data in accordance with the update of step 410 (step 412).

[0086] In one embodiment, after receiving the positioning related data from the second network node 402 (in step 408 and optionally step 412), if an AI / ML model is not available yet at the first network node 400, the first network node 400 uses received data to create training data and train the AI / ML model (step 414). If an AI / ML model is already available, the first network node 400 can use the received positioning related data to monitor model performance or to retrain the AI / ML model (or both) (step 414).

[0087] In one embodiment, the first to fifth messages between the second network node 402 and the first network node 400 are NRPPa messages, either existing or new messages.

[0088] In another embodiment, the first to fifth messages between the second network node 402 and the first network node 400 are F1AP messages, either existing or new messages, and the first network node 400 is a DU of a RAN node (e.g., a gNB-DU) and the second network node 402 is a CU of a RAN node (e.g., gNB-CU).

[0089] Figure 5 illustrates the operation of an NG-RAN node 500 and an LMF 502 in accordance with an example embodiment of the present disclosure. Note that the process of Figure 5 is one example implementation of the process of Figure 4 in which steps 506, 508, 512, 516, 520, and 526 correspond to steps 404, 406, 408, 410, 412, and 414 of Figure 4, respectively. Note that not all steps shown in Figure 5 are required. As illustrated, the LMF 502 gets a list of positioning information that can help in data collection (step 504) and sends, to the NG-RAN node 500, an NRPPa DATA COLLECTION NOTIFICATION including indices associated to positioning related data (e.g., positioning information, measurements) that can be requested (step 506). The NG-RAN node 500 sends, to the LMF 502, an NRPPa DATA COLLECTION INFORMATION REQUEST including indices associated to requested positioning related dataand, optionally, a requested reporting periodicity (step 508). The LMF 502 sends an NRPPa DATA COLLECTION INFORMATION RESPONSE to the NG-RAN node 500 (step 510).

[0090] The LMF 502 sends, to the NG-RAN node 500, an NRPPa DATA COLLECTION INFORMATION UPDATE including the requested data (step 510). In this example, the requested data may include any one or more of the following: (a) UE locations and corresponding timestamps for UE(s) that satisfy the request (e.g., for UE(s) indicated in the request, for UE(s) located in coverage area(s) indicated in the request, for UE(s) for which one or more requested metrics can be reported, or the like), (b) timing measurements that satisfy the request and corresponding time stamps, (c) quality information or (a) and / or (b). Further updates may also be sent (e.g., at the requested periodicity) (step 514).

[0091] Optionally, the NG-RAN node 500 may send, to the LMF 502, an NRPPa DATA COLLECITON INFORMATION REQUEST that provides an update to the which data is requested by the NG-RAN node (step 516), and, in response, the LMF 502 may send, to the NG- RAN node 500, an NRPPa DATA COLLECTION INFORMATION RESPONSE (step 518). The LMF 502 may then send, to the NG-RAN node 500, NRPPa DATA COLLECTION INFORMATION UPDATE including the requested data, in accordance with the updated request of step 516 (step 520).

[0092] Optionally, the LMF 502 may send, to the NG-RAN node 500, an NRPPa DATA COLLECTION INFORAMTION TERMINATION message to terminate data collection (step 522). In addition or alternatively, the NG-RAN node 500 may send, to the LMF 502, an NRPPa DATA COLLECTION INFORMATION ABORT to stop the reporting of the data by the LMF 502 (step 524).

[0093] The NG-RAN node 500 may then perform AI / ML model training or retraining, feedback, and / or model performance monitoring, based on the received data (step 526).

[0094] Figure 6 illustrates an alternative embodiment in which existing NRPPa message(s) can be reused with additional enhancements / extensions and with limited number of new messages, in accordance with another embodiment of the present disclosure. In this example, only one new message is needed where this message is highlighted in Figure 6 using bold text. The procedure of Figure 6 involves a RAN node 600 (e.g., a gNB), an LMF 602, and an Access and Mobility Management Function (AMF) 604.

[0095] As illustrated in Figure 6, during an ongoing positioning session, the LMF 602 indicates to the RAN node 600 that the LMF 602 can support provisioning of data collection (step 606). The LMF 602 may indicate this either on its own (i.e., unsolicited) or after receiving a request from the RAN node 600, e.g., during ongoing measurement reporting, e.g., by appendinga flag in a measurement response that data collection is needed or learning that RAN node 600 can support inference using AI / ML (Note: Supporting inference using AI / ML may not imply that AI / ML model is available for usage).

[0096] The RAN node 600 may indicate for which cells and / or TRPs the data collection needs to be performed and may also report a preferred duration for data collection (step 608).

[0097] The LMF 602 checks the PRUs present in that area (i.e., in the coverage area indicated by the indicated cells and / or TRPs) and if not sufficient, sends a request to the AMF for additional UEs that are present in the indicated coverage area (steps 610, 612, 614).

[0098] The LMF at the preferred training time invokes an uplink based procedure such as UL-TDOA or multi-Round Trip Time (RTT) for each of the UEs / PRUs present in the indicated coverage area (step 616).

[0099] The RAN node 600 performs the measurement (RTOA, gNB Rx-Tx) and obtains UE Location or RTOA as ground truth from the LMF 602 or PRU (steps 618 and 620).

[0100] The data collection procedure may be updated / stopped / aborted using NRPPa signaling / message and this may happen due to many reasons such as UE user consent for data collection getting revoked or the RAN node / LMF needs computations capacity for other tasks than the data collection (step 622). Hence step 622 can be initiated by either end.

[0101] Below the potential specification impacts to TS 38.455 vl8.3.0:9.1.X.1 DATA COLLECTION NOTIFICATIONThis message is sent by LMF to notify the gNB that it can start requesting positioning information for data collection.Direction: node.9.1.x.2 DATA COLLECTION INFORMATION REQUESTThis message is sent by NG-RAN node to request data collection information. Direction: NG-RAN node9.1.x.3 DATA COLLECTION INFORMATION RESPONSE / UPDATEThis message is sent by the LMF to provide data collection information. Direction: node.

[0102] Below an example of spec change using existing measurement procedures specified in TS 38.455: 9.1.4.1 MEASUREMENT REQUESTThis message is sent by the LMF to request the NG-RAN node to configure a positioning measurement.Direction: node.9.1.4.2 MEASUREMENT RESPONSE This message is sent by the NG-RAN node to report positioning measurements.Direction: NG-RAN node.1.XX Data Collection9.X.10Data Label ProvisioningThis message is sent by the LMF to provide data label.Direction: node.

[0103] In addition to the problems and solutions described above, there currently exist further certain challenge(s). A further problem addressed in this disclosure is how to define the communication between an LMF and a RAN node for cases where the LMF is in need of AI / ML based inferred measurements from the RAN (see use case 3a described in the Background section above). One of the issues addressed is how the LMF and RAN handle the case in which such inferred outputs cannot be provided by the RAN, for various reasons. In this respect, some aspects of the interaction between an LMF and a RAN node have been previously proposed. However, these previous proposal fall short of describing details about how to handle failure cases when the LMF requests for inference outputs to the RAN node.

[0104] Another aspect touched upon in the present disclosure is how and when the LMF requests the RAN to stop reporting AI / ML inferred measurements for the purpose of positioning, or alternatively, in which circumstance the RAN should stop measurement reporting. Additionally, the present disclosure addresses whether stopping of such reporting is due to issues that require actions at the RAN side for the purpose of restarting the reporting process.

[0105] Finally, the present disclosure addresses how the RAN can be made aware of the fact that some configurations triggered from the LMF are specifically provided for the purpose of triggering an AI / ML inference process. Without such communication, resource utilization at the RAN may be used inefficiently, for example in cases where the RAN adopts some configurations implying signaling towards the UE and collection of UE measurements, but where the RAN is not capable of providing inference outputs.

[0106] Certain aspects of the disclosure and their embodiments may provide solutions to these additional or other challenges. Systems and methods are disclosed herein that enable a RAN node to communicate an LMF that inferred measurements to support AI / ML based positioning cannot be reported, together with information concerning why such measurements cannot be reported. Asa consequence of such information exchange between the RAN node and the LMF, specific actions can be taken, such as, for example, triggering of training data collection from the RAN node (e.g., after initiation of such process by the LMF), to collect data with which to derive an AI / ML model that can produce inferred measurements for positioning.

