MDT for AIML positioning

Abstract

Various examples of embodiments described herein relate to methods and apparatuses for MDT for AIML positioning. One such example of an embodiment relates to a method that includes receiving a trace session activation message comprising a minimization of drive test, MDT, configuration for collecting positioning related data for training or validating an artificial intelligence and machine learning, AIML, model for positioning; configuring, through the LTE Positioning Protocol, LPP, an endpoint terminal to collect and send positioning related data to be used for training or validating the AIML model for positioning based on the modified MDT configuration; receiving, from an endpoint or at least one gNodeB, positioning related data; inserting the positioning related data into payloads of trace records; and providing the trace records to a network entity or function managing trace collection.

Description

MDT FOR AIML POSITIONING Related Applications

[0001] This patent application claims the benefit of priority of United Kingdom Patent Application No. 2312286.4 filed August 10, 2023, which is hereby incorporated by reference as if reproduced in its entirety. Technical Field

[0002] Various examples of embodiments described herein relate to a method and an apparatus for MDT for AIML positioning. Background

[0003] Over time, an increasing extension of communication networks has taken place all over the world. Various organizations, such as the European Telecommunications Standards Institute (ETSI), the 3rdGeneration Partnership Project (3GPP), Telecoms & Internet converged Services & Protocols for Advanced Networks (TISPAN), the International Telecommunication Union (ITU), 3rdGeneration Partnership Project 2 (3GPP2), Internet Engineering Task Force (IETF), the IEEE (Institute of Electrical and Electronics Engineers), the WiMAX Forum and the like are working on standards or specifications for telecommunication networks and access environments.

[0004] By way of example, 3GPP defines 5G Core Network Function’s (NF’s) interfaces and relevant Application Programming Interfaces (APIs) for each NF to communicate between NFs. SUMMARY

[0005] Various examples of embodiments described in the subject disclosure provide certain advantages, for example in the form of one or more improvements that are either explicitly described herein or otherwise apparent to a person skilled in the art from the subject disclosure. Hence, at least some examples of embodiments of the subject disclosure aim to provide (or otherwise contribute to) at least part of the aforementioned advantages and improvements.

[0006] Various aspects of examples of embodiments of the subject disclosure are set forth in the claims and pertain to methods, apparatuses and computer program products in the context of MDT for AIML positioning.

[0007] At least some of aforementioned advantages and improvements may be achieved through the methods, apparatuses and non-transitory storage media as specified in the claims. Further advantages and improvements may be achieved through the methods, apparatuses and non-transitory storage media set forth in respective dependent claims.

[0008] Insofar, according to various examples of embodiments, an apparatus may comprise: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, may cause the apparatus at least to: generate a minimization of drive test, MDT, configuration for collecting data for training or validating an artificial intelligence and machine learning, AIML, model for positioning endpoint terminal; and provide a trace session activation message comprising the MDT configuration to a network entity or function managing data, wherein the MDT configuration comprises at least one of: a job type field comprising an indication to collect data for the training of the AIML model for positioning, a field for list of positioning related data to be obtained for positioning, a field for configuration details, a field for assistance data, a field for a reporting trigger for collecting positioning related data, or AIML-specific fields comprising at least one of label type or label value for the positioning related data.

[0009] According to various examples of embodiments, the indication in the job type field may be used to route a data collection request for collecting positioning reference data to a network entity or function managing a location of an endpoint terminal; the field for list of measurements for positioning may comprise measurement configurations that support collection of different types of measurements for positioning, the different types of measurements for positioning comprising at least one of reference symbol received power, RSRP, reference symbol received path power, RSRPP, time of arrival, ToA, angle of arrival, AoA, or time / angle / power / phase-based measurements; the field for a reporting trigger for collecting positioning related data may be indicative of at least one of a periodic trigger, an aperiodic trigger, an immediate trigger, a deferred trigger, or an event-based trigger to support a configuration in an endpoint terminal to transmit positioning related data; the label type and / or label value may be useable to configure the endpoint terminal to collect labels associated with positioning related data collected by the endpoint terminal to assist in training or validating the AIML model for positioning.

[0010] Moreover, according to various examples of embodiments, an apparatus may comprise: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, may cause the apparatus at least to: receive a trace session activation message comprising a minimization of drive test, MDT, configuration for artificial intelligence and machine learning, AIML, positioning; verify user consent for data collection for positioning based on the MDT configuration; and provide the trace session activation message to a network entity or function managing endpoint terminal mobility.

[0011] Furthermore, according to various examples of embodiments, an apparatus may comprise: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, may cause the apparatus at least to: receive a trace session activation message comprising a minimization of drive test, MDT, configuration for artificial intelligence and machine learning, AIML, positioning; store trace control parameters and configuration parameters related to the MDT configuration; and provide the trace session activation message to a network entity or function managing endpoint terminal location.

[0012] Further, according to various examples of embodiments, an apparatus may comprise: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, may cause the apparatus at least to: receive a trace session activation message comprising a minimization of drive test, MDT, configuration for collecting positioning related data for training or validating an artificial intelligence and machine learning, AIML, model for positioning; configure, through the LTE Positioning Protocol, LPP, an endpoint terminal to collect and send positioning related data to be used for training or validating the AIML model for positioning based on the MDT configuration; receive, from an endpoint terminal or at least one gNodeB, positioning related data; insert the positioning related data into payloads of trace records; and provide the trace records to a network entity or function managing trace collection.

[0013] According to at least some examples of embodiments, the trace records may be provided: in a first data format comprising an encoded LPP message, or in a second data format comprising a structured message generated by the apparatus, the structured message type comprising the positioning related data.

[0014] According to at least some examples of embodiments, the apparatus may further be caused to receive from an endpoint terminal, through at least one LPP message, logged positioning measurement reports, comprising of positioning related data, taken over a predetermined period of time while the endpoint terminal is in RRC IDLE mode or RRC INACTIVE mode.

[0015] According to at least some examples of embodiments, the apparatus may further be caused to configure, through the LTE Positioning Protocol, LPP, an endpoint terminal to include in its positioning related data the positioning related measurements used to estimate the position of the endpoint terminal.

[0016] According to at least some examples of embodiments, the MDT configuration may include an indication of at least one positioning method to be used by the location management function to collect positioning related data.

[0017] According to at least some examples of embodiments, the apparatus may further be caused to insert, in the payloads of the trace records, configuration details and assistance data provided when configuring, through the LTE Positioning Protocol, LPP, the endpoint to collect and send positioning related data, wherein the configuration details comprise a positioning reference signal, PRS, configuration related to the endpoint terminal, and wherein the assistance data comprise transmission-reception point, TRP, information related to the endpoint terminal.

[0018] Moreover, according to various examples of embodiments, an apparatus may comprise: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, may cause the apparatus at least to: receive, through the LTE Positioning Protocol, LPP, a configuration for configuring the apparatus for collecting positioning related data for training or validating an artificial intelligence and machine learning, AIML, for positioning; perform positioning measurements basedon the configuration; generate positioning related data based on the performed positioning measurements; and provide the positioning related data to a network entity or function managing endpoint terminal location.

[0019] According to at least some examples of embodiments, the apparatus may further be caused to perform the positioning measurements and / or provide the positioning related data during different network-connection status, comprising RRC IDLE / INACTIVE / CONNECTED mode; and provide the location information message to the network entity or function managing endpoint terminal location.

[0020] Furthermore, according to various examples of embodiments, an apparatus may comprise: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, may cause the apparatus at least to: receive trace records comprising positioning related data comprising location information related to an endpoint terminal, the location information obtained based on a minimization of drive test, MDT, configuration for collecting data for training or validating an artificial intelligence and machine learning, AIML, model for positioning endpoint terminal; and make the positioning related data available for usage as input in AIML positioning model training.

[0021] Further, according to various examples of embodiments, an apparatus may comprise means for generating a minimization of drive test, MDT, configuration for collecting data for training or validating an artificial intelligence and machine learning, AIML, model for positioning endpoint terminal; and means for providing a trace session activation message comprising the MDT configuration to a network entity or function managing data, wherein the MDT configuration comprises at least one of: a job type field comprising an indication to collect data for the training of the AIML model for positioning, a field for list of positioning related data to be obtained for positioning, a field for configuration details, a field for assistance data, a field for a reporting trigger for collecting positioning related data, or AIML-specific fields comprising at least one of label type or label value for the positioning related data.

[0022] According to various examples of embodiments, the indication in the job type field may be used to route a data collection request for collecting positioning referencedata to a network entity or function managing a location of an endpoint terminal; the field for list of measurements for positioning may comprise measurement configurations that support collection of different types of measurements for positioning, the different types of measurements for positioning comprising at least one of reference symbol received power, RSRP, reference symbol received path power, RSRPP, time of arrival, ToA, angle of arrival, AoA, or time / angle / power / phase-based measurements; the field for a reporting trigger for collecting positioning related data may be indicative of at least one of a periodic trigger, an aperiodic trigger, an immediate trigger, a deferred trigger, or an event- based trigger to support a configuration in an endpoint terminal to transmit positioning related data; the label type and / or label value may be useable to configure the endpoint terminal to collect labels associated with positioning related data collected by the endpoint terminal to assist in training or validating the AIML model for positioning.

[0023] Moreover, according to various examples of embodiments, an apparatus may comprise: means for receiving a trace session activation message comprising a minimization of drive test, MDT, configuration for artificial intelligence and machine learning, AIML, positioning; means for verifying user consent for data collection for positioning based on the MDT configuration; and means for providing the trace session activation message to a network entity or function managing endpoint terminal mobility.

[0024] Furthermore, according to various examples of embodiments, an apparatus may comprise: means for receiving a trace session activation message comprising a minimization of drive test, MDT, configuration for artificial intelligence and machine learning, AIML, positioning; means for storing trace control parameters and configuration parameters related to the MDT configuration; and means for providing the trace session activation message to a network entity or function managing endpoint terminal location.

[0025] Further, according to various examples of embodiments, an apparatus may comprise: means for receiving a trace session activation message comprising a minimization of drive test, MDT, configuration for collecting positioning related data for training or validating an artificial intelligence and machine learning, AIML, model for positioning; means for configuring, through the LTE Positioning Protocol, LPP, an endpoint terminal to collect and send positioning related data to be used for training orvalidating the AIML model for positioning based on the MDT configuration; means for receiving, from an endpoint terminal or at least one gNodeB, positioning related data ; means for inserting the positioning related data into payloads of trace records; and means for providing the trace records to a network entity or function managing trace collection.

[0026] According to at least some examples of embodiments, the trace records may be provided: in a first data format comprising an encoded LPP message, or in a second data format comprising a structured message generated by the apparatus, the structured message type comprising the positioning related data.

