System and method for discovering network node for data collection for an ai / ML model for positioning

Abstract

Disclosed is a method for execution by a first network node. The method involves sending, to a second network node, an inquiry for discovering a third network node that can facilitate data collection for an AI / ML (Artificial Intelligence / Machine Learning) model for predicting positioning of a UE (User Equipment) in a defined area. The method also involves receiving, from the second network node, a response identifying the third network node that can facilitate the data collection in the defined area. In this way, it becomes clear which network node will initiate data collection for training and how different network nodes can be discovered which can facilitate DTA during the data collection process. The method also involves executing at least one step initiating the data collection in the defined area using the third network node.

Description

[0001] SYSTEM AND METHOD FOR DISCOVERING NETWORK NODE FOR DATA COLLECTION FOR AN AI / ML MODEL FOR POSITIONING

[0002] Related Application

[0003] [1] This application claims priority from European application no. 24383041.1 filed on September 27, 2024 and entitled “System and Method for Discovering Network Node for Data Collection for an AI / ML Model for Positioning”, which is incorporated by reference in its entirety.

[0004] Field of the Disclosure

[0005] [2] This disclosure relates to mobile communication systems, and more particularly data collection for an AI / ML (Artificial Intelligence I Machine Learning) model for predicting positioning of a UE (User Equipment) in a defined area.

[0006] Background

[0007] [3] None of the material in the background section should be interpreted to be Applicant’s admitted prior art.

[0008] AI / ML modeling and associated principles

[0009] [4] An Al (Artificial Intelligence) or ML (Machine Learning) technique comprises one or more algorithms, which use a set of data as input for training one or more AI / ML models. An output of the AI / ML model is used by a device (e.g. UE, base station or another node) for performing certain operations or taking certain decisions (e.g. handover etc.) fully or partially based on the output, which in turn depends on the trained AI / ML model. The AI / ML model can be trained in the device online (e.g. on-the-fly while processing data) or offline in the background. More specifically:

[0010] • Online training is an AI / ML training process where the model being used for inference is (typically continuously) trained in (near) real-time with the arrival of new training samples or data. • Offline training is an AI / ML training process where the model is trained based on collected samples or data, and where the trained model is later used or delivered for inference.

[0011] 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.

[0012] [5] The AL / ML models can be trained in a device, which can be a UE, a network node, or another node. In this respect the AI / ML modes can be broadly classified as:

[0013] • Case I: UE-side (AI / ML) model. It is an AI / ML model whose inference is performed entirely at the UE.

[0014] • Case II: Network-side (AI / ML) model. It is an AI / ML model whose inference is performed entirely at the network.

[0015] • Case III: One-sided (AI / ML) model. It is a UE-side (AI / ML) model or a Network-side (AI / ML) model.

[0016] • Case IV: Two-sided (AI / ML) model. It is a paired AI / ML model(s) over which joint inference is performed, where joint inference comprises AI / ML Inference whose inference is performed jointly across the UE and the network, i.e. , the first part of inference is firstly performed by UE and then the remaining part is performed by gNB, or vice versa.

[0017] [6] 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.

[0018] [7] The term LCM (Lifecycle Management) of an AI / ML model refers to the process of developing, deploying and maintaining the AI / ML model. An example of an AI / ML model training pipeline illustrating different stages is shown in Figure 1. The AI / ML model training pipeline includes several processing stages as gathering unprocessed input data from data repositories (data ingestion), finding high-quality input features (data preprocessing), 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), evaluate 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 comprise of operations to make the ML model runnable via compilation to a specific HW and of steps like versioning and packaging of the model so that it can be executed.

[0019] AI / ML model for positioning

[0020] [8] An AI / ML model can be used for UE positioning. A UE or a gNB, depending on capability, can have a trained model stored inside the device, or have an untrained AI / ML that can be trained on-the-fly to either produce measurements that are required to localize a UE within a RAN (Radio Access Network) coverage area or directly predict / determine the UE location by exploiting the measurements performed by the UE or gNB on reference signals such as PRS (Positioning Reference Signal), SRS (Sounding Reference Signal) etc. within a RAN coverage area.

[0021] [9] 3GPP (3rd Generation Partnership Project) TR (Technical Report) 23.700.84 entitled “Study on Core Network Enhanced Support for Artificial Intelligence (Al) I Machine Learning (ML)” version 1.0.0 uploaded on 2024-06-10 (hereinafter “TR 23.700.84”) reached conclusions stating that in Release 19 it will be possible to do UE Positioning using an AI / ML Model. This AI / ML Model may be initially available at an LMF (Location Management Function), then the LMF performs data collection to train the AI / ML model. The following are principles listed into the conclusions in TR 23.700.84:

[0022] • Principle #1 : The LMF is enhanced to perform location calculation based on ML model. The interaction between LMF and MTLF is only for model provisioning and possibly, based on coordination with RAN WGs, for data collection for training.

[0023] NOTE 1 : When receiving the request for a UE location, the LMF selects an appropriate method to determine the UE location and may select AI / ML positioning as method. • Principle #2: LMF is enhanced to perform model training for AI / ML based Positioning, the trigger for data collection and for model training in LMF is up to implementation.

[0024] • Principle #3: MTLF is enhanced to perform model training for AI / ML based Positioning.

[0025] Principle #3.1 : To retrieve a model to perform location calculation, the LMF discovers a suitable MTLF via NRF.

[0026] Principle #3.2: NWDAF containing MTLF trains ML model for AI / ML based Positioning based on request from the LMF or internal trigger.

[0027] Principle #3.3: MTLF collects training data (e.g. data as defined by RAN for AI / ML based Positioning and historical data stored in ADRF) from data sources.

[0028] NOTE 2: Whether LMF can also retrieve a trained model from OAM (Operations, Administration, and Maintenance) or not will be discussed in normative phase and need to coordinate with SA5.

[0029] • Principle #4: LMF or MTLF performs model performance monitoring for AI / ML based Positioning.

[0030] Principle #4.1 The result of model performance monitoring may trigger the LMF to change the positioning method, e.g. from AI / ML based positioning to the legacy positioning, or vice versa.

[0031] Principle #4.2 The result of model performance monitoring may trigger ML model retraining in the training entity.

[0032] Principle #4.3 The detailed procedure and service for this model performance monitoring will be decided in normative phase.

[0033] • Principle #5: Data used for model training, inference and model performance monitoring for AI / ML based positioning will be decided by RAN WGs, and SA WG2 will align with RAN WGs. The related procedures for data collection will be coordinated with RAN WGs in the normative phase. NOTE 3: The user’s authorization / consent of collecting UE related training data is needed as specified in 3GPP TS 23.288 entitled ’’Architecture enhancements for 5G System (5GS) to support network data analytics services” version 18.10.0 uploaded on 2025-06-19 and 3GPP TS 23.273 entitled ” 5G System (5GS) Location Services (LCS); Stage 2” version 18.6.0 uploaded on 2024-06-26 (hereinafter ”TS 23.273”).

[0034] NOTE 4: Whether and how the NF profile of the LMF at the NRF will be extended to support selection of an LMF that supports AI / ML based Positioning will be discussed in normative phase.

[0035] Al ML for Beam prediction

[0036]

[0010] The use case of beam prediction which will be standardized as part of 3GPP Release 19 work item consists of spatial beam prediction, and temporal beam prediction. The core idea of this use case is to predict the “best” beam (or beams) from a Set A of beams using measurement results from another Set B of beams.