[0107] Systems and methods are also disclosed herein that enable both an LMF and a RAN node to stop inferred measurements reporting, where such interruption can be signaled by the RAN node and / or the LMF with information describing the reason why the measurements reporting was stopped and consequently triggering actions at any of the two nodes, e.g. to enable non-inferred measurement reporting to resume.

[0108] Finally, systems and methods are disclosed herein that apply to configuration of positioning reference signals transmission from the UE. When the LMF signals to the RAN node recommendations of positioning reference signals to be configured to the UE, the LMF also specifies the purpose of the positioning reference signal configuration and measurements collection at the RAN node for the configured positioning reference signals. With this information, the RAN node can better manage situations where, e.g., the LMF is planning to request for measurements collection for AI / ML related reasons, e.g. to enable the RAN to collect training data or to request input data at the TRP to derive inferred measurements at the RAN. It is also applicable when the RAN is already aware that it cannot fulfil the needs and requirements of the LMF, in that case, the RAN is able to avoid RS configuration at the UE and RS measurement collection, as the final purpose of this process cannot be fulfilled and notify the LMF accordingly.

[0109] Certain embodiments may provide one or more of the following technical advantage(s). Embodiments of the solutions described herein define procedures that will have to be in place between the RAN and the positioning server (e.g., LMF) in order to manage AI / ML based positioning processes. Embodiments of the solutions described herein enable a more efficient behavior at RAN and LMF especially in cases where the information required by the LMF to derive the UE's position on the basis of inferred positioning measurements from the RAN cannot be provided, or can be provided but then it becomes unavailable. Embodiments of the solutions described herein help to address and resolve the reasons why the inferred measurements cannot be provided, as well as coordinating RAN and LMF behaviors in such failure cases.

[0110] Now, further details regarding embodiments of the present disclosure will be provided. [OHl] Note that the embodiments described herein can be applicable to 3 GPP NR but are not limited to 3GPP NR. Hence, a base station (BS) or RAN node can be an NR gNB, or 6G-RAT base station, or any other device with similar function.

[0112] In the present disclosure, the communication between the positioning function (LMF) and the RAN is described. However, the methods described herein apply equally to the associated communication between a CU and a DU within the same RAN node (e.g., the gNB-CU and the gNB-DU within the same gNB).

[0113] Figure 7 illustrates the operation of a RAN node 700 and an LMF 702 for handling of failure cases at the RAN for AI / ML inference output requests, in accordance with an example embodiment of the present disclosure. Optional steps are represented by dashed lines / boxes. As illustrated, the LMF 702 sends, to the RAN node 700, a request for inferred measurements, e.g. as the result of an AI / ML inference process (step 704). The LMF 702 may add in the request for inferred measurements details such as, for example, any one or more of the following:• Information that indicates what inferred measurements are requested;• information that indicates a requested periodicity for such measurement reports (i.e., for measurement reports reporting the requested inferred measurements);• information that indicates one or more accuracy targets that the inferred measurements should fulfil;• information that indicates an expected inferred measurement value, e.g. expected RTOA. In one example embodiment, this is provided with min, max threshold (i.e., an expected value range) and is, for example, based upon LMF knowledge from historical information about the value being reported from the RAN node for other UEs in the same area (cell, beam).• information that indicates one or more specific UEs for which such measurements should be provided, where such UEs might be identified by UE identifiers or by reference signals configurations such as SRS configurations and where such SRS configurations have been previously signaled to the UEs in question to enable appropriate transmission of SRS and consequent measurement at the network side of SRS signals.

[0114] In one embodiment, the RAN node 700 is not able to provide the inferred measurements requested by the LMF 702. In this case, the RAN node 700 sends, to the LMF, a notification that the requested inferred measurements cannot be provided (step 706). The notification sent by the RAN node 700 may include information describing or indicating why the measurements are not available and cannot be reported. Such information can be provided in the form of one or more cause values or one or more dedicated Information Elements (IES) within a message (e.g., a response providing the notification of step 706) that the RAN node 700 sends to the LMF 702 following the request inferred measurements request.

[0115] The LMF 702 may perform one or more actions in response to the notification and, optionally, the reason(s) that the requested inferred measurements cannot be provided by the RAN node 700 as indicated in the notification of step 706 (step 708). Below is a list of example reasons for which the RAN node 700 may not be able to provide the requested inferred measurements (which may be indicated in the notification of step 706), as well as possible consequent actions performed by the LMF 702 in step 708. Any one or more of the reasons indicted in the list below may be indicated in the notification sent by the RAN node 700 in step 706.• In one example, the RAN node 700 is not able to provide the requested inferred measurements because an AI / ML model enabling such inference is not available. In this case, the RAN node 700 may either explicitly, namely by means of signaling a dedicated flag or IE, or implicitly, namely by signaling to the LMF that inferred measurements are not available due to lack of an AI / ML model to infer them, trigger at the LMF 702 a procedure for data collection at the RAN node 700. This procedure may be triggered by the LMF 702 to inform the RAN node 700 for what network areas and / or for what UEs data collection is possible. Therefore, by receiving a notification from the RAN node 700 that inferred measurements are not available due to lack of a model that can derive them, the LMF 702 may trigger a process that will enable the RAN node 700 to collect data to be used for model training (step 710). Eventually, the training process for a model that can infer the requested inferred measurements will complete (step 712), and the model will be available at the RAN node 700. In one dependent embodiment, after a model becomes available at the RAN node 700, the RAN node 700 signals to the LMF 702 a notification message stating that inferred measurements previously requested are available (step 714). Additionally, the RAN may include in such notification, details of the previous inferred measurements request process for which the RAN replied with a rejection. As an example, such details could be in the form of an identifier for the inferred measurements request process, or they could be the list of inferred measurements that the LMF previously requested.• In another example, the RAN node 700 is not able to provide the requested inferred measurements because of lack of resources. As an example, such issue could consist of lack of processing power resources, or lack of memory resources.• In another example, the RAN node 700 is not able to provide the requested inferred measurements because of the reporting configuration conditions. As an example, the RAN node 700 is not able to provide to the LMF 702 measurements at the periodicity requested. Alternatively, the RAN node 700 is not able to provide measurements to the LMF 702 forthe area or for parts of the area, requested by the LMF (details of the area where measurements cannot be provided can also be signaled by the RAN node 700).• In another example, the RAN node 700 is not able to provide the requested inferred measurement with the requested accuracy. Note that for such case, the RAN node 700 may issue a response (e.g., in addition to the notification of step 706 or as an alternative to the notification of step 706) stating that inferred measurements will be reported, but with a lower accuracy with respect to what was requested. In this case the RAN node 700 may either explicitly, namely by means of signaling a dedicated flag or IE, or implicitly, namely by signaling to the LMF 702 that inferred measurements are not available for the required accuracy, trigger at the LMF 702 the procedure for data collection at the RAN node 700. This procedure may be triggered by the LMF 702 to inform the RAN node 700 for what network areas, and / or for what UEs data collection is possible. Therefore, by receiving a notification from the RAN node 700 that inferred measurements are not available for the requested accuracy, the LMF 702 may trigger the process that will enable the RAN node 700 to collect data to be used for model improvements so that the requested accuracy is achieved. Eventually, the training process for a model that can infer the requested inferred measurements with the requested accuracy will complete and the model will be available at the RAN. In one dependent embodiment, after a model at the RAN node 700 is able to provide inferred measurements with the accuracy previously requested by the LMF 702, the RAN 700 signals to the LMF 702 a notification message stating that inferred measurements previously requested are available (see steps 710-714). Additionally, the RAN node 700 may include in such notification details of the previous inferred measurements request process for which the RAN replied with a rejection. As an example, such details could be in the form of an identifier for the inferred measurements request process, or they could be the list of inferred measurements that the LMF previously requested.• In another example, the RAN node 700 is not able to provide the inferred measurements for the amount of UEs requested by the LMF 702. This may prompt the LMF 702 to request measurements for a lower number of UEs.• In another example, the RAN node 700 is not able to provide the inferred measurements for one or more specific UEs. This may prompt the LMF 702 to request measurements for a list of UEs excluding those where inferred measurements are not available.• In another example, the RAN node 702 is not able to provide inferred measurements due to the combination of measurements requested. In this case, the RAN node 700 mayindicate to the LMF 702 the combination of measurements for which inferred measurements could be calculated, and the LMF could provide a new request accordingly.

