Method performed by radio access network node and method performed by location management function

Abstract

A wireless communication system is described that may have at least a non-distributed and / or distributed RAN architecture (e.g., a RAN node CU, and at least one RAN node DU), and at least one UE. In particular, herein is described are means and methods of implementing AI / ML models in a wireless communication system to improve the accuracy of UE position determinations in the communication system.

Description

METHOD PERFORMED BY RADIO ACCESS NETWORK NODE AND METHOD PERFORMED BY LOCATION MANAGEMENT FUNCTION

[0001] The present disclosure relates to a communication system and to parts thereof.

[0002] The disclosure has particular but not exclusive relevance to wireless communication systems and devices thereof operating according to the 3rd Generation Partnership Project (3GPP) standards or equivalents or derivatives thereof (including Long-Term Evolution (LTE)-Advanced, Next Generation or 5G / 6G networks, future generations, and beyond). The present disclosure in particular, but not exclusively, relates to the use of AI / ML models to improve the accuracy of UE position determinations in the communication system.

[0003] Earlier developments of the 3GPP standards were referred to as the LTE of Evolved Packet Core (EPC) network and Evolved Universal Mobile Telecommunications System UMTS Terrestrial Radio Access Network (E-UTRAN), also commonly referred as '4G'. More recently, the term '5G' and 'new radio' (NR) has started to be used to refer to an evolving communication technology that is expected to support a variety of applications and services. Various details of 5G networks are described in, for example, the 'NGMN 5G White Paper' V1.0 by the Next Generation Mobile Networks (NGMN) Alliance, which document is available from https: / / www.ngmn.org / 5g-white-paper.html. 3GPP intends to support 5G by way of the so-called 3GPP Next Generation (NextGen) radio access network (RAN) and the 3GPP NextGen core network.

[0004] Under the 3GPP standards, a NodeB (or e.g., an eNB in LTE, and gNB in 5G) is the radio access network (RAN) node (or simply 'access node', 'access network node' or 'base station') via which communication devices (user equipments or 'UEs') connect to a core network and communicate with other communication devices or remote servers. For simplicity, the present application will use the term access network node, RAN node or base station to refer to any such access nodes.

[0005] For simplicity, the present application will use the term mobile device, user device, or UE to refer to any communication device that is able to connect to the core network via one or more RAN nodes. Although the present application may refer to mobile devices in the description, it will be appreciated that the technology described can be implemented on any communication devices (mobile and / or generally stationary) that can connect to a communication network for sending / receiving data, regardless of whether such communication devices are controlled by human input or software instructions stored in memory.

[0006] The RAN node structure may be split into two or more parts. In some RAN implementations there are two parts, known as the Central Unit (CU or sometimes gNB-CU) - sometimes referred to as a 'control unit' - and the Distributed Unit (DU or sometimes gNB-DU), connected by an F1 interface. This enables the use of a 'split' architecture in which the typically 'higher' CU layers (for example, but not necessarily or exclusively, Packet Data Convergence Protocol (PDCP) and Radio Resource Control (RRC) layers) and the, 'lower' DU layers (for example, but not necessarily or exclusively, Radio Link Control (RLC), Media (sometimes referred to as 'Medium') Access Control (MAC), and Physical (PHY) layers) are separated between a particular CU, and one or more DUs that are connected to and controlled by that CU via the F1 interface. Thus, for example, the higher layer CU functionality for a number of gNBs may be implemented centrally (for example, by a single processing unit, or in a cloud-based or virtualised system), whilst retaining the lower layer DU functionality locally separately for each gNB.

[0007] In more recently proposed RAN distributed architectures, in addition to the CU and DU, the concept of a Radio Unit (RU) - sometimes referred to as a 'remote unit' - has been introduced. In this architecture the RU is responsible for handling the digital front end (DFE), digital beamforming functionality and, typically, the functionality of the lower parts of the PHY layer, whilst the DU typically handles the higher parts of the PHY layer and the RLC and MAC layers. The CU in this architecture continues to be responsible for controlling one or more DUs (each DU corresponding to a different respective gNB) and to handle higher layer signalling (typically RRC and PDCP layers).

[0008] The actual functional split between the CU and DUs (and potentially RUs where applicable) of these distributed architectures is flexible allowing the functionality to be optimised for different use cases. Effectively, the split architecture enables a network to use a different distribution of protocol stacks between CU and DUs (and potentially RUs) depending on, for example, mid-haul availability and network design.

[0009] The choice of how to split functions in the architecture depends on, among other things, factors related to radio network deployment scenarios, constraints and intended supported use cases. Key considerations include: the need to support a specific quality of service for each service offered and for real / non-real time applications; support of specific user density and load demand in a given geographical area; and available transport networks with different performance levels.

[0010] The coverage in many modern communication systems is often beam-based rather than cell based. There is no cell-level reference channel from where the coverage of the cell could be measured. Instead, each cell has one or more so-called synchronization signal / physical broadcast channel (PBCH) block (SSB) beams. SSB beams form a matrix of beams covering an entire cell area. Each SSB beam carries an SSB comprising a primary synchronization signal (PSS), secondary synchronization signal (SSS), and physical broadcast channel (PBCH).

[0011] The UE searches for and performs measurements on the SSB beams (e.g., of the synchronization signal reference signal received power, 'SS-RSRP,' synchronization signal reference signal received quality, 'SS-RSRQ,' and / or the synchronization signal to noise and interference ratio, 'SS-SINR'). The UE maintains a set of candidate beams which may contain beams from multiple cells. A PCI and beam ID (or SSB index) thus distinguish the SSB beams from each other. Effectively, therefore, the SSB beams are like mini cells which may be within a larger cell. Once a UE has detected and selected a cell (and / or an SSB beam) it may attempt to access that cell and / or SSB beam using an initial RRC connection setup procedure comprising a random-access procedure.

[0012] For example, once a UE has detected and selected a cell (and / or a beam) it may attempt to access that cell and / or beam using an initial radio resource control (RRC) connection setup procedure comprising a random access (RACH) procedure that typically involves four distinct steps. Alternatively, the UE may attempt to access that cell and / or beam using a so-called two-step RACH procedure. Both the four step and two step RACH procedures are well known to those skilled in the art.

[0013] The ability to accurately locate the position of a UE has long been an important and developing part of cellular communication technology. Originally driven by regulatory requirements for emergency calls, cellular positioning technology has been developed to provide significant improvements in accuracy, coverage extent (both indoors and outdoors), latency, reliability etc.

[0014] Positioning in developing communication technologies is anticipated to provide or improve support for many and varied positioning use cases, each coming with its own respective performance requirements. These use cases include, for example: enhanced indoor navigation (e.g., in shopping malls, hospitals, or underground facilities); tracking of unmanned (autonomous) vehicles; public safety applications (e.g., assisting first responders to reach emergencies more quickly, or monitoring the location of vulnerable people); smart factories; localised sensing; digital twins; augmented / virtual reality; etc.

[0015] Positioning methods supported in modern communication systems include, amongst other things: RAT-dependent methods including Observed Time Difference Of Arrival (OTDOA) based positioning; Uplink Time Difference of Arrival (UTDOA) based positioning; Roundtrip time (RTT) based positioning; Angle of Arrival (AOA) based positioning; and RAT-independent methods including Global Navigation Satellite System (GNSS) based positioning; barometric sensor based positioning; and Bluetooth based positioning.

[0016] New reference signals and related measurements have been introduced to support enhanced (e.g., more accurate / precise) positioning related measurements (compared to earlier techniques). These signals include newly defined dedicated positioning reference signals (PRS) for positioning in the downlink and sounding reference signals (SRS) for positioning in the uplink. For example, a UE can perform downlink reference signal time difference (DL RSTD) measurements for each RAN node's PRSs and report these to the location server for downlink positioning. Similarly, each RAN node can measure the uplink relative time of arrival (UL-RTOA) of SRS and report the measurements to the location server for uplink positioning. Moreover, channel state information reference signals (CSI-RS) and synchronisation signal blocks (SSBs) can also be used (e.g., as part of an enhanced cell ID (E-CID) positioning method). More recent developments include: the provision of positioning for UEs in the RRC inactive state; on-demand transmission and reception of downlink PRS; enhancements for angle based methods; enhancements of information reporting from the UE, and the RAN node, for supporting mitigation of multipath / non-line of sight (NLOS) effects; enhancements of signalling and procedures for reducing positioning latency; and signalling and procedures to support global navigation satellite system (GNSS) positioning integrity.

[0017] Some recent developments in 3GPP relate to the use of artificial intelligence and ML, often abbreviated to AI / ML. Predictions or inferences generated using an AI / ML model can be used as part of various methods for improving the reliability or efficiency of communication in the network. For example, AI / ML models can be used to predict the path of a UE based on previous mobility of the UE, used for cell and / or beam management, or used in methods of encoding and transmitting information. An AI / ML model may be hosted at a RAN node, and the RAN node may perform control of communication resources or control related to the status of a UE (e.g., control of UE mobility, or control of a radio resource control (RRC) state of the UE) based on an inference (e.g., determination or prediction) generated using the AI / ML model. The RAN node may also transmit an inference generated using the model to another node in the network, for use at the other node.

[0018] Alternatively, an AI / ML model may be hosted at two nodes of the network, for example at a RAN node and at a UE. In this case, the RAN node and the UE may both make determinations or predictions using the model. For example, the UE may use the model as part of an encoding process for encoding (and / or compressing) channel state information (CSI) for transmission to the RAN node, and the RAN node may use the same model as part of a corresponding decoding (and / or decompression) process for decoding the CSI received from the UE.

[0019] NPL 1: 'NGMN 5G White Paper' V1.0 by the Next Generation Mobile Networks (NGMN), available from https: / / www.ngmn.org / 5g-white-paper.html.

[0020] It will nevertheless be appreciated that the use of AI / ML models may also be extended to other procedures and methods performed in a communication system to further improve the reliability or efficiency of communication in the network, for example, for the improved determination (e.g., improved accuracy) of UE positions in the communication system.

[0021] In the context of improved positioning accuracy, for example, there is a need for positioning accuracy enhancements in respect either or both of: direct AI / ML positioning (in which an AI / ML model at one more particular entities provides, as an output, an estimated UE position); and AI / ML assisted positioning (in which an AI / ML model at one more particular entities provides, as an output, information from which an estimated UE position can be derived at the same or a different entity).

[0022] For example, direct AI / ML positioning may involve: UE-based positioning in which a UE-side model provides an inferred AI / ML positioning output directly (which may be referred to as 'Case 1'); UE-assisted LMF-based positioning in which an LMF-side model provides an inferred AI / ML positioning output, based on 'assistance information' or the like from the UE (which may be referred to as 'Case 2b'); and / or RAN node assisted positioning in which an LMF-side model provides an inferred AI / ML positioning output, based on 'assistance information' or the like from the RAN node (which may be referred to as 'Case 3b').

[0023] Similarly, AI / ML assisted positioning may involve: UE-assisted LMF-based positioning in which a UE-side model provides inferred AI / ML assistance information, or the like, and the LMF (or possibly the UE itself) determines an associated positioning output, based on that inferred assistance information (which may be referred to as 'Case 2a'); and / or RAN node assisted positioning in which a RAN node-side model provides inferred AI / ML assistance information, or the like, and the LMF may determine an associated positioning output, based on that inferred assistance information (which may be referred to as 'Case 3a').

[0024] For example, AI / ML models may be extended to generate inferences about the positions of UEs in a communication system using, for example, UE-assisted information and / or location management function (LMF)-based positioning assistance, and / or RAN node-based positioning assistance to facilitate life cycle management (LCM) operations specific to positioning accuracy enhancements in the communication system.

[0025] In order to support such positioning accuracy enhancements, there is a respective need to: specify necessary procedures / signalling to support input data and inference output exchange between RAN node and LMF; develop methods to associate information from LMF with information from the same UE in the same location.

[0026] The present specification aims to disclose apparatus and methods that at least contribute to addressing one or more of the above needs and / or issues.

[0027] The disclosure has a method performed by a radio access network, RAN, node, the method comprising transmitting, to a Location Management Function, LMF, a first message comprising first information indicating requested positioning information for data collection of an artificial intelligence / machine learning, AI / ML, model at the RAN node; and receiving, from the LMF, a second message for reporting positioning information for the data collection.

[0028] The disclosure has a method performed by a radio access network, RAN, node, the method comprising receiving, from a Location Management Function, LMF, a first message indicating requested positioning measurements at the LMF; and transmitting, to the LMF, positioning measurement results for the data collection at the LMF.

[0029] The disclosure has a method performed by a Location Management Function, LMF, the method comprising receiving, from a radio access network, RAN, node, a first message comprising first information indicating requested positioning information for data collection of an artificial intelligence / machine learning, AI / ML, model at the RAN node; and transmitting, to the RAN node, a second message for reporting positioning information for the data collection.

[0030] The disclosure has a method performed by a Location Management Function, LMF, the method comprising transmitting, to a radio access network, RAN, node, a first message indicating requested positioning measurements at the LMF; and receiving, from the RAN node, positioning measurement results for the data collection at the LMF.

[0031] The various functional means described below that are part of the UE may be provided by a memory and one or more processors that execute instructions stored in the memory. Similarly, the various functional means described below that are part of the access network node may be provided by a memory and one or more processors that execute instructions stored in the memory.

[0032] Various example described below may be implemented by means of a computer program product comprising computer implementable instructions for causing a programmable computer to carry out the any of the methods described below. The computer implementable instructions may be provided as a signal or on a tangible computer readable medium.