[0037]

[0011] According to 3GPP TR 38.843 entitled “Study on Artificial Intelligence (Al) / Machine Learning (ML) for NR air interface” version 18.0.0 uploaded on 2024-01-16, the spatial-domain beam prediction for Set A of beams is based on measurement results of Set B of beams, whereas the temporal beam prediction for Set A of beams is based on the historic measurement results of Set B of beams.

[0038]

[0012] Set A and Set B of beams have not been defined yet, however, the following two examples illustrate some scenarios that will likely be studied in Release 18:

[0039] • Set B is a subset of a Set A. For example, Set A is a set of 8 SSB / CSI-RS beams shown in Figure 4 (both light and dark circles). The UE measures Set B (the 4 beams indicated by dark circles). The AI / ML model should predict the best beam (or beams) in Set A using only measurements from Set B.

[0040] Figure 2A depicts an example where Set B is a subset of Set A. The figure illustrates a grid-of-beam type radiation pattern: Each row (resp. column) depicts a certain zenith (resp. azimuth) angle from the antenna array. Set A has 8 beams and Set B has 4 beams (indicated by dark circles).

[0041] • Set A and Set B correspond to two different sets of beams. For example, Set A is a set of 30 narrow CSI-RS beams, and Set B is a set of 8 wide SSB beams. The UE measures beams in Set B and the AI / ML model should predict the best beam(s) from Set A.

[0042] Figure 2B depicts an example where Set A is a set of narrow beams, and Figure 2C depicts an example where Set B is a set of wide beams.

[0043]

[0013] The beam prediction can be performed in the gNB and in the UE, and the gain is twofold. From the UE point of view, the UE would be able to generate good radio measurement estimations without really measuring certain resources, thereby saving energy, whereas from the gNB point of view, the gNB can get good radio measurements estimation from the UE without providing the measuring resources, thereby limiting the overhead over the air-interface.

[0044]

[0014] The following terminology may be used in the art:

[0045] • ML model: a manageable representation of an ML model algorithm.

[0046] NOTE 1 : an ML model algorithm is a mathematical algorithm through which running a set of input data can generate a set of inference output.

[0047] NOTE 2: ML model algorithm is proprietary and not in scope for standardization and therefore not treated in this specification.

[0048] NOTE 3: ML model may include metadata. Metadata may include e.g. information related to the trained model, and applicable runtime context.

[0049] • ML model training: a process performed by an ML training function to take training data, run it through an ML model algorithm, derive the associated loss and adjust the parameterization of that ML model iteratively based on the computed loss and generate the trained ML model.

[0050] • ML model initial training: a process of an initial version of an ML model.

[0051] • ML model re-training: a process of training a previous version of an ML model and generate a new version.

[0052] NOTE 4: a new version of a trained ML model supports the same type of inference as the previous version of the ML model, i.e. , the data type of inference input and data type of inference output remain unchanged between the two versions of the ML model, but parameter values might be different for the re-trained model.

[0053] • ML model joint training: a process of training a group of ML models.

[0054] • ML training function: a logical function with ML model training capabilities.

[0055] • ML model testing: a process of testing an ML model using testing data.

[0056] • ML testing function: a logical function with ML model testing capabilities.

[0057] • AI / ML inference: a process of running a set of input data through a trained ML model to produce set of output data, such as predictions.

[0058] NOTE 5: the inference represents the process to realize the Al capabilities by utilizing a trained ML model and other Al enablers if needed, hence the AI / ML prefix is used when referring to inference as compared to training and testing.

[0059] • AI / ML inference function: a logical function that employs trained ML model(s) to conduct inference.

[0060] • AI / ML inference emulation: running the inference process to evaluate the performance of an ML model in an emulation environment before deploying it into the target environment. • ML model deployment: a process of making a trained ML model available for use in the target environment.

[0061] Summary of the Disclosure

[0062]

[0015] 3GPP Rel-19 AI / ML for physical layer allows the AI / ML model to be trained (e.g. collection of data and performing training) in different entities such as UE, gNB, LMF etc. It is unclear though which entity will initiate data collection for training and how different network entities can be discovered which can facilitate DTA during the data collection process.

[0063]

[0016] An example is that, for gNB side data collection, the gNB may require assistance from LMF, but how to identify which LMF may facilitate such data is a question that so far remains unsolved by prior approaches. Similarly, if LMF has to collect data, it may require assistance from the gNB, but how to identify which gNBs can be involved for data collection is of question.

[0064]

[0017] Some embodiments disclosed herein set out to solve, address, or mitigate one or more of the foregoing deficiencies.

[0065]

[0018] Disclosed is a method for execution by a first network node. The method involves sending, to a second network node, an inquiry for discovering a third network node that can facilitate data collection for an AI / ML (Artificial Intelligence I Machine Learning) model for predicting positioning of a UE (User Equipment) in a defined area. The method also involves receiving, from the second network node, a response identifying the third network node that can facilitate the data collection in the defined area. The method also involves executing at least one step initiating the data collection in the defined area using the third network node.

[0066]

[0019] In this way, it becomes clear which network node will initiate data collection for training and how different network nodes can be discovered which can facilitate DTA during the data collection process. Note that there are many possibilities for the network nodes. Example possibilities are summarized below.

[0020] In some implementations, the first network node includes an AMF (Access and Mobility Management Function), the second network node includes an NRF (Network Repository Function), and the third network node includes an LMF (Location Management Function). For such implementations, the method can involve receiving, from a RAN (Radio Access Network) node, a request for data collection for the AI / ML model for predicting positioning of the UE in the defined area, wherein the defined area is identified from the request from the RAN node. Furthermore, executing at least one step initiating the data collection in the defined area using the third network node can involve the AMF forwarding to the LMF the request thereby initiating data collection by the RAN node from the LMF.

[0067]

[0021] In some implementations, the first network node includes an AMF, the second network node includes an NRF, and the third network node includes an LMF. For such implementations, executing at least one step initiating the data collection in the defined area using the third network node can involve the AMF sending to the LMF a request for data collection for the AI / ML model for predicting positioning of the UE in the defined area, thereby initiating data collection by a RAN node from the LMF.

[0068]

[0022] In some implementations, the first network node includes an LMF, the second network node includes an NRF, and the third network node includes an AMF. For such implementations, executing at least one step initiating the data collection in the defined area using the third network node can involve the LMF identifying which UEs are in the defined area, and the LMF initiating data collection by a RAN node from the LMF.

[0069]

[0023] In some implementations, the first network node includes an AMF, the second network node includes an NRF, and the third network node includes an LMF. For such implementations, the method can involve receiving, from a fourth node, a request for data collection for the AI / ML model for predicting positioning of the UE in the defined area, wherein the defined area is identified from the request from the UE. Furthermore, executing at least one step initiating the data collection in the defined area using the third network node can involve the AMF forwarding to the LMF the request thereby initiating data collection by the fourth node from the LMF. The fourth node can include a UE.

[0024] In some implementations, the first network node includes an AMF, the second network node includes an NRF, and the third network node includes an OAM (Operations, Administration, and Maintenance). For such implementations, receiving, from a fourth node, a request for data collection for the AI / ML model for predicting positioning of the UE in the defined area, wherein the defined area is identified from the request from the UE. Furthermore, executing at least one step initiating the data collection in the defined area using the third network node can involve the AMF forwarding to the OAM the request thereby initiating data collection by the fourth node from the OAM. The fourth node can include a UE or a RAN node.

[0070]

[0025] Also disclosed is a non-transitory CRM (computer readable medium) having recorded thereon statements and instructions that, when executed by a processor of a first network node, configure the first network node to implement a method as summarized above.