[0116] Figure 8 illustrates the operation of a RAN node 800 and an LMF 802 to enable exit conditions for the reporting of inferred measurements from the RAN node 800, in accordance with an example embodiment of the present disclosure. Optional steps are represented by dashed lines / boxes. As illustrated, the LMF 802 sends, to the RAN node 800, a request for reporting of inferred measurements (step 804), and the RAN node 800 reports, to the LMF 802, the requested inferred measurements (step 806). The reporting of step 806 may be performed repetitively, e.g., at a desired (e.g., requested) periodicity and / or in response to a defined triggering condition. Note that the details of steps 804 and 806 ae not the focus of this procedure; rather, the focus of this procedure how such reporting is stopped.

[0117] In one embodiment, the LMF 802 signals, to the RAN node 800, a message stating that inferred measurements are not needed any longer (i.e., a message that indicates that reporting of the inferred measurements is to be stopped or is requested to be stopped) (step 808). The RAN node 800 may then stop reporting of the inferred measurements in accordance with the received message (step 810). Note that, in another embodiment, the RAN node 800 may autonomously decide to step reporting of inferred measurements in step 810.

[0118] In one embodiment, the message of step 808 indicates that reporting of all of the previously requested inferred measurements (e.g., all inferred measurements requested in step 804) is to be stopped, and the RAN node 800, in response, stops reporting of all of the previously requested inferred measurements. In another embodiment, in step 808, the LMF 802 requests that reporting of only certain previously requested inferred measurements is to stop. For example, , in step 808, the LMF 802 signals to the RAN node 800 that inferred measurements are not needed for one or more specific UEs, where such UEs can be directly identified by means of UE identifiers, or they can be indirectly identified by means of identifying the configurations (e.g. the SRS configuration) they were configured with. In response, in step 810, the RAN node 800 stops reporting of inferred measurements for those specific UEs. As another example, the LMF 802 may signal (in step 808) to the RAN node 800 an updated list of UEs (directly or indirectly identified) which is an updated list with respect to the list previously sent to the RAN node 800 when requesting the reporting of inferred measurements. For any UEs no longer in the updated list, the RAN node 800 stops reporting inferred measurements in step 810. For any new UEs present in the list, the RAN node 800 starts report inferred measurements. For all other UEs, reporting of inferred measurements continues as before.

[0119] In one embodiment, the RAN node 800 stops reporting (e.g., autonomously or in response to the request of step 808) of inferred measurements for a UE in step 810 if any one or more of the conditions below occur:• The positioning process for a given UE is stopped. In this case, inferred measurements for one or more of the affected UEs is stopped. In this case, the LMF 802 can explicitly signal to the RAN node 800 (in step 808) that inferred measurements reporting for the one or more affected UEs is to be stopped. The LMF 802 may include information describing the cause for which the process is stopped, e.g. a cause stating “positioning process is stopped”. Alternatively, the RAN node 800 may (e.g., autonomously) stop inferred measurement reporting assuming that the RAN node 800 can deduce that the positioning process has stopped for the one or more UE affected (e.g. in case the UEs are reconfigured to stop signaling SRS signals). In this case, the RAN node 800 may signal to the LMF 802 that inferred measurements reporting is stopped (step 812). Additionally, the RAN node may include in the message information that explain the reasons why the reporting stopped, e.g. a “cause” stating “positioning process is stopped”.• The RAN node 800 reporting inferred measurements no longer receives inputs for the AI / ML model that should infer such measurements. In one example, this can occur if the UE for which measurements are inferred moves out of coverage of the RAN node 800 and if the UE s signaled SRS are no more detectable by the RAN node 800. In this case, the RAN node 800 may signal to the LMF 802 that inferred measurements reporting is stopped (in step 812). Additionally, the RAN node 800 may include in the message information that explain the reasons why the reporting stopped, e.g. a “cause” stating “inputs not available”.• The RAN node 800 reporting inferred measurements does not have the resources to infer the measurements. For example, the RAN node 800 does not have the processing power to derive such measurements. In this case, the RAN node 800 may signal to the LMF 802 that inferred measurements reporting is stopped. Additionally, the RAN node 800 may include in the message information that explain the reasons why the reporting stopped, e.g. a “cause” stating “insufficient resources available”.

[0120] In another embodiment, a RAN node 800 may autonomously decide to• fallback to non- AI / ML based derivation (e.g.: RTOA derivation using pre-Rel-19 UL- TDOA method) if for any reason it is unable to perform inference; OR• initiate failure indication procedure. In such case, the LMF 802 is notified that the RAN node 800 cannot provide any inferred measurements and the procedure may abruptly end. The LMF 802 may select another RAN node 800 for inference; OR,• report an error. In such case, LMF 802 may configure another positioning method or request RAN node 802 to provide measurement report without using AI / ML.

[0121] In an alternate embodiment, the LMF 802 may provide configuration or a request message on the actions that can be taken by RAN node 800 if inference cannot be performed. The actions may include fallback to non-AI / ML based method / measurement.

[0122] In another embodiment, the LMF 802 may signal to the RAN node (in step 808) that inferred measurements reporting is to be stopped due to poor inferred measurements accuracy. As a dependent embodiment, the LMF 802 may also signal (e.g., in step 808 or a separate message) to the RAN node 800 the target accuracy requested for the inferred measurements and additionally the accuracy calculated for the inferred measurements received. If the LMF 802 has not triggered a data collection process for data (e.g., training data) transferring from the LMF 802 to the RAN node 802, the LMF 802 may trigger such process after signaling to the RAN node 800 that inferred measurements reporting is to be stopped due to insufficient measurements accuracy. Alternatively, if the LMF 802 has already triggered this process, the RAN node 800, after receiving from the LMF 802 an indication that inferred measurements reporting is to be stopped due to poor inferred measurements accuracy, may request data (to be used for e.g. model training) from the LMF 802 with the intention to improve the inference models and achieve the accuracy requested by the LMF 802.

[0123] In another embodiment, the RAN node 800 may signal to the LMF 802 that inferred measurements reporting is stopped due to poor inferred measurements accuracy (e.g., in step 812). The RAN node 800 may derive this information by collecting model performance feedback data and deriving the goodness of its inference. As a dependent embodiment, the RAN node 800 may also signal to the LMF 802 the accuracy calculated for the inferred measurements reported. If the LMF 802 has not triggered a data collection process for data (e.g., training data) transferring from the LMF 802 to the RAN node 800, the LMF 802 may trigger such process after receiving such indication from the RAN node 802. Alternatively, if the LMF 802 has already triggered this process, the RAN node 800, after sending such indication to the LMF 802, may request data (to be used for e.g. model training) from the LMF 802 with the intention to improve the inference models and achieve better accuracy.

[0124] Figure 9 illustrates the operation of a RAN node 900 and an LMF 902 that enables indication of SRS configuration purpose from the LMF 902 to the RAN node 900, in accordancewith another embodiment of the present disclosure. Optional steps are represented by dashed lines / boxes. As illustrated, the LMF 902 signals, to the RAN node 900, SRS configurations that are assumed to be configured at specific UEs for one or more of the following reasons (step 904):• Triggering SRS measurement collection at the RAN node 900 to enable the RAN node 900 to collect inputs that would allow generation of inferred measurements to be reported to the LMF 902;• Triggering SRS measurement collection at the RAN node 900 to enable the RAN node 900 to collect data from the LMF 902 that can enable model training / retraining, e.g. such data could consist of “labels” that the RAN can use to generate training data samples;• Triggering SRS measurement collection at the RAN node 900 to enable the RAN node 900 to collect data from the LMF 902 that can enable model performance monitoring, e.g. such data could consist of “labels” that the RAN can use to deduce how good its inferred measurements are.

[0125] In one embodiment, the LMF 902 may signal to the RAN 900 (e.g., in step 904) the purpose of signaling the SRS configuration, where such purpose could be generic, e.g. it could state that the SRS configuration and measurements is for AI / ML based positioning, or it could be more detailed, e.g. it could describe that the SRS configuration and measurements is for model training / retraining at the RAN, or for model performance monitoring.

[0126] In one embodiment, the RAN node 900 may configure the SRS information at an associated UE 903, and the RAN node 900 may indicate to the UE 903 that the configuration is for AI / ML based positioning, or it could be more detailed, e.g. it could describe that the SRS configuration and measurements is for model training / retraining at the RAN, or for model performance monitoring (step 906).