[0027] According to at least some examples of embodiments, the apparatus may further comprise means for receiving from an endpoint terminal, through at least one LPP message, logged positioning measurement reports, comprising of positioning related data, taken over a predetermined period of time while the endpoint terminal is in RRC IDLE mode or RRC INACTIVE mode.

[0028] According to at least some examples of embodiments, the apparatus may further comprise means for configuring, through the LTE Positioning Protocol, LPP, an endpoint terminal to include in its positioning related data the positioning related measurements used to estimate the position of the endpoint terminal.

[0029] According to at least some examples of embodiments, the MDT configuration may include an indication of at least one positioning method to be used by the location management function to collect positioning related data.

[0030] According to at least some examples of embodiments, the apparatus may further comprise means for inserting, in the payloads of the trace records, configuration details and assistance data provided when configuring, through the LTE Positioning Protocol, LPP, the endpoint to collect and send positioning related data, wherein the configuration details comprise a positioning reference signal, PRS, configuration related to the endpoint terminal, and wherein the assistance data comprise transmission-reception point, TRP, information related to the endpoint terminal.

[0031] Moreover, according to various examples of embodiments, an apparatus may comprise: means for receiving, through the LTE Positioning Protocol, LPP, aconfiguration for configuring the apparatus for collecting positioning related data for training or validating an artificial intelligence and machine learning, AIML, for positioning; means for performing positioning measurements based on the configuration; means for generatinge positioning related data based on the performed positioning measurements; and means for providing the positioning related data to a network entity or function managing endpoint terminal location.

[0032] According to at least some examples of embodiments, the apparatus may further comprise means for performing the positioning measurements and / or providing the positioning related data during different network-connection status, comprising RRC IDLE / INACTIVE / CONNECTED mode; and provide the location information message to the network entity or function managing endpoint terminal location.

[0033] Furthermore, according to various examples of embodiments, an apparatus may comprise means for receiving trace records comprising positioning related data comprising location information related to an endpoint terminal, the location information obtained based on a minimization of drive test, MDT, configuration for collecting data for training or validating an artificial intelligence and machine learning, AIML, model for positioning endpoint terminal; and means for making the positioning related data available for usage as input in AIML positioning model training.

[0034] Furthermore, according to at least some examples of embodiments, a method may comprise generating a minimization of drive test, MDT, configuration for collecting data for training or validating an artificial intelligence and machine learning, AIML, model for positioning endpoint terminal; and providing a trace session activation message comprising the MDT configuration to a network entity or function managing data, wherein the MDT configuration comprises at least one of a job type field comprising an indication to collect data for the training of the AIML model for positioning, a field for list of positioning related data to be obtained for positioning, a field for configuration details,

[0035] A field for assistance data, a field for a reporting trigger for collecting positioning related data, or AIML-specific fields comprising at least one of label type or label value for the positioning related data.

[0036] According to various examples of embodiments, the indication in the job type field may be used to route a data collection request for collecting positioning reference data to a network entity or function managing a location of an endpoint terminal; the field for list of measurements for positioning may comprise measurement configurations that support collection of different types of measurements for positioning, the different types of measurements for positioning comprising at least one of reference symbol received power, RSRP, reference symbol received path power, RSRPP, time of arrival, ToA, angle of arrival, AoA, or time / angle / power / phase-based measurements; the field for a reporting trigger for collecting positioning related data may be indicative of at least one of a periodic trigger, an aperiodic trigger, an immediate trigger, a deferred trigger, or an event- based trigger to support a configuration in an endpoint terminal to transmit positioning related data; the label type and / or label value may be useable to configure the endpoint terminal to collect labels associated with positioning related data collected by the endpoint terminal to assist in training or validating the AIML model for positioning.

[0037] Furthermore, according to at least some examples of embodiments, a method may comprise receiving a trace session activation message comprising a minimization of drive test, MDT, configuration for collecting positioning related data for training or validating an artificial intelligence and machine learning, AIML, model for positioning; configuring, through the LTE Positioning Protocol, LPP, an endpoint terminal to collect and send positioning related data to be used for training or validating the AIML model for positioning based on the MDT configuration; receiving, from an endpoint or at least one gNodeB, positioning related data ; inserting the positioning related data into payloads of trace records; and providing the trace records to a network entity or function managing trace collection.

[0038] Moreover, according to at least some examples of embodiments, a method may comprise receiving from an endpoint terminal, through at least one LPP message, logged positioning measurement reports, comprising of positioning related data, taken over a predetermined period of time while the endpoint terminal is in RRC IDLE mode or RRC INACTIVE mode.

[0039] Further, according to at least some examples of embodiments, a method may comprise receiving a trace session activation message comprising a minimization of drivetest, MDT, configuration for artificial intelligence and machine learning, AIML, positioning; configuring, through the LTE Positioning Protocol, LPP, an endpoint terminal for data collection for positioning based on the modified MDT configuration; receiving positioning measurement reports comprising location information related to the endpoint terminal; packing the positioning measurement reports into trace records; and providing the trace records to a network entity or function managing trace collection.

[0040] According to various examples of embodiments, the trace records may be provided: in a first data format comprising an encoded LPP message, or in a second data format comprising a structured message generated by the apparatus, the structured message type comprising the positioning related data.

[0041] According to various examples of embodiments, the method may further comprise receiving from an endpoint terminal, through at least one LPP message, logged positioning measurement reports, comprising of positioning related data, taken over a predetermined period of time while the endpoint terminal is in RRC IDLE mode or RRC INACTIVE mode.

[0042] According to various examples of embodiments, the method may further comprise configuring, through the LTE Positioning Protocol, LPP, an endpoint terminal to include in its positioning related data the positioning related measurements used to estimate the position of the endpoint terminal.

[0043] According to various examples of embodiments, the MDT configuration may include an indication of at least one positioning method to be used by the location management function to collect positioning related data.

[0044] According to various examples of embodiments, the method may further comprise inserting, in the payloads of the trace records, configuration details and assistance data provided when configuring, through the LTE Positioning Protocol, LPP, the endpoint terminal to collect and send positioning related data, wherein the configuration details comprise a positioning reference signal, PRS, configuration related to the endpoint terminal, and wherein the assistance data comprise transmission-reception point, TRP, information related to the endpoint terminal.

[0045] Further, according to at least some examples of embodiments, a method may comprise receiving, at an endpoint terminal, through an LTE Positioning Protocol, LPP, a configuration for configuring the endpoint terminal for collecting positioning related data for training or validating an artificial intelligence and machine learning, AIML, for positioning; performing positioning measurements based on the configuration; generating positioning related data based on the performed positioning measurements; and providing the positioning related data to a network entity or function managing endpoint terminal location.

[0046] According to various examples of embodiments, the method may further comprise performing the positioning measurements and / or providing the positioning related data during different network-connection status of the endpoint terminal, comprising RRC IDLE / INACTIVE / CONNECTED mode; and providing the location information message to the network entity or function managing endpoint terminal location.

[0047] Further, according to at least some examples of embodiments, a method may comprise receiving trace records comprising positioning related data comprising location information related to an endpoint terminal, the location information obtained based on a minimization of drive test, MDT, configuration for collecting data for training or validating an artificial intelligence and machine learning, AIML, model for positioning endpoint terminal; and making the positioning related data available for usage as input in AIML positioning model training.

[0048] Furthermore, according to various examples of embodiments, there may be provided a computer program product for a computer, including software code portions for performing the steps of any of the above-outlined methods, when said product is run on the computer.

[0049] According to at least some examples of embodiments, the computer program product may include a computer-readable medium on which said software code portions are stored, and / or the computer program product may be directly loadable into the internal memory of the computer and / or transmittable via a network by means of at least one of upload, download and push procedures.

[0050] Any one of the aspects mentioned according to the claims facilitates MDT for AIML positioning, thereby providing at least part of the aforementioned advantages and improvements.

[0051] In more detail, the subject disclosure describes MDT for AIML positioning, which may, according to some examples of embodiments, prove advantageous over a proprietary solution for at least the following reasons.

[0052] Examples of embodiments described in the subject disclosure may, for instance, enable the collection of positioning-related measurements through additions to MDT signaling through positioning-based modifications to the MDT configuration, adding MDT signaling to the interface between the Access and Mobility Management Function (AMF) and the Location Management Function (LMF), and connecting the LMF to a Trace Collection Entity (TCE).

[0053] Further advantages may become apparent to a person skilled in the art from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Some examples of embodiments of the subject disclosure are described below, by way of example only, with reference to the accompanying drawings, in which:

[0055] FIG. 1 shows MDT Trace Session Activation Flow Adapted for Positioning according to various examples of embodiments of the subject disclosure;

[0056] FIG. 2 shows Measurement Transmission in MDT for AIML Positioning according to various examples of embodiments of the subject disclosure;

[0057] FIG. 3 shows a flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the subject disclosure;

[0058] FIG. 4 shows a flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the subject disclosure;

[0059] FIG. 5 shows a flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the subject disclosure;

[0060] FIG. 6 shows a flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the subject disclosure;

[0061] FIG. 7 shows a flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the subject disclosure;

[0062] FIG. 8 shows a flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the subject disclosure;

[0063] FIG.9 shows a block diagram illustrating an example apparatus according to various examples of embodiments of the subject disclosure;

[0064] FIG. 10 shows a block diagram illustrating an example apparatus according to various examples of embodiments of the subject disclosure;

[0065] FIG. 11 shows a block diagram illustrating an example apparatus according to various examples of embodiments of the subject disclosure;

[0066] FIG. 12 shows a block diagram illustrating an example apparatus according to various examples of embodiments of the subject disclosure;

[0067] FIG. 13 shows a block diagram illustrating an example apparatus according to various examples of embodiments of the subject disclosure; and

[0068] FIG. 14 shows a block diagram illustrating an example apparatus according to various examples of embodiments of the subject disclosure. DEATILED DESCRIPTION

[0069] In general, two or more end points involved in a communication therebetween may be implemented as a particular type of end point (e.g. a communication station or entity or function, such as a terminal device, user equipment (UE), or other communication network element, a database, a server, host, etc.), or as one or more network elements or functions (e.g. virtualized network functions), such as communication network control elements or functions, for example access network elements like access points (APs), radio base stations (BSs), relay stations, eNBs, gNBsetc., and core network elements or functions, for example control nodes, support nodes, service nodes, gateways, user plane functions, access and mobility functions, etc.. These end points may belong to a single communication network system, different communication network systems, or a combination of at least one same communication network system and at least one different communication network system.