[0033] Examples of apparatus and methods will now be described, by way of example, with reference to the accompanying drawings in which:

[0034] Fig. 1 schematically illustrates a mobile ('cellular' or 'wireless') communication system;Fig. 2 illustrates a functional framework for AI / ML models, and how various entities of the framework may interact with one another, that may be implemented in the communication system of Fig. 1;Fig. 3 schematically illustrates a method of training an AI / ML model, and of monitoring the performance of the AI / ML model, which may be implemented in the communication system of Fig. 1;Fig. 4A illustrates a simplified sequence diagram of an example procedure for configuring and using an AI / ML model for UE location assisted information prediction stored at the RAN node in the communication system illustrated in Fig. 1;Fig. 4B illustrates a simplified sequence diagram of an example procedure for configuring and using an AI / ML model for UE location assisted information prediction stored at the RAN node in the communication system illustrated in Fig. 1;Fig. 5A illustrates a simplified sequence diagram of an example procedure for configuring and using an AI / ML model for UE location prediction stored in the LMF of the core network in the communication system illustrated in Fig. 1;Fig. 5B illustrates a simplified sequence diagram of an example procedure for configuring and using an AI / ML model for UE location prediction stored in the LMF of the core network in the communication system illustrated in Fig. 1;Fig. 6 illustrates a simplified sequence diagram of an example procedure for additional configuration and use of the AI / ML model in the procedure of Figs. 4A and 4B when the RAN node is a distributed RAN node;Fig. 7 illustrates a simplified sequence diagram of an example procedure for additional configuration and use of the AI / ML model in the procedure of Fig. 5 when the RAN node is a distributed RAN node;Fig. 8 is a simplified block schematic illustrating the main components of a UE for implementation in the communication system of Fig. 1;Fig. 9 is a simplified block schematic illustrating the main components of a non-distributed RAN node for implementation in the communication system of Fig. 1; andFig. 10 is a schematic block diagram illustrating the main components of a distributed RAN node for the communication system of Fig. 1.

[0035] <Overview> 　　An exemplary telecommunication system will now be described in general terms, by way of example only, with reference to Figs. 1 to 3.

[0036] Fig. 1 schematically illustrates a mobile ('cellular' or 'wireless') communication system (e.g., communication system 1) to which examples of the present disclosure are applicable.

[0037] In the communication system 1, user equipments (UEs) 3 (3-1, 3-2, 3-3) (e.g., mobile telephones and / or other mobile devices) can communicate with each other via a corresponding (radio) access network ((R)AN) node 5-1, 5-2 that operates according to one or more compatible radio access technologies (RATs). In the illustrated example, each RAN node 5 (5-1, 5-2) comprises a base station that respectively operates one or more associated cells. Communication via the RAN nodes 5 are typically routed through a core network 7 (e.g., a 5G / 6G or later generations core network or evolved packet core network (EPC)).

[0038] As those skilled in the art will appreciate, whilst three UEs 3 and two RAN nodes 5-1, 5-2 are shown in Fig. 1 for illustration purposes, the system, when implemented, will typically include other RAN nodes and UEs.

[0039] Each RAN node 5 controls one or more associated cells either directly, or indirectly via one or more other nodes (such as home base stations, relays, remote radio heads, distributed units, and / or the like). It will be appreciated that the RAN nodes 5 may be configured to support 4G, 5G, 6G and / or later generation, and / or any other 3GPP or non-3GPP communication protocols.

[0040] In this example one of the illustrated RAN nodes 5 is a non-distributed RAN node 5-1, while another RAN node 5 is a distributed RAN node 5-2. That distributed RAN node 5-2 forms part of a distributed RAN (which may be referred to as a 'distributed base station'). The RAN node 5-2 of a distributed RAN comprises at least one distributed unit (DU) 5-2DU(e.g., a gNB-DU or the like), and a central unit (CU) 5-2CU(e.g., a gNB-CU or the like). The CU 5-2CUemploys a separated control plane and user plane and so is, itself, split between a control plane function (CU-CP) and a user plane function (CU-UP) which respectively communicate, with the DU 5-2DUvia a first interface (e.g., an F1-C logical interface) and a second interface (e.g., an F1-C logical interface) (the interfaces together forming a combined interface such as an F1 interface (or 'reference point')), and with one another via another interface (e.g., an E1 logical interface). It will be appreciated that while, in this example, the DU 5-2DUincludes the physical and virtual elements required to provide the functionality of the lower parts of the PHY layer and hence communicate with the UEs 3 over the air interface, the distributed RAN node 5-2 may alternatively (or additionally) include one or more separate radio units (RUs) (e.g., providing this functionality of the lower parts of the PHY layer).

[0041] Whilst one non-distributed ('integrated') RAN node 5-1 and one distributed RAN node 5-2 are shown, it will, nevertheless, be appreciated that either (or both) of the RAN nodes 5 may be provided in a distributed or non-distributed form. It will also be appreciated that whilst the term 'RAN node' is generally used herein to refer to a whole base station, the CU 5-2CUand DU 5-2DUare also both parts of a corresponding distributed RAN and are therefore each a distinct 'RAN node' albeit that they each form part of the same RAN, and that each may have only a subset of the functionality provided by a whole base station. References to a RAN node as used herein should, therefore, be understood, accordingly.

[0042] The UEs 3 are configured for communication with their serving RAN node 5 via an appropriate air interface (for example a so-called 'Uu' interface and / or the like). Neighbouring RAN nodes 5 may be connected to each other via an appropriate RAN node to RAN node interface (such as the so-called 'X2' interface, 'Xn' interface and / or the like - not shown in Fig. 1).

[0043] The core network 7 includes a number of logical nodes (or 'functions') for supporting communication in the communication system 1. In this example, the core network 7 comprises control plane functions (CPFs) 10 and one or more network node entities for the communication of user data (e.g. user plane functions (UPFs) 11). The CPFs 10 include one or more network node entities for the communication of control signalling (e.g. Access and Mobility Management Functions (AMFs) 10-1), one or more network node entities for session management (e.g. Session Management Functions (SMFs) 10-2), one or more network node entities for the communication of location management (e.g., Location Management Functions (LMFs) 10-3), and a number of other functions 10-n. Additional functions may include, for example: an Authentication Server Function (AUSF) which facilitates security processes; a Unified Data Management (UDM) entity for managing user specific data (e.g., for access authorisation, user registration, and data network profiles); a Policy Control Function (PCF); an Application Function (AF); a Security Anchor Function (SEAF) which is in a serving network and acts as a "middleman" during an authentication process between the UE 3 and its home network; an Authentication credential Repository and Processing Function (ARPF) which maintains the authentication credentials; and / or the like. It will be appreciated that the nodes or functions may have different names in different systems.

[0044] Each RAN node 5 is connected to the core network nodes via appropriate interfaces (or 'reference points') such as an N2 reference point between the RAN node 5 and the AMF 10-1 for the communication of control signalling, and an N3 reference point between the RAN node 5 and each UPF 11 for the communication of user data. The UEs 3 are each connected to the AMF 10-1 via a non-access stratum (NAS) connection over an appropriate reference point (e.g., N1 reference point (analogous to the S1 reference point in LTE)). It will be appreciated, that N1 communication is routed transparently via the RAN node 5.

[0045] One or more UPFs 11 are connected to an external data network 40 (e.g., an IP network such as the internet) via an appropriate reference point (e.g., N6 reference point) for communication of the user data.

[0046] The AMF 10-1 performs mobility management related functions, maintains the NAS connection with each UE 3 and manages UE registration. The AMF 10-1 is also responsible for managing paging. The AMF 10-1 receives user information sent through the network and forwards the information to the SMF 10-2.

[0047] The SMF 10-2 is connected to the AMF 10-1 via an appropriate reference point (e.g., N11 reference point). The SMF 10-2 provides session management functionality (that formed part of MME functionality in LTE) and additionally combines some control plane functions (provided by the serving gateway and packet data network gateway in LTE). The SMF 10-2 uses user information provided via the AMF 10-1 to determine what session manager would be best assigned to the user. The SMF 10-2 may be considered effectively to be a gateway from the user plane to the control plane of the network. The SMF 10-2 also allocates IP addresses to each UE 3.

[0048] The LMF 10-3 manages the support of different location services for UEs 3 whose location is unknown and needs to be located ('target UEs'), including positioning of the UEs 3 and delivery of assistance data to the UEs 3. The LMF 10-3 may interact with the RAN node 5-1 for a target UE 3 in order to obtain position measurements for that UE 3, including uplink measurements made by the RAN node 5 (e.g., of sounding reference signals (SRS)) and downlink measurements made by the UE 3 (e.g., of positioning reference signals (PRS)) and provided to the RAN node 5. The LMF 10-3 may interact with a target UE 3 in order to deliver assistance data if requested for a particular location service, or to obtain a location estimate if requested.

[0049] For positioning of a target UE 3, the LMF 10-3 decides on the position methods to be used, based on factors that may include, for example, a location services (LCS) client type, a required quality of service (QoS), UE positioning capabilities, and / or RAN node positioning capabilities. The LMF 10-3 can invoke these positioning methods in the UE 3 and / or serving RAN node 5. The positioning methods may yield a location estimate for UE-based position methods and / or positioning measurements for UE-assisted and network-based position methods. The LMF 10-3 may combine the received results and determine a single location estimate for the target UE 3. Additional information like accuracy of the location estimate and velocity may also be determined.

[0050] The LMF 10-3 is connected to the AMF 10-1 via appropriate reference points (e.g., an NLs reference point). The LMF 10-3 is configured to receive measurement results (e.g., for PRS) and assistance information from the RAN node 5 and / or UEs 3, via the AMF 10-1 over the appropriate interfaces (e.g., NLs interface), and to compute the position of the UEs 3 based on the measurement results. The communication of positioning information between the RAN node 5 and the LMF 10-3 makes use of an appropriate positioning protocol (such as the NR Positioning Protocol A (NRPPa) or the like). The LMF 10-3 is also configured for configuring the UEs 3 using an appropriate positioning protocol (e.g., the LTE positioning protocol (LPP) or the like) via AMF 10-1.

[0051] Each RAN node 5 is also configured for transmission of, and the UEs 3 are configured for the reception of, control information and user data via a number of downlink (DL) physical channels and for transmission of a number of physical signals. The DL physical channels correspond to resource elements (REs) carrying information originated from a higher layer, and the DL physical signals are used in the physical layer and correspond to REs which do not carry information originated from a higher layer.

[0052] The physical channels may include, for example, a physical downlink shared channel (PDSCH), a physical broadcast channel (PBCH), and a physical downlink control channel (PDCCH). The PDSCH carries data sharing the PDSCH's capacity on a time and frequency basis. The PDSCH can carry a variety of items of data including, for example, user data, UE-specific higher layer control messages mapped down from higher channels, system information blocks (SIBs), and paging. The PDCCH carries downlink control information (DCI) for supporting a number of functions including, for example, scheduling the downlink transmissions on the PDSCH and also the uplink data transmissions on a physical uplink shared channel (PUSCH). The PBCH provides the UEs 3 with the Master Information Block (MIB). It also, in conjunction with the PDCCH, supports the synchronisation of time and frequency, which aids cell acquisition, selection and re-selection.

[0053] The DL physical signals may include, for example, reference signals (RSs) and synchronisation signals (SSs). A reference signal (sometimes known as a pilot signal) is a signal with a predefined special waveform known to both the UE 3 and its serving RAN node 5. The reference signals may include, for example, cell specific reference signals, UE-specific reference signal (UE-RS), downlink demodulation signals (DMRS), channel state information reference signal (CSI-RS), and / or PRS used for DL channel measurement.

[0054] Similarly, the UEs 3 are configured for transmission of, and their serving RAN node 5 is configured for the reception of, control information and user data via a number of uplink (UL) physical channels corresponding to REs carrying information originated from a higher layer, and UL physical signals which are used in the physical layer and correspond to REs which do not carry information originated from a higher layer. The physical channels may include, for example, the PUSCH, a physical uplink control channel (PUCCH), and / or a physical random-access channel (PRACH). The UL physical signals may include, for example, demodulation reference signals (DMRS) for a UL control / data signal, and / or SRS used for UL channel measurement.

[0055] The UEs 3 and their serving RAN node 5 are configured to support positioning in the communication system 1, for example by transmitting appropriate reference signals (e.g., SRS in the uplink and PRS in the downlink respectively), and by performing appropriate measurements on those reference signals (e.g., PRS in the downlink and SRS in the uplink respectively) and reporting the results to the LMF 10-3 for position determination. A UE 3 may, for example, perform measurements of the times at which reference signals (e.g., PRS) are received from different RAN nodes 5 to determine downlink reference signal time difference (DL RSTD) for the purposes of DL time difference of arrival (DL-TDOA) based positioning. A UE 3 may, for example, perform measurements of downlink reference signal receive power (DL RSRP) per beam / RAN node for use in determining the downlink angle of departure (DL AoD) based on UE beam location for each RAN node 5. The LMF 10-3 can then use the AoDs to estimate the UE position. A RAN node 5 may, for example, perform measurements of the times at which reference signals (e.g., SRS) are received at the RAN node 5 to determine uplink relative time of arrival (UL RTOA) for the purposes of UL time difference of arrival (UL-TDOA) based positioning. A RAN node 5 may, for example, perform measurements of an angle of arrival of received reference signals based on a beam the UE 3 is located in for the purposes of UL angle of arrival (UL-AOA) based positioning. The UE 3 and RAN node 5 may also perform receiver transmitter (Rx-Tx) time difference measurements for signals in each cell. Measurement reports including the results of these measurements from the UE 3 and RAN node 5 can then be used by the LMF 10-3 to derive corresponding round trip times (RTTs) for the purposes of multi-cell RTT based positioning.

[0056] Moreover, the UEs 3 and the RAN nodes 5 are mutually configured for performing a random-access channel (RACH) procedure for those UEs 3 to access the network. Specifically, on detection and selection of a cell (and / or a beam), the UE 3 is able to attempt access to that cell and / or beam using an initial radio resource control (RRC) connection setup procedure comprising a random-access procedure with the RAN node 5.