[0127] Upon receiving such information in step 904, the RAN node 900 may reply to the LMF 902 with a response (step 908). If SRS configuration is successful, the response is a successful response, indicating that the SRS configuration is successful. Alternatively, if SRS configuration fails, the response is a failure message, indicating a reason(s) for failure. Such reasons for failure may be any one or more of the following:• The RAN node 900 does not support AI / ML based positioning hence any process that is related with AI / ML based positioning is not supported• The RAN node 900 is temporarily prevented from running AI / ML based positioning hence any process that is related with AI / ML based positioning is temporarily not available• The RAN node 900 does not have the resources to run the AI / ML process for which the SRS configuration is meant• The RAN node 900 does not need to run the AI / ML process for which the SRS configuration is meant.

[0128] Figure 10 illustrates the operation of a RAN node 1000, LMF 1002, and UE 1004, in accordance with another example embodiment of the present disclosure. Note that not all steps shown in Figure 10 are required. The procedure of Figure 10 includes any one or more of the following steps:• Step 1006: The RAN node 1000 sends, to the LMF 1002, an NRPPa Positioning Information Request with a request to configure the UE 1004 for SRS and optionally an AI / ML inference purpose indication.• Step 1008: The RAN node 1000 determines an SRS configuration for the UE 1004 for AI / ML inference.• Step 1010: The RAN node 1000 sends, to the UE 1004, a message (e.g., a Radio Resource Control (RRC) message in this example) with the SRS configuration and optionally an AI / ML inference purpose indication.• Step 1012: The RAN node 1000 sends, to the LMF 1002, an NRPPa Positioning Information Response with the SRS configuration of the UE 1004 and optionally an AI / ML inference purpose indication.• Step 1014: The LMF 1002 sends, to the RAN node 1000, an NRPPa Request Message (e.g., a Measurement Request message or a new message) with the SRS configuration, area information (e.g., list of cells, Tracking Area Identifies (TAIs), TRP IDs, or the like), and a list of inferred measurements to be reported (e.g., predicted UL-RTOA).• Step 1016: The RAN node 1000 perform AI / ML inference in accordance with the request of step 5.• Step 1018: The RAN node 1000 sends, to the LMF 1002, an NRPPa Response Message (e.g., a Measurement Response message or a new message) confirming the information and optionally reporting a configuration to which the RAN node 1000 can commit. For instance, the response may confirm that the RAN node 1000 can commit provide inferred measurements in accordance to the request or confirm that the RAN node 1000 can commit to provide inferred measurements in accordance with only some of the configurations included in the request of step 1014.• Step 1020: The RAN node 1000 sends, to the LMF 1002, an NRPPa Data Collection Update Message, which may be used in the case of a new NRPPa procedure, and may include the requested inferred measurements. This message may be signaled as a one off message or on a periodic basis.• Step 1022: The RAN node 1000 may send, to the LMF 1002, an NRPPa Data Collection Configuration Update (e.g., a Measurement Update or a new message) with updated configurations for inferred measurement reporting.• Step 1024: The RAN node 1000 may send, to the LMF 1002, an NRPPa measurement reporting failure message, upon occurrence of an exit condition.

[0129] Other embodiments may include any one or more of the following:• In one embodiment, a first network node (e.g., a RAN node) receives a request from a second network node (e.g., an LMF) to configure SRS with an indication that the configuration is for deriving inferred measurements from this configuration.• In a second embodiment, once the first network node has configured the SRS for the purpose of inference, an indication is sent to UE (e.g., via RRC message) and the response to the second network node.• In one embodiment, the first network and second network node are NG-RAN and LMF, respectively, and the interface in between in NRPPa.• In one embodiment, the first network and second network node are gNB-DU and gNB-CU, respectively, and the interface in between in F1AP.• In one embodiment, when the first network node cannot infer any predicted measurements, it sends a failure message with a cause value.• In one embodiment, when the second network node decides to stop to the reporting of the inferred predicted measurements, it sends an abort message to the first network node.

[0130] Figure 11 shows an example of a communication system 1100 in accordance with some embodiments.

[0131] In the example, the communication system 1100 includes a telecommunication network 1102 that includes an access network 1104, such as a Radio Access Network (RAN), and a core network 1106, which includes one or more core network nodes 1108. The access network 1104 includes one or more access network nodes, such as network nodes 1110A and 1110B (one or more of which may be generally referred to as network nodes 1110), or any other similar Third Generation Partnership Project (3GPP) access nodes or non-3GPP Access Points (APs). Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 1102 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 1102 that supports an ORANspecification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network 1102, including one or more network nodes 1110 and / or core network nodes 1108.

[0132] Examples of an ORAN network node include an Open Radio Unit (O-RU), an Open Distributed Unit (O-DU), an Open Central Unit (O-CU), including an O-CU Control Plane (O- CU-CP) or an O-CU User Plane (O-CU-UP), a RAN intelligent controller (near-real time or non- real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes 1110 facilitate direct or indirect connection of User Equipment (UE), such as by connecting UEs 1112A, 1112B, 1112C, and 1112D (one or more of which may be generally referred to as UEs 1112) to the core network 1106 over one or more wireless connections.

[0133] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 1100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 1100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0134] The UEs 1112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 1110 and other communication devices. Similarly, the network nodes 1110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs1112 and / or with other network nodes or equipment in the telecommunication network 1102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 1102.

[0135] In the depicted example, the core network 1106 connects the network nodes 1110 to one or more hosts, such as host 1116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 1106 includes one more core network nodes (e.g., core network node 1108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 1108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-Concealing Function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0136] The host 1116 may be under the ownership or control of a service provider other than an operator or provider of the access network 1104 and / or the telecommunication network 1102, and may be operated by the service provider or on behalf of the service provider. The host 1116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0137] As a whole, the communication system 1100 of Figure 11 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 1100 may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable Second, Third, Fourth, or Fifth Generation (2G, 3G, 4G, or 5G) standards, or any applicable future generation standard (e.g., Sixth Generation (6G)); Wireless Local Area Network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication(NFC) ZigBee, LiFi, and / or any Low Power Wide Area Network (LPWAN) standards such as LoRa and Sigfox.

[0138] In some examples, the telecommunication network 1102 is a cellular network that implements 3 GPP standardized features. Accordingly, the telecommunication network 1102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 1102. For example, the telecommunication network 1102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing enhanced Mobile Broadband (eMBB) services to other UEs, and / or massive Machine Type Communication (mMTC) / massive Internet of Things (loT) services to yet further UEs.

[0139] In some examples, the UEs 1112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 1104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1104. Additionally, a UE may be configured for operating in single- or multi -Radio Access Technology (RAT) or multi -standard mode. For example, a UE may operate with any one or combination of WiFi, New Radio (NR), and LTE, i.e. being configured for Multi -Radio Dual Connectivity (MR-DC), such as Evolved UMTS Terrestrial RAN (E-UTRAN) NR - Dual Connectivity (EN-DC).

[0140] In the example, a hub 1114 communicates with the access network 1104 to facilitate indirect communication between one or more UEs (e.g., UE 1112C and / or 1112D) and network nodes (e.g., network node 1 HOB). In some examples, the hub 1114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1114 may be a broadband router enabling access to the core network 1106 for the UEs. As another example, the hub 1114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 1110, or by executable code, script, process, or other instructions in the hub 1114. As another example, the hub 1114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 1114 may be a content source. For example, for a UE that is a Virtual Reality (VR) headset, display, loudspeaker or other media delivery device, the hub 1114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example,the hub 1114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0141] The hub 1114 may have a constant / persistent or intermittent connection to the network node 1 HOB. The hub 1114 may also allow for a different communication scheme and / or schedule between the hub 1114 and UEs (e.g., UE 1112C and / or 1112D), and between the hub 1114 and the core network 1106. In other examples, the hub 1114 is connected to the core network 1106 and / or one or more UEs via a wired connection. Moreover, the hub 1114 may be configured to connect to a Machine-to-Machine (M2M) service provider over the access network 1104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1110 while still connected via the hub 1114 via a wired or wireless connection. In some embodiments, the hub 1114 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 1110B. In other embodiments, the hub 1114 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and the network node 1110B, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0142] Figure 12 shows a UE 1200 in accordance with some embodiments. 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 Internet Protocol (VoIP) phone, wireless local loop phone, desktop computer, Personal Digital Assistant (PDA), wireless camera, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, Laptop Embedded Equipment (LEE), Laptop Mounted Equipment (LME), smart device, wireless Customer Premise Equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3 GPP, including a Narrowband Internet of Things (NB-IoT) UE, a Machine Type Communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0143] 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), Vehi cl e-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).