[0070] In the following, various examples of embodiments will be described using, as an example of a communication network to which examples of embodiments may be applied, a communication network architecture based on 3GPP standards for a communication network, such as a 5G / NR, without restricting the examples of embodiments to such an architecture. It, however, is obvious for a person skilled in the art that the examples of embodiments may also be applied to other kinds of communication networks like 4G and / or LTE (and even 6G and higher) where mobile communication principles are integrated, e.g. Wi-Fi, worldwide interoperability for microwave access (WiMAX), Bluetooth®, personal communications services (PCS), ZigBee®, wideband code division multiple access (WCDMA), systems using ultra- wideband (UWB) technology, mobile ad-hoc networks (MANETs), wired access, etc.. Furthermore, without loss of generality, the description of some examples of embodiments is related to a mobile communication network, but the subject disclosure can be extended and applied to any other type of communication network, such as a wired communication network or datacenter networking.

[0071] The following examples of embodiments are to be understood only as illustrative in nature. Although portions of the subject disclosure may refer to the expressions “an”, “one”, or “some” example(s) of embodiment(s) in several specific locations, this does not necessarily mean that each such reference is related to the same example(s) of embodiment(s), or that the described feature only applies to a single example of an embodiment. Individual features from different examples of embodiments may also be combined to provide other examples of embodiments. Furthermore, terms like “comprising” and “including” should be understood as not limiting the described examples of embodiments to consist of only those features that have been mentioned; such examples of embodiments can also contain features, structures, units, modules etc. that have not been specifically mentioned.

[0072] A simplified system architecture of a (tele)communication network including a mobile communication system, where some examples of embodiments, are applicable may include an architecture of one or more communication networks. The one or more communication networks may include wireless access network subsystem(s) and core network(s). Such an architecture may include one or more communication network control elements or functions, access network elements, radio access network elements, access service network gateways or base transceiver stations, such as a base station (BS), an access point (AP), a NodeB (NB), an eNB or a gNB, a distributed or a centralized unit (CU), which controls a respective coverage area or cell(s) and with which one or more communication stations such as communication elements or functions, like user devices (e.g. customer devices), mobile devices, or terminal devices, like a UE, or another device having a similar function, such as a modem chipset, a chip, a module etc., which can also be part of a station, an element, a function or an application configured to conduct a communication, such as a UE, an entity or function usable in a machine-to-machine communication architecture, or attached as a separate element to such an element, function or application capable of conducting a communication, or the like, are configured to communicate via one or more channels via one or more communication beams for transmitting several types of data in a plurality of access domains. Furthermore, (core) network elements or network functions ((core) network control elements or network functions, (core) network management elements or network functions), such as gateway network elements / functions, mobility management entities, a mobile switching center, servers, databases and the like may be included.

[0073] Functions and interconnections of the described elements and functions, which also depend on the actual network type, are apparent to those skilled in the art and may be described in corresponding specifications, so that a description thereof is omitted herein. However, it is to be noted that several additional network elements and signaling links may be employed for a communication to or from an element, function or application, like a communication endpoint, a communication network control element, such as a server, a gateway, a radio network controller, and other elements of the same or other communication networks besides those described in detail herein below.

[0074] It should be appreciated that according to some examples of embodiments, a so-called “liquid” or flexible network concept may be implemented where the operations and functionalities of a network element, a network function, or of another entity of the network, may be performed in different entities or functions, such as in a node, host or server, in a flexible manner. Thus, a “division of labor” between involved network elements, functions or entities may vary case by case.

[0075] The subject disclosure, in some examples, relates to MDT for collecting positioning related data to be used for training or validating a AIML model for positioning.

[0076] Data collection, particularly for the offline training of UE-side models, i.e., models for which inference is performed on the UE, is an ongoing topic in 3GPP RAN2. The agreed way forward for the study item includes “considering the use of existing frameworks as starting points to be considered for data collection.” This specification proposes a solution to augment the MDT framework and to use the LTE Positioning Protocol (LPP), which are among the listed data collection frameworks to be studied, with the ability to collect positioning related measurements.

[0077] It has been expressed by most network operators that it is desirable that collected data would also be available for use by the network, e.g., for training and / or validating an AI / ML model (referred to herein as AI / ML training). The present specification uses a network-driven approach in which the network is aware of the data collection, which would enable the network to store the collected data even if the eventual target of the data collection is external to the network.

[0078] The framework to be modified in this specification is called minimization of drive test (MDT). The purpose of MDT is to use commercial UEs to perform the function of a drive test by reporting signal measurements such as reference symbol received power (RSRP) and reference symbol received quality (RSRQ) on signals received from nearby gNodeBs (e.g., gNodeBs in close proximity to the fielded UEs). The collected data is used to optimize network configurations and increase reliability. MDT is initiated in an operations, administration, and management (OAM) system, and trigger radio access network (RAN)-based measurements via the 5G core network. A measurementconfiguration is propagated from an OAM system to the access and mobility function (AMF) of a 5G core network, and the AMF configures a gNodeB to provide a UE with a measurement configuration. The UE transmits measurement reports to the gNodeB, and the gNodeB forwards the measurement reports to a trace collection entity (TCE) with which the gNodeB has been configured.

[0079] Figure 4.1.2.17.3.1 from TS32.422 (v17.10.0) depicts a configuration procedure for signaling based immediate MDT trace session activation toward a gNodeB and UE. Note that the configuration procedure essentially begins with the propagation of a trace session activation, or MDT configuration, from the Management System (MaS) to a Unified Data Management (UDM) of a 5GC, from the UDM to an AMF of a 5GC, and the AMF to the gNodeB of a NG-RAN. From that point, MDT simply reuses the measurement configuration and collection procedures defined for radio resource control (RRC) in TS 38.331.

[0080] The MDT configuration includes an IP address of a TCE, which is used by the gNodeB to forward measurements received from the UE. The measurement transmission process is depicted in Figure 6.4.1 from TS 32.422 (v17.10.0).

[0081] The MDT configuration is received through RRC Reconfiguration, while the MDT reporting is achieved through sending RRC Measurement Reports from the UE to the gNodeB. Once the gNodeB has collected measurements from the UE, it packs them into trace records, which contain a header to identify a trace, and a payload, which comes with a schema URI pointing to a decoding definition for the payload.

[0082] More details about MDT can be found in TS 37.320 and TS 32.422.

[0083] TS 37.355 (LPP Specification) – The LTE Positioning Protocol (LPP) specification will be reused as the transport mechanism for location estimates, including, but not limited to, the messages defined in the specification: LPP Provide / Request Location Information; LPP Provide / Request Assistance Data; and LPP Provide / Request Capability. The protocol defines message and data structures that carry the data to be collected through enhancements to MDT that are presented in this IR.

[0084] TS 37.320 and TS 32.422 (MDT) – The MDT specifications are used as a starting point to carry the functionality to support data collection from an LMF. In the current specification, MDT supports the configuration of a gNodeB through the MaS, UDM, and AMF, and the transport of collected data from the gNodeB to a TCE.

[0085] As shown in the technical background section, LPP (defined in TS 37.355 and TS 38.305) and MDT (defined in TS 32.422 and TS 37.320) are being considered by the 3GPP RAN2 working group. These data collection frameworks are in existence and in use today. For the purpose of positioning, LPP can be reused to enable the LMF to configure positioning sessions on UEs, to transmit assistance data to the UEs, and to acquire positioning measurements, e.g., location information from the UEs. However, LPP terminates in the LMF, which is not a suitable location for data collection purposes, especially considering the training of UE-side models, which might be trained at an external server. MDT is currently used to collect physical layer measurement data from UEs for the purpose of network optimization, but it only supports communication with the gNodeB. Both of these solutions fall short of solving the data collection problem for AIML positioning because there is no method available to collect positioning measurement data from UEs and make those measurements available to data collection entities suitable for performing AIML functions.

[0086] To support the creation of AIML models for each of the use cases: CSI compression; CSI prediction; beam management; and enhanced positioning, defined in 3GPP RAN, vendors of UEs and modem chipsets need a way to collect measurement data and labels from UEs on live networks. For the CSI and beam management cases, there is at least a solution, minimization of drive test (MDT), that collects and stores measurement data internal to the network. For positioning, while the LMF can collect detailed positioning data, i.e., measurements such as reference symbol received power (RSRP), reference symbol received path power (RSRPP), angle of arrival (AoA), and time of arrival (ToA) made for positioning, there is no mechanism to collect and store that data in a network for AIML purposes. Currently, only “available” (by chance) UE locations can be queried and stored, which on their own are not useful for training AIML models.

[0087] According to various examples of embodiments of the subject disclosure, the present specification enables the collection of positioning-related measurements throughadditions to MDT signaling through positioning-based modifications to the MDT configuration, adding MDT signaling to the interface between the AMF and the LMF, and connecting the LMF to a TCE.

[0088] With the proposed method, there is a further advantage that LPP in its current version and in future versions can be directly reused by MDT for positioning. The transmission of configurations and assistance information, e.g., transmission-reception point (TRP) information, reference symbol configurations, etc., can also be transmitted to the TCE with the collected data.

[0089] Among others, the following features are shown by the present specification according to various examples of embodiments of the subject disclosure: • Configuration of an LMF through MDT, including the addition of new configuration fields; • Transmission of detailed positioning measurements made by UEs through the network to a TCE; and • Transmission of configurations and assistance information to the TCE.

[0090] In the following, several aspects in relation to MDT for AIML positioning according to various examples of embodiments of the subject disclosure are described in detail. Configuration

[0091] According to various examples of embodiments of the subject disclosure and with reference to FIG. 1, the following may be considered at least in parts for configuration of MDT for AIML positioning.

[0092] In Step 1 of FIG.1, the trace session activation message containing the MDT configuration for AIML positioning is sent from MaS 110 to the UDM 120. The following modifications and new fields are added to the trace activation message: a new job type for Positioning is added; a new field for the list measurements for positioning is added; anew field for the reporting trigger for positioning is added; and AIML-specific fields such as label type and label value for the collected data are added.

[0093] The new positioning job type is used to properly route the data collection request to the correct entity, which in this case will be the LMF 140.

[0094] The list of measurements for positioning include different measurement profiles that support the collection of different types of measurements for positioning such as RSRP, RSRPP, ToA, and AoA. The list of measurements to collect is used by the LMF 140 to configure the UE 150 over LPP, and for the LMF 140 to configure the gNodeB over NRPPa to collect and transmit positioning measurements to the LMF 140, and for the LMF 140 to transmit the MDT records to the TCE.

[0095] The reporting trigger for positioning supports periodic, aperiodic, and event- based triggers to support configurations in the UE 150 to transmit positioning related data comprising positioning related measurements used to estimate the position of a UE.

[0096] The label type and value can be used to configure the UE 150 to collect labels associated with the collected data to assist in AIML model training.

[0097] In Step 2 of FIG.1, the trace control and configuration parameters are stored in the UDM 120, and user consent for data collection for positioning is checked.