[0071]

[0026] Also disclosed is a first network node having a network interface configured to communicate with other network nodes and control circuitry coupled to the network interface. The control circuitry is configured to send, to a second network node via the network interface, an inquiry for discovering a third network node that can facilitate data collection for an AI / ML model for predicting positioning of a UE in a defined area. The control circuitry is also configured to receive, from the second network node via the network interface, a response identifying the third network node that can facilitate the data collection in the defined area. The control circuitry is also configured to execute at least one step initiating the data collection in the defined area using the third network node.

[0072]

[0027] Also disclosed is a method for execution by a second network node. The method involves receiving, from a first network node, an inquiry for discovering a third network node that can facilitate data collection for an AI / ML (Artificial Intelligence I Machine Learning) model for predicting positioning of a UE (User Equipment) in a defined area. The method also involves sending, to the first network node, a response identifying the third network node that can facilitate the data collection in the defined area.

[0028] In this way, it becomes clear which network node will initiate data collection for training and how different network nodes can be discovered which can facilitate DTA during the data collection process. Note that there are many possibilities for the network nodes. Example possibilities are summarized below.

[0073]

[0029] In some implementations, the first network node includes an AMF (Access and Mobility Management Function), the second network node includes an NRF (Network Repository Function), and the third network node includes an LMF (Location Management Function). In some implementations, the method also involves receiving, from the LMF, information indicating support of data collection in certain areas.

[0074]

[0030] In some implementations, the first network node includes an LMF, the second network node includes an NRF, and the third network node includes an AMF. In some implementations, the method also involves receiving, from the AMF, information indicating support of data collection in certain areas.

[0075]

[0031] In some implementations, the first network node includes an AMF, the second network node includes an NRF, and the third network node includes an OAM (Operations, Administration, and Maintenance). In some implementations, the method also involves receiving, from the OAM, information indicating support of data collection in certain areas.

[0076]

[0032] Also disclosed is a non-transitory CRM (computer readable medium) having recorded thereon statements and instructions that, when executed by a processor of a second network node, configure the second network node to implement a method as summarized above.

[0077]

[0033] Also disclosed is a second network node having a network interface configured to communicate with other network nodes, and control circuitry coupled to the network interface. The control circuitry is configured to receive, from a first network node, an inquiry for discovering a third network node that can facilitate data collection for an AI / ML (Artificial Intelligence I Machine Learning) model for predicting positioning of a UE (User Equipment) in a defined area. The control circuitry is also configured to send, to the first network node, a response identifying the third network node that can facilitate the data collection in the defined area.

[0078]

[0034] Also disclosed is a communication system having the first network node as summarized above, and the second network node as summarized above.

[0079]

[0035] Other aspects and features of the present disclosure will become apparent, to those ordinarily skilled in the art, upon review of the following description of the various embodiments of the disclosure.

[0080] Brief Description of the Drawings

[0081]

[0036] Embodiments will now be described with reference to the attached drawings in which:

[0082] Figure 1 is a block diagram of an example of an AI / ML model training pipeline;

[0083] Figures 2A to 2C are graphs showing radiation patterns;

[0084] Figure 3 is a block diagram of a communication system, in accordance with an embodiment of the disclosure;

[0085] Figures 4A to 4D are flowcharts of methods of discovering a network node that can facilitate data collection for an AI / ML model for predicting positioning of a UE in a defined area;

[0086] Figure 5 is a sequence diagram of a data collection initiation procedure for gNB side model;

[0087] Figure 6 is a sequence diagram of a gNB-side data collection - based on CN (Core Network) triggered process;

[0088] Figure 7 is a sequence diagram of a data collection initiation procedure for LMF side model;

[0089] Figure 8 is a sequence diagram of a data collection initiation procedure for LMF side model; Figure 9 is a sequence diagram of a data collection initiation procedure for 0AM side model;

[0090] Figure 10 is a sequence diagram of a process for requesting data collection;

[0091] Figure 11 is a schematic of an example cellular communications system in which some embodiments of the present disclosure may be implemented;

[0092] Figures 12A and 12B are block diagrams of a wireless communication system represented as a 5G network architecture in which some embodiments of the present disclosure may be implemented;

[0093] Figures 13 and 15 are block diagrams of a radio access node according to some embodiments of the present disclosure;

[0094] Figure 14 is a block diagram that illustrates a virtualized embodiment of a radio access node according to some embodiments of the present disclosure; and

[0095] Figures 16 and 17 are block diagrams of a wireless communication device.

[0096] Detailed Description of Embodiments

[0097]

[0037] It should be understood at the outset that although illustrative implementations of one or more embodiments of the present disclosure are provided below, the disclosed systems and / or methods may be implemented using any number of techniques. The disclosure should in no way be limited to the illustrative implementations, drawings, and techniques illustrated below, including the exemplary designs and implementations illustrated and described herein, but may be modified within the scope of the appended embodiment along with their full scope of equivalents.

[0098] Introduction

[0099]

[0038] Referring first to Figure 3, shown is a block diagram of a communication system 100, in accordance with an embodiment of the disclosure. The communication system 100 has a first network node 110 and a second network node 120 operatively coupled to one or more other network nodes 130a-130c via at least one network 102. Details of the other network nodes 130a-130c are omitted for simplicity. The communication system 100 may have other components that are not shown for simplicity. For example, the communication system 100 may include components of a core network and components of a radio access network.

[0100]

[0039] The first network node 110 has a network interface 115 configured to communicate with other nodes of the communication system 100, a CRM 119, and control circuitry 116 coupled to the network interface 115 and the CRM 119. In some implementations, the control circuitry 116 includes a processor 117 that executes software, which can stem from a memory 118. However, other implementations are possible and are within the scope of this disclosure. The first network node 110 can have additional components, but these are not shown for simplicity.

[0101]

[0040] The second network node 120 has a network interface 125 configured to communicate with other nodes of the communication system 100, a CRM 129, and control circuitry 126 coupled to the network interface 125 and the CRM 129. In some implementations, the control circuitry 126 includes a processor 127 that executes software, which can stem from a memory 128. However, other implementations are possible and are within the scope of this disclosure. The second network node 120 can have additional components, but these are not shown for simplicity.

[0102]

[0041] The control circuitry 116 of the first network node 110 and the control circuitry 126 of the second network node 120 operates to implement a method of discovering a network node that can facilitate data collection for an AI / ML model for predicting positioning of a UE in a defined area. The operation by the first network node 110 and the second network node 120 will be described below with reference to Figure 4A. Although the method of Figure 4A is described below with reference to the communication system 100 shown in Figure 1 , it is to be understood that the method of Figure 4A is applicable to other communication systems. In general, the method of Figure 4A is applicable to any appropriately configured communication system.

[0103]

[0042] At step 4-1 , the first network node 110 sends, to the second network node 120, an inquiry for discovering a third network node 130a-c that can facilitate data collection for an AI / ML model for predicting positioning of a UE in a defined area. The second network node 120 might for example select the third network node 130a for data collection in the defined area. Then, at step 4-2, the first network node 110 receives, from the second network node 120, a response identifying the third network node 130a that can facilitate the data collection in the defined area. In this case, the third network node 130a that has been identified is the first of the network nodes 130a-c. Finally, at step 4-3 the first network node 110 executes at least one step initiating the data collection in the defined area using the third network node 130a. For example, in some implementations as shown at step 4-3a, the first network node 110 sends a request for data collection to the third network node 130a. However, other implementations are possible.

[0104]

[0043] In this way, it becomes clear which network node will initiate data collection for training and how different network nodes can be discovered which can facilitate DTA during the data collection process. Note that there are many possibilities for the network nodes. Example possibilities are described later with reference to Figures 5 through 9.