[0144] 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, 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 Figure 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] The memory 1210 may be or be configured to include memory such as Random Access Memory (RAM), Read Only Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM), Electrically EPROM (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.

[0149] 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 Data Storage (HDDS) optical disc drive, external mini Dual In-line Memory Module (DIMM), Synchronous Dynamic RAM (SDRAM), external micro-DIMM SDRAM, smartcard memory such as a tamper resistant module in the form of a Universal Integrated Circuit Card (UICC) including one or more Subscriber Identity Modules (SIMs), such as a Universal SIM (USIM) and / or Internet Protocol Multimedia Services Identity Module (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 a ‘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.

[0150] 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 includeone 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., the antenna 1222) and may share circuit components, software, or firmware, or alternatively be implemented separately.

[0151] In the illustrated embodiment, communication functions of the communication interface 1212 may include cellular communication, WiFi communication, LPWAN communication, data communication, voice communication, multimedia communication, short- range communications such as Bluetooth, NFC, 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 according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband CDMA (WCDMA), GSM, LTE, NR, UMTS, WiMax, Ethernet, Transmission Control Protocol / Intemet Protocol (TCP / IP), Synchronous Optical Networking (SONET), Asynchronous Transfer Mode (ATM), Quick User Datagram Protocol Internet Connection (QUIC), Hypertext Transfer Protocol (HTTP), and so forth.

[0152] 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 to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0153] 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.

[0154] A UE, when in the form of an 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 area device which is or which is embedded in: a connected refrigerator or freezer, a television, 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 head-mounted display for Augmented Reality (AR) or VR, 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 Figure 12.

[0155] 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 3 GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship, an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0156] 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.

[0157] Figure 13 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, APs (e.g.,radio APs), Base Stations (BSs) (e.g., radio BSs, Node Bs, evolved Node Bs (eNBs), NR Node Bs (gNBs)), and 0-RAN nodes or components of an 0-RAN node (e.g., O-RU, O-DU, O-CU).

[0158] 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 0-RAN access node), and / or Remote Radio Units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such RRUs 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).

[0159] 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 BS 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).

[0160] The network node 1300 includes processing circuitry 1302, 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 an 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 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, Long Range Wide Area Network (LoRaWAN), Radio Frequency Identification (RFID), or Bluetoothwireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within the network node 1300.

[0161] The processing circuitry 1302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, CPU, DSP, ASIC, FPGA, 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.

[0162] 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 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 the RF transceiver circuitry 1312 and the baseband processing circuitry 1314 may be on the same chip or set of chips, boards, or units.

[0163] 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, RAM, 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 the memory 1304 are integrated.

[0164] 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. The radio front-end circuitry 1318 comprises filters 1320 and amplifiers 1322. The radio front-end circuitry 1318 may be connected to the antenna 1310 and the processingcircuitry 1302. The radio front-end circuitry 1318 may be configured to condition signals communicated between the antenna 1310 and the processing circuitry 1302. The radio front-end circuitry 1318 may receive 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 the filters 1320 and / or the 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 1306 may comprise different components and / or different combinations of components.

[0165] 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 the 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).

[0166] 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.

[0167] The antenna 1310, the 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 1300. 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 1300. Any information, data, and / or signals may be transmitted to a UE, another network node, and / or any other network equipment.

[0168] The power source 1308 provides power to the various components of the 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 or an electricity outlet) via input circuitry or an 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.

[0169] Embodiments of the network node 1300 may include additional components beyond those shown in Figure 13 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.

[0170] Figure 14 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 virtualization environments 1400 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, a UE, a core network node, or a 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, a UE, a core network node, or a host.

[0171] 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 1400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0172] 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, an 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 the functions, 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.

[0173] 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.

[0174] 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 the 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.

[0175] The hardware 1404 may be implemented in a standalone network node with generic or specific components. The hardware 1404 may implement some functions via virtualization. Alternatively, the 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 the applications 1402. In some embodiments, the hardware 1404 is coupled to one or more radio units that each includeone 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.

[0176] 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 stored in 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.

[0177] 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 processingcircuitry 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.

[0178] Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.

[0179] Some exemplary embodiments of the present disclosure are as follows:

[0180] Embodiment 1 : A method performed by a first network node (400) for data collection for training, retraining or evaluating an Artificial Intelligence, Al, or Machine Learning, ML, (AI / ML) model for positioning in a wireless communication system, the method comprising any one or more of the following: receiving (404), from a second network node (402), a first message that indicates that the second network node supports providing positioning-related data to the first network node (400) for data collection; sending (406), to the second network node (402), a second message that indicates requested positioning-related data; receiving (408), from the second network node (402), a third message comprising the requested positioning-related data; and training, retraining, and / or evaluating a performance of an AI / ML model for positioning in the wireless communication system, based on the received positioning-related data.

[0181] Embodiment 2: The method of embodiment 1, wherein the first message comprise information that indicates a set of available positioning-related data, and the second message comprises information that indicates at least as subset of the available positioning-related data as the requested positioning-related data.

[0182] Embodiment 3 : The method of embodiment 2, wherein the information that indicates the set of available positioning-related data comprises any one or more of the following: information that indicates one or more positioning related metrics that are available to be reported to the first network node (400); information that indicates one or more User Equipments, UEs, for which positioning-related data can be reported to the first network node (400); information that indicates an overall maximum number of UEs for which positioning-related data can be reported to the first network node (400); information that indicates one or more coverage areas where UEs for which positioning-related data can be reported to the first network node (400); information that indicates one or more UEs for which one or more specific positioning-related metrics can be reported to the first network node (400); information that indicates a number of UEs for which one or more specific positioning-related metrics can be reported to the first network node (400).

[0183] Embodiment 4: The method of embodiment 2, wherein the information that indicates the set of available positioning-related data comprises any one or more of the following: a list ofUE IDs for which positioning-related data can be reported to the first network node (400); a list of positioning measurement codepoints; a list of IDs of SRS configurations used for positioning.

[0184] Embodiment 5 : The method of any of embodiments 1 to 4, wherein the second message indicates the requested positioning-related data via an implicit indication.

[0185] Embodiment 6: The method of any of embodiments 1 to 4, wherein the second message indicates the requested positioning-related data via an explicit indication.

[0186] Embodiment 7 : The method of any of embodiments 1 to 4, wherein the second message further indicates a requested reporting periodicity of the requested positioning-related data.

[0187] Embodiment 8: The method of any of embodiments 1 to 7, wherein the second message further indicates an overall number of UEs for which the requested positioning-related data is desired.

[0188] Embodiment 9: The method of any of embodiments 1 to 8, wherein the third message comprises, for each of a plurality of UEs, a set of UE locations for that UE and corresponding time stamp.

[0189] Embodiment 10: The method of any of embodiments 1 to 9, wherein the third message comprises a set of positioning measurements and corresponding time stamps.

[0190] Embodiment 11 : The method of any of embodiments 1 to 9, wherein the third message comprises a list of positioning SRS configurations.

[0191] Embodiment 12: The method of any of embodiments 1 to 11, further comprising sending (410), to the second network node (402), a fourth message comprising an update to the positioning-related data requested by the first network node (400).

[0192] Embodiment 13 : The method of any of embodiments 1 to 12, wherein the first network node (400) is a RAN node, and the second network node (402) is a positioning node.

[0193] Embodiment 14: The method of any of embodiments 1 to 12, wherein the first network node (400) is a RAN node, the second network node (402) is an LMF, and the first message, the second message, and the third message are NRPPa messages.

[0194] Embodiment 15: The method of any of embodiments 1 to 12, wherein the first network node (400) is a Distributed Unit, DU, of a RAN node, and the second network node (402) is a Central Unit, CU, of the RAN node.

[0195] Embodiment 16: The method of embodiment 15, wherein the first message, the second message, and the third message are F1AP messages.

[0196] Embodiment 17: A first network node comprising: a network interface; and processing circuitry associated with the network interface, the processing circuitry configured to cause the first network node to perform any of the steps of any of embodiments 1 to 16.