[0098] In Steps 3 through 6 of FIG.1, the trace session activation is forwarded from the UDM 120 to the AMF 130, and from the AMF 130 to the LMF 140. Each entity stores the trace control and configuration parameters.

[0099] In Step 7 of FIG. 1, the LMF 140 starts the trace recording session in for positioning.

[0100] In Steps 8 and 9, the LMF 140 configures the UE 150 targeted for data collection for positioning using standard LPP procedures, using the MDT configuration for data collection for positioning.

[0101] A new field, includeMeasurementPosNr, is introduced to have the UE 150 report measurements used in location estimation along with the location estimate for UE-based positioning, e.g., when the AIML model is executed on the UE 150 and the output of inference is the location estimate. The positioning method, such as GNSS, or A-GNSS, can be set in the PositioningMethodMdt parameter and additional positioning methods, including new AIML-based methods that will be implemented in the future, such as observed time difference of arrival (OTDOA) and AoA, are added to the parameter.

[0102] According to various examples of embodiments of the present disclosure, the LMF 140 can be configured by MDT indirectly through other network functions, which would propagate the configuration to the LMF 140.

[0103] At least some embodiments exemplifying the modifications to parameters, and the addition of fields for the configuration of MDT for positioning are provided below. Measurement Transmission

[0104] This specification captures positioning measurements transmitted through LPP between the UE and the LMF, and NRPPa between the gNodeB and the LMF. According to various examples of embodiments of the subject disclosure for the Capture UE Positioning Measurements, at least part of the following may be considered with reference to FIG.2.

[0105] To transmit the measurements from the LMF 220 to the TCE 240, the LMF 220 packages positioning measurements, configurations, and assistance data into trace records.

[0106] Step 1 in FIG.2 is covered in the previous section regarding configuration for MDT for AIML Positioning.

[0107] In Step 2 in FIG.2, which may be repeated for as long as there are positioning measurements configured in the UE 210, the UE 210 transmits positioning measurement reports, e.g., location information, to the LMF 220.

[0108] In Step 3 in FIG.2, which may be repeated for as long as there are positioning measurements configured in the UE 210, the LMF 220 stores or buffers the location information transmitted by the UE 210.

[0109] In Step 4 in FIG.2, whether management-based MDT (steps 4-1a, and 4-1b) or signaling-based MDT (step 4-2) is used, the LMF 220 packages the positioning measurements from the UE 210 into trace records to send to the TCE. There are two options for the data format: send encoded LPP messages directly; or create a new structured message type that the LMF 220 fills with the measurements received from the UE 210.

[0110] According to various examples of embodiments of the subject disclosure, to support logged MDT, wherein measurements are made in RRC IDLE and RRC INACTIVE modes, a new log for MDT for AIML Positioning data structure may be added to LPP to support the transmission of many measurements taken over the period of time while the UE 210 is in either mode.

[0111] According to various examples of embodiments of the subject disclosure, the new log message may be added as a list of measurements in a ProvideLocationInformation message once the UE 210 reentered RRC CONNECTED mode and could transfer the log to the LMF 220.

[0112] At least some embodiments exemplifying the modifications to parameters, and the addition of fields for the capture positioning measurements for MDT for AIML positioning are provided below.

[0113] According to various examples of embodiments of the subject disclosure for the Capture of Configuration and Assistance Data Associated with UE Positioning Measurements, at least part of the following may be considered with reference to FIG.2.

[0114] In addition to positioning measurements, configuration details such as the PRS configuration and assistance data from the LMF, such as TRP information, may be transmitted to the TCE 240 during an MDT for AIML positioning session. This information does not change with each new measurement, so it is only be transmitted alongside the measurements with the first measurement and when the information does change. If the MDT configuration is set to trace all LPP activity on a UE 210, then the information could change if different positioning methods are used.

[0115] To transmit the configuration and assistance data, the trace record and trace record payload could also be used. According to various examples of embodiments of the subject disclosure, two options are presented below as implementation options.

[0116] According to various examples of embodiments, the configuration and assistance data can be transmitted to the TCE 240 directly using the encoded LPP messages containing the configuration, e.g., LPPRequestLocationInformation, and the assistance data, e.g., LPPProvideAssistanceData.

[0117] Moreover, according to various examples of embodiments, the configuration and assistance data can be transmitted to the TCE 240 using a new structured message type for carrying the configuration and assistance data.

[0118] At least several embodiments exemplifying the modifications to parameters, and the addition of fields for the capture positioning configurations and assistance data for MDT for AIML positioning are provided below. Details on Configuration

[0119] According to various examples of embodiments for Augmentation of MDT Configuration for Positioning Measurements, the following items highlighted in bold could be added to the MdtConfiguration type () and to its field JobType (). According to various examples of embodiments, further illustrations of the new configuration IEs, MeasurementPosNrForMdt, ReportingTriggerPosNr, and ReportIntervalPosNr are depicted in illustrations 3 to 5.

[0120] Illustration 1, MdtConfiguration Modifications for MDT for Positioning (adapted from TS29571_CommonData.yaml v18.2.0): MdtConfiguration: description: contains contain MDT configuration data. type: object required:- jobType properties: jobType: $ref: '# / components / schemas / JobType' reportType: $ref: '# / components / schemas / ReportTypeMdt' areaScope: $ref: '# / components / schemas / AreaScope' measurementPosNrList: type: array items: $ref: ‘# / components / schemas / MeasurementPosNrForMdt’ minItems: 1 reportingTriggerPosNrList: type: array items: $ref: '# / components / schemas / ReportingTriggerPosNr' minItems: 1 reportIntervalPosNrList: type: array items:$ref: ‘# / components / schemas / ReportIntervalPosNrList’ minItems: 1 includeMeasurementPosNr: -type: boolean

[0121] According to various examples of embodiments for addition of job types for MDT for AIML Positioning, to support the configuration of MDT for positioning, a new JobType may be added in for immediate MDT for positioning.

[0122] Illustration 2, JobType Modifications for MDT for Positioning (adapted from TS29.571 (v18.2.0), Table 5.6.3.3-1): JobType: anyOf: - type: string enum: - IMMEDIATE_MDT_ONLY - LOGGED_MDT_ONLY - TRACE_ONLY - IMMEDIATE_MDT_AND_TRACE - RLF_REPORTS_ONLY - RCEF_REPORTS_ONLY - LOGGED_MBSFN_MDT - IMMEDIATE_MDT_POS_ONLY - LOGGED_MDT_POS_ONLY- type: string description: >

[0123] (The enumeration JobType defines Job Type in the trace. See 3GPP TS 32.422 for further description of the values. It shall comply with the provisions defined in table 5.6.3.3-1 from TS29.571 (v18.2.0))

[0124] According to various examples of embodiments for addition of Measurement Configuration for MDT for AIML Positioning, in illustration 3, M1 – MN correspond to different types of measurement configurations. This may e.g. enable the definition of measurement profiles that correspond to types of measurements and also profiles corresponding to positioning methods for which the LMF would configure the appropriate measurements. According to various examples of embodiments, the measurement configuration corresponds to types of measurements, such as RSRP, RSRPP, ToA, and AoA. According to various examples of embodiments, the measurement configuration corresponds to the various combinations of positioning method, e.g., DL TDOA, and positioning mode, e.g., UE-based, for which data collection would be configured.

[0125] Illustration 3, New IE MeasurementPosNrForMdt (adapted from TS29.571 (v18.2.0), Table 5.6.3.6-1): MeasurementPosNrForMdt: anyOf: - type: string enum: - M1 - M2 - M3 - …- MN - type: string description: >

[0126] According to various examples of embodiments for addition of Reporting Triggers for MDT for AIML Positioning, in illustrations 4 and 5, two example report trigger options are shown. These examples do not include every possibility. The two triggers shown are periodical and all. For periodical reporting, a report interval, shown in illustration 6, is applied to the configuration such that the UE reports positioning measurements and / or position estimates on a periodical basis. When MDT for AIML positioning is configured to trigger a report for all event triggers, it means that positioning measurements and / or position estimates are captured positioning sessions that were configured outside of the MDT for AIML Positioning framework, e.g., ongoing requests for positioning requested by other network functions and / or LCS clients. For example, with reference to an event-based trigger, according to various examples of embodiments, such an event-based trigger may represent, as examples only without excluding any other triggering events not named in the following: a UE having moved by some predetermined distance and / or a UE having changed cells.

[0127] Illustration 4, New ReportingTriggerPosNr Parameter (adapted from TS29.571 (v18.2.0), Table 5.6.3.8-1): ReportingTriggerPosNr: anyOf: - type: string enum: - PERIODICAL - ALL_POS_EVENT_TRIGGERS - type: stringdescription: >

[0128] Illustration 4, New ReportIntervalPosNrMdt Parameter (adapted from TS29.571 (v18.2.0), Table 5.6.3.9-1): ReportIntervalPosNrMdt: anyOf: - type: string enum: - 1 - 2 - 4 - 8 - 10 - 16 - 20 - 32 - 64 - type: string description: > Measurement, Configuration, and Assistance Data Transmission

[0129] According to various examples of embodiments, addition to the definition of Trace Record for UE location info is considered. The TS32.423 specification defines a table, 4.34.2, of positioning measurements that can be transmitted in a trace record. According to various examples of embodiments, this table may be augmented to support additional measurements such as, but not limited to, downlink (DL) TDOA, (DL) AoD.

[0130] According to various examples of embodiments, Transmission of Configuration and Assistance Data Using MDT for Positioning with LPP is considered. If encoded LPP is used as the format for the transmission of measurements for MDT for positioning according to various examples of embodiments, then the configuration, provided in LPPRequestLocationInformation may be provided using the LPP message directly, and the assistance data, such as TRP Info, may be provided in LPPProvideAssistanceData using the LPP message directly.

[0131] The following illustration 6 adapted from TS32.423, D.1.1, shows an example of an XML trace file, with a subset of potential modifications highlighted in bold according to various examples of embodiments, that allows for the transmission of a raw protocol message. This approach could also work for sending raw, encoded, LPP to the TCE instead of defining a new message type to contain measurements.