[0057] Prior to attempting initial access, the UE 3 will choose random access resources (including, for example, a preamble) to use to initiate the RACH procedure. The UE 3 sends the selected preamble (e.g., in 'Msg1') to a RAN node 5 over a physical random-access channel (PRACH) for initiating the process to obtain synchronisation in the uplink (UL). In response, the serving RAN node 5 responds with a random-access response (RAR) (or 'Msg2'). The RAR indicates reception of the preamble and includes: a timing-alignment (TA) command for adjusting the transmission timing of the UE 3 based on the timing of the received preamble; an uplink grant field indicating the resources to be used in the uplink for a physical uplink shared channel (PUSCH); a frequency hopping flag to indicate whether the UE 3 is to transmit on the PUSCH with or without frequency hopping; a modulation and coding scheme (MCS) field from which the UE 3 can determine the MCS for the PUSCH transmission; and a transmit power control (TPC) command value for setting the power of the PUSCH transmission. The UE 3 then sends a third message ('Msg3') to that RAN node 5 over a physical uplink shared channel (PUSCH) based on the information in the RAR. The specific message sent by the UE 3 in this step, and the content of the message, depends on the context in which the random-access procedure is being used. In the example of initial RRC connection setup, however, Msg3 typically comprises an RRC Setup request or similar message carrying a temporary randomly generated UE identifier. That RAN node 5 responds with a fourth message ('Msg4') which carries the randomly generated UE identifier received in Msg3 for contention purposes to resolve any collisions between different UEs 3 using the same preamble sequence. When successful, Msg4 also transfers the UE 3 to a connected state.

[0058] The UEs 3 and the RAN nodes 5 are also mutually configured for performing a two-step RACH procedure that involves the UE 3-2, 3-3 sending one message ('MsgA') to the RAN node 5 and the RAN node 5 sending one message ('MsgB') to the UE 3-1, 3-3. MsgA, in effect, combines Msg1 and Msg3 of the four-step procedure, and MsgB, in effect, combines Msg2 and Msg4 of the four-step procedure.

[0059] While contention-based RACH procedures are described it will be appreciated that a UE 3 and the RAN nodes 5 may also perform a non-contention based (or 'contention free') procedure in which a dedicated preamble is assigned by a RAN node 5 to a UE 3.

[0060] <AI / ML> 　　The communication system 1 supports the use of artificial intelligence (AI) and machine learning (ML), often abbreviated to AI / ML in accordance with recent developments in cellular communication technology (e.g., as part of the work of the 3GPP) that those skilled in the art will be familiar with. These AI / ML features make use of trained AI / ML models to make one or more predictions or inferences, from a set of one or more input vectors, which can be used in the network (e.g., for improving the reliability or efficiency of communication in the network).

[0061] In respect of the communication system 1, for example, AI / ML models could potentially be trained and used for predicting the path of a UE 3 based on previous mobility of the UE 3, used for beam management, or used in methods of encoding and transmitting information. An AI / ML model may be hosted at a RAN node 5 (or any other suitable network node), and the RAN node 5 may perform control of communication resources for UEs 3 it serves, and / or perform control related to the status of a UE 3 (e.g. control of UE mobility, or control of a radio resource control, RRC, state of the UE 3) based on an inference (e.g. determination or prediction) generated using the AI / ML model. The RAN node 5 may also transmit an inference generated using the model to another node in the network, for use at the other node. An AI / ML model may also be hosted the UE 3, or at a plurality of locations within the network, for example at both the RAN node 5 and at the UE 3. For example, the RAN node 5 and the UE 3 may both make determinations and / or predictions using the same model or different models.

[0062] The support for such AI / ML features may involve different levels of collaboration between the network (RAN node 5 and / or core network 7) and a UE 3 served by the network when deploying and using such AI / ML features. For example, three possible 'network-UE collaboration levels' that may be supported are: - Level x: Involving no collaboration between the network and the UE 3. Specifically, level x is an implementation-based AI / ML operation without any dedicated AI / ML-specific enhancement. - Level y: Signalling-based collaboration without AI / ML model transfer. For example, this level is applicable when model training is performed offline, and models are registered to both a RAN node 5 and the UE 3. Here, the RAN node 5 and the UE 3 are aware of available models (before operation), and the RAN node 5 is only required to activate / deactivate the models residing at the UE 3 when needed. - Level z: Signalling-based collaboration with AI / ML model transfer (e.g., where an AI / ML model is transferred to the UE 3 when needed

[0063] The AI / ML model types that are supported in the communication system 1 may include, for example: - Single-sided model: A single-sided AI / ML model is an AI / ML model that is deployed (hosted) only at the UE side or at the network side. For example, an AI / ML model may be hosted (stored, for generating inferences) at a UE 3, RAN node 5 , or a central entity of the communication system 1, or an operations, administration, and maintenance (OAM) / over-the-top (OTT) server. When the AI / ML model is used at a UE 3, a RAN node 5, a central entity of the communication system 1, or an OAM / OTT server only, the AI / ML model may be referred to as a 'single-sided' model. An example of this type of 'single-sided' model is an AI / ML model for beam prediction in time, which can be deployed at the UE side. - However, even when the model is a single-sided model, it will be appreciated that the model need not necessarily be trained at the node at which it is deployed (e.g., a UE 3 or a RAN node 5). For example, the model could be trained at the RAN node 5 (or at another node in the network such as a core network node / function - e.g., a central entity of the communication system 1, or an OAM / OTT server - and then transferred to the UE 3 for use at the UE 3. - Two-sided model: A 'two-sided' model is an AI / ML model (or model pair) that has one AI / ML model hosted at one node (e.g., the UE 3), and a corresponding AI / ML model hosted at another node (e.g., a RAN node 5) - it will be appreciated that any pair of network nodes may be used. Such a two-sided model may also be referred to as a 'paired' AI / ML model. An inference using a two-sided model is performed jointly across the nodes at which the AI / ML models of the two-sided model are deployed. The joint inference may comprise, for example, a first part of the inference being performed at one node (e.g., the UE 3 or RAN node 5), and then the remaining part may be performed by the other (e.g., the RAN node 5 or UE 3). It will be appreciated that whilst the AI / ML model hosted at the different nodes may be the same AI / ML model, they need not necessarily be the same model. One example of this type of model is, for example only, channel state information (CSI) compression, where the UE performs CSI compression and network performs CSI decompression. As with the single-sided model case, the two-sided model (or models) may be trained at any suitable network node, and then transmitted to the UE 3 and the RAN node 5 (or other respective node or nodes).

[0064] A general discussion of how AI / ML may be implemented in the communication system 1 will now be provided, by way of example only, with reference to Figs. 2 and 3.

[0065] Fig. 2 illustrates a functional framework for AI / ML models, and how various entities of the framework may interact with one another, that may be implemented in the communication system 1.

[0066] The entities include a data collection entity 241, a model training function 243, a model inference function 245, an actor 247, a management function 249, and a model storage entity 251.

[0067] The model storage entity 251 may be a reference point for protocol terminations for model transfer and delivery. The AI / ML models could be stored at any suitable node in the network.

[0068] The data collection entity 241 provides training data to the model training function 243, inference data to the model inference function 245, and monitoring data to the management function 249. The collected data may be, for example, data regarding mobility (e.g., handover of a UE 3, or a location of the UE 3). The data may be obtained, for example, by a UE 3 or a RAN node 5 (e.g., by receiving a measurement report from a UE 3, or by receiving data from another RAN node 5 or a core network node / function) and transmitted to another RAN node 5 or core network node that generates the AI / ML model inference output (or alternatively, the same RAN node 5 that obtains the data may generate the AI / ML model output).

[0069] The model training function 243 performs the AI / ML model training, validation, and testing, and may generate model performance metrics as part of a model testing procedure. The model training function 243 may output a trained AI / ML model to the model storage entity 251 (though it will be appreciated that the output model may be stored at locations other than model storage entity 251).

[0070] The model inference function 245 provides AI / ML model inference output (e.g., predictions or decisions), and the actor 247 is a function or node that receives the output from the model inference function 245 and triggers or performs corresponding actions (e.g., a RAN node 5 that increases / reduces its transmit power or initiates a handover procedure for a UE 3). The AI / ML model inference output may be, for example, a prediction of mobility (e.g., expected path, route or trajectory, inter-cell, or inter-beam mobility, or expected handover) of the UE 3, or one or more parameters for use in encoding or decoding transmissions between the RAN node 5 and the UE 3. The model inference function 245 may receive an AI / ML model from the model storage entity 251, and inference data from the data collection entity 241 for use with the AI / ML model. The model inference function 245 may also output monitoring data for use at the management function 249 and receive information indicating an AI / ML to activate or deactivate from the management function 249.

[0071] The management function 249 receives monitoring data from the data collection entity 241 and may also receive monitoring data from the model inference function 245. The management function 249 may transmit to the model storage entity 251, an indication of an AI / ML model to be transmitted for use at the model inference function 245. The management function 249 may also transmit to the model training function 243, performance feedback or a retraining request for the AI / ML model.

[0072] The functions and entities illustrated in Fig. 2 may be co-located at a single node of the communication system 1 (e.g., at the RAN node 5 or core network node / function) or may be distributed amongst a plurality of network nodes (e.g., a plurality of the RAN nodes 5).

[0073] By way of example only, terms referred to by 3GPP in the context of this framework include: - AI / ML model training: A process to train an AI / ML Model [by learning the input / output relationship] in a data driven manner and obtain the trained AI / ML Model for inference. Model training can be performed offline or online or combination of both. - AI / ML model validation: A subprocess of training, to evaluate the quality of an AI / ML model using a dataset different from one used for model training, which helps selecting model parameters that generalize beyond the dataset used for model training. - AI / ML model testing: A subprocess of training, to evaluate the performance of a final AI / ML model using a dataset different from one used for model training and validation. Differently from AI / ML model validation, testing does not assume subsequent tuning of the model. - AI / ML model Inference: A process of using a trained AI / ML model to produce a set of outputs based on a set of inputs. - Data collection: A process of collecting data by the network nodes, management entity, or UE for the purpose of AI / ML model training, data analytics and inference. - Model monitoring: A procedure that monitors the inference performance of the AI / ML model. - Model activation: Enable an AI / ML model for a specific function. - Model deactivation: Disable an AI / ML model for a specific function. - Model switching: Deactivating a currently active AI / ML model and activating a different AI / ML model for a specific function. - Supervised learning: A process of training a model from input and its corresponding labels. - Unsupervised leaning: A process of training a model without labelled data. - Semi-supervised learning: A process of training a model with a mix of labelled data and unlabelled data. - Reinforcement Learning (RL): A process of training an AI / ML model from input (also referred to as 'state') and a feedback signal (also referred to as 'reward') resulting from the model's output (also referred to as 'action') in an environment the model is interacting with.

[0074] The data collection by the data collection entity 241 may be performed at various nodes of the communication system 1 (e.g., at one or more RAN nodes 5 or UEs 3). Particularly advantageous methods of obtaining, at the UE 3, data for an AI / ML model, and transmitting the AI / ML data from the UE 3 to the RAN node 5, will be described in more detail later.

[0075] Fig. 3 schematically illustrates of a method of training an AI / ML model, and of monitoring the performance of the AI / ML model. As illustrated in Fig. 3, stored data / features may first be extracted in a data extraction step. In the data validation step, a determination of whether to proceed with training or retraining the AI / ML model is made (e.g., based on the extracted data). In the data preparation stage, the data is prepared for use in training the AI / ML model. For example, the data may be cleaned (e.g., filtered), subject to a transformation, or modified in any other suitable manner. The data may also be divided in training data, validation data and test data sets in the data preparation stage.

[0076] In the model training step, the AI / ML model is trained (or retrained) using training data prepared in the data preparation step. It will be appreciated that any suitable training method can be used to train the AI / ML model (e.g., a method that comprises supervised learning or unsupervised learning). In the model evaluation step, the AI / ML model is evaluated (e.g., a prediction accuracy of the AI / ML model is evaluated) using a test data set (which may be generated in the data preparation step). In the model validation step, a determination of whether the AI / ML model is suitable for deployment in the communication system 1 is made (e.g., based on the results of the model evaluation step).

[0077] In the model serving step, the AI / ML model is deployed for use in the communication system 1. AI / ML model deployment may comprise compiling a trained AI / ML model, packaging the model into an executable format, and delivering the AI / ML model to a target device. For example, the AI / ML model may be transmitted to the RAN node 5 and / or the UE 3, for use at the RAN node 5 and / or the UE 3 to generate predictions or determinations using the AI / ML model as part of a prediction service. In the performance monitoring step, the performance of the deployed AI / ML model is monitored. The predictive performance of the AI / ML model may be monitored by comparing predictions generated using the model with one or more measurements. For example, when the AI / ML model is used to predict a location of a UE 3, the prediction accuracy of the AI / ML model may be assessed using a measurement of an actual location of the UE 3. If the AI / ML model is used for predicting future measurement results (e.g., the measured Reference Signal Received Power (RSRP) of reference signals) at some point in time, the prediction accuracy of the AI / ML model may be assessed using actual measurement results acquired by the UE 3 when that point in time is reached. If the AI / ML model is used for determining parameters for use in encoding and decoding data transmitted between a RAN node 5 and a UE 3, the model may be assessed based on the performance of the encoding and / or decoding processes. In the retraining trigger step, retraining of the AI / ML model is triggered (e.g., because the prediction accuracy of the AI / ML model has fallen below an acceptable threshold accuracy, or because a performance of a method that uses inferences from the AI / ML model has fallen below an acceptable threshold performance), and the method returns to the data extraction step.

[0078] Each step of the method of Fig. 3 may be executed at a single node of the communication system 1 (including at the RAN node 5), or alternatively steps of the method may be distributed between a plurality of different nodes (or indeed one or more of these steps may be performed online or offline).