[0105]

[0044] Some embodiments described herein define how the data collection procedure can be initiated and how different entities can be discovered which can support the data collection process. In some implementations, the entity which requires the data has to initiate the procedure for data collection and the entity which can facilitate data collection may registers with a network database for the purpose of better data collection service discovery. Alternatively, the capability of different nodes or functions to provide data for data collection processes can be made aware within the network, e.g. by means of pre-configuration. With such pre-known information, the selection of appropriate nodes or functions to support data collection can be achieved.

[0106]

[0045] Some embodiments described herein define a method whereby an entity operating in cellular network requiring positioning data (e.g. channel measurements, UE location, UE / gNB measurements) for AIML purpose can acquire the data by interacting and discovering different entities of the network node.

[0107]

[0046] Figures 4B and 4C are flowcharts showing a process for database registration. Figure 4D is a flowchart showing a process for case of preconfigured data collection support information. In some implementations, one or more of the processes of Figures 4B to 4D are combined with the method of Figure 4A.

[0108]

[0047] According to another embodiment of the disclosure, there is provided a non- transitory CRM having recorded thereon statements and instructions that, when executed by the processor 117 of the first network node 110, implement a method as described herein. The non-transitory computer readable medium can be the memory 118 and / or the CRM 119 of the first network node 110 shown in Figure 3, or some other non-transitory CRM.

[0109]

[0048] According to another embodiment of the disclosure, there is provided a non- transitory CRM having recorded thereon statements and instructions that, when executed by the processor 127 of the second network node 120, implement a method as described herein. The non-transitory computer readable medium can be the memory 128 and / or the CRM 129 of the second network node 120 shown in Figure 3, or some other non-transitory CRM.

[0110]

[0049] Examples of a non-transitory CRM include memory, an SSD (Solid State Drive), a hard disk drive, a CD (Compact Disc), a DVD (Digital Video Disc), a BD (Blu-ray Disc), a memory stick, etc. Other non-transitory CRMs are also possible.

[0111]

[0050] The illustrated examples described herein focus on software implementations. However, other implementations are possible and are within the scope of this disclosure. Other implementations can include additional or alternative hardware components, such as any appropriately configured FPGA (Field-Programmable Gate Array), ASIC (Application-Specific Integrated Circuit), and / or microcontroller, for example. Thus, the control circuitry 116 of the first network node 110 and the control circuitry 126 of the second network node 120 can instead be implemented with any suitable combination of hardware, software and / or firmware.

[0112]

[0051] Further example details are provided in the following sections. It is to be understood that the following sections are very specific and are provided merely for exemplary purposes, such that other implementations are possible and within the scope of the disclosure.

[0113] Further Example Details

[0114]

[0052] It is noted that the below embodiments can be applicable but not limited to 3GPP NR (New Radio). Hence a base station, can be a NR gNB (5G Node B), or 6G-RAT base station, or any other device with similar function. Equivalently, the CN nodes may be 4G or 6G nodes in charge of equivalent functions as those nodes used for the description herein. gNB side data collection - based on RAN triggered process

[0115]

[0053] Referring now to Figure 5, shown is a sequence diagram of a data collection initiation procedure for gNB side model.

[0116]

[0054] Step 5-0 is a prerequisite step whereby the location server, e.g. LMF, registers to a repository server, e.g. the NRF (Network Repository Function), information such as for which tracking area, list of cells, list of TRPs, list of gNBs that it can support for data collection. It registers its network profile with NRF. In a depending embodiment, the LMF declares to the AMF whether it is capable of supporting AI / ML based mobility, for example whether it is able to support RAN assisted AI / ML based mobility.

[0117]

[0055] At step 5-1 , the gNB which has to collect the data shall initiate the procedure towards a Core Network function such as the AMF, using a protocol such as NGAP to generate a request for data collection. The request may also contain the cell ID or geographical area (list of cells, list of TRPs belonging to the gNB, RAN Coverage area, RAN paging area etc.) for which data collection is requested. The request may additionally consist of number of UEs, time duration by which data collection activity should be ended, periodicity of the information reporting.

[0118]

[0056] At step 5-2, the AMF enquires with NRF which LMF can support data collection in that area.

[0057] At step 5-3, the NRF provides to the AMF, the details of an appropriate LMF (IP address etc.) fulfilling such request, and by means of which the AMF can establish a connection with LMF.

[0119]

[0058] At step 5-4, the AMF establishes the connection with LMF and forwards the request from gNB (RAN node) to LMF indicating that gNB needs to collect data for a particular region along with other criteria.

[0120]

[0059] At step 5-5, the LMF identifies which UEs are in that area. Some of the UEs can be PRUs which it can find it locally if stored or via another LMF or for non-PRU UEs it can subscribe to AMF. In a depending embodiment, the LMF selects the UEs based on whether the user associated to the UE has provided user consent for the data collection process.

[0121]

[0060] At step 5-6, the LMF initiates data collection procedure by invoking UL based Positioning procedure towards target UE and gNBs for the purpose of data collection (e.g: UL-TDOA as defined in 3GPP TS 38.305 entitled “NG Radio Access Network (NG-RAN); Stage 2 functional specification of User Equipment (UE) positioning in NG-RAN” version 18.3.0 uploaded on 2024-09-26 (hereinafter “TS 38.305”). This procedure can be based upon legacy procedure such as Network Assisted Positioning Procedure (clause 6.11.2 in TS 23.273) where LMF contacts gNB via NRPPa protocol. gNB side data collection - based on CN triggered process

[0122]

[0061] Referring now to Figure 6, shown is a sequence diagram of a gNB-side data collection - based on CN triggered process. The method described herein allows for data collection at a RAN to be based on triggering of the overall procedure in the CN.

[0123]

[0062] Step 6-0 is a prerequisite step whereby the location server, e.g. LMF, registers to a repository server, e.g. the NRF, information such as for which tracking area, list of cells, list of TRPs, list of gNBs it can support for data collection. It registers its network profile with NRF. In a depending embodiment, the LMF declares to the AMF whether it is capable of supporting AI / ML based mobility, for example whether it is able to support RAN assisted AI / ML based mobility.

[0124]

[0063] At step 6-1 , the AMF needs to trigger a positioning process for a specific UE served in a specific coverage area, e.g. in a specific TAI (Tracking Area identity). In one depending embodiment the AMF is assumed to know which RAN nodes support AI / ML based positioning and therefore it knows whether the RAN node serving the UE supports AI / ML based positioning. In another depending embodiment the AMF enquiries the NRF about the LMF that could support data collection from LMF to RAN in support of AI / ML based positioning. In some implementations, the AMF decides to enquire about an LMF that could support data collection from LMF to RAN in support of AI / ML based positioning in light of whether the RAN node serving the UE supports AI / ML based positioning or not, or in light of whether the RAN node serving the UE has been connected to an LMF that could support data collection from LMF to RAN in support of AI / ML based positioning before. For example, if the RAN node supports AI / ML based positioning and it has never been connected to an LMF that could support data collection from LMF to RAN in support of AI / ML based positioning, the AMF will enquiry about an LMF that could support data collection from LMF to RAN in support of AI / ML based positioning.

[0125]

[0064] At step 6-2, the NRF provides to the AMF information about the LMF that could support data collection from LMF to RAN in support of AI / ML based positioning. Such information could be transport layer addresses and port numbers. By means of this information the AMF can establish a connection with the LMF.

[0126]

[0065] At step 6-3, the AMF establishes the connection with LMF and forwards the request for positioning for the specific UE.