[0132] Illustration 6, adaptation of Example of XML Trace File with a Raw Message from TS32.423 (v17.6.0), D.1.1: <?xml version="1.0" encoding="UTF-8"?> <traceCollecFile xmlns="http: / / www.3gpp.org / ftp / specs / archive / 32_series / 32.423#traceData" xmlns:xsi="http: / / www.w3.org / 2001 / XMLSchema-instance" xsi:schemaLocation="http: / / www.3gpp.org / ftp / specs / archive / 32_series / 32.423#traceDat a http: / / www.3gpp.org / ftp / specs / archive / 32_series / 32423#traceData"> <fileHeader fileFormatVersion="32.423 V6.0" vendorName="Company NN"> <pOPLMN> <MCC>460< / MCC><MNC>10< / MNC> < / pOPLMN> <fileSender elementDn="DC=a1.companyNN.com,SubNetwork=1, ManagedElement=LMF-1" elementType="LMF" / > <traceCollec beginTime="2001-09-11T09:30:47-05:00" / > < / fileHeader> <traceRecSession dnPrefix="DC=a1.companyNN.com,SubNetwork=1" traceRecSessionRef=" A1" stime="2001-09-11T09:30:47-05:00"> <ue idType="IMSI" idValue="32795" / > <msg function="LPP" name="LPPProvideLocationInformation" changeTime="0.005" vendorSpecific="false"> <target type="Cell">SubNetwork=1,ManagedElement=Cell- 1< / target> <rawMsg protocol="LPP" version="17p5">A9FD64E12C< / rawMsg> < / msg> <traceSessionRef> <MCC>460< / MCC> <MNC>10< / MNC> <TRACE_ID>000122< / TRACE_ID> < / traceSessionRef> < / traceRecSession>< / traceCollecFile>

[0133] According to various examples of embodiments, Transmission of Configuration and Assistance Data Using MDT for Positioning with a Message Type is considered. If a new message type is created to transmit positioning measurements from the LMF to TCE according to various examples of embodiments, then the message would contain a header which would identify the type of data contained, which in this case would be configuration or assistance data.

[0134] According to various examples of embodiments, the following adapted from TS32.423, D.1.4, shows an example dedicated message for sending MDT measurements to a TCE with a subset of potential modifications highlighted in bold, to show how positioning related measurements could be transmitted. In the example, the TRP ID is added, as well as a new example measurement, TDOA. Measurements that are related to one another could be defined hierarchically to preserve the grouping.

[0135] Illustration 7, adaptation of Example of MDT XML File from TS32.423 (v17.6.0), D.1.4: <?xml version="1.0" encoding="UTF-8"?> <traceCollecFile xmlns="http: / / www.3gpp.org / ftp / specs / archive / 32_series / 32.423#traceData" xmlns:xsi="http: / / www.w3.org / 2001 / XMLSchema-instance" xsi:schemaLocation="http: / / www.3gpp.org / ftp / specs / archive / 32_series / 32.423#traceDat a http: / / www.3gpp.org / ftp / specs / archive / 32_series / 32.423#traceData"> <fileHeader fileFormatVersion="32.423 V6.0" vendorName="Company NN"> <pOPLMN> <MCC>460< / MCC> <MNC>10< / MNC> < / pOPLMN><fileSender elementDn="DC=a1.companyNN.com,SubNetwork=1, ManagedElement=LMF-1" elementType="LMF" / > <traceCollec beginTime="2001-09-11T09:30:47-05:00" / > < / fileHeader> <traceRecSession dnPrefix="DC=a1.companyNN.com,SubNetwork=1" traceRecSessionRef=" A1", stime="2001-09-11T09:30:47-05:00"> <ue idType="IMSI" idValue="32795" / > <meas name="RSRP" changeTime="0.005" vendorSpecific="false" targetCell="Cell-1" targetTRP=”TRP-1”> 97 < / meas> <meas name="RSRP" changeTime="0.010" vendorSpecific="false" targetCell="Cell-1" targetTRP=”TRP-1”> 34 < / meas> <meas name="TDOA" changeTime="0.015" vendorSpecific="false" targetCell="Cell-1" targetTRP=”TRP-1”> 5 < / meas> <traceSessionRef> <MCC>460< / MCC> <MNC>10< / MNC> <TRACE_ID>000150< / TRACE_ID> < / traceSessionRef> < / traceRecSession> < / traceCollecFile>

[0136] According to various examples of embodiments, it may be assumed that the configuration and / or assistance data is only transmitted the first time or upon any change in the configuration and / or assistance data. At the TCE, each of the collectedmeasurement datasets should be associated with a pointer to the configuration and / or assistance data that was provided at the time of measurement collection.

[0137] In the following, further examples of embodiments are described in relation to the aforementioned methods and / or apparatuses.

[0138] Referring now to FIG. 3, there is shown a flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the subject disclosure.

[0139] In particular, according to FIG. 3, in S310, the method generating a minimization of drive test, MDT, configuration for collecting data for training or validating an artificial intelligence and machine learning, AIML, model for positioning endpoint terminal. The MDT configuration comprises at least one of a job type field comprising an indication to collect data for the training of the AIML model for positioning, a field for list of positioning related data to be obtained for positioning, a field for configuration details, a field for assistance data, a field for a reporting trigger for collecting positioning related data, or AIML-specific fields comprising at least one of label type or label value for the positioning related data.

[0140] S310 may represent at least part of Step 1 as shown in FIG.1.

[0141] Further, in S320, the method comprises providing a trace session activation message comprising the MDT configuration to a network entity or function managing data.

[0142] S320 may represent at least part of Step 1 as shown in FIG.1.

[0143] It shall be noted that the method may be applied at a network entity or function managing the network system, like e.g. such MaS 110, 230 as outlined above with reference to FIGS.1 and 2. Moreover, the network entity or function managing data may be such UDM 120 as outlined above with reference to FIG.1.

[0144] Moreover, according to at least some examples of embodiments the indication in the job type field may be used to route a data collection request for collecting positioning reference data to a network entity or function managing a location of anendpoint terminal; the field for list of measurements for positioning may comprise measurement configurations that support collection of different types of measurements for positioning, the different types of measurements for positioning comprising at least one of reference symbol received power, RSRP, reference symbol received path power, RSRPP, time of arrival, ToA, angle of arrival, AoA, or time / angle / power / phase-based measurements; the field for a reporting trigger for collecting positioning related data may be indicative of at least one of a periodic trigger, an aperiodic trigger, an immediate trigger, a deferred trigger, or an event-based trigger to support a configuration in an endpoint terminal to transmit positioning related data; the label type and / or label value may be useable to configure the endpoint terminal to collect labels associated with positioning related data collected by the endpoint terminal to assist in training or validating the AIML model for positioning.

[0145] It shall be noted that the network entity or function managing endpoint terminal location may represent such LMF 140, 220 as outlined above with reference to FIGS.1 and 2.

[0146] Referring now to FIG. 4, there is shown a flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the subject disclosure.

[0147] In particular, according to FIG.4, in S410, the method comprises receiving a trace session activation message comprising a minimization of drive test, MDT, configuration for artificial intelligence and machine learning, AIML, positioning.

[0148] S410 may represent at least part of Step 1 as shown in FIG.1.

[0149] In addition, in S420, the method comprises verifying user consent for data collection for positioning based on the MDT configuration.

[0150] S420 may represent at least part of Step 2 as shown in FIG.1.

[0151] Further, in S430, the method comprises providing the trace session activation message to a network entity or function managing endpoint terminal mobility.

[0152] S430 may represent at least part of Step 3 as shown in FIG.1.

[0153] It shall be noted that the network entity or function managing endpoint terminal mobility may represent such AMF 130 as outlined above with reference to FIG. 1.

[0154] Referring now to FIG. 5, there is shown a flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the subject disclosure.

[0155] In particular, according to FIG.5, in S510, the method comprises receiving a trace session activation message comprising a minimization of drive test, MDT, configuration for artificial intelligence and machine learning, AIML, positioning.

[0156] S510 may represent at least part of Step 3 as shown in FIG.1.

[0157] In addition, in S520, the method comprises storing trace control parameters and configuration parameters related to the MDT configuration.

[0158] S520 may represent at least part of Step 4 as shown in FIG.1.

[0159] Further, in S530, the method comprises providing the trace session activation message to a network entity or function managing endpoint terminal location.

[0160] S530 may represent at least part of Step 5 as shown in FIG.1.

[0161] It shall be noted that the network entity or function managing endpoint terminal location may represent such LMF 140, 220 as outlined above with reference to FIGS.1 and 2.

[0162] Referring now to FIG. 6, there is shown a flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the subject disclosure.

[0163] In particular, according to FIG.6, in S610, the method comprises receiving a trace session activation message comprising a minimization of drive test, MDT, configuration for collecting positioning related data for training or validating an artificial intelligence and machine learning, AIML, model for positioning.

[0164] S610 may represent at least part of Step 5 as shown in FIG.1.

[0165] In addition, in S620, the method comprises configuring, through the LTE Positioning Protocol, LPP, an endpoint terminal to collect and send positioning related data to be used for training or validating the AIML model for positioning based on the MDT configuration.

[0166] S620 may represent at least part of Step 8 as shown in FIG.1.

[0167] Further, in S630, the method comprises receiving, from an endpoint or at least one gNodeB, positioning related data .

[0168] S630 may represent at least part of Step 9 as shown in FIG.1.

[0169] Further, in S640, the method comprises inserting the positioning related data into payloads of trace records.

[0170] S640 may represent at least part of Step 4 as shown in FIG.2.

[0171] Further, in S650, the method comprises providing the trace records to a network entity or function managing trace collection.

[0172] S650 may represent at least part of Step 4 as shown in FIG.2.

[0173] It shall be noted that the network entity or function managing trace collection may represent such TCE 240 as outlined above with reference to FIG.2.

[0174] Moreover, according to at least some examples of embodiments, the method as outlined with reference to FIG.6 may be applied at a network entity or function managing endpoint terminal location, and wherein the trace records may be provided: in a first data format comprising an encoded LPP message, or in a second data format comprising a structured message generated by the network entity or function managing endpoint terminal location, the structured message type comprising the positioning related data.

[0175] Furthermore, according to at least some examples of embodiments, the method may further receiving from an endpoint terminal, through at least one LPPmessage, logged positioning measurement reports, comprising of positioning related data, taken over a predetermined period of time while the endpoint terminal is in RRC IDLE mode or RRC INACTIVE mode.

[0176] Further, according to at least some examples of embodiments, the method may further comprise configuring, through the LTE Positioning Protocol, LPP, an endpoint terminal to include in its positioning related data the positioning related measurements used to estimate the position of the endpoint terminal.

[0177] It shall be noted that such field may represent such field “includeMeasurementPosNr” as outlined above.

[0178] Moreover, according to at least some examples of embodiments, the MDT configuration may include an indication of at least one positioning method to be used by the location management function to collect positioning related data.

[0179] Furthermore, according to at least some examples of embodiments, the method may further comprise inserting, in the payloads of the trace records, configuration details and assistance data provided when configuring, through the LTE Positioning Protocol, LPP, the endpoint terminal to collect and send positioning related data, wherein the configuration details comprise a positioning reference signal, PRS, configuration related to the endpoint terminal, and wherein the assistance data comprise transmission- reception point, TRP, information related to the endpoint terminal.