[0079] As discussed above, information collected by nodes / functions in the communication system 1 (e.g., at a UE 3 and / or RAN nodes 5) can be used as training data for an AI / ML model and used as inference data for use in generating one or more model inferences using the AI / ML model. The information used as training data, monitoring data, and / or to generate the one or more model inferences may be referred to as 'AI / ML information' or 'AI / ML data.'

[0080] <Configuration information for AI / ML> 　　Configuration information for an AI / ML model (which may be referred to as "AI / ML configuration information") may be exchanged between nodes in the communication system. For example, a core network node may transmit AI / ML configuration information to a RAN node 5 (or any other entity of the network that supports an AI / ML-based prediction functionality) that hosts an AI / ML model. The AI / ML configuration information may include a list of supported use cases for the AI / ML model (the AI / ML model need not necessarily be for predicting UE mobility). The supported use cases may be, for example: energy saving; traffic steering; anomaly detection; quality of experience (QoE) optimisation; mobility robustness optimisation (MRO); RAN slice service level agreement (SLA) assurance; massive multiple-input multiple-output (MIMO) beamforming optimisation; network slice subnet instance (NSSI) resource allocation; optimisation coverage and capacity optimisation (CCO); mobility load balancing (MLB); RACH optimisation; or UE transmission power optimisation. The AI / ML configuration information may include an indication of a particular AI / ML model to use for a particular use case. The AI / ML configuration information may also include an indication of whether feedback is required (e.g., from another network node). The feedback may include, for example, communication performance feedback (e.g., indicating a communication performance for communication between a UE 3 and a RAN node 5).

[0081] <AI / ML Enhanced UE Positioning Determination> 　　The communication system 1 is beneficially configured to support one or more enhanced procedures for inferring / predicting the positions of UEs 3 within the communication system 1 using an appropriately trained AI / ML model maintained at either a RAN node 5 or the LMF 10-3 of the communication system 1.

[0082] For example, as described in more detail later, the communication system 1 may be beneficially configured to support one or more enhanced procedures for inferring / predicting the positions or the assisted information for positions of UEs 3 within the communication system 1 using an appropriately trained AI / ML model maintained at either the RAN node 5 or LMF 10-3 of the communication system 1.

[0083] The communication system 1 may be beneficially configured to support one or more enhanced procedures for appropriately training, retraining, and tuning the AI / ML model used in the one or more enhanced procedures for predicting the positions of UEs 3 within the communication system 1.

[0084] A number of enhanced procedures and techniques that may be implemented in the communication system 1 to make positioning (e.g., UE positioning) inferences / predictions, and to train / retrain / tune the AI / ML models will now be described, by way of example only, with reference to Figs. 4 to 7.

[0085] <Implementation of an AI / ML Positioning Prediction Model> <Configuring and using an AI / ML Positioning Prediction Model - Model located at RAN Node> 　　Figs. 4A and 4B illustrate a simplified sequence diagram of an example procedure for configuring and using an AI / ML model for UE location assisted information prediction located at the RAN node 5 in the communication system 1.

[0086] As also shown in Fig. 4A and Fig. 4B, the RAN node 5 stores an appropriate AI / ML model and is in communication with the AMF 10-1 and LMF 10-3, as well as one or more UEs 3.

[0087] At step S402 the LMF 10-3 can co-ordinate with the AMF 10-1 to perform a user privacy and user consent verification procedure, or the like, for one or more UEs 3.

[0088] Optionally, at step S404, the RAN node 5 (which stores the AI / ML model) may send, to the LMF 10-3, a request for the LMF 10-3 to provide a list of available UEs 3 that are in a specific area of interest and support AI / ML assisted positioning, to the RAN node 5 (e.g., a list of available UEs 3 within a specific cell coverage area provided by the RAN node 5, or the like).

[0089] When a request for the LMF 10-3 to provide a list of available UEs 3 in a specific area of interest to the RAN node 5 is sent by the RAN node 5 to the LMF 10-3, that request may be sent by the RAN node 5 to the LMF 10-3 via any appropriate message - for example, a data collection information request type message that includes an 'AI / ML assisted' indication or the like, or the like, or alternatively a dedicated positioning protocol (e.g., a '5G NR Positioning Protocol A' (NRPPa)) type message for use with AI / ML assisted operations.

[0090] The message sent at step S404 may, by way of example only, include: a list of UE identifiers (IDs) of the UEs 3 in a specific area of interest, and / or cell IDs of cells covering the specific area of interest, and / or tracking areas corresponding to a specific area of interest.

[0091] At step S406 (which may occur in response to optional step S404 when performed), the LMF 10-3 provides, to the RAN node 5, a list of available UEs 3 in a specific area of interest. For example, if the list of available UEs 3 provided at step S406 is sent in response to the optional request sent at S404, the list of available UEs 3 may include the UEs 3 that are available within the specific area of interest as defined by the RAN node 5 in the request (e.g., within a specific cell coverage area indicated by the RAN node 5, among the UEs 3 identified in a list of UE IDs indicated by the RAN node 5, within a tracking area indicated by the RAN node 5, and / or the like). That list of available UEs 3 may, for example, be sent by the LMF 10-3 to the RAN node 5 via an appropriate message - for example, a data collection information notification / response type message that includes an 'AI / ML assisted' indication, or alternatively a dedicated positioning protocol (e.g., NRPPa) type message for use with AI / ML assisted operations. Nevertheless, if the list of available UEs 3 provided at step S406 is not sent in response to a specific request, then the specific area of interest may be any suitable area of interest (e.g., determined at, or provided to, the LMF 10-3 at some other earlier time).

[0092] The message sent at step S406 may, by way of example only, include: a list of UE identifiers (IDs) for the available UEs 3 in the specific area of interest to the RAN node 5, and / or cell IDs of cells covering the specific area of interest to the RAN node 5, tracking areas (TAs corresponding to the specific area of interest to the RAN node 5.

[0093] Additionally (or alternatively), the message sent at step S406 may, by way of example only, include an appropriate mapping of UE IDs to deferred routing IDs (and / or to new temporary IDs assigned to the UEs 3 by the LMF 10-3). By way of example only, the deferred routing IDs / new temporary IDs, may include global IDs (e.g., an IP address, a universally unique ID (UUID), or a uniform resource ID (URI)), or local IDs.

[0094] Additionally (or alternatively), the message sent at step S406 may, by way of example only, include: an appropriate indication (e.g., a new dedicated AI / ML assisted indication) to indicate whether or not the list of available UEs 3 in the specific area of interest is provided for an AI / ML assisted procedure. For example, where the list of available UEs 3 in the specific area of interest is provided for an AI / ML assisted procedure, the message containing that list of UEs 3 may additionally include an AI / ML assisted indication, or the like.

[0095] At step S408, the RAN node 5 performs UE selection for AI / ML-assisted positioning purposes based, by way of example, upon user consent, the list of available UEs 3 provided to the RAN node 5 by the LMF 10-3 (e.g., at step S406). For example, the RAN node 5 may select one or more UEs 3 listed in the list of available UEs 3 provided to the RAN node 5, and for which the RAN node 5 wishes to generate UE location assisted information predictions / inferences using the AI / ML model stored at the RAN node 5.

[0096] Having selected one or more UEs 3 for AI / ML-assisted positioning purposes, the RAN node 5 triggers an appropriate data collection initiation procedure with the LMF 10-3. For example, at step S410, the RAN node 5 may send, to the LMF 10-3, an appropriate message to request data collection (e.g., a data collection request message, or the like), to request the LMF 10-3 to provide 'ground-truth' labels (or the like) for the one or more selected UEs 3.

[0097] A ground-truth label represents the exact location of a UE. In the context of AI / ML models for UE positioning predictions, ground-truth labels may, for example, include (previously) known positions of a UE 3 at specific times.

[0098] The message to request data collection sent at step S410 may include, for example: one measurement ID allocated by the RAN node 5; one measurement ID allocated by the LMF 10-3; a start / stop indication for triggering (or stopping) the data collection reporting procedure; the data type requested to be reported (e.g., ground-truth label, or the like); a UE positioning information collection configuration (e.g., respectively indicating an appropriate UE ID for each of one or more selected UEs, and the corresponding time at which UE position information should be collected for the associated UE, or the like); and / or a UE positioning information report configuration (e.g., indicating one or more UE IDs, cell IDs, TA identities (TAIs), and a reporting periodicity, or the like).

[0099] At step S412, the LMF 10-3 replies to the request for data collection sent by the RAN node 5 at step S410, by sending an appropriate response message (e.g., data collection response, or the like) to the RAN node 5. The response message sent at step S412 may, for example, include an indication of UEs 3 for which a ground-truth can be provided to the RAN node 5.

[0100] Additionally (or alternatively), the appropriate response message sent at step S412 may include: the measurement ID allocated by the RAN node 5; and the measurement ID allocated by the LMF 10-3.

[0101] The response message sent at step S412 may also include a list of selected UEs 3 for which data collection failed to be initiated. For example, the response message sent at step S412 may include, for each of the selected UEs 3 for which data collection failed to be initiated, an ID of the UE 3, and a cause value (or the like) indicating a reason why data collection failed to be initiated for the UE 3.

[0102] At step S414, the LMF 10-3 obtains UE position information for selected UEs 3 for which the LMF 10-3 has managed to initiate data collection. For example, for each of the selected UEs 3 that the LMF 10-3 has respectively managed to initiate data collection for, the LMF 10-3 may determine a position for the UE 3 using appropriate UE positioning methods, and then generate a ground-truth label that indicates the determined position of that UE 3 together with a timestamp for the ground-truth label indicating the time at which that ground-truth label was generated.

[0103] At step S416a, the LMF 10-3 may send an appropriate message (e.g., a data collection update message, or the like) to the RAN node 5 to report to the RAN node 5 one or more ground-truth labels generated at step S414 (e.g., to indicate to the RAN node 5 the respective position / ground truth label determined for each of the selected UEs 3 that the LMF 10-3 has managed to initiate and complete data collection for successfully).

[0104] The message sent at step S416a may, for example, include: the measurement ID allocated by the RAN node 5; the measurement ID allocated by the LMF 10-3; a respective UE deferred routing ID or new temporary UE ID for each UE 3 being reported; a respective quality indicator for each of the ground-truth labels; and / or a respective timestamp for each ground-truth label indicating the time at which that ground-truth label was generated.

[0105] It will be appreciated that, if at step S414 the LMF 10-3 is unable to obtain UE position information for any of the UEs 3, then the LMF 10-3 may send, at step S416b, an appropriate message (e.g., a data collection failure message, or the like) to the RAN node 5 to indicate to the RAN node 5 that the LMF 10-3 was unable to obtain UE position information for a corresponding one or more of the selected UEs 3, along with a cause value indicating a reason why the LMF 10-3 was unable to obtain UE position information for the corresponding selected UEs 3. By way of example only, the cause value may be i) the determination and provision of UE position information is not supported; ii) user consent has changed; and / or iii) no information is available for the selected UEs 3.

[0106] It will be appreciated that such a failure message could potentially be sent to indicate that all of the requested objects to be reported cannot be initiated.

[0107] It will be appreciated that the message sent at step S416b may include, together with the cause value: the measurement ID allocated by the RAN node 5; and the measurement ID allocated by the LMF 10-3.

[0108] Assuming that data collection for at least one UE 3 is successful, the procedure of Fig. 4A may continue as set out in in Fig. 4B. Specifically, the LMF 10-3 having sent an appropriate message (e.g., a data collection update message, or the like) to the RAN node 5 at step S416a, at step S418, the RAN node 5 sends one or more appropriate DL / UL reference signal configurations to the selected UEs 3. For example, the RAN node 5 may send, to the selected UEs 3, a PRS (e.g., DL reference signal) configuration and / or an SRS (e.g., UL reference signal) configuration to configure the selected UEs 3 to receive and measure PRSs and / or transmit SRSs to the RAN node 5.

[0109] It will be appreciated that the configuration (or configurations) for the PRSs / SRSs may be indicated by the RAN node 5 using an appropriate existing measurement configuration information element (IE), with an appropriate AI / ML assisted indication, to indicate that the measured PRSs / SRSs are for use by an AI / ML model for generating AI / ML inferences / predictions. Alternatively, a new dedicated measurement configuration IE for AI / ML assisted operation may be used for the DL / UL reference signal configuration.

[0110] In a case where PRSs are configured the UE 3 may thus receive and measure PRSs from the RAN node 5. Having received and measured PRSs from the RAN node 5 (step S420), the selected UEs 3 send an appropriate measurement report of those measured PRSs (e.g., UE measurement report) to the RAN node 5 at step S422. That measurement report may also include channel measurements, quality indicators of the channel measurements, and / or time stamps of channel measurements.

[0111] In a case where SRSs are configured the UE 3 may thus transmit SRSs in accordance with the configuration. At step S424, the RAN node 5 receives and measures any SRSs, transmitted in accordance with the corresponding configuration, from the selected UEs 3.

[0112] At step S426, the RAN node 5 collects / collates data for AI / ML model training / inference. Collecting / collating data for AI / ML model training / inference may include, by way of example, i) collecting / collating the SRS measurements made by the RAN node 5 at step S424, ii) collecting / collating the PRS measurement results, and the like, received from the selected UEs 3 (e.g., that are sent to the RAN node 5 at step S422), and iii) collecting / collating the ground-truth labels for the selected UEs 3, which are sent by the LMF 10-3 at step S416a.

[0113] For example, to collect / collate all of the necessary data for AI / ML model training / inference as described above, the RAN node 5 may use the IDs of the selected UEs 3, the timestamps of the ground-truth labels, and the time stamps of the reference signal measurements (e.g., PRS / SRS measurements) to identify which ground-truth labels, reference signal measurements, and time stamps are associated with which specific selected UE 3.

[0114] Having collected / collated the data (e.g., SRS measurements, PRS measurements) for AI / ML model training / inference, that data may be input to the AI / ML model for training purposes and / or for generating a model inference for another purpose (not shown).