[0127]

[0066] At step 6-4, the LMF triggers a positioning procedure over NRPPA for the specific UE. This procedure may involve configuring the UE with UL RS signalling and it might involve NRPPA signalling with the RAN node serving the UE.

[0128]

[0067] At step 6-5, a process of data collection, where data is signalled from the LMF to the RAN, is started via signalling procedures over NRPPA.

[0068] It should be noted that step 6-5 could happen earlier than step 6-4, namely, the data collection process may start even before the positioning process starts. In a variant of this embodiment, the AMF is preconfigured with information about the LMFs that could support data collection from LMF to RAN in support of AI / ML based positioning. Additionally or alternatively, the AMF is preconfigured with information concerning the LMFs that support AI / ML based positioning. In this variant, when the AMF needs to trigger a positioning process for a specific UE served in a specific coverage area, e.g. in a specific TAI, the AMF would not need to query the NRF to determine which LMF could support data collection from LMF to RAN in support of AI / ML based positioning. Therefore, step 6-0, step 6-1 and step 6-2 would not be needed in the procedure shown in the figure above. Instead, the AMF would be able to trigger step 6-3 as soon as a positioning process to position a specific UE needs to be initiated.

[0129] LMF side data collection

[0130]

[0069] Referring now to Figure 7, shown is a sequence diagram of a data collection initiation procedure for LMF side model.

[0131]

[0070] Step 7-0 is a prerequisite step whereby AMF registers to NRF information such as for which tracking area, list of cells, list of TRPs, list of gNBs that it can support for data collection. It registers its network profile with NRF.

[0132]

[0071] At step 7-1 , the LMF which has to collect the data shall initiate the procedure for data collection by requesting NRF to provide information of which AMF can support data collection in a specific area.

[0133]

[0072] At step 7-2, the NRF provides to the LMF, the details of an appropriate AMF (IP address etc.) fulfilling such request, and by means of which the LMF can establish the connection with AMF.

[0134]

[0073] At step 7-3, the LMF identifies which UEs are in that area. Some of the UEs can be PRUs which it can find it locally if stored or via another LMF or for non-PRU UEs it can subscribe to AMF. In a depending embodiment, the LMF selects the UEs based on whether the user associated to the UE has provided user consent for the data collection process.

[0135]

[0074] At step 7-4, the LMF initiates data collection procedure by invoking UL based Positioning procedure towards target UE and gNBs for the purpose of data collection (e.g: UL-TDOA as defined in TS 38.305). This procedure can be based upon legacy procedure such Network Assisted Positioning Procedure (clause 6.11.2 in TS 23.273) where LMF contacts gNB via NRPPa protocol.

[0136] UE side data collection for positioning

[0137]

[0075] Referring now to Figure 8, shown is a sequence diagram of a data collection initiation procedure for LMF side model.

[0138]

[0076] Step 8-0 is a prerequisite procedure whereby the location server, e.g. LMF, registers to a repository server, e.g. the NRF, information such as for which tracking area, list of cells, list of TRPs, list of gNBs that it can support for data collection. It registers its network profile with NRF. In a depending embodiment, the LMF declares to the AMF whether it is capable of supporting AI / ML based mobility, for example whether it is able to support RAN assisted AI / ML based mobility.

[0139]

[0077] At step 8-1 , the UE which has to collect the data (which is not necessarily the UE being tracked, although it could be) shall initiate the procedure towards a core network function such as the AMF, using a protocol such as NAS to generate a request for data collection. The request also contains the cell ID or geographical area (list of cells, list of TRPs belonging to the gNB, RAN Coverage area, RAN paging area etc.) for which data collection is requested. The request may additionally comprise time duration by which data collection activity should be ended, periodicity of the information reporting.

[0140]

[0078] Note: It is also possible that the UE uses MO-LR (LCS service request) transparently to AMF and thus inform the data collection request to LMF directly. However, it could happen that this LMF may not be capable of handling data collection and thus this is less preferred solution.

[0079] At step 8-2, the AMF enquires with NRF which LMF can support data collection in that area.

[0141]

[0080] At step 8-3, the NRF provides to the AMF, the details of an appropriate LMF (IP address etc.) fulfilling such request, and by means of which the AMF can establish a connection with LMF.

[0142]

[0081] At step 8-4, the AMF establishes the connection with LMF and forwards the request from UE to LMF indicating that UE needs to collect data for a particular region along with other criteria.

[0143]

[0082] At step 8-5, the LMF initiates data collection procedure by invoking DL based Positioning procedure towards target UE for the purpose of data collection (e.g: DL-TDOA as defined in TS 38.305). This procedure can be based upon legacy procedure such as Network Assisted Positioning Procedure (clause 6.11.2 in TS 23.273) where LMF contacts gNB via NRPPa protocol. The LMF may also identify which PRUs are in that area which can assist in data collection and thus obtain the measurement and location from PRU and provide it to target UE.

[0144] UE / gNB side data collection for beam management

[0145]

[0083] Referring now to Figure 9, shown is a sequence diagram of a data collection initiation procedure for 0AM side model. The procedure is similar to the data collection initiation procedure for LMF side model described above in connection with Figure 8. However, instead of a location server such as the LMF, the procedure uses an 0AM system, or a server or any other core network entity.

[0146]

[0084] Step 9-0 is a prerequisite procedure whereby the location server, e.g. 0AM, registers to a repository server, e.g. the NRF, information such as for which tracking area, list of cells, list of TRPs, list of gNBs that it can support for data collection. It registers its network profile with NRF. In a depending embodiment, the 0AM declares to the AMF whether it is capable of supporting AI / ML based mobility, for example whether it is able to support RAN assisted AI / ML based mobility.

[0085] At step 9-1 , the UE which has to collect the data (which is not necessarily the UE being tracked, although it could be) shall initiate the procedure towards a core network function such as the AMF, using a protocol such as NAS to generate a request for data collection. The request also contains the cell ID or geographical area (list of cells, list of TRPs belonging to the gNB, RAN Coverage area, RAN paging area etc.) for which data collection is requested. The request may additionally comprise time duration by which data collection activity should be ended, periodicity of the information reporting.

[0147]

[0086] Note: It is also possible that the UE uses MO-LR (LCS service request) transparently to AMF and thus inform the data collection request to 0AM directly. However, it could happen that this 0AM may not be capable of handling data collection and thus this is less preferred solution.

[0148]

[0087] At step 9-2, the AMF enquires with NRF which 0AM can support data collection in that area.

[0149]

[0088] At step 9-3, the NRF provides to the AMF, the details of an appropriate 0AM (IP address etc.) fulfilling such request, and by means of which the AMF can establish a connection with 0AM.

[0150]

[0089] At step 9-4, the AMF establishes the connection with 0AM and forwards the request from UE to 0AM indicating that UE needs to collect data for a particular region along with other criteria.

[0151]

[0090] At step 9-5, the 0AM initiates data collection procedure by invoking DL based Positioning procedure towards target UE for the purpose of data collection (e.g: DL-TDOA as defined in TS 38.305). This procedure can be based upon legacy procedure such as Network Assisted Positioning Procedure (clause 6.11.2 in TS 23.273) where 0AM contacts gNB via NRPPa protocol. The 0AM may also identify which PRUs are in that area which can assist in data collection and thus obtain the measurement and location from PRU and provide it to target UE.

[0152]

[0091] This section describes how some embodiments disclosed herein can be implemented in one or more standards. It is note that this section is very specific and that other implementations are possible.

[0153]

[0092] Regarding 3GPP TS 38.413 entitled “NG-RAN; NG Application Protocol (NGAP)” version 18.2.0 uploaded on 2024-07-03, the following subject matter could be adopted.