[0180] Referring now to FIG. 7, there is shown a flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the subject disclosure.

[0181] In particular, according to FIG. 7, in S710, the method comprises receiving, at an endpoint terminal, through an LTE Positioning Protocol, LPP, a configuration for configuring the endpoint terminal for collecting positioning related data for training or validating an artificial intelligence and machine learning, AIML, for positioning.

[0182] S710 may represent at least part of Step 8 as shown in FIG.1.

[0183] In addition, in S720, the method comprises performing positioning

[0184] S720 may represent at least part of Step 8 as shown in FIG.1.

[0185] Further, in S730, the method comprises generating positioning related data based on the performed positioning measurements.

[0186] S730 may represent at least part of Step 8 as shown in FIG.1.

[0187] Furthermore, in S740, the method comprises providing the positioning related data to a network entity or function managing endpoint terminal location.

[0188] S740 may represent at least part of Step 9 as shown in FIG.1.

[0189] Furthermore, according to at least some examples of embodiments, the method may further comprise performing the positioning measurements and / or providing the positioning related data during different network-connection status of the endpoint terminal, comprising RRC IDLE / INACTIVE / CONNECTED mode; and providing the location information message to the network entity or function managing endpoint terminal location.

[0190] It shall be noted that the location information message may represent such ProvideLocationInformation message as outlined above.

[0191] Referring now to FIG. 8, there is shown a flowchart illustrating steps corresponding to an example method according to various examples of embodiments of the subject disclosure.

[0192] In particular, according to FIG. 8, in S810, the method comprises receiving trace records comprising positioning related data comprising location information related to an endpoint terminal, the location information obtained based on a minimization of drive test, MDT, configuration for collecting data for training or validating an artificial intelligence and machine learning, AIML, model for positioning endpoint terminal.

[0193] S810 may represent at least part of Step 4 as shown in FIG.2.

[0194] In addition, in S820, the method comprises making the positioning related data available for usage as input in AIML positioning model training.

[0195] Referring now to FIG. 9, FIG. 9 shows a block diagram illustrating an example apparatus according to various examples of embodiments of the subject disclosure.

[0196] Specifically, FIG. 9 shows a block diagram illustrating an apparatus 900, which may represent a network entity or function managing the network system (e.g. MaS 110, 230) as outlined above with reference to FIGS. 1 and 2, according to various examples of embodiments, which may participate in MDT for AIML positioning. Furthermore, even though reference is made to a network entity or function managing the network system, the network entity or function managing the network system may be also another device or function having a similar task, such as a chipset, a chip, a module, an application etc., which can also be part of a network element or attached as a separate element to a network element, or the like. It should be understood that each block and any combination thereof may be implemented by various means or their combinations, such as hardware, software, firmware, one or more processors and / or circuitry.

[0197] The apparatus 900 shown in FIG. 9 may include a processing circuitry, a processing function, a control unit or a processor 910, such as a CPU or the like, which is suitable to enable MDT for AIML positioning. The processor 910 may include one or more processing portions or functions dedicated to specific processing as described below, or the processing may be run in a single processor or processing function. Portions for executing such specific processing may be also provided as discrete elements or within one or more further processors, processing functions or processing portions, such as in one physical processor like a CPU or in one or more physical or virtual entities, for example. Reference signs 931 and 932 denote input / output (I / O) units or functions (interfaces) connected to the processor or processing function 1410. The I / O units 931 and 932 may be a combined unit including communication equipment towards several entities / elements, or may include a distributed structure with a plurality of different interfaces for different entities / elements. Reference sign 920 denotes a memory usable, for example, for storing data and programs / instructions to be executed by the processor or processing function 910 and / or as a working storage of the processor or processingfunction 910. It is to be noted that the memory 920 may be implemented by using one or more memory portions of the same or different type of memory, but may also represent an external memory, e.g. an external database provided on a cloud server.

[0198] The processor or processing function 910 is configured to execute processing related to the above described processing. In particular, the processor or processing circuitry or function 910 includes one or more of the following sub-portions. Sub-portion 911 is a generating portion, which is usable as a portion for generating a MDT configuration. The portion 911 may be configured to perform processing according to S310 of FIG. 3. Further, sub-portion 912 is a providing portion, which is usable as a portion for providing a trace session activation message. The portion 912 may be configured to perform processing according to S320 of FIG.3.

[0199] Referring now to FIG. 10, FIG. 10 shows a block diagram illustrating an example apparatus according to various examples of embodiments of the subject disclosure.

[0200] Specifically, FIG. 10 shows a block diagram illustrating an apparatus 1000, which may represent a network entity or function managing data (e.g. UDM 120) as outlined above with reference to FIG.1, according to various examples of embodiments, which may participate in MDT for AIML positioning. Furthermore, even though reference is made to a network entity or function managing data, the network entity or function managing data may be also another device or function having a similar task, such as a chipset, a chip, a module, an application etc., which can also be part of a network element or attached as a separate element to a network element, or the like. It should be understood that each block and any combination thereof may be implemented by various means or their combinations, such as hardware, software, firmware, one or more processors and / or circuitry.

[0201] The apparatus 1000 shown in FIG. 10 may include a processing circuitry, a processing function, a control unit or a processor 1010, such as a CPU or the like, which is suitable to enable MDT for AIML positioning. The processor 1010 may include one ormore processing portions or functions dedicated to specific processing as described below, or the processing may be run in a single processor or processing function. Portions for executing such specific processing may be also provided as discrete elements or within one or more further processors, processing functions or processing portions, such as in one physical processor like a CPU or in one or more physical or virtual entities, for example. Reference signs 1031 and 1032 denote input / output (I / O) units or functions (interfaces) connected to the processor or processing function 1410. The I / O units 1031 and 1032 may be a combined unit including communication equipment towards several entities / elements, or may include a distributed structure with a plurality of different interfaces for different entities / elements. Reference sign 1020 denotes a memory usable, for example, for storing data and programs / instructions to be executed by the processor or processing function 1010 and / or as a working storage of the processor or processing function 1010. It is to be noted that the memory 1020 may be implemented by using one or more memory portions of the same or different type of memory, but may also represent an external memory, e.g. an external database provided on a cloud server.

[0202] The processor or processing function 1010 is configured to execute processing related to the above described processing. In particular, the processor or processing circuitry or function 1010 includes one or more of the following sub-portions. Sub-portion 1011 is a receiving portion, which is usable as a portion for receiving a MDT configuration. The portion 1011 may be configured to perform processing according to S410 of FIG. 4. Further, sub-portion 1012 is a verifying portion, which is usable as a portion for verifying user consent for data collection. The portion 1012 may be configured to perform processing according to S420 of FIG. 4. Moreover, sub-portion 1013 is a providing portion, which is usable as a portion for providing the trace session activation message. The portion 1013 may be configured to perform processing according to S430 of FIG.4.

[0203] Referring now to FIG. 11, FIG. 11 shows a block diagram illustrating an example apparatus according to various examples of embodiments of the subject disclosure.

[0204] Specifically, FIG. 11 shows a block diagram illustrating an apparatus 1100, which may represent a network entity or function managing endpoint terminal mobility (e.g. AMF 130) as outlined above with reference to FIG.1, according to various examples of embodiments, which may participate in MDT for AIML positioning. Furthermore, even though reference is made to a network entity or function managing endpoint terminal mobility, the network entity or function managing endpoint terminal mobility may be also another device or function having a similar task, such as a chipset, a chip, a module, an application etc., which can also be part of a network element or attached as a separate element to a network element, or the like. It should be understood that each block and any combination thereof may be implemented by various means or their combinations, such as hardware, software, firmware, one or more processors and / or circuitry.

[0205] The apparatus 1100 shown in FIG. 11 may include a processing circuitry, a processing function, a control unit or a processor 1110, such as a CPU or the like, which is suitable to enable MDT for AIML positioning. The processor 1110 may include one or more processing portions or functions dedicated to specific processing as described below, or the processing may be run in a single processor or processing function. Portions for executing such specific processing may be also provided as discrete elements or within one or more further processors, processing functions or processing portions, such as in one physical processor like a CPU or in one or more physical or virtual entities, for example. Reference signs 1131 and 1132 denote input / output (I / O) units or functions (interfaces) connected to the processor or processing function 1410. The I / O units 1131 and 1132 may be a combined unit including communication equipment towards several entities / elements, or may include a distributed structure with a plurality of different interfaces for different entities / elements. Reference sign 1120 denotes a memory usable, for example, for storing data and programs / instructions to be executed by the processor or processing function 1110 and / or as a working storage of the processor or processing function 1110. It is to be noted that the memory 1120 may be implemented by using one or more memory portions of the same or different type of memory, but may also represent an external memory, e.g. an external database provided on a cloud server.

[0206] The processor or processing function 1110 is configured to execute processing related to the above described processing. In particular, the processor orprocessing circuitry or function 1110 includes one or more of the following sub-portions. Sub-portion 1111 is a receiving portion, which is usable as a portion for receiving a trace session activation message. The portion 1111 may be configured to perform processing according to S510 of FIG. 5. Further, sub-portion 1112 is a storing portion, which is usable as a portion for storing trace control parameters and configuration parameters. The portion 1112 may be configured to perform processing according to S520 of FIG. 5. Moreover, sub-portion 1113 is a providing portion, which is usable as a portion for providing the trace session activation message. The portion 1113 may be configured to perform processing according to S530 of FIG.5.

[0207] Referring now to FIG. 12, FIG. 12 shows a block diagram illustrating an example apparatus according to various examples of embodiments of the subject disclosure.

[0208] Specifically, FIG. 12 shows a block diagram illustrating an apparatus 1200, which may represent a network entity or function managing endpoint terminal location (e.g. LMF 140, 220) as outlined above with reference to FIGS. 1 and 2, according to various examples of embodiments, which may participate in MDT for AIML positioning. Furthermore, even though reference is made to a network entity or function managing endpoint terminal location, the network entity or function managing endpoint terminal location may be also another device or function having a similar task, such as a chipset, a chip, a module, an application etc., which can also be part of a network element or attached as a separate element to a network element, or the like. It should be understood that each block and any combination thereof may be implemented by various means or their combinations, such as hardware, software, firmware, one or more processors and / or circuitry.