[0197] Embodiment 18: A method performed by a second network node (402) for data collection for training, retraining or evaluating an Artificial Intelligence, Al, or Machine Learning, ML, (AI / ML) model for positioning in a wireless communication system, the method comprising any one or more of the following: sending (404), to a first network node (400), a first message that indicates that the second network node supports providing positioning-related data to the first network node (400) for data collection; receiving (406), from the first network node (400), a second message that indicates requested positioning-related data; sending (408), to the first network node (400), a third message comprising the requested positioning-related data.

[0198] Embodiment 19: The method of embodiment 18, wherein the first message comprise information that indicates a set of available positioning-related data, and the second message comprises information that indicates at least as subset of the available positioning-related data as the requested positioning-related data.

[0199] Embodiment 20: The method of embodiment 19, wherein the information that indicates the set of available positioning-related data comprises any one or more of the following: information that indicates one or more positioning related metrics that are available to be reported to the first network node (400); information that indicates one or more User Equipments, UEs, for which positioning-related data can be reported to the first network node (400); information that indicates an overall maximum number of UEs for which positioning-related data can be reported to the first network node (400); information that indicates one or more coverage areas where UEs for which positioning-related data can be reported to the first network node (400); information that indicates one or more UEs for which one or more specific positioning-related metrics can be reported to the first network node (400); information that indicates a number of UEs for which one or more specific positioning-related metrics can be reported to the first network node (400).

[0200] Embodiment 21 : The method of embodiment 19, wherein the information that indicates the set of available positioning-related data comprises any one or more of the following: a list of UE IDs for which positioning-related data can be reported to the first network node (400); a list of positioning measurement codepoints; a list of IDs of SRS configurations used for positioning.

[0201] Embodiment 22: The method of any of embodiments 18 to 21, wherein the second message indicates the requested positioning-related data via an implicit indication.

[0202] Embodiment 23: The method of any of embodiments 18 to 21, wherein the second message indicates the requested positioning-related data via an explicit indication.

[0203] Embodiment 24: The method of any of embodiments 18 to 21, wherein the second message further indicates a requested reporting periodicity of the requested positioning-related data.

[0204] Embodiment 25: The method of any of embodiments 18 to 24, wherein the second message further indicates an overall number of UEs for which the requested positioning-related data is desired.

[0205] Embodiment 26: The method of any of embodiments 18 to 25, wherein the third message comprises, for each of a plurality of UEs, a set of UE locations for that UE and corresponding time stamp.

[0206] Embodiment 27: The method of any of embodiments 18 to 26, wherein the third message comprises a set of positioning measurements and corresponding time stamps.

[0207] Embodiment 28: The method of any of embodiments 18 to 26, wherein the third message comprises a list of positioning SRS configurations.

[0208] Embodiment 29: The method of any of embodiments 18 to 28, further comprising receiving (410), from the first network node (400), a fourth message comprising an update to the positioning-related data requested by the first network node (400).

[0209] Embodiment 30: The method of any of embodiments 18 to 29, wherein the first network node (400) is a RAN node, and the second network node (402) is a positioning node.

[0210] Embodiment 31 : The method of any of embodiments 18 to 29, wherein the first network node (400) is a RAN node, the second network node (402) is an LMF, and the first message, the second message, and the third message are NRPPa messages.

[0211] Embodiment 32: The method of any of embodiments 18 to 29, wherein the first network node (400) is a Distributed Unit, DU, of a RAN node, and the second network node (402) is a Central Unit, CU, of the RAN node.

[0212] Embodiment 33 : The method of embodiment 32, wherein the first message, the second message, and the third message are F1AP messages.

[0213] Embodiment 34: A second network node comprising: a network interface; and processing circuitry associated with the network interface, the processing circuitry configured to cause the second network node to perform any of the steps of any of embodiments 18 to 33.

[0214] Embodiment 35: A method performed by a User Equipment, UE, (903; 1004), the method comprising: receiving (906; 1010), from aRadio Access Network, RAN, node (900; 1000), a Sounding Reference Signal, SRS, configuration and an indication that a purpose of the SRS configuration is AI / ML inference; and operating in accordance with the SRS configuration.

[0215] Embodiment 36: The method embodiment 35, wherein operating in accordance with the SRS configuration comprises operating in accordance with the SRS configuration and the indication that the purpose of the SRS configuration is AI / ML inference.

[0216] Embodiment 37: A method performed by a Radio Access Network, RAN, node (700), the method comprising: receiving (704), from a positioning node (702), a request for reporting of interfered measurements; and sending (706), to the positioning node (702), a notification that the requested inferred measurements cannot be provided.

[0217] Embodiment 38: The method of embodiment 37, wherein the request comprises any one or more of the following information: information that indicates what inferred measurements are requested; information that indicates a requested periodicity for reporting of the inferred measurements; information that indicates one or more accuracy targets that the inferred measurements should fulfil; information that indicates an expected inferred measurement value or an expected range of values for the inferred measurements; information that indicates one or more specific UEs for which such measurements should be provided.

[0218] Embodiment 39: The method of embodiment 37 or 38, wherein the notification comprises information that indicates one or more reasons that the requested inferred measurements cannot be provided.

[0219] Embodiment 40: The method of embodiment 5, wherein the one or more reasons that the requested inferred measurements cannot be provide comprise any one or more of the following: the RAN node is not able to provide the requested inferred measurements because an AI / ML model enabling such inference is not available; the RAN node is not able to provide the requested inferred measurements due to a lack of resources; the RAN node is not able to provide the requested inferred measurements because the RAN node is not able to provide the requested inferred measurements in accordance with at least one configuration (e.g., a reporting periodicity, measurement accuracy, a requested number of UEs for which the inferred measurements are to be provided, one or more specific UEs for which the inferred measurements are to be provided, or the like ) indicated in the request.

[0220] Embodiment 41 : The method of any of embodiments 37 to 40, further comprising: in response to the request, performing (710) data collection via a data collection procedure triggered by the positioning node (702); training (712) an AI / ML model enabling inference of the requested inferred measurements; and sending (714), to the positioning node (702), a notification that the requested inferred measurements can now be provided.

[0221] Embodiment 42: The method of any of embodiments 37 to 40, further comprising, sometime after sending the notification, sending (714), to the positioning node (702), a second notification that indicates that the requested inferred measurements can now be provided.

[0222] Embodiment 43: A method performed by a positioning node (702), the method comprising: sending (704), to a Radio Access Network, RAN, node (700), a request for reporting of interfered measurements; and receiving (706), from the RAN node (700), a notification that the requested inferred measurements cannot be provided.

[0223] Embodiment 44: The method of embodiment 43, wherein the request comprises any one or more of the following information: information that indicates what inferred measurements are requested; information that indicates a requested periodicity for reporting of the inferred measurements; information that indicates one or more accuracy targets that the inferred measurements should fulfil; information that indicates an expected inferred measurement value or an expected range of values for the inferred measurements; information that indicates one or more specific UEs for which such measurements should be provided.

[0224] Embodiment 45: The method of embodiment 43 or 44, wherein the notification comprises information that indicates one or more reasons that the requested inferred measurements cannot be provided.

[0225] Embodiment 46: The method of embodiment 45, wherein the one or more reasons that the requested inferred measurements cannot be provide comprise any one or more of the following: the RAN node is not able to provide the requested inferred measurements because an AI / ML model enabling such inference is not available; the RAN node is not able to provide the requested inferred measurements due to a lack of resources; the RAN node is not able to provide the requested inferred measurements because the RAN node is not able to provide the requested inferred measurements in accordance with at least one configuration (e.g., a reporting periodicity, measurement accuracy, a requested number of UEs for which the inferred measurements are to be provided, one or more specific UEs for which the inferred measurements are to be provided, or the like ) indicated in the request.

[0226] Embodiment 47: The method of any of embodiments 43 to 46, further comprising performing (708) one or more actions based on the notification.

[0227] Embodiment 48: The method of embodiment 47, wherein performing (708) the one or more actions based on the notification comprises initiating (708) a data collection procedure at the RAN node (700).

[0228] Embodiment 49: The method of any of embodiments 43 to 48, further comprising subsequently receiving (714) a second notification from the RAN node (700) that indicates that the RAN node (700) is able to provide the requested inferred measurements.