[0154] 8.X Data Collection Request Procedures

[0155] 8.X.1 Data Collection request

[0156] 8.X.1.1 General

[0157] The purpose of the Data Collection Request procedure is to request AMF that the NG-RAN node needs to collect data for the purpose of AIML for positioning and hence it requires assistance from an LMF. The NG-RAN node includes the area (list of cells) where the data collection needs to be performed. The AMF upon receiving this request queries with the NRF to identify an LMF that serves the requested area for location or data collection purposes. This procedure uses non-UE associated signaling.

[0158] 8.X.1.2Successful Operation

[0159] Figure 10 (8.X.1.2-1): Data Collection Request

[0160] The NG-RAN node initiates the procedure by sending an Data collection Request message to the AMF.

[0161] Upon reception of the Data collection Request message, the AMF shall identify an LMF and forward the NG-RAN node request to the LMF.

[0162] 8.X.1.3 Abnormal Conditions

[0163] The AMF may return failure, if it is unable to identify any LMF.

[0093] Regarding 3GPP TS 24.501 entitled “Non-Access-Stratum (NAS) protocol for 5G System (5GS); Stage 3” version 18.2.0 uploaded on 2023-04-03, the following subject matter could be adopted.

[0164] 4.X Data Collection Assistance to UE

[0165] A 5GS can support a UE to collect data for the purpose of AIML. This subclause describes NAS- specific aspects of the 5GS features to support the authorization of the UE, and identification of an LMF for the provisioning of assistance data for the purpose of data collection.

[0166] The AMF authorizes the UE's request for data collection based on the subscription information. The AMF can indicate to the UE that it is not allowed to collect the data.

[0167] The UE requests for data collection from an area (list of cells, tracking area) and the AMF identifies the suitable LMF that can provide the assistance data for the purpose of data collection.

[0168] Additional Details

[0169]

[0094] Additional details are provided below with reference to Figures 11 through 18. It is to be understood that these details are very specific for exemplary purposes only.

[0170]

[0095] Figure 11 illustrates one example of a cellular communications system 500 in which embodiments of the present disclosure may be implemented. In the embodiments described herein, the cellular communications system 500 is a 5GS (5G system) including a NG-RAN (Next Generation RAN) and a 5GC (5G Core). In this example, the RAN includes base stations 102-1 and 502-2, which in the 5GS include NR base stations (gNBs) and optionally next generation eNBs (ng-eNBs) (e.g., LTE RAN nodes connected to the 5GC), controlling corresponding (macro) cells 504-1 and 504-2. The base stations 502-1 and 502-2 are generally referred to herein collectively as base stations 502 and individually as base station 502. Likewise, the (macro) cells 504-1 and 504-2 are generally referred to herein collectively as (macro) cells 504 and individually as (macro) cell 504. The RAN may also include a number of low power nodes 506-1 through 506-4 controlling corresponding small cells 508-1 through 508-4. The low power nodes 506-1 through 506-4 can be small base stations (such as pico or femto base stations) or RRHs (Remote Radio Heads), or the like. Notably, while not illustrated, one or more of the small cells 508-1 through 508-4 may alternatively be provided by the base stations 502. The low power nodes 506-1 through 506-4 are generally referred to herein collectively as low power nodes 506 and individually as low power node 506. Likewise, the small cells 508-1 through 508-4 are generally referred to herein collectively as small cells 508 and individually as small cell 508. The cellular communications system 500 also includes a core network 510, which in the 5G System (5GS) is referred to as the 5GC. The base stations 502 (and optionally the low power nodes 506) are connected to the core network 510.

[0171]

[0096] The base stations 502 and the low power nodes 506 provide service to wireless communication devices 512-1 through 512-5 in the corresponding cells 504 and 508. The wireless communication devices 512-1 through 512-5 are generally referred to herein collectively as wireless communication devices 512 and individually as wireless communication device 512. In the following description, the wireless communication devices 512 are oftentimes UEs, but the present disclosure is not limited thereto.

[0172]

[0097] Referring now to Figure 12A, shown is a block diagram of a wireless communication system represented as a 5G network architecture composed of core NFs (Network Functions), where interaction between any two NFs is represented by a point-to- point reference point / interface. Figure 12A can be viewed as one particular implementation of the system 500 of Figure 11 .

[0173]

[0098] Seen from the access side the 5G network architecture shown in Figure 12A includes a plurality of UEs 613 connected to either a RAN 607 or an (Access Network) as well as an AMF 600. Typically, the R(AN) 607 comprises base stations, e.g. such as eNBs or gNBs or similar. Seen from the core network side, the 5GC NFs shown in Figure 12A include a NSSF 602, an AUSF 604, a UDM 606, the AMF 600, a SMF 608, a PCF 610, and an AF (Application Function) 612.

[0174]

[0099] Reference point representations of the 5G network architecture are used to develop detailed call flows in the normative standardization. The N1 reference point is defined to carry signaling between the UE 613 and AMF 600. The reference points for connecting between the AN 607 and AMF 600 and between the AN 607 and UPF 614 are defined as N2 and N3, respectively. There is a reference point, N11 , between the AMF 600 and SMF 608, which implies that the SMF 608 is at least partly controlled by the AMF 600. N4 is used by the SMF 608 and UPF 614 so that the UPF 614 can be set using the control signal generated by the SMF 608, and the UPF 614 can report its state to the SMF 608. N9 is the reference point for the connection between different UPFs 614, and N14 is the reference point connecting between different AMFs 600, respectively. N15 and N7 are defined since the PCF 610 applies policy to the AMF 600 and SMF 608, respectively. N12 is utilized for the AMF 600 to perform authentication of the UE 613. N8 and N10 are defined because the subscription data of the UE 613 is utilized for the AMF 600 and SMF 608.

[0175]

[0100] The 5GC network aims at separating UP and CP. The UP carries user traffic while the CP carries signaling in the network. In Figure 12A, the UPF 614 is in the UP and all other NFs, i.e., the AMF 600, SMF 608, PCF 610, AF 612, NSSF 602, AUSF 604, and UDM 606, are in the CP. Separating the UP and CP guarantees each plane resource to be scaled independently. It also allows UPFs to be deployed separately from CP functions in a distributed fashion. In this architecture, UPFs may be deployed very close to UEs to shorten the RTT (Round Trip Time) between UEs and data network for some applications involving low latency.

[0176]

[0101] The core 5G network architecture is composed of modularized functions. For example, the AMF 600 and SMF 608 are independent functions in the CP. Separated AMF 600 and SMF 608 allow independent evolution and scaling. Other CP functions like the PCF 610 and AUSF 604 can be separated as shown in Figure 12A. Modularized function design enables the 5GC network to support various services flexibly.

[0177]

[0102] Each NF interacts with another NF directly. It is possible to use intermediate functions to route messages from one NF to another NF. In the CP, a set of interactions between two NFs is defined as service so that its reuse is possible. This service enables support for modularity. The UP supports interactions such as forwarding operations between different UPFs.

[0178]

[0103] Referring now to Figure 12B, shown is a block diagram of a 5G network architecture using service-based interfaces between the NFs in the CP, instead of the point-to-point reference points / interfaces used in the 5G network architecture of Figure 12A. However, the NFs described above with reference to Figure 12B correspond to the NFs shown in Figure 12A. The service(s) etc. that a NF provides to other authorized NFs can be exposed to the authorized NFs through the service-based interface. In Figure 12B, the service based interfaces are indicated by the letter “N” followed by the name of the NF, e.g. Namf for the service based interface of the AMF 600 and Nsmf for the service based interface of the SMF 608, etc. The NEF 603 and the NRF 601 in Figure 12B are not shown in Figure 12A discussed above. However, it should be clarified that all NFs depicted in Figure 12A can interact with the NEF 603 and the NRF 601 of Figure 12B as necessary, though not explicitly indicated in Figure 12A.