[0115] Optionally, at step S427, the RAN node 5 may calculate one or more performance monitoring metrics for the AI / ML model if model performance monitoring locates in the RAN node 5. For example, the RAN node 5 may use one or more outputs (e.g., generated inferences) from the AI / ML model generated using the data input to the AI / ML model described above, and calculate the performance monitoring metric (or metrics) based on a comparison of the output (or outputs) to one or more ground-truth labels received from the LMF 10-3, to determine how well the AI / ML model is functioning. Based on the calculated performance monitoring metric (or metrics) for the AI / ML model, the AI / ML model can be retuned or retrained (e.g., by adjusting weights, and the like) to improve the performance of the AI / ML model.

[0116] At step S428, the RAN node 5 sends one or more predicted / inferred UE location assisted information generated by the AI / ML model for the selected UEs 3, to the LMF 10-3. For example, the RAN node 5 may send to the LMF 10-3, for the selected UEs 3, predicted / inferred UL RTOA information, predicted / inferred RAN node Rx-Tx time difference information, or the like. The predicted / inferred UE location assisted information may also include a respective 'predicted' timestamp associated with each predicted / inferred UE location, which indicates the available time point of the UE location for each selected UE 3, and (optionally) a line-of-sight (LOS) / non-LOS (NLOS) indication.

[0117] The LMF 10-3 may, in turn, determine a location of each selected UE 3 e.g., at the corresponding indicated time point indicated (not shown).

[0118] The predicted UE location assisted information sent to the LMF 10-3 at step S428 may be sent via an appropriate message. For example, the appropriate message may be a measurement report type message that includes an 'AI / ML assisted' indication or a new set of assistance information IEs for AI / ML assistance information, or alternatively the appropriate message may be a dedicated positioning protocol (e.g., NRPPa) type message for use with AI / ML assistance operations. The appropriate message may, for example, include requested AI / ML model predicted information for a list of UEs 3 (e.g., the selected UEs 3), and may respectively include, for each UE 3, a UE ID, predicted UE location information for the UE 3, a predicted timestamp for the UE 3, and a LOS / NLOS indication for the UE 3.

[0119] Optionally, at step S430, if model performance monitoring locates in the LMF 10-3, then the LMF 10-3 may calculate performance monitoring metric(s) for the AI / ML model. For example, the LMF 10-3 may use the output (e.g., generated inference) from the AI / ML model generated using the data input to the AI / ML model described above and sent to the LMF 10-3 at step S428, and calculate the performance monitoring metric (or metrics) based on a comparison of the output (or outputs) to the ground-truth labels generated by LMF 10-3 at step S414, to determine how well the AI / ML model is functioning.

[0120] At step S432, having calculated a performance monitoring metric for the AI / ML model, the LMF 10-3 may send the calculated monitoring metric for the AI / ML model to the RAN node 5 in an appropriate message (e.g., model performance feedback message, or the like). The model performance feedback may, for example, be an accuracy indicator and / or a precision indicator.

[0121] Based on the calculated performance monitoring metric for the AI / ML model, the AI / ML model can be retuned or retrained (e.g., by adjusting weights, and the like) to improve the performance of the AI / ML model.

[0122] < Configuring and using an AI / ML Positioning Prediction Model - Model located at RAN Node with Signalling via AMF> 　　In the procedure of Figs. 4A and 4B above, steps S404, S406, S410, S412, S416a, S416b, S428, and S432 involve transmissions directly between the RAN node 5 and the LMF 10-3. Nevertheless, it will be appreciated that in the procedure of Figs. 4A and 4B transmissions between the RAN node 5 and the LMF 10-3 may occur indirectly via the AMF 10-1.

[0123] For example, the information transmitted between the RAN node 5 and the LMF 10-3 at steps S404, S406, S410, S412, S416a, S416b, S428, and S432 may be sent indirectly between the RAN node 5 and the LMF 10-3 via he AMF 10-3 using messages of an appropriate application protocol - for example the so called 'NG application protocol' (NGAP) or similar.

[0124] For example, the RAN node 5 may co-ordinate with the AMF 10-1, during an appropriate setup procedure for establishing an initial connection between RAN node 5 and the core network 7 using the application protocol (e.g., an NGAP setup procedure, or the like), to indicate to the RAN node 5 that the AMF 10-1 supports AI / ML assisted positioning for UEs e.g., by including an appropriate indication (e.g., AI / ML assisted positioning supported indication, or the like) in a response message from the AMF 10-1 to the RAN node 5 during the appropriate setup procedure.

[0125] It will also be appreciated that the AMF 10-1 may co-ordinate with the LMF 10-3 in advance of the procedure of Figs. 4A and 4B to indicate to the LMF 10-3 that the AMF 10-1 supports (or does not support) AI / ML assisted positioning.

[0126] The AMF 10-1 may also perform a user consent check (if needed), or the like, when it receives an appropriate message to request data collection (e.g., a data collection request message, or the like) - for example, prior to forwarding that data collection request message to the LMF 10-3 to request the LMF 10-3 to provide 'ground-truth' labels for the one or more selected UEs 3.

[0127] <Configuring and using an AI / ML Positioning Prediction Model - Model located at the LMF> 　　Figs. 5A and 5B illustrate a simplified sequence diagram of an example procedure for configuring and using an AI / ML model for UE location prediction located at the LMF 10-3 in the communication system 1.

[0128] As also shown in Fig. 5A and Fig 5B, the LMF 10-3 stores an AI / ML model and is in communication with the AMF 10-1 and the RAN node 5, as well as one or more UEs 3.

[0129] At step S502 the LMF 10-3 performs a user privacy and user consent verification procedure, or the like, for the UE 3.

[0130] At step S504, the LMF 10-3 performs UE selection for AI / ML-assisted positioning purposes based, by way of example, upon the user consent and privacy verification performed at step S502, and an area of interest. For example, the LMF 10-3 may select one or more UEs 3 in an area (e.g., cell) of interest to generate AI / ML-assisted positioning predictions / inferences using the AI / ML model located at the LMF 10-3 for those selected UEs 3.

[0131] At step S506, having selected one or more UEs 3 for AI / ML-assisted positioning purposes, the LMF 10-3 triggers an appropriate data collection procedure with the RAN node 5. For example, the LMF 10-3 may send to the RAN node 5, an appropriate message to request data collection (e.g., a data collection request message, or the like).

[0132] That data collection request message may, for example, be a dedicated positioning protocol (e.g., NRPPa) type message for use with AI / ML assisted operations, or alternatively the data collection request message may be an existing measurement request message that includes an 'AI / ML assisted' indication' or the like. Alternatively, the data collection request message may be an existing measurement request message that includes a separate IE group for AI / ML assisted measurements including specific AI / ML assisted SRS measurements.

[0133] That data collection request message may, for example, include an appropriate indication for the RAN node 5 to provide an appropriate SRS configuration to the selected UEs 3. For example, the data collection request message sent to the RAN node 5 may include an indication of an explicit SRS configuration that the LMF 10-3 wishes the RAN node 5 to provide to the selected UEs 3. In another example, the data request message sent to the RAN node 5 may include a request for the RAN node 5 to provide any appropriate SRS configuration to the selected UEs 3.

[0134] Additionally, the data collection request message may include new sampling parameters specific for AI / ML assisted positioning determinations for UEs. For example, where the data collection request message includes an indication for the RAN node 5 to provide an appropriate SRS configuration to the selected UEs 3, the data collection request message may also include, by way of example only, measurement type (e.g., should the SRSs be measured in sample-based, path-based, etc., manner); the number of samples to be measured; the number of samples to be reported; the number of reported paths, a timing reporting granularity (e.g., factor); and / or the like.

[0135] That appropriate message to request data collection sent at step S506 may also include, for example: one measurement ID allocated by the RAN node 5; one measurement ID (allocated by the LMF 10-3; a candidate transmission-reception point (TRP) ID, and / or a cell ID of a candidate TRP used to receive UL SRSs from the selected UEs 3; an UL SRS configuration; UL timing information, together with timing uncertainty information, for the reception of SRSs by candidate TRPs; the report characteristics for the SRS and / or PRS measurements; an indication of the quantity of the measurements to be made (e.g., SRS and / or PRS measurements); the periodicity of the measurements and the number of measurements to be made; a measurement beam information request; search window information; an expected UL angle of arrival (AoA) / Zenith angle of arrival (ZoA) and uncertainty range; a number of TRP receiver (Rx) timing error groups (TEGs); a response time; a measurement characteristics request indicator; an indication of measurement time occasions for a measurement instance; information for timing windows in which measurements are made; and / or an AI / ML model assistance indication.

[0136] At step S508, the RAN node 5 replies to the request for data collection sent by the LMF 10-3 at step S506, by sending an appropriate response message (e.g., a data collection response message, or the like) to the LMF 10-3.

[0137] The response message may, for example, be a dedicated positioning protocol (e.g., NRPPa) type message for use with AI / ML assisted operations, or alternatively the response message may be an existing measurement response message that includes an 'AI / ML assisted' indication' or the like.

[0138] That appropriate response message sent at step S508 may include, for example: one measurement ID allocated by the RAN node 5; a measurement ID allocated by the LMF 10-3; and a list of selected UEs 3 that the RAN node 5 was unable to initiate data collection for, along with a respective cause value indicating a corresponding reason why the RAN node 5 was unable initiate data collection for each of those listed UEs 3, if RAN node is capable of providing some but not all of the requested information.

[0139] At step S510, the RAN node 5 sends one or more appropriate DL / UL reference signal configurations to the selected UEs 3. For example, the RAN node 5 may send, to the selected UEs 3, a PRS (e.g., DL reference signal) configuration and / or an SRS (e.g., UL reference signal) configuration to configure the selected UEs 3 to receive and measure PRSs and / or transmit SRSs to the RAN node 5, respectively.

[0140] It will be appreciated that the configuration (or configurations) for the PRSs / SRSs may be indicated by the RAN node 5 using an appropriate existing measurement configuration IE, with an appropriate AI / ML-assisted indication, to indicate that the measured PRSs / SRSs are for use by an AI / ML model for generating AI / ML inferences / predictions. Alternatively, a new dedicated measurement configuration IE for AI / ML assisted operations may be used for the DL / UL reference signal configuration.

[0141] In a case where PRSs are configured the UE 3 may thus receive and measure PRSs from the RAN node 5. Having received and measured PRSs from the RAN node 5 (step S512), the selected UEs 3 send an appropriate measurement report of those measured PRSs (e.g., UE measurement report) to the RAN node 5 at step S514. That measurement report may also include channel measurements, quality indicators of the channel measurements, and time stamps of the channel measurements.

[0142] In a case where SRSs are configured the UE 3 may thus transmit SRSs in accordance with the configuration. At step S516, the RAN node 5 receives and measures any SRSs transmitted, in accordance with the corresponding configuration, from the selected UEs 3.

[0143] At step S518a, the RAN node 5 may send an appropriate message (e.g., a data collection update message, or the like) to the LMF 10-3 to indicate to the LMF 10-3 the PRS measurement results that the RAN node 5 received from the selected UEs 3 at step S514, the SRS measurement results obtained by the RAN node 5 at step S516, and / or one or more appropriate positioning metrics based on the results of the PRS / SRS measurement results (e.g., UL RTOA, AOA, RAN node Rx-Tx Time Difference, a new UE positioning measurement metric, or the like).

[0144] The message sent at S518a may, for example, be a dedicated positioning protocol (e.g., NRPPa) type message for use with AI / ML assisted operations, or alternatively the message may be an existing measurement report message that includes an 'AI / ML assisted' indication' or the like. Alternatively, the message may be an existing measurement report message that includes a separate IE group for AI / ML assisted measurements including specific AI / ML assisted measurements (e.g., SRS and / or PRS measurements).

[0145] The message sent at step S518a may, for example, include: the measurement ID allocated by the RAN node 5; the measurement ID allocated by the LMF 10-3; a cell global identity (CGI) (e.g., an NR CGI (NCGI) or the like) and TRP ID of the SRS and / or PRS measurements; one or more UL-RTOAs for each UE 3; one or more reference signal received powers (RSRPs) of the SRSs (UL-SRS-RSRP) for each UE 3; one or more reference signal received quality (RSRQ) of the SRSs (UL-SRS-RSRQ for each UE 3; one or more UL reference signal carrier phase (UL-RSCP) measurements for each UE 3; one or more UL angle of arrivals (e.g., azimuth value and / or elevation value) for each UE 3 one or more indications of an SRS resource type; one or more time stamps of the SRS and / or PRS measurements; one or more quality indications of the SRS and / or PRS measurements for each measurement or set of measurements; appropriate beam information associated with the SRS and / or PRS measurements for each measurement or set of measurements; appropriate LoS / NLoS information associated with the SRS and / or PRS measurements for each measurement or set of measurements; one or more address resolution protocol (ARP) IDs associated with the SRS and / or PRS measurements for each measurement or set of measurements; appropriate mobile TRP location information; measured frequency hops; a list of IDs of aggregated positioning SRS resources; SRS and / or PRS measurements based on an indication of aggregated positioning SRS resources; and an AI / ML-assisted indication, UL SRS Reference Signal Received Path Power (UL-SRS-RSRPP), or the like.

[0146] Additionally (or alternatively), that appropriate message sent at step S518a may include the results of channel measurements (e.g., SRS and / or PRS measurements) for multiple paths.

[0147] It will be appreciated that if the RAN node 5 is unable to obtain UE position information for any of the UEs 3, then the RAN node 5 may send, at step S518b, an appropriate message (e.g., a data collection failure message, or the like) to the LMF 10-3 to indicate to the LMF 10-3 that the RAN node 5 was unable to obtain the requested measurement information in step S506 for a corresponding one or more of the selected UEs 3, along with a cause value indicating a reason why the RAN node 5 was unable to obtain that SRS and / or PRS measurement information for the corresponding selected UEs 3. By way of example only, the cause value may be i) AI / ML assisted positioning is not supported; ii) user consent has changed; and / or iii) no information is available for the selected UEs 3.