[0229] Embodiment 50: A method performed by a Radio Access Network, RAN, node (800), the method comprising: receiving (804), from a positioning node (802), a request for reporting of interfered measurements; sending (806), to the positioning node (802), one or more reports comprising the requested inferred measurements; and either in response to receiving (808) a request from the positioning node (802) or an autonomous decision at the RAN node (800), stopping reporting (810) of at least some of the requested inferred measurements to the positioning node (802).

[0230] Embodiment 51 : The method of embodiment 50, further comprising receiving (808) a request from the positioning node (802) to stop reporting of at least some of the requested inferred measurements, wherein stopping reporting (810) is responsive to receiving (808) the request.

[0231] Embodiment 52: The method of embodiment 51, wherein the request to stop reporting is a request to stop reporting of all of the requested inferred measurements, and stopping reporting (810) comprising stopping reporting (810) of all of the requested inferred measurements responsive to receiving the request to stop reporting of all of the requested inferred measurements.

[0232] Embodiment 53 : The method of embodiment 51, wherein the request to stop reporting is a request to stop reporting of a certain subset of the requested inferred measurements, and stopping reporting (810) comprising stopping reporting (810) of the certain subset of the requested inferred measurements responsive to receiving the request to stop reporting of all of the requested inferred measurements.

[0233] Embodiment 54: The method of any of embodiments 50 to 53, wherein the RAN node stops reporting of the at least some of the requested inferred measurements if one or more of the following conditions is satisfied: a positioning process for a given UE is stopped; the RAN node stops receiving inputs for an AI / ML model used to infer at least some of the inferred measurements; and the RAN node does not have sufficient resources to infer the inferred measurements.

[0234] Embodiment 55: The method of any of embodiments 50 to 54, wherein the request to stop reporting comprises an indication of one or more reasons (e.g., accuracy of reported inferred measurements falls below a certain minimum accuracy requirement) for the request to stop reporting:

[0235] Embodiment 56: The method of embodiment 50, wherein stopping reporting (810) is responsive an autonomous decision to stop reporting of at least some of the requested inferred measurements.

[0236] Embodiment 57: The method of embodiment 56, wherein the autonomous decision to stop reporting of at least some of the requested inferred measurements is based on one or more of the following conditions: a positioning process for a given UE is stopped; the RAN node stops receiving inputs for an AI / ML model used to infer at least some of the inferred measurements; and the RAN node does not have sufficient resources to infer the inferred measurements.

[0237] Embodiment 58: The method of any of embodiments 50 to 57, further comprising any one or more of the following: falling back to a non-AI / ML based derivation of the at least some of the requested inferred measurements; initiating a failure indication procedure; reporting an error.

[0238] Embodiment 59: A method performed by a positioning node (802), the method comprising: sending (804), to a Radio Access Network, RAN, node (800), a request for reporting of interfered measurements; receiving (806), from the RAN node (800), one or more reports comprising the requested inferred measurements; and either: sending (808), to the RAN node (800), a request to stop reporting of at least some of the requested inferred measurements to the positioning node (802) or receiving (812), from the RAN node (800), a notification that reporting of at least some of the requested inferred measurements has stopped.

[0239] Embodiment 60: The method of embodiment 59, wherein the method comprises sending (808), to the RAN node (800), a request to stop reporting of at least some of the requested inferred measurements to the positioning node (802).

[0240] Embodiment 61 : The method of embodiment 60, wherein the request to stop reporting is a request to stop reporting of all of the requested inferred measurements.

[0241] Embodiment 62: The method of embodiment 60, wherein the request to stop reporting is a request to stop reporting of a certain subset of the requested inferred measurements.

[0242] Embodiment 63 : The method of any of embodiments 60 to 62, wherein the request to stop reporting comprises an indication of one or more reasons (e.g., accuracy of reported inferred measurements falls below a certain minimum accuracy requirement) for the request to stop reporting:

[0243] Embodiment 64: The method of embodiment 59, wherein the method comprises receiving (812), from the RAN node (800), a notification that reporting of at least some of the requested inferred measurements has stopped.

[0244] Embodiment 65: The method of embodiment 64, wherein the notification comprises an indication of one or more reasons (e.g., accuracy of reported inferred measurements falls below a certain minimum accuracy requirement) that the reporting has stopped.

[0245] Embodiment 66: A method performed by a Radio Access Network, RAN, node (900), the method comprising: receiving (904), from a positioning node (902), an SRS configuration for one or more UEs (903) and an indication of one or more reasons for the SRS configuration; and sending (908) a response to the positioning node (902).

[0246] Embodiment 67: The method of embodiment 66, wherein the one or more reasons for the SRS configuration comprise any one or more of the following: triggering of SS measurement collection at the RAN node (900) for generation of inferred measurements using an AI / ML model for reporting to the positioning node (902); triggering of SRS measurement collection at the RAN node (900) to enable AI / ML model training or retraining at the RAN node (900); triggering of SRS measurement collection at the RAN node (900) to enable monitoring of a performance of an AI / ML model at the RAN node (900).

[0247] Embodiment 68: The method of embodiment 66, wherein the one or more reasons for the SRS configuration comprise a reason that the SRS configuration is associated measurements are for AI / ML based positioning.

[0248] Embodiment 69: The method of embodiment 66, wherein the one or more reasons for the SRS configuration comprise a reason that the SRS configuration is associated measurements are for AI / ML model training or retraining or AI / ML model performance monitoring.

[0249] Embodiment 70: The method of any of embodiments 66 to 68, further comprising sending (906) the SRS configuration to the one or more UEs (903).

[0250] Embodiment 71 : The method of embodiment 67, further comprising sending (906), to the one or more UEs (903), an indication of one or more reasons for the SRS configuration.

[0251] Embodiment 72: The method of embodiment 71, wherein the one or more reasons for the SRS configuration sent to the one or more UEs comprises a reason that the SRS configuration is associated measurements are for AI / ML based positioning.

[0252] Embodiment 73: The method of embodiment 71, wherein the one or more reasons for the SRS configuration sent to the one or more UEs comprises a reason that the SRS configuration and associated measurements are for AI / ML model training or retraining or AI / ML model performance monitoring.

[0253] Embodiment 74: The method of any of embodiments 66 to 73, wherein the response indicates that SRS configuration is successful.

[0254] Embodiment 75: The method of any of embodiments 66 to 73, wherein the response indicates that SRS configuration has failed.

[0255] Embodiment 76: The method of embodiment 75, wherein the response further comprises one or more reasons that the SRS configuration has failed.

[0256] Embodiment 77: A method performed by a positioning node (902), the method comprising: sending (904), to a Radio Access Network, RAN, node (900), an SRS configuration for one or more UEs (903) and an indication of one or more reasons for the SRS configuration; and receiving (908) a response from the RAN node (900).

[0257] Embodiment 78: The method of embodiment 77, wherein the one or more reasons for the SRS configuration comprise any one or more of the following: triggering of SS measurement collection at the RAN node (900) for generation of inferred measurements using an AI / ML model for reporting to the positioning node (902); triggering of SRS measurement collection at the RAN node (900) to enable AI / ML model training or retraining at the RAN node (900); triggering of SRS measurement collection at the RAN node (900) to enable monitoring of a performance of an AI / ML model at the RAN node (900).

[0258] Embodiment 79: The method of embodiment 77, wherein the one or more reasons for the SRS configuration comprise a reason that the SRS configuration is associated measurements are for AI / ML based positioning.

[0259] Embodiment 80: The method of embodiment 77, wherein the one or more reasons for the SRS configuration comprise a reason that the SRS configuration is associated measurements are for AI / ML model training or retraining or AI / ML model performance monitoring.

[0260] Embodiment 81 : The method of any of embodiments 77 to 80, wherein the response indicates that SRS configuration is successful.

[0261] Embodiment 82: The method of any of embodiments 77 to 80, wherein the response indicates that SRS configuration has failed.

[0262] Embodiment 83: The method of embodiment 82, wherein the response further comprises one or more reasons that the SRS configuration has failed.

[0263] Embodiment 84: A user equipment comprising: processing circuitry configured to perform any of the steps of any of embodiments 35 to 36; and power supply circuitry configured to supply power to the processing circuitry.

[0264] Embodiment 85: A network node comprising: a network interface; and processing circuitry associated with the network interface, the processing circuitry configured to cause the network node to perform any of the steps of any of embodiments 37 to 83.