[0209] The apparatus 1200 shown in FIG. 12 may include a processing circuitry, a processing function, a control unit or a processor 1210, such as a CPU or the like, which is suitable to enable MDT for AIML positioning. The processor 1210 may include one or more processing portions or functions dedicated to specific processing as described below, or the processing may be run in a single processor or processing function. Portions for executing such specific processing may be also provided as discrete elements or within one or more further processors, processing functions or processing portions, such as inone physical processor like a CPU or in one or more physical or virtual entities, for example. Reference signs 1231 and 1232 denote input / output (I / O) units or functions (interfaces) connected to the processor or processing function 1410. The I / O units 1231 and 1232 may be a combined unit including communication equipment towards several entities / elements, or may include a distributed structure with a plurality of different interfaces for different entities / elements. Reference sign 1220 denotes a memory usable, for example, for storing data and programs / instructions to be executed by the processor or processing function 1210 and / or as a working storage of the processor or processing function 1210. It is to be noted that the memory 1220 may be implemented by using one or more memory portions of the same or different type of memory, but may also represent an external memory, e.g. an external database provided on a cloud server.

[0210] The processor or processing function 1210 is configured to execute processing related to the above described processing. In particular, the processor or processing circuitry or function 1210 includes one or more of the following sub-portions. Sub-portion 1211 is a receiving portion, which is usable as a portion for receiving a trace session activation message. The portion 1211 may be configured to perform processing according to S610 of FIG.6. Further, sub-portion 1212 is a configuring portion, which is usable as a portion for configuring, through an LTE Positioning Protocol, LPP, an endpoint terminal. The portion 1212 may be configured to perform processing according to S620 of FIG.6. Moreover, sub-portion 1213 is a receiving portion, which is usable as a portion for receiving positioning related data. The portion 1213 may be configured to perform processing according to S630 of FIG.6. Further, sub-portion 1214 is an inserting portion, which is usable as a portion for inserting the positioning measurement reports into trace records. The portion 1214 may be configured to perform processing according to S640 of FIG.6. Moreover, sub-portion 1215 is a providing portion, which is usable as a portion for providing the trace records. The portion 1213 may be configured to perform processing according to S650 of FIG.6.

[0211] Referring now to FIG. 13, FIG. 13 shows a block diagram illustrating an example apparatus according to various examples of embodiments of the subject disclosure.

[0212] Specifically, FIG. 13 shows a block diagram illustrating an apparatus 1300, which may represent an endpoint terminal (e.g. UE 150, 210) as outlined above with reference to FIGS. 1 and 2, according to various examples of embodiments, which may participate in MDT for AIML positioning. Furthermore, even though reference is made to an endpoint terminal, the endpoint terminal may be also another device or function having a similar task, such as a chipset, a chip, a module, an application etc., which can also be part of a network element or attached as a separate element to a network element, or the like. It should be understood that each block and any combination thereof may be implemented by various means or their combinations, such as hardware, software, firmware, one or more processors and / or circuitry.

[0213] The apparatus 1300 shown in FIG. 13 may include a processing circuitry, a processing function, a control unit or a processor 1310, such as a CPU or the like, which is suitable to enable MDT for AIML positioning. The processor 1310 may include one or more processing portions or functions dedicated to specific processing as described below, or the processing may be run in a single processor or processing function. Portions for executing such specific processing may be also provided as discrete elements or within one or more further processors, processing functions or processing portions, such as in one physical processor like a CPU or in one or more physical or virtual entities, for example. Reference signs 1331 and 1332 denote input / output (I / O) units or functions (interfaces) connected to the processor or processing function 1410. The I / O units 1331 and 1332 may be a combined unit including communication equipment towards several entities / elements, or may include a distributed structure with a plurality of different interfaces for different entities / elements. Reference sign 1320 denotes a memory usable, for example, for storing data and programs / instructions to be executed by the processor or processing function 1310 and / or as a working storage of the processor or processing function 1310. It is to be noted that the memory 1320 may be implemented by using one or more memory portions of the same or different type of memory, but may also represent an external memory, e.g., an external database provided on a cloud server.

[0214] The processor or processing function 1310 is configured to execute processing related to the above-described processing. In particular, the processor or processing circuitry or function 1310 includes one or more of the following sub-portions.Sub-portion 1311 is a receiving portion, which is usable as a portion for receiving, through LPP, a configuration. The portion 1311 may be configured to perform processing according to S710 of FIG.7. Further, sub-portion 1312 is a performing portion, which is usable as a portion for performing positioning measurements. The portion 1312 may be configured to perform processing according to S720 of FIG. 7. Moreover, sub-portion 1313 is an generating portion, which is usable as a portion for generating positioning measurement reports. The portion 1313 may be configured to perform processing according to S730 of FIG. 7. Further, sub-portion 1314 is a providing portion, which is usable as a portion for providing the positioning measurement reports. The portion 1314 may be configured to perform processing according to S740 of FIG.7.

[0215] Referring now to FIG. 14, FIG. 14 shows a block diagram illustrating an example apparatus according to various examples of embodiments of the subject disclosure.

[0216] Specifically, FIG. 14 shows a block diagram illustrating an apparatus 1400, which may represent a network entity or function managing trace collection (e.g. TCE 240) as outlined above with reference to FIG. 2, according to various examples of embodiments, which may participate in MDT for AIML positioning. Furthermore, even though reference is made to a network entity or function managing trace collection, the network entity or function managing trace collection may be also another device or function having a similar task, such as a chipset, a chip, a module, an application etc., which can also be part of a network element or attached as a separate element to a network element, or the like. It should be understood that each block and any combination thereof may be implemented by various means or their combinations, such as hardware, software, firmware, one or more processors and / or circuitry.

[0217] The apparatus 1400 shown in FIG. 14 may include a processing circuitry, a processing function, a control unit or a processor 1410, such as a CPU or the like, which is suitable to enable MDT for AIML positioning. The processor 1410 may include one or more processing portions or functions dedicated to specific processing as described below, or the processing may be run in a single processor or processing function. Portions for executing such specific processing may be also provided as discrete elements or within one or more further processors, processing functions or processing portions, such as inone physical processor like a CPU or in one or more physical or virtual entities, for example. Reference signs 1431 and 1432 denote input / output (I / O) units or functions (interfaces) connected to the processor or processing function 1410. The I / O units 1431 and 1432 may be a combined unit including communication equipment towards several entities / elements, or may include a distributed structure with a plurality of different interfaces for different entities / elements. Reference sign 1420 denotes a memory usable, for example, for storing data and programs / instructions to be executed by the processor or processing function 1410 and / or as a working storage of the processor or processing function 1410. It is to be noted that the memory 1420 may be implemented by using one or more memory portions of the same or different type of memory, but may also represent an external memory, e.g. an external database provided on a cloud server.

[0218] The processor or processing function 1410 is configured to execute processing related to the above described processing. In particular, the processor or processing circuitry or function 1410 includes one or more of the following sub-portions. Sub-portion 1411 is a receiving portion, which is usable as a portion for receiving trace records comprising positioning related data. The portion 1411 may be configured to perform processing according to S810 of FIG. 8. Further, sub-portion 1412 is a making available portion, which is usable as a portion for making the positioning related data available for usage as input in AIML positioning model training. The portion 1412 may be configured to perform processing according to S820 of FIG.8.

[0219] It shall be noted that the apparatuses 900 to 1400 as outlined above with reference to FIGS. 9 to 14 may comprise further / additional sub-portions, which may allow the apparatuses 900 to 1400 to perform such further methods / method steps as outlined above with reference to FIGS.3 to 8.

[0220] Further, according to various examples of embodiments, an apparatus may comprise means for generating a minimization of drive test, MDT, configuration for collecting data for training or validating an artificial intelligence and machine learning, AIML, model for positioning endpoint terminal; and means for providing a trace session activation message comprising the MDT configuration to a network entity or function managing data, wherein the MDT configuration comprises at least one of: a job type field comprising an indication to collect data for the training of the AIML model forpositioning, a field for list of positioning related data to be obtained for positioning, a field for configuration details, a field for assistance data, a field for a reporting trigger for collecting positioning related data, or AIML-specific fields comprising at least one of label type or label value for the positioning related data.

[0221] According to various examples of embodiments, the indication in the job type field may be used to route a data collection request for collecting positioning reference data to a network entity or function managing a location of an endpoint terminal; the field for list of measurements for positioning may comprise measurement configurations that support collection of different types of measurements for positioning, the different types of measurements for positioning comprising at least one of reference symbol received power, RSRP, reference symbol received path power, RSRPP, time of arrival, ToA, angle of arrival, AoA, or time / angle / power / phase-based measurements; the field for a reporting trigger for collecting positioning related data may be indicative of at least one of a periodic trigger, an aperiodic trigger, an immediate trigger, a deferred trigger, or an event- based trigger to support a configuration in an endpoint terminal to transmit positioning related data; the label type and / or label value may be useable to configure the endpoint terminal to collect labels associated with positioning related data collected by the endpoint terminal to assist in training or validating the AIML model for positioning.

[0222] Moreover, according to various examples of embodiments, an apparatus may comprise: means for receiving a trace session activation message comprising a minimization of drive test, MDT, configuration for artificial intelligence and machine learning, AIML, positioning; means for verifying user consent for data collection for positioning based on the MDT configuration; and means for providing the trace session activation message to a network entity or function managing endpoint terminal mobility.

[0223] Furthermore, according to various examples of embodiments, an apparatus may comprise: means for receiving a trace session activation message comprising a minimization of drive test, MDT, configuration for artificial intelligence and machine learning, AIML, positioning; means for storing trace control parameters and configuration parameters related to the MDT configuration; and means for providing the trace session activation message to a network entity or function managing endpoint terminal location.

[0224] Further, according to various examples of embodiments, an apparatus may comprise: means for receiving a trace session activation message comprising a minimization of drive test, MDT, configuration for collecting positioning related data for training or validating an artificial intelligence and machine learning, AIML, model for positioning; means for configuring, through the LTE Positioning Protocol, LPP, an endpoint terminal to collect and send positioning related data to be used for training or validating the AIML model for positioning based on the MDT configuration; means for receiving, from an endpoint terminal or at least one gNodeB, positioning related data ; means for inserting the positioning related data into payloads of trace records; and means for providing the trace records to a network entity or function managing trace collection.

[0225] According to at least some examples of embodiments, the trace records may be provided: in a first data format comprising an encoded LPP message, or in a second data format comprising a structured message generated by the apparatus, the structured message type comprising the positioning related data.

[0226] According to at least some examples of embodiments, the apparatus may further comprise means for receiving from an endpoint terminal, through at least one LPP message, logged positioning measurement reports, comprising of positioning related data, taken over a predetermined period of time while the endpoint terminal is in RRC IDLE mode or RRC INACTIVE mode.

[0227] According to at least some examples of embodiments, the apparatus may further comprise means for configuring, through the LTE Positioning Protocol, LPP, an endpoint terminal to include in its positioning related data the positioning related measurements used to estimate the position of the endpoint terminal.

[0228] According to at least some examples of embodiments, the MDT configuration may include an indication of at least one positioning method to be used by the location management function to collect positioning related data.