[0179]

[0104] Some properties of the NFs shown in Figures 12A and 12B may be described in the following manner. The AMF 600 provides UE-based authentication, authorization, mobility management, etc. A UE 613 even using multiple access technologies is basically connected to a single AMF 600 because the AMF 600 is independent of the access technologies. The SMF 608 is responsible for session management and allocates IP (Internet Protocol) addresses to UEs. It also selects and controls the UPF 614 for data transfer. If a UE 613 has multiple sessions, different SMFs 608 may be allocated to each session to manage them individually and possibly provide different functionalities per session. The AF 612 provides information on the packet flow to the PCF 610 responsible for policy control in order to support QoS. Based on the information, the PCF 610 determines policies about mobility and session management to make the AMF 600 and SMF 608 operate properly. The AUSF 604 supports authentication function for UEs or similar and thus stores data for authentication of UEs or similar while the UDM 606 stores subscription data of the UE 613. The DN (Data Network), not part of the 5GC network, provides Internet access or operator services and similar.

[0180]

[0105] An NF may be implemented either as a network element on a dedicated hardware, as a software instance running on a dedicated hardware, or as a virtualized function instantiated on an appropriate platform, e.g., a cloud infrastructure.

[0181]

[0106] Figure 13 is a schematic block diagram of a radio access node 700 according to some embodiments of the present disclosure. Optional features are represented by dashed boxes. The radio access node 700 may be, for example, a base station 102 or 106 or a network node that implements all or part of the functionality of the base station 102 or gNB described herein. As illustrated, the radio access node 700 includes a control system 702 that includes one or more processors 704 (e.g., CPUs (Central Processing Units), ASICs (Application Specific Integrated Circuits), FPGAs (Field Programmable Gate Arrays), and / or the like), memory 706, and a network interface 708. The one or more processors 704 are also referred to herein as processing circuitry. In addition, the radio access node 700 may include one or more radio units 710 that each includes one or more transmitters 712 and one or more receivers 714 coupled to one or more antennas 716. The radio units 710 may be referred to or be part of radio interface circuitry. In some embodiments, the radio unit(s) 710 is external to the control system 702 and connected to the control system 702 via, e.g., a wired connection (e.g., an optical cable). However, in some other embodiments, the radio unit(s) 710 and potentially the antenna(s) 716 are integrated together with the control system 702. The one or more processors 704 operate to provide one or more functions of a radio access node 700 as described herein. In some embodiments, the function(s) are implemented in software that is stored, e.g., in the memory 706 and executed by the one or more processors 704.

[0182]

[0107] Figure 14 is a schematic block diagram that illustrates a virtualized embodiment of the radio access node 700 according to some embodiments of the present disclosure. This discussion is equally applicable to other types of network nodes. Further, other types of network nodes may have similar virtualized architectures. Again, optional features are represented by dashed boxes.

[0183]

[0108] As used herein, a “virtualized” radio access node is an implementation of the radio access node 700 in which at least a portion of the functionality of the radio access node 700 is implemented as a virtual component(s) (e.g., via a virtual machine(s) executing on a physical processing node(s) in a network(s)). As illustrated, in this example, the radio access node 700 may include the control system 702 and / or the one or more radio units 710, as described above. The control system 702 may be connected to the radio unit(s) 710 via, for example, an optical cable or the like. The radio access node 700 includes one or more processing nodes 800 coupled to or included as part of a network(s) 802. If present, the control system 702 or the radio unit(s) 710 are connected to the processing node(s) 800 via the network 802. Each processing node 800 includes one or more processors 804 (e.g., CPUs, ASICs, FPGAs, and / or the like), memory 806, and a network interface 808.

[0184]

[0109] In this example, functions 810 of the radio access node 700 described herein are implemented at the one or more processing nodes 800 or distributed across the one or more processing nodes 800 and the control system 702 and / or the radio unit(s) 810 in any desired manner. In some particular embodiments, some or all of the functions 810 of the radio access node 700 described herein are implemented as virtual components executed by one or more virtual machines implemented in a virtual environment(s) hosted by the processing node(s) 800. As will be appreciated by one of ordinary skill in the art, additional signaling or communication between the processing node(s) 800 and the control system 702 is used in order to carry out at least some of the desired functions 810. Notably, in some embodiments, the control system 702 may not be included, in which case the radio unit(s) 810 communicates directly with the processing node(s) 800 via an appropriate network interface(s).

[0185]

[0110] In some embodiments, a computer program including instructions which, when executed by at least one processor, causes the at least one processor to carry out the functionality of radio access node 700 or a node (e.g., a processing node 800) implementing one or more of the functions 810 of the radio access node 700 in a virtual environment according to any of the embodiments described herein is provided. In some embodiments, a carrier comprising the aforementioned computer program product is provided. The carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium (e.g., a non-transitory computer readable medium such as memory).

[0186]

[0111] Figure 15 is a schematic block diagram of the radio access node 700 according to some other embodiments of the present disclosure. The radio access node 700 includes one or more modules 800, each of which is implemented in software. The module(s) 800 provide the functionality of the radio access node 700 described herein. This discussion is equally applicable to the processing node 800 of Figure 14 where the modules 800 may be implemented at one of the processing nodes 800 or distributed across multiple processing nodes 800 and / or distributed across the processing node(s) 800 and the control system 702.

[0187]

[0112] Figure 16 is a schematic block diagram of a wireless communication device 900 according to some embodiments of the present disclosure. As illustrated, the wireless communication device 900 includes one or more processors 902 (e.g., CPUs, ASICs, FPGAs, and / or the like), memory 904, and one or more transceivers 906 each including one or more transmitters 908 and one or more receivers 910 coupled to one or more antennas 912. The transceiver(s) 906 includes radio-front end circuitry connected to the antenna(s) 912 that is configured to condition signals communicated between the antenna(s) 912 and the processor(s) 902, as will be appreciated by on of ordinary skill in the art. The processors 902 are also referred to herein as processing circuitry. The transceivers 906 are also referred to herein as radio circuitry. In some embodiments, the functionality of the wireless communication device 900 described above may be fully or partially implemented in software that is, e.g., stored in the memory 904 and executed by the processor(s) 902. Note that the wireless communication device 900 may include additional components not illustrated in Figure 16 such as, e.g., one or more user interface components (e.g., an input / output interface including a display, buttons, a touch screen, a microphone, a speaker(s), and / or the like and / or any other components for allowing input of information into the wireless communication device 900 and / or allowing output of information from the wireless communication device 900), a power supply (e.g., a battery and associated power circuitry), etc.

[0188]

[0113] In some embodiments, a computer program including instructions which, when executed by at least one processor, causes the at least one processor to carry out the functionality of the wireless communication device 900 according to any of the embodiments described herein is provided. In some embodiments, a carrier comprising the aforementioned computer program product is provided. The carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium (e.g., a non-transitory computer readable medium such as memory).

[0114] Figure 17 is a schematic block diagram of the wireless communication device 900 according to some other embodiments of the present disclosure. The wireless communication device 900 includes one or more modules 1000, each of which is implemented in software. The module(s) 1000 provide the functionality of the wireless communication device 900 described herein.