[0265] Embodiment 86: A user equipment (UE) comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of embodiments 35 to 36; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a battery connected to the processing circuitry and configured to supply power to the UE.

Claims

CLAIMS1. A method performed by a Radio Access Network, RAN, node (400) for data collection for training, retraining or evaluating an Artificial Intelligence, Al, or Machine Learning, ML, (AI / ML) model for positioning in a wireless communication system, the method comprising: sending (406), to a Location Management Function, LMF, (402), a New Radio Positioning Protocol a, NRPPa, message that indicates requested positioning-related data; receiving (408), from the LMF (402), a NRPPa message comprising the requested positioning-related data; and training, retraining, and / or evaluating a performance of (414) an AI / ML model for positioning in the wireless communication system, based on the received positioning-related data.

2. The method of claim 1, wherein the sent NRPPa message is a measurement response comprising a flag that indicates that data collection is needed.

3. The method of claim 1, wherein the sent NRPPa message indicates the requested positioning-related data via an implicit indication.

4. The method of claim 1, wherein the sent NRPPa message indicates the requested positioning-related data via an explicit indication.

5. The method of any of claims 1 to 3, wherein the requested positioning-related data indicated by the sent NRPPa messages comprises one or more requested positioning related metrics indicated by the sent NRPPa message.

6. The method of claim 4, wherein the one or more requested positioning related metrics indicated by the sent NRPPa message comprise either or both of: uplink relative time of arrival and gNodeB, gNB, Rx-Tx time difference.

7. The method of any of claims 1 to 6, wherein the sent NRPPa message further indicates a requested reporting periodicity of the requested positioning-related data.

8. The method of any of claims 1 to 7, wherein the sent NRPPa message further indicates an overall number of User Equipments, UEs, for which the requested positioning-related data is desired.

9. The method of any of claims 1 to 8, wherein the received NRPPa message comprises, for each of a plurality of UEs, a set of UE locations for that UE and corresponding time stamp.

10. The method of any of claims 1 to 9, wherein the received NRPPa message comprises a set of positioning measurements and corresponding time stamps.

11. The method of any of claims 1 to 10, wherein the received NRPPa message comprises a list of positioning Sounding Reference Signal, SRS, configurations.

12. The method of any of claims 1 to 11, further comprising sending (410), to the LMF (402), a further NRPPa message comprising an update to the positioning-related data requested by the RAN node (400).

13. The method of any of claims 1 to 12, further comprising receiving (404), from the LMF (402), an NRPPa message that indicates that the LMF supports providing positioning-related data to the RAN node (400) for data collection.

14. The method of claim 13, wherein the NRPPa message that indicates that the LMF supports providing positioning-related data to the RAN node (400) for data collection comprises information that indicates a set of available positioning-related data, and the sent NRPPa message comprises information that indicates at least as subset of the available positioning-related data as the requested positioning-related data.

15. The method of claim 14, wherein the information that indicates the set of available positioning-related data comprises any one or more of the following: information that indicates one or more positioning related metrics that are available to be reported to the RAN node (400); information that indicates one or more User Equipments, UEs, for which positioning- related data can be reported to the RAN node (400); information that indicates an overall maximum number of UEs for which positioning- related data can be reported to the RAN node (400); information that indicates one or more coverage areas where UEs for which positioning- related data can be reported to the RAN node (400);information that indicates one or more UEs for which one or more specific positioning- related metrics can be reported to the RAN node (400); information that indicates a number of UEs for which one or more specific positioning- related metrics can be reported to the RAN node (400).

16. The method of claim 14, wherein the information that indicates the set of available positioning-related data comprises any one or more of the following: a list of UE IDs for which positioning-related data can be reported to the RAN node (400); a list of positioning measurement codepoints; a list of IDs of SRS configurations used for positioning.

17. A Radio Access Network, RAN, node comprising: a network interface; and processing circuitry associated with the network interface, the processing circuitry configured to cause the RAN node to: send (406), to a Location Management Function, LMF, (402), a New Radio Positioning Protocol a, NRPPa, message that indicates requested positioning-related data; receive (408), from the LMF (402), an NRPPa message comprising the requested positioning-related data; and train, retrain, and / or evaluate a performance of an AI / ML model for positioning in the wireless communication system, based on the received positioning-related data.

18. The RAN node of claim 17, wherein the processing circuitry is further configured to cause the RAN node to perform the method of any of claims 2 to 16.

19. A method performed by a Location Management Function, LMF, (402) for data collection for training, retraining or evaluating an Artificial Intelligence, Al, or Machine Learning, ML, (AI / ML) model for positioning in a wireless communication system, the method comprising: receiving (406), from a Radio Access Network, RAN, node (400), a New Radio Positioning Protocol a, NRPPa, message that indicates requested positioning-related data; sending (408), to the RAN node (400), an NRPPa message comprising the requested positioning-related data.

20. The method of claim 19, wherein the received NRPPa message is a measurement responsecomprising a flag that indicates that data collection is needed.

21. The method of claim 19, wherein the received NRPPa message indicates the requested positioning-related data via an implicit indication.

22. The method of claim 19, wherein the received NRPPa message indicates the requested positioning-related data via an explicit indication.

23. The method of any of claims 19 to 22, wherein the requested positioning-related data indicated by the received NRPPa messages comprises one or more requested positioning related metrics indicated by the received NRPPa message.

24. The method of claim 23, wherein the one or more requested positioning related metrics indicated by the received NRPPa message comprise either or both of: uplink relative time of arrival and gNodeB, gNB, Rx-Tx time difference.

25. The method of any of claims 19 to 24, wherein the received NRPPa message further indicates a requested reporting periodicity of the requested positioning-related data.

26. The method of any of claims 19 to 25, wherein the received NRPPa message further indicates an overall number of User Equipments, UEs, for which the requested positioning-related data is desired.

27. The method of any of claims 19 to 26, wherein the sent NRPPa message comprises, for each of a plurality of UEs, a set of UE locations for that UE and corresponding time stamp.

28. The method of any of claims 19 to 27, wherein the sent NRPPa message comprises a set of positioning measurements and corresponding time stamps.

29. The method of any of claims 19 to 28, wherein the sent NRPPa message comprises a list of positioning Sounding Reference Signal, SRS, configurations.

30. The method of any of claims 19 to 29, further comprising receiving (410), from the RAN node (400), an NRPPa message comprising an update to the positioning-related data requested bythe RAN node (400).

31. The method of any of claims 19 to 30, further comprising sending (404), to the RAN node (400), an NRPPa message that indicates that the LMF supports providing positioning-related data to the RAN node (400) for data collection.

32. The method of claim 31, wherein the NRPPa message that indicates that the LMF supports providing positioning-related data to the RAN node (400) for data collection comprises information that indicates a set of available positioning-related data, and the received NRPPa message comprises information that indicates at least as subset of the available positioning-related data as the requested positioning-related data.

33. The method of claim 32, wherein the information that indicates the set of available positioning-related data comprises any one or more of the following: information that indicates one or more positioning related metrics that are available to be reported to the RAN node (400); information that indicates one or more User Equipments, UEs, for which positioning- related data can be reported to the RAN node (400); information that indicates an overall maximum number of UEs for which positioning- related data can be reported to the RAN node (400); information that indicates one or more coverage areas where UEs for which positioning- related data can be reported to the RAN node (400); information that indicates one or more UEs for which one or more specific positioning- related metrics can be reported to the RAN node (400); information that indicates a number of UEs for which one or more specific positioning- related metrics can be reported to the RAN node (400).

34. The method of claim 32, wherein the information that indicates the set of available positioning-related data comprises any one or more of the following: a list of UE IDs for which positioning-related data can be reported to the RAN node (400); a list of positioning measurement codepoints; a list of IDs of SRS configurations used for positioning.

35. A Location Management Function, LMF, comprising:a network interface; and processing circuitry associated with the network interface, the processing circuitry configured to cause the LMF to: receive (406), from a Radio Access Network, RAN, node (400), a New Radio Positioning Protocol a, NRPPa, message that indicates requested positioning-related data; send (408), to the RAN node (400), an NRPPa message comprising the requested positioning-related data.

36. The LMF of claim 35, wherein the processing circuitry is further configured to cause the LMF to perform the method of any of claims 20 to 34.

Images