[0148] It will be appreciated that such a failure message could potentially be sent to indicate that all of the requested objects to be reported cannot be initiated.

[0149] It will be appreciated that the message sent at step S518b may include, together with the cause value: the measurement ID allocated by the RAN node 5; and the measurement ID allocated by the LMF 10-3.

[0150] At step S522, the LMF 10-3 collects / collates data for AI / ML model training / inference and predicts UE position for the selected UEs. Collecting / collating data for AI / ML model training / inference may include, by way of example, i) collecting / collating the SRS measurements made by the RAN node 5 at step S516, ii) collecting / collating the channel measurement results (e.g., PRS measurements) and / or other information (e.g., UL RTOA, AOA, RAN node Rx-Tx Time Difference, a new UE positioning measurement metric, or the like) of the selected UEs 3 (e.g., that were sent to the RAN node 5 at step S514 and forwarded to the LMF at S518a), and iii) collecting / collating the ground-truth labels for the selected UEs 3. For example, to collect / collate all of the necessary data for AI / ML model training / inference as described above, the LMF 10-3 may use the IDs of the selected UEs 3, the timestamps of the ground-truth labels, and the time stamps of the reference signal measurements (e.g., PRS / SRS measurements) to identify which ground-truth labels, reference signal measurements, and time stamps are associated with which selected UE 3.

[0151] Having collected / collated the data for AI / ML model training / inference that data is input to the AI / ML model for training and / or generating a model inference (not shown).

[0152] Optionally, at step S524, the LMF 10-3 may calculate a performance monitoring metric for the AI / ML model if model performance monitoring locates in the LMF 10-3. For example, the LMF 10-3 may obtain the ground-truth labels of UEs using one or more legacy methods previously described, and the LMF 10-3 may use the output (e.g., generated inference) from the AI / ML model generated using the data input to the AI / ML model described above and compare that output to the ground-truth labels generated by the LMF 10-3 to determine how well the AI / ML model is functioning. Based on the calculated performance monitoring metric for the AI / ML model, the AI / ML model can be retuned (e.g., by adjusting weights, and the like) or retrained to improve the performance of the AI / ML model.

[0153] <Configuring and using an AI / ML Positioning Prediction Model - Model located at a RAN node DU> <Adaption for the Procedure of Figs. 4A and 4B > 　　In the procedure of Figs. 4A and 4B, the AI / ML model is located at the RAN node 5. In the procedure described above with reference Figs. 4A and 4B the RAN node 5 may be a non-distributed RAN node 5 (e.g., RAN node 5-1).

[0154] Nevertheless, it will be appreciated that the RAN node 5 may in fact be a distributed RAN node 5-2 comprising one or more RAN node DUs 5-2DUand a RAN node CU 5-2CU. In this scenario, appropriate mechanisms and procedures may be required to enable the messages sent between the LMF 10-3, the distributed RAN node 5-2, and the UEs 3 to be transmitted between the one or more RAN node DUs 5-2DUof the distributed RAN node 5-2 and the RAN node CU 5-2CU.

[0155] Fig. 6 illustrates a simplified sequence diagram of an example procedure for additional configuration and use of the AI / ML model in the procedure of Figs. 4A and 4B when the RAN node 5 is a distributed RAN node 5-2.

[0156] As shown in Fig. 6 the distributed RAN node 5-2 comprises a RAN node DU 5-2DUand a RAN node CU 5-2CU. The RAN node CU 5-2CUand the RAN node DU 5-2DUare in communication with one another via an appropriate interface (e.g., F1 interface, or the like).

[0157] In the procedure of Figs. 4A and 4B, at step S410, the RAN node 5 may send, to the LMF 10-3, an appropriate message to request data collection (e.g., a data collection request message, or the like), to request the LMF 10-3 to provide 'ground-truth' labels for the one or more selected UEs 3.

[0158] In the scenario where the RAN node 5 is a distributed RAN node 5-2, the RAN node CU 5-2CUmay first receive the message to request data collection (e.g., a data collection request message, or the like) from a RAN node DU 5-2DUat step S410-1. That message to request data collection may then be sent by the RAN node CU 5-2CUto the LMF 10-3 to request the LMF 10-3 to provide 'ground-truth' labels for the one or more selected UEs 3.

[0159] In the procedure of Figs. 4A and 4B, at step S412 the LMF 10-3 replies to the request for data collection sent by the RAN node 5 at step S410, by sending an appropriate response message (e.g., data collection response, or the like) to the RAN node 5.

[0160] In the scenario where the RAN node 5 is a distributed RAN node 5-2, the LMF 10-3 may first send an appropriate response message (e.g., data collection response, or the like) to the RAN node CU 5-2CU, and the RAN node CU 5-2CUmay then send the response message on to the RAN node DU 5-2DUat step S412-1.

[0161] In the procedure of Figs. 4A and 4B, at step S416a, the LMF 10-3 sends an appropriate message (e.g., a data collection update message, or the like) to the RAN node 5 to indicate to the RAN node 5 the ground-truth labels generated at step S414 (e.g., to indicate to the RAN node 5 the determined positions for each of the selected UEs 3).

[0162] In the scenario where the RAN node 5 is a distributed RAN node 5-2, the LMF 10-3 may first send the message (e.g., a data collection update message, or the like) to the RAN node CU 5-2CU, and then the RAN node CU 5-2CU may send that message on to the RAN node DU 5-2DU at step S416a-1.

[0163] Alternatively, if at step S414 the LMF 10-3 is unable to report UE position information for selected UEs 3, then the LMF 10-3 may send, at step S416b, an appropriate message (e.g., a data collection failure message, or the like) to the RAN node CU 5-2CU to indicate to the RAN node CU 5-2CU that the LMF 10-3 was unable to obtain UE position information for selected UEs 3, along with a cause value indicating a reason why the LMF 10-3 was unable to obtain UE position information for selected UEs 3. That appropriate message (e.g., a data collection failure message, or the like) may then be forwarded by the RAN node CU 5-2CU to the RAN node DU 5-2DU at step S416b-1.

[0164] It will be appreciated that in this scenario, the data collection update and / or failure sent at steps S416a and S416b may terminate at the RAN node CU 5-2CU - for example because it uses a positioning protocol (such as the NR Positioning Protocol A (NRPPa) or the like) that terminates at the RAN node CU 5-2CU. Accordingly, in this case, the RAN node CU 5-2CU may be configured to send / forward the ground truth labels (e.g., received from the LMF 10-3 in the data collection update message at S416) - or a subset of those ground truth labels - to the RAN node DU 5-2DU using an appropriate message over the CU to DU (e.g., F1) interface (e.g., where the AI / ML model is stored at that RAN node DU 5-2DU),

[0165] Alternatively, rather than sending the above messages at steps S410-1, S412-1, S416a-1, and S416b-1 as described above, where the procedure of Figs. 4A and 4B is implemented with a distributed RAN node 5-2, an appropriate RAN node DU configuration update procedure and / or an appropriate RAN node CU configuration update procedure may be used to support a data collection procedure such as those described above.

[0166] For example, where the procedure of Figs. 4A and 4B is implemented with a distributed RAN node 5-2, an intra-RAN, inter-node, information exchange procedure comprising a DU configuration update procedure may be implemented whereby the information conveyed by the data collection request message at step S410-1 is instead communicated via an appropriate DU configuration update message (e.g., a RAN node DU configuration update message, or the like) sent by the RAN node DU 5-2DU to update the RAN node CU 5-2CU.

[0167] It will be appreciated that in this scenario, the DU configuration update message (e.g., a RAN node DU configuration update message, or the like) sent by the RAN node DU 5-2DU to update the RAN node CU 5-2CU may include a new IE group for AI / ML-assisted positioning (e.g., which may include the information typically conveyed by the data collection request message at step S410-1).

[0168] In response to the DU configuration update message (e.g., a RAN node DU configuration update message, or the like), the RAN node CU 5-2CUmay send an appropriate dedicated DU configuration update acknowledgement message (e.g., a RAN node DU configuration acknowledge message or the like) that may convey information typically included in the data response message sent at step S412-1.

[0169] It will also be appreciated that in this scenario, the data collection update and / or failure message sent at steps S416a-1 and S416b-1 (or the information typically conveyed by those messages) may be sent as part of a RAN node CU configuration update procedure (e.g., using a RAN node CU configuration update message or the like) - e.g., including a new IE group for AI / ML-assisted positioning.

[0170] <Adaption for the Procedure of Figs. 5A and 5B> 　　In the procedure of Figs. 5A and 5B the AI / ML model is located at the LMF 10-3. In the procedure described above with reference Figs. 5A and 5B, the RAN node 5 may be a non-distributed RAN node 5 (e.g., RAN node 5-1).

[0171] Nevertheless, it will be appreciated that the RAN node 5 may in fact be a distributed RAN node 5-2 comprising one or more RAN node DUs 5-2DUand a RAN node CU 5-2CU. In this scenario, appropriate mechanisms and procedures may be required to enable the messages sent between the LMF 10-3, the distributed RAN node 5-2, and the UEs 3 to be transmitted between the one or more RAN node DUs 5-2DUof the distributed RAN node 5-2 and the RAN node CU 5-2CU.

[0172] Fig. 7 illustrates a simplified sequence diagram of another example procedure for additional configuration and use of the AI / ML model in the procedure of Figs. 5A and 5B when the RAN node 5 is a distributed RAN node 5-2.

[0173] As shown in Fig. 7 the distributed RAN node 5-2 comprises a RAN node DU 5-2DUand a RAN node CU 5-2CU. The RAN node CU 5-2CUand the RAN node DU 5-2DUare in communication with one another via an appropriate interface (e.g., F1 interface, or the like).

[0174] In the procedure of Figs. 5A and 5B, at step S506, having selected one or more UEs 3 for AI / ML-assisted positioning purposes, the LMF 10-3 triggers an appropriate data collection initiation procedure with the RAN node 5. For example, the LMF 10-3 may send to the RAN node 5, a message to request data collection (e.g., a data collection request message, or the like) to configure SRSs for the selected UEs 3.

[0175] In the scenario where the RAN node 5 is a distributed RAN node 5-2, the LMF 10-3 may first send to the RAN node CU 5-2CU, a message to request data collection (e.g., a data collection request message, or the like) to configure the selected UEs 3 and then the RAN node CU 5-2CUmay send that message on to the RAN node DU 5-2DUat step S506-1 to configure SRSs for the selected UEs 3.

[0176] In the procedure of Figs. 5A and 5B, at step S508, the RAN node 5 replies to the request for data collection sent by the LMF 10-3 at step S506, by sending an appropriate response message (e.g., data collection response, or the like) to the LMF 10-3.

[0177] In the scenario where the RAN node 5 is a distributed RAN node 5-2, the RAN node DU 5-2DUmay first send to the RAN node CU 5-2CUa response message (e.g., data collection response, or the like) at step S508-1, and the RAN node CU 5-2CUmay then send that response message (e.g., data collection response, or the like) to the LMF 10-3 at step S508.

[0178] In the procedure of Figs. 5A and 5B, at step S518a, the RAN node 5 sends an appropriate message (e.g., a data collection update message, or the like) to the LMF 10-3 to indicate to the LMF 10-3 measurement results that the RAN node 5 received from the selected UEs 3 at step S514, and / or measurement results obtained by the RAN node 5 at step S516.

[0179] In the scenario where the RAN node 5 is a distributed RAN node 5-2 however, the RAN node DU 5-2DUmay first send a message (e.g., a data collection update message, or the like) to the RAN node CU 5-2CUat step S518a-1, and then the RAN node CU 5-2CUmay then send that appropriate message to the LMF 10-3 at step S518a.

[0180] In the procedure of Figs. 5A and 5B, at step S518b, the RAN node 5 may send an appropriate message (e.g., a data collection failure message, or the like) to the LMF 10-3 to indicate to the LMF 10-3 that the RAN node 5 was unable to obtain requested information for selected UEs 3 in step S506, along with a cause value indicating a reason why the RAN node 5 was unable to obtain that requested information for selected UEs 3. By way of example only, the cause value may be i) AI / ML assisted positioning is not supported; ii) user consent has changed; and / or iii) no information is available for the selected UEs 3.

[0181] In the scenario where the RAN node 5 is a distributed RAN node 5-2 however, the RAN node DU 5-2DUmay first send an appropriate message (e.g., a data collection failure message, or the like) to the RAN node CU 5-2CUat step S518b-1, and then the RAN node CU 5-2CUmay then send that appropriate message to the LMF 10-3 at step S518b.

[0182] Alternatively, rather than sending the above messages at steps S506-1, S508-1, S518a-1, and S518b-1 as described above, where the procedure of Figs. 5A and 5B is implemented with a distributed RAN node 5-2, an appropriate RAN node CU configuration update procedure and / or an appropriate RAN node DU configuration update procedure may be used to support a data collection procedure such as those described above.

[0183] For example, where the procedure of Figs. 5A and 5B is implemented with a distributed RAN node 5-2, an intra-RAN, inter-node, information exchange procedure comprising a CU configuration update procedure may be implemented whereby the information conveyed by the data collection request message at step S506-1 is instead communicated via an appropriate CU configuration update message (e.g., a RAN node CU configuration update message, or the like) sent by the RAN node CU 5-2CUto update the RAN node DU 5-2DU.

[0184] It will be appreciated that in this scenario, the CU configuration update message (e.g., a RAN node CU configuration update message, or the like) sent by the RAN node CU 5-2CUto update the RAN node DU 5-2DUmay include a new IE group for AI / ML-assisted positioning (e.g., which may include the information typically conveyed by the data collection request message at step S506-1).