[0189]

[0115] Each station 1106A, 1106B, 1106C is connectable to the core network 1104 over a wired or wireless connection 1110. A first UE 1112 located in coverage area 1108C is configured to wirelessly connect to, or be paged by, the corresponding base station 1106C. A second UE 1114 in coverage area 1108A is wirelessly connectable to the corresponding base station 1106A. While a plurality of UEs 1112, 1114 are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole UE is in the coverage area or where a sole UE is connecting to the corresponding base station 1106.

[0190]

[0116] The telecommunication network 1100 is itself connected to a host computer 1116, which may be embodied in the hardware and / or software of a standalone server, a cloud-implemented server, a distributed server, or as processing resources in a server farm. The host computer 1116 may be under the ownership or control of a service provider, or may be operated by the service provider or on behalf of the service provider. Connections 1118 and 1120 between the telecommunication network 1100 and the host computer 1116 may extend directly from the core network 1104 to the host computer 1116 or may go via an optional intermediate network 1122. The intermediate network 1122 may be one of, or a combination of more than one of, a public, private, or hosted network; the intermediate network 1122, if any, may be a backbone network or the Internet; in particular, the intermediate network 1122 may comprise two or more sub-networks (not shown).

[0191]

[0117] Any appropriate steps, methods, features, functions, or benefits disclosed herein may be performed through one or more functional units or modules of one or more virtual apparatuses. Each virtual apparatus may comprise a number of these functional units. These functional units may be implemented via processing circuitry, which may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include DSPs (Digital Signal Processor), special-purpose digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as ROM (Read Only Memory), RAM (Random Access Memory), cache memory, flash memory devices, optical storage devices, etc. Program code stored in memory includes program instructions for executing one or more telecommunications and / or data communications protocols as well as instructions for carrying out one or more of the techniques described herein. In some implementations, the processing circuitry may be used to cause the respective functional unit to perform corresponding functions according one or more embodiments of the present disclosure.

[0192]

[0118] While processes in the figures may show a particular order of operations performed by certain embodiments of the present disclosure, it should be understood that such order is exemplary (e.g., alternative embodiments may perform the operations in a different order, combine certain operations, overlap certain operations, etc.).

[0119] Numerous modifications and variations of the present disclosure are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended embodiments, the disclosure may be practised otherwise than as specifically described herein.

Claims

Claims:1 . A method for execution by a first network node, comprising: sending, to a second network node, an inquiry for discovering a third network node that can facilitate data collection for an AI / ML (Artificial Intelligence I Machine Learning) model for predicting positioning of a UE (User Equipment) in a defined area; receiving, from the second network node, a response identifying the third network node that can facilitate the data collection in the defined area; and executing at least one step initiating the data collection in the defined area using the third network node.

2. The method of claim 1 , further comprising: receiving, from a RAN (Radio Access Network) node, a request for data collection for the AI / ML model for predicting positioning of the UE in the defined area, wherein the defined area is identified from the request from the RAN node; wherein the first network node comprises an AMF (Access and Mobility Management Function), the second network node comprises an NRF (Network Repository Function), and the third network node comprises an LMF (Location Management Function); and wherein executing at least one step initiating the data collection in the defined area using the third network node comprises the AMF forwarding to the LMF the request thereby initiating data collection by the RAN node from the LMF.

3. The method of claim 1 , wherein: the first network node comprises an AMF, the second network node comprises an NRF, and the third network node comprises an LMF; and executing at least one step initiating the data collection in the defined area using the third network node comprises the AMF sending to the LMF a request for datacollection for the AI / ML model for predicting positioning of the UE in the defined area, thereby initiating data collection by a RAN node from the LMF.

4. The method of claim 1 , wherein: the first network node comprises an LMF, the second network node comprises an NRF, and the third network node comprises an AMF; and executing at least one step initiating the data collection in the defined area using the third network node comprises the LMF identifying which UEs are in the defined area, and the LMF initiating data collection by a RAN node from the LMF.

5. The method of claim 1 , further comprising: receiving, from a fourth node, a request for data collection for the AI / ML model for predicting positioning of the UE in the defined area, wherein the defined area is identified from the request from the UE; the first network node comprises an AMF, the second network node comprises an NRF, and the third network node comprises an LMF; and wherein executing at least one step initiating the data collection in the defined area using the third network node comprises the AMF forwarding to the LMF the request thereby initiating data collection by the fourth node from the LMF; wherein the fourth node comprises a UE.

6. The method of claim 1 , further comprising: receiving, from a fourth node, a request for data collection for the AI / ML model for predicting positioning of the UE in the defined area, wherein the defined area is identified from the request from the UE; the first network node comprises an AMF, the second network node comprises an NRF, and the third network node comprises an OAM (Operations, Administration, and Maintenance); andwherein executing at least one step initiating the data collection in the defined area using the third network node comprises the AMF forwarding to the OAM the request thereby initiating data collection by the fourth node from the OAM; wherein the fourth node comprises a UE or a RAN node.

7. A non-transitory CRM (computer readable medium) having recorded thereon statements and instructions that, when executed by a processor of a first network node, configure the first network node to implement a method according to any one of claims 1 to 6.

8. A first network node, comprising: a network interface configured to communicate with other network nodes; control circuitry coupled to the network interface and configured to: send, to a second network node via the network interface, an inquiry for discovering a third network node that can facilitate data collection for an AI / ML (Artificial Intelligence I Machine Learning) model for predicting positioning of a UE (User Equipment) in a defined area; receive, from the second network node via the network interface, a response identifying the third network node that can facilitate the data collection in the defined area; and execute at least one step initiating the data collection in the defined area using the third network node.

9. The first network node of claim 8, wherein the control circuitry is further configured to implement a method according to any one of claims 2 to 6.

10. A method for execution by a second network node, comprising: receiving, from a first network node, an inquiry for discovering a third network node that can facilitate data collection for an AI / ML (Artificial Intelligence I Machine Learning) model for predicting positioning of a UE (User Equipment) in a defined area; sending, to the first network node, a response identifying the third network node that can facilitate the data collection in the defined area.

11. The method of claim 10, wherein the first network node comprises an AMF (Access and Mobility Management Function), the second network node comprises an NRF (Network Repository Function), and the third network node comprises an LMF (Location Management Function).

12. The method of claim 11 , further comprising: receiving, from the LMF, information indicating support of data collection in certain areas.

13. The method of claim 10, wherein the first network node comprises an LMF, the second network node comprises an NRF, and the third network node comprises an AMF.

14. The method of claim 13, further comprising: receiving, from the AMF, information indicating support of data collection in certain areas.

15. The method of claim 10, wherein the first network node comprises an AMF, the second network node comprises an NRF, and the third network node comprises an OAM (Operations, Administration, and Maintenance).

16. The method of claim 15, further comprising: receiving, from the OAM, information indicating support of data collection in certain areas.

17. A non-transitory CRM (computer readable medium) having recorded thereon statements and instructions that, when executed by a processor of a second network node, configure the second network node to implement a method according to any one of claims 10 to 16.

18. A second network node, comprising: a network interface configured to communicate with other network nodes; control circuitry coupled to the network interface and configured to: receive, from a first network node, an inquiry for discovering a third network node that can facilitate data collection for an AI / ML (Artificial Intelligence I Machine Learning) model for predicting positioning of a UE (User Equipment) in a defined area; and send, to the first network node, a response identifying the third network node that can facilitate the data collection in the defined area.

19. The second first network node of claim 18, wherein the control circuitry is further configured to implement a method according to any one of claims 11 to 16.

20. A communication system comprising the first network node of claim 8 or claim 9, and the second network node of claim 18 or claim 19.

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