[0185] In response to the CU configuration update message (e.g., a RAN node CU configuration update message, or the like), the RAN node DU 5-2DUmay send an appropriate dedicated CU configuration update acknowledgement message (e.g., a RAN node CU configuration acknowledge message or the like) that may convey information typically included in the data response message sent at step S508-1.

[0186] It will also be appreciated that in this scenario, the data collection update and / or failure message sent at steps S518a-1 and S518b-1 (or the information typically conveyed by those messages) may be sent as part of a RAN node DU configuration update procedure (e.g., using a RAN node DU configuration update message or the like) - e.g., including a new IE group for AI / ML-assisted positioning.

[0187] <Devices of the Communication System> <User Equipment> 　　Fig. 8 is a schematic block diagram illustrating the main components of a UE 3 as shown in Fig. 1.

[0188] As shown, the UE 3 has a transceiver circuit 31 that is operable to transmit signals to and to receive signals from a RAN node 5 via one or more antennas 33 (e.g., comprising one or more antenna elements). The UE 3 has a controller 37 to control the operation of the UE 3. The controller 37 is associated with a memory 39 and is coupled to the transceiver circuit 31. Although not necessarily required for its operation, the UE 3 might, of course, have all the usual functionality of a conventional UE 3 (e.g., a user interface 35, such as a touch screen / keypad / microphone / speaker and / or the like for, allowing direct control by and interaction with a user) and this may be provided by any one or any combination of hardware, software, and firmware, as appropriate. Software may be pre-installed in the memory 39 and / or may be downloaded via the communication system 1 or from a removable data storage device (RMD), for example.

[0189] The controller 37 is configured to control overall operation of the UE 3 by, in this example, program instructions or software instructions stored within memory 39. As shown, these software instructions include, among other things, an operating system 41, and a communications control module 43.

[0190] The communication control module 43 is operable to control the communication between the UE 3 and its serving RAN node 5 (and other communication devices connected to the RAN node 5, such as further UEs and / or core network nodes). The communication control module 43 is configured for the overall handling of uplink communications via associated uplink channels (e.g., via a physical uplink control channel (PUCCH), random access channel (RACH), and / or a physical uplink shared channel (PUSCH)) including both dynamic and semi-static signalling (e.g., SRS). The communication control module 43 is also configured for the overall handling of receipt of downlink communications via associated downlink channels (e.g., of DCI via a physical downlink control channel (PDCCH) and / or a physical downlink shared channel (PDSCH)) including both dynamic and semi-persistent scheduling (e.g., SPS). The communication control module 43 is responsible, for example: for determining where to monitor for downlink control information; for determining the resources to be used by the UE 3 for transmission / reception of UL / DL communications (including interleaved resources and resources subject to frequency hopping); for managing frequency hopping at the UE side; for determining how slots / symbols are configured (e.g., for UL, DL or full duplex communication, or the like); for determining which bandwidth parts are configured for the UE 3; for determining how uplink transmissions should be encoded and the like.

[0191] It will be appreciated that the communication control module 43 may include a number of sub-modules ('layers' or 'entities') to support specific functionalities. For example, the communication control module 43 may include a PHY sub-module, a MAC sub-module, an RLC sub-module, a PDCP sub-module, an RRC sub-module, etc.

[0192] The communication control module 43 is configured, in particular, to control the UE's communications, where applicable, in accordance with any of the methods described herein.

[0193] <RAN node (non-distributed)> 　　Fig. 9 is a schematic block diagram illustrating the main components of a non-distributed RAN node 5-1 for the communication system 1 shown in Fig. 1. As shown, the RAN node 5-1 has a transceiver circuit 51 for transmitting signals to and for receiving signals from the communication devices (such as UEs 3) via one or more antennas 53 (e.g., a single or multi-panel antenna array / massive antenna), and a core network interface 55 (e.g., comprising the N2, N3 and other reference points / interfaces) for transmitting signals to and for receiving signals from network nodes in the core network 7. Although not shown, the RAN node 5-1 may also be coupled to other RAN nodes via an appropriate interface (e.g., the so-called 'Xn' interface in NR). The RAN node 5-1 has a controller 57 to control the operation of the RAN node 5-1. The controller 57 is associated with a memory 59. Software may be pre-installed in the memory 59 and / or may be downloaded via the communication system 1 or from a removable data storage device (RMD), for example. The controller 57 is configured to control the overall operation of the RAN node 5-1 by, in this example, program instructions or software instructions stored within memory 59.

[0194] As shown, these software instructions include, among other things, an operating system 61, and a communications control module 63.

[0195] The communications control module 63 is operable to control the communication between the RAN node 5-1 and UEs 3 and other network entities that are connected to the RAN node 5-1. The communications control module 63 is configured for the overall control of the reception and decoding of uplink communications, via associated uplink channels (e.g., via a physical uplink control channel (PUCCH), a random-access channel (RACH), and / or a physical uplink shared channel (PUSCH)) including both dynamic and semi-static signalling (e.g., SRS). The communications control module 63 is also configured for the overall handling the transmission of downlink communications via associated downlink channels (e.g., via a physical downlink control channel (PDCCH) and / or a physical downlink shared channel (PDSCH)) including both dynamic and semi-static signalling (e.g., CSI-RS, SSBs etc.). The communications control module 63 is also responsible, for example, for determining and scheduling the resources to be used by the UE 3 for receiving in DL / transmitting in UL, for configuring slots / symbols appropriately (e.g., for UL, DL, flexible, full duplex communication, or the like), for configuring one or more bandwidth parts for the UE 3, and for providing related configuration signalling to the UE 3.

[0196] It will be appreciated that the communications control module 63 may include a number of sub-modules (or 'layers') to support specific functionalities. For example, the communications control module 63 may include a PHY sub-module, a MAC sub-module, an RLC sub-module, a PDCP sub-module, an SDAP sub-module, an IP sub-module, an RRC sub-module, etc.

[0197] The communication control module 63 is configured, in particular, to control the RAN Node's communications, where applicable, in accordance with any of the methods described herein.

[0198] < RAN node (distributed)> 　　Fig. 10 is a simplified block schematic illustrating the main components of a distributed RAN node 5-2 comprising a distributed type of base station for implementation in the communication system 1 of Fig. 1. As shown, the distributed RAN node 5-2 includes a central unit (RAN node CU) 5-2CUand a distributed unit (RAN node DU) 5-2DU(although it may include other RAN node DUs 5-2DUas described above). Each unit 5-2CU, 5-2DUincludes respective transceiver circuitry 51c, 51d.

[0199] The transceiver circuitry 51d of the distributed unit 5-2DUis operable to transmit signals to and to receive signals from UEs 3 via an air interface 53d and one or more antennas and is also operable to transmit signals to and to receive signals from the central unit 5-2CUvia an interface, for example the distributed unit side of an F1 interface (which may be provided over a satellite radio interface).

[0200] The transceiver circuitry 51c of the central unit 5-2CUis operable to transmit signals to and to receive signals from functions of the core network 7 and / or other RAN nodes via a network interface 55c. The network interface typically includes an N2 and / or N3 interfaces for communicating with the core network and a RAN node to RAN node (e.g., Xn) interface for communicating with other RAN nodes 5. The transceiver circuitry 51c of the central unit 5-2CUis also operable to transmit signals to and to receive signals from one or more distributed units 5-2DU, for example the central unit side of the F1 interface provided.

[0201] Each unit 5-2CU, 5-2DUincludes a respective controller 57c, 57d which controls the operation of the corresponding transceiver circuitry 51c, 51d in accordance with software stored in the respective memories 59c and 59d of the central unit 5-2CUand the distributed unit 5-2CU. The software of each unit may be pre-installed in the memory 59c, 59d and / or may be downloaded via the communication system 1 or from a removable data storage device (RMD), for example. The software of each unit includes, among other things, a respective operating system 61c, 61d, and a respective communications control module 63c, 63d.

[0202] Each communications control module 63c, 63d is operable to control the communication of its corresponding unit 5-2CU, 5-2DUincluding the communication from one unit to the other. The communications control module 63d of the distributed unit 5-2DUcontrols communication between the distributed unit 5-2DUand the UEs 3, and the communications control module 63c of the central unit 5-2CUcontrols communication between the central unit 5-2CUand other network entities that are connected to the distributed RAN node 5-2.

[0203] The communications control modules 63c, 63d also respectively control the part played by the central unit 5-2CUand distributed unit 5-2DUin the flow of uplink and downlink user traffic and control data to be received from and transmitted to the communications devices served by the distributed RAN node 5-2 including, for example, control data for managing operation of the UEs 3. Each communication control module 63c, 63d is responsible, for example, for controlling the respective part played by the central unit 5-2CUand distributed unit 5-2DUin the reception and decoding of uplink communications, via associated uplink channels (e.g., via a physical uplink control channel (PUCCH), a random-access channel (RACH), and / or a physical uplink shared channel (PUSCH)) including both dynamic and semi-static signalling (e.g., SRS). Each communication control module 63c, 63d is responsible, for example, for controlling the respective part played by the central unit 5-2CUand distributed unit 5-2DUin the overall handling the transmission of downlink communications via associated downlink channels (e.g., via a physical downlink control channel (PDCCH) and / or a physical downlink shared channel (PDSCH)) including both dynamic and semi-static signalling (e.g., CSI-RS, SSBs etc.). Each communication control module 63c, 63d is responsible, for example, for controlling the respective part played by the central unit 5-2CUand distributed unit 5-2DUin determining and scheduling the resources to be used by the UE 3 for receiving in DL / transmitting in UL, for configuring slots / symbols appropriately (e.g., for UL, DL, flexible, full duplex communication, or the like), for configuring one or more bandwidth parts for the UE 3, and for providing related configuration signalling to the UE 3.

[0204] It will be appreciated that each communication control module 63c, 63d may include a number of sub-modules (or 'layers') to support specific functionalities supported by the by the central unit 5-2CUand distributed unit 5-2DU. For example, a communications PHY sub-module, a MAC sub-module, an RLC sub-module, a PDCP sub-module, an SDAP sub-module, an IP sub-module, an RRC sub-module, etc may be distributed between the central unit 5-2CUand distributed unit 5-2DUappropriately depending on where the functional split is configured between the central unit 5-2CUand distributed unit 5-2DU.

[0205] Each communication control module 63c, 63d is configured, in particular, to control the respective communications of the central unit 5-2CU and distributed unit 5-2DU, where applicable, in accordance with any of the methods described herein.

[0206] <Modifications and Alternatives> 　　Detailed examples been described above. As those skilled in the art will appreciate, a number of modifications and alternatives can be made to the above examples whilst still benefiting from the concepts embodied therein.

[0207] It will be appreciated that description of features of and actions performed by a RAN node (base station), apply equally to distributed type base stations as to non-distributed type base stations.

[0208] It will also be appreciated that whilst information elements having specific names have been described differently named information elements but having a similar purpose may be used.

[0209] In the above description the UE and the base station are described for ease of understanding as having a number of discrete functional components or modules. Whilst these modules may be provided in this way for certain applications, for example where an existing system has been modified to implement the disclosed enhancements, in other applications, for example in systems designed with the inventive features in mind from the outset, these modules may be built into the overall operating system or code and so these modules may not be discernible as discrete entities.

[0210] Whilst it is described above that messages to request data collection may include, for example: one measurement ID allocated by the RAN node and one measurement ID allocated by the LMF, it will be appreciated that it may be possible for that those messages to request data collection may include one or more measurement IDs (e.g., a SRS and / or a PRS measurement ID) allocated by the RAN node; and / or one or more measurement IDs (e.g., an SRS and / or a PRS measurement ID) allocated by the LMF.

[0211] In the above examples, a number of software modules were described. As those skilled in the art will appreciate, the software modules may be provided in compiled or un-compiled form and may be supplied to the UE or base station as a signal over a computer network, or on a recording medium. Further, the functionality performed by part, or all, of this software may be performed using one or more dedicated hardware circuits. However, the use of software modules is preferred as it facilitates the updating of the UE or the base station in order to update their functionalities.

[0212] Each controller may comprise any suitable form of processing circuitry including (but not limited to), for example: one or more hardware implemented computer processors; microprocessors; central processing units (CPUs); arithmetic logic units (ALUs); input / output (IO) circuits; internal memories / caches (program and / or data); processing registers; communication buses (e.g. control, data and / or address buses); direct memory access (DMA) functions; hardware or software implemented counters, pointers and / or timers; and / or the like. Various other modifications will be apparent to those skilled in the art and will not be described in further detail here.

[0213] The User Equipment (or "UE," "mobile station," "mobile device" or "wireless device") in the present disclosure is an entity connected to a network via a wireless interface.

[0214] It should be noted that the present disclosure is not limited to a dedicated communication device and can be applied to any device having a communication function as explained in the following paragraphs.

[0215] The terms "User Equipment" or "UE" (as the term is used by 3GPP), "mobile station", "mobile device", and "wireless device" are generally intended to be synonymous with one another, and include standalone mobile stations, such as terminals, cell phones, smart phones, tablets, cellular IoT devices, IoT devices, and machinery. It will be appreciated that the terms "mobile station" and "mobile device" also encompass devices that remain stationary for an extended period of time.

[0216] A UE may, for example, be an item of equipment for production or manufacture and / or an item of energy related machinery (for example equipment or machinery such as: boilers; engines; turbines; solar panels; wind turbines; hydroelectric generators; thermal power generators; nuclear electricity generators; batteries; nuclear systems and / or associated equipment; heavy electrical machinery; pumps including vacuum pumps; compressors; fans; blowers; oil hydraulic equipment; pneumatic equipment; metal working machinery; manipulators; robots and / or their application systems; tools; moulds or dies; rolls; conveying equipment; elevating equipment; materials handling equipment; textile machinery; sewing machines; printing and / or related machinery; paper converting machinery; chemical machinery; mining and / or construction machinery and / or related equipment; machinery and / or implements for agriculture, forestry and / or fisheries; safety and / or environment preservation equipment; tractors; precision bearings; chains; gears; power transmission equipment; lubricating equipment; valves; pipe fittings; and / or application systems for any of the previously mentioned equipment or machinery etc.).

[0217] A UE may, for example, be an item of transport equipment (for example transport equipment such as: rolling stocks; motor vehicles; motorcycles; bicycles; trains; buses; carts; rickshaws; ships and other watercraft; aircraft; rockets; satellites; drones; balloons etc.).

[0218] A UE may, for example, be an item of information and communication equipment (for example information and communication equipment such as: electronic computer and related equipment; communication and related equipment; electronic components etc.).

[0219] A UE may, for example, be a refrigerating machine, a refrigerating machine applied product, an item of trade and / or service industry equipment, a vending machine, an automatic service machine, an office machine or equipment, a consumer electronic and electronic appliance (for example a consumer electronic appliance such as: audio equipment; video equipment; a loud speaker; a radio; a television; a microwave oven; a rice cooker; a coffee machine; a dishwasher; a washing machine; a dryer; an electronic fan or related appliance; a cleaner etc.).

[0220] A UE may, for example, be an electrical application system or equipment (for example an electrical application system or equipment such as: an x-ray system; a particle accelerator; radio isotope equipment; sonic equipment; electromagnetic application equipment; electronic power application equipment etc.).

[0221] A UE may, for example, be an electronic lamp, a luminaire, a measuring instrument, an analyser, a tester, or a surveying or sensing instrument (for example a surveying or sensing instrument such as: a smoke alarm; a human alarm sensor; a motion sensor; a wireless tag etc.), a watch or clock, a laboratory instrument, optical apparatus, medical equipment and / or system, a weapon, an item of cutlery, a hand tool, or the like.

[0222] A UE may, for example, be a wireless-equipped personal digital assistant or related equipment (such as a wireless card or module designed for attachment to or for insertion into another electronic device (for example a personal computer, electrical measuring machine)).

[0223] A UE may be a device or a part of a system that provides applications, services, and solutions described below, as to "internet of things (IoT)," using a variety of wired and / or wireless communication technologies.

[0224] Internet of Things devices (or "things") may be equipped with appropriate electronics, software, sensors, network connectivity, and / or the like, which enable these devices to collect and exchange data with each other and with other communication devices. IoT devices may comprise automated equipment that follow software instructions stored in an internal memory. IoT devices may operate without requiring human supervision or interaction. IoT devices might also remain stationary and / or inactive for an extended period of time. IoT devices may be implemented as a part of a (generally) stationary apparatus. IoT devices may also be embedded in non-stationary apparatus (e.g., vehicles) or attached to animals or persons to be monitored / tracked.

[0225] It will be appreciated that IoT technology can be implemented on any communication devices that can connect to a communication system for sending / receiving data, regardless of whether such communication devices are controlled by human input or software instructions stored in memory.

[0226] It will be appreciated that IoT devices are sometimes also referred to as Machine-Type Communication (MTC) devices or Machine-to-Machine (M2M) communication devices. It will be appreciated that a UE may support one or more IoT or MTC applications. Some examples of MTC applications are listed in the following table. This list is not exhaustive and is intended to be indicative of some examples of machine type communication applications.

[0227] Further, the above-described UE categories are merely examples of applications of the technical ideas and exemplary examples described in the present document. Needless to say, these technical ideas and examples are not limited to the above-described UE and various modifications can be made thereto.

[0228] Various other modifications will be apparent to those skilled in the art and will not be described in further detail here.

[0229] For example, the whole or part of the exemplary embodiments disclosed above can be described as, but not limited to, the following supplementary notes. 　　(Supplementary note 1) 　　A method performed by a radio access network, RAN, node, the method comprising: 　　transmitting, to a Location Management Function, LMF, a first message comprising first information indicating requested positioning information for data collection of an artificial intelligence / machine learning, AI / ML, model at the RAN node; and 　　receiving, from the LMF, a second message for reporting positioning information for the data collection. 　　(Supplementary note 2) 　　The method of supplementary note 1, wherein the positioning information comprises at least ground-truth labels for UEs in an area of interest and a timestamp. 　　(Supplementary note 3) 　　The method of supplementary note 1 or 2, wherein the first message further comprises AI / ML assisted indication. 　　(Supplementary note 4) 　　The method of supplementary note 1, further comprising: 　　transmitting, to the LMF, a first request for the LMF to provide to the RAN node, a list of available User Equipments, UEs, that are in a specific area and that support artificial intelligence, AI, and machine learning, ML, assisted positioning. 　　(Supplementary note 5) 　　The method of supplementary note 4, further comprising: 　　receiving, from the LMF, the list of available UEs. 　　(Supplementary note 6) 　　The method of any one of supplementary notes 1-5, further comprising: 　　performing UE selection for AI / ML-assisted positioning purposes. 　　(Supplementary note 7) 　　The method of any one of supplementary notes 1-6, further comprising: 　　receiving, from the LMF, a first response including an indication of UEs for which a ground-truth can be provided to the RAN node. 　　(Supplementary note 8) 　　The method of any one of supplementary notes 1-7, further comprising: 　　transmitting Downlink / Uplink, DL / UL, reference signal, RS, configurations to one or more UEs. 　　(Supplementary note 9) 　　The method of supplementary note 8, wherein the DL / UL RS configurations comprise at least one of a PRS, Positioning RS, a Sounding RS, SRS. 　　(Supplementary note 10) 　　The method of any one of supplementary notes 1-9, further comprising: calculating one or more performance monitoring metrics for AI / ML model. 　　(Supplementary note 11) 　　The method of supplementary note 10, further comprising: 　　transmitting, to the LMF, one or more predicted / inferred UE location assisted information generated by the AI / ML model for selected UEs. 　　(Supplementary note 12) 　　A method performed by a radio access network, RAN, node, the method comprising: 　　receiving, from a Location Management Function, LMF, a first message indicating requested positioning measurements at the LMF; and transmitting, to the LMF, positioning measurement results for the data collection at the LMF. 　　(Supplementary note 13) 　　The method of supplementary note 12, wherein the positioning measurement results comprise at least one of positioning reference signals, PRS, measurement results, sounding reference signals, SRS, measurement results, and one or more positioning metrics based on the PRS / SRS measurement results. 　　(Supplementary note 14) 　　The method of supplementary note 12 or 13, wherein the positioning metrics comprises at least uplink relative time of arrival, UL RTOA, UL angle of arrival, AOA, RAN node Rx-Tx Time Difference, and a new UE positioning measurement metric. 　　(Supplementary note 15) 　　The method of any one of supplementary note 12-14, wherein the first message comprises at least either an LMF measurement ID or a RAN measurement ID. 　　(Supplementary note 16) 　　The method of any one of supplementary notes 12-15, wherein the first message further comprises a parameter indicating a timing related to a Transmission-Reception Point, TRP. 　　(Supplementary note 17) 　　The method of any one of supplementary notes 12-16, wherein the first message further indicates: 　　at least one of a time window for measurements, and a number of samples to be reported. 　　(Supplementary note 18) 　　A method performed by a Location Management Function, LMF, the method comprising: 　　receiving, from a radio access network, RAN, node, a first message comprising first information indicating requested positioning information for data collection of an artificial intelligence / machine learning, AI / ML, model at the RAN node; and 　　transmitting, to the RAN node, a second message for reporting positioning information for the data collection. 　　(Supplementary note 19) 　　The method of supplementary note 18, wherein the positioning information comprises at least ground-truth labels for UEs in an area of interest and a timestamp. 　　(Supplementary note 20) 　　The method of supplementary note 18 or 19, wherein the first message further comprises AI / ML assisted indication. 　　(Supplementary note 21) 　　A method performed by a Location Management Function, LMF, the method comprising: 　　transmitting, to a radio access network, RAN, node, a first message indicating requested positioning measurements at the LMF; and receiving, from the RAN node, positioning measurement results for the data collection at the LMF. 　　(Supplementary note 22) 　　The method of supplementary note 21, wherein the positioning measurement results comprise at least one of positioning reference signals, PRS, measurement results, sounding reference signals, SRS, measurement results, and one or more positioning metrics based on the PRS / SRS measurement results. 　　(Supplementary note 23) 　　The method of any one of supplementary notes 21 or 22, wherein the first message further indicates: 　　at least one of a time window for measurements, and a number of samples to be reported.

[0230] This application is based upon and claims the benefit of priority from Great Britain Patent Application No. 2501065.3, filed on January 24, 2025, the disclosure of which is incorporated herein in its entirety by reference.

[0231] 1 COMMUNICATION SYSTEM 3 USER EQUIPMENT 5 RAN NODE 7 CORE NETWORK 10 CONTROL PLANE FUNCTIONS 11 USER PLANE FUNCTIONS 20 EXTERNAL DATA NETWORK 241　　DATA COLLECTION 243 MODEL TRAINING 245 INFERENCE 247 ACTOR 249 MANAGEMENT 251 MODEL STORAGE 31 TRANSCEIVER CIRCUIT 33 ANTENNA 35 USER INTERFACE 37 CONTROLLER 39 MEMORY 41 OPERATING SYSTEM 43 COMMUNICATIONS CONTROL MODULE 51 TRANSCEIVER CIRCUIT 51c TRANSCEIVER CIRCUIT (CU) 51d TRANSCEIVER CIRCUIT (DU) 53 ANTENNA 53d AIR INTERFACE 55 CORE NETWORK INTERFACE 55c NETWORK INTERFACE 57 CONTROLLER 57c CU CONTROLLER 57d DU CONTROLLER 59 MEMORY 59c CU MEMORY 59d DU MEMORY 61 OPERATING SYSTEM 61c CU OPERATING SYSTEM 61d DU OPERATING SYSTEM 63 COMMUNICATIONS CONTROL MODULE 63c CU COMMUNICATIONS CONTROL MODULE 63d DU COMMUNICATIONS CONTROL MODULE

Claims

1. A method performed by a radio access network, RAN, node, the method comprising: 　　transmitting, to a Location Management Function, LMF, a first message comprising first information indicating requested positioning information for data collection of an artificial intelligence / machine learning, AI / ML, model at the RAN node; and 　　receiving, from the LMF, a second message for reporting positioning information for the data collection.

2. The method of claim 1, wherein the positioning information comprises at least ground-truth labels for UEs in an area of interest and a timestamp.

3. The method of claim 1 or 2, wherein the first message further comprises AI / ML assisted indication.

4. The method of claim 1, further comprising: 　　transmitting, to the LMF, a first request for the LMF to provide to the RAN node, a list of available User Equipments, UEs, that are in a specific area and that support artificial intelligence, AI, and machine learning, ML, assisted positioning.

5. The method of claim 4, further comprising: 　　receiving, from the LMF, the list of available UEs.

6. The method of any one of claims 1-5, further comprising: 　　performing UE selection for AI / ML-assisted positioning purposes.

7. The method of any one of claims 1-6, further comprising: 　　receiving, from the LMF, a first response including an indication of UEs for which a ground-truth can be provided to the RAN node.

8. The method of any one of claims 1-7, further comprising: 　　transmitting Downlink / Uplink, DL / UL, reference signal, RS, configurations to one or more UEs.

9. The method of claim 8, wherein the DL / UL RS configurations comprise at least one of a PRS, Positioning RS, a Sounding RS, SRS.

10. The method of any one of claims 1-9, further comprising: calculating one or more performance monitoring metrics for AI / ML model.

11. The method of claim 10, further comprising: 　　transmitting, to the LMF, one or more predicted / inferred UE location assisted information generated by the AI / ML model for selected UEs.

12. A method performed by a radio access network, RAN, node, the method comprising: 　　receiving, from a Location Management Function, LMF, a first message indicating requested positioning measurements at the LMF; and transmitting, to the LMF, positioning measurement results for the data collection at the LMF.

13. The method of claim 12, wherein the positioning measurement results comprise at least one of positioning reference signals, PRS, measurement results, sounding reference signals, SRS, measurement results, and one or more positioning metrics based on the PRS / SRS measurement results.

14. The method of claim 12 or 13, wherein the positioning metrics comprises at least uplink relative time of arrival, UL RTOA, UL angle of arrival, AOA, RAN node Rx-Tx Time Difference, and a new UE positioning measurement metric.

15. The method of any one of claims 12-14, wherein the first message comprises at least either an LMF measurement ID or a RAN measurement ID.

16. The method of any one of claims 12-15, wherein the first message further comprises a parameter indicating a timing related to a Transmission-Reception Point, TRP.

17. The method of any one of claims 12-16, wherein the first message further indicates: 　　at least one of a time window for measurements, and a number of samples to be reported.

18. A method performed by a Location Management Function, LMF, the method comprising: 　　receiving, from a radio access network, RAN, node, a first message comprising first information indicating requested positioning information for data collection of an artificial intelligence / machine learning, AI / ML, model at the RAN node; and 　　transmitting, to the RAN node, a second message for reporting positioning information for the data collection.

19. The method of claim 18, wherein the positioning information comprises at least ground-truth labels for UEs in an area of interest and a timestamp.

20. The method of claim 18 or 19, wherein the first message further comprises AI / ML assisted indication.

21. A method performed by a Location Management Function, LMF, the method comprising: 　　transmitting, to a radio access network, RAN, node, a first message indicating requested positioning measurements at the LMF; and receiving, from the RAN node, positioning measurement results for the data collection at the LMF.

22. The method of claim 21, wherein the positioning measurement results comprise at least one of positioning reference signals, PRS, measurement results, sounding reference signals, SRS, measurement results, and one or more positioning metrics based on the PRS / SRS measurement results.

23. The method of any one of claims 21 or 22, wherein the first message further indicates: 　　at least one of a time window for measurements, and a number of samples to be reported.

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