[0229] According to at least some examples of embodiments, the apparatus may further comprise means for inserting, in the payloads of the trace records, configuration details and assistance data provided when configuring, through the LTE PositioningProtocol, LPP, the endpoint to collect and send positioning related data, wherein the configuration details comprise a positioning reference signal, PRS, configuration related to the endpoint terminal, and wherein the assistance data comprise transmission-reception point, TRP, information related to the endpoint terminal.

[0230] Moreover, according to various examples of embodiments, an apparatus may comprise: means for receiving, through the LTE Positioning Protocol, LPP, a configuration for configuring the apparatus for collecting positioning related data for training or validating an artificial intelligence and machine learning, AIML, for positioning; means for performing positioning measurements based on the configuration; means for generating positioning related data based on the performed positioning measurements; and means for providing the positioning related data to a network entity or function managing endpoint terminal location.

[0231] According to at least some examples of embodiments, the apparatus may further comprise means for performing the positioning measurements and / or providing the positioning related data during different network-connection status, comprising RRC IDLE / INACTIVE / CONNECTED mode; and provide the location information message to the network entity or function managing endpoint terminal location.

[0232] Furthermore, according to various examples of embodiments, an apparatus may comprise means for receiving trace records comprising positioning related data comprising location information related to an endpoint terminal, the location information obtained based on a minimization of drive test, MDT, configuration for collecting data for training or validating an artificial intelligence and machine learning, AIML, model for positioning endpoint terminal; and means for making the positioning related data available for usage as input in AIML positioning model training.

[0233] Furthermore, according to various examples of embodiments, there may be provided a computer program product for a computer, including software code portions for performing the steps of any of the above-outlined methods, e.g. with reference to FIGS.3 to 8, when said product is run on the computer.

[0234] According to at least some examples of embodiments, the computer program product may include a computer-readable medium on which said software code portions are stored, and / or the computer program product may be directly loadable into the internal memory of the computer and / or transmittable via a network by means of at least one of upload, download and push procedures.

[0235] It should be appreciated that - an access technology via which traffic is transferred to and from an entity in the communication network may be any suitable present or future technology, such as WLAN (Wireless Local Access Network), WiMAX (Worldwide Interoperability for Microwave Access), LTE, LTE-A, 5G, 6G, Bluetooth, Infrared, and the like may be used; additionally, examples of embodiments may also apply wired technologies, e.g. IP based access technologies like cable networks or fixed lines. - examples of embodiments suitable to be implemented as software code or portions thereof and being run using a processor or processing function are software code independent and can be specified using any known or future developed programming language, such as a high-level programming language, such as objective-C, C, C++, C#, Java, Python, Javascript, other scripting languages etc., or a low-level programming language, such as a machine language, or an assembler. - implementation of examples of embodiments is hardware independent and may be implemented using any known or future developed hardware technology or any hybrids of these, such as a microprocessor or CPU (Central Processing Unit), MOS (Metal Oxide Semiconductor), CMOS (Complementary MOS), BiMOS (Bipolar MOS), BiCMOS (Bipolar CMOS), ECL (Emitter Coupled Logic), and / or TTL (Transistor-Transistor Logic). - examples of embodiments may be implemented as individual devices, apparatuses, units, means or functions, or in a distributed fashion, for example, one or more processors or processing functions may be used or shared in the processing, or one or more processing sections or processing portions may be used and shared in the processing, wherein one physical processor or more than one physical processor may be used forimplementing one or more processing portions dedicated to specific processing as described, - an apparatus may be implemented by a semiconductor chip, a chipset, or a (hardware) module including such chip or chipset; - examples of embodiments may also be implemented as any combination of hardware and software, such as ASIC (Application Specific IC (Integrated Circuit)) components, FPGA (Field-programmable Gate Arrays) or CPLD (Complex Programmable Logic Device) components or DSP (Digital Signal Processor) components. - examples of embodiments may also be implemented as computer program products, including a computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to execute a process as described in embodiments, wherein the computer usable medium may be a non-transitory medium.

[0236] The term “circuitry” may refer to one or more or all of the following examples of embodiments: (a) hardware-only circuit implementations (such as implementations in only analog and / or digital circuitry) and (b) combinations of hardware circuits and software, such as (as applicable): (i) a combination of analog and / or digital hardware circuit(s) with software / firmware and (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) andhardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.”

[0237] This definition of circuitry applies to all uses of the term “means” herein, including in any claims. As a further example, as used herein, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0238] The term “non-transitory,” as used herein, is a limitation of the medium itself (e.g., tangible, not a signal ) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM).

[0239] As used herein, “at least one of the following: ” and “at least one of ” and similar wording, where the list of two or more elements are joined by “and” or “or”, mean at least any one of the elements, or at least any two or more of the elements, or at least all the elements.

[0240] Although the subject disclosure has been described herein before with reference to various examples of embodiments thereof, the subject disclosure is not limited thereto and it will be apparent to a person skilled in the art that various modifications can be made to the subject disclosure.

[0241] The following meanings for the abbreviations used herein apply: 3GPP 3rdGeneration Partnership Project 3GGP2 3rdGeneration Partnership Project 2 4G Fourth Generation 5G Fifth Generation5GC 5G Core Network 6G Sixth Generation AMF Access and Mobility Management Function AoA Angle of Arrival AP Access Point API Application Programming Interface BS Base Station CDMA Code Division Multiple Access eNB Evolved Node B ETSI European Telecommunications Standards Institute gNB Next Generation Node B IEEE Institute of Electrical and Electronics Engineers ITU International Telecommunication Union LMF Location Management Function LPP LTE Positioning Protocol LTE Long Term Evolution LTE-A Long Term Evolution-Advanced MaS Management System MDT Minimization of Drive Test NAS Non-Access Stratum NB Node BNF Network Function NR New Radio RAM Random Access Memory RAN Radio Access Network Rel Release ROM Read Only Memory RRC Radio Resource Control RSRP Reference Symbol Received Power RSRQ Reference Symbol Received Quality TCE Trace Collection Entity TDOA Time Difference of Arrival TISPAN Telecoms & Internet converged Services & Protocols for Advanced Networks ToA Time of Arrival TRP Transmission-Reception Point UDM Unified Data Management UE User Equipment UL Uplink UWB Ultra-Wideband WCDMA Wideband Code Division Multiple Access WiMAX Worldwide Interoperability for Microwave AccessWLAN Wireless Local Area Network ĵĶ

Claims

CLAIMS:

1. An apparatus for an operation, administration, and maintenance system, comprising, at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: generate a minimization of drive test, MDT, configuration for collecting data for training or validating an artificial intelligence and machine learning, AIML, model for positioning an endpoint terminal; and provide a trace session activation message comprising the MDT configuration to a network entity or function managing data, wherein the MDT configuration comprises at least one of: a job type field comprising an indication to collect data for the training of the AIML model for positioning, a field for list of positioning related data to be obtained for positioning, a field for configuration details, a field for assistance data, a field for a reporting trigger for collecting positioning related data, or AIML-specific fields comprising at least one of label type or label value for the positioning related data.

2. The apparatus according to claim 1, wherein the indication in the job type field is used to route a data collection request for collecting positioning reference data to a network entity or function managing a location of an endpoint terminal;the field for list of measurements for positioning comprises measurement configurations that support collection of different types of measurements for positioning, the different types of measurements for positioning comprising at least one of reference symbol received power, RSRP, reference symbol received path power, RSRPP, time of arrival, ToA, angle of arrival, AoA, or time / angle / power / phase-based measurements; the field for a reporting trigger for collecting positioning related data is indicative of at least one of a periodic trigger, an aperiodic trigger, an immediate trigger, a deferred trigger, or an event-based trigger to support a configuration in an endpoint terminal to transmit positioning related data; the label type and / or label value are useable to configure the endpoint terminal to collect labels associated with positioning related data collected by the endpoint terminal to assist in training or validating the AIML model for positioning.

3. An apparatus, comprising, at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: receive a trace session activation message comprising a minimization of drive test, MDT, configuration for artificial intelligence and machine learning, AIML, positioning; verify user consent for data collection for positioning based on the MDT configuration; and provide the trace session activation message to a network entity or function managing endpoint terminal mobility.

4. An apparatus, comprising, at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to:receive a trace session activation message comprising a minimization of drive test, MDT, configuration for artificial intelligence and machine learning, AIML, positioning; store trace control parameters and configuration parameters related to the MDT configuration; and provide the trace session activation message to a network entity or function managing endpoint terminal location.

5. An apparatus, comprising, at least one processor; and at least one memory storing instructions for a location management function, wherein the instructions, when executed by the at least one processor, cause the apparatus at least to: receive a trace session activation message comprising a minimization of drive test, MDT, configuration for collecting positioning related data for training or validating an artificial intelligence and machine learning, AIML, model for positioning; configure, through the LTE Positioning Protocol, LPP, an endpoint terminal to collect and send positioning related data to be used for training or validating the AIML model for positioning based on the MDT configuration; receive, from an endpoint terminal or at least one gNodeB, positioning related data ; insert the positioning related data into payloads of trace records; and provide the trace records to a network entity or function managing trace collection.

6. The apparatus according to claim 5, wherein the apparatus is further caused to receive from an endpoint terminal, through at least one LPP message, logged positioning measurement reports, comprising of positioning related data, taken over apredetermined period of time while the endpoint terminal is in RRC IDLE mode or RRC INACTIVE mode.

7. The apparatus according to claim 5 or 6, wherein the endpoint terminal is configured based on the MDT configuration, wherein the apparatus is further caused to configure, through the LTE Positioning Protocol, LPP, an endpoint terminal to include in its positioning related data the positioning related measurements used to estimate the position of the endpoint terminal.

8. The apparatus according to any of claims 5 to 7, wherein the MDT configuration includes an indication of at least one positioning method to be used by the location management function to collect positioning related data.

9. The apparatus according to any of claims 5 to 8, wherein the trace records are provided: in a first data format comprising an encoded LPP message, or in a second data format comprising a structured message generated by the apparatus, the structured message type comprising the positioning related data.

10. The apparatus according to any of claims 5 to 9, wherein the apparatus is further caused to insert, in the payloads of the trace records, configuration details and assistance data provided when configuring, through the LTE Positioning Protocol, LPP, the endpoint to collect and send positioning related data, wherein the configuration details comprise a positioning reference signal, PRS, configuration related to the endpoint terminal, and wherein the assistance data comprise transmission-reception point, TRP, information related to the endpoint terminal.

11. An apparatus, comprising,at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: receive, through the LTE Positioning Protocol, LPP, a configuration for configuring the apparatus for collecting positioning related data for training or validating an artificial intelligence and machine learning, AIML, for positioning; perform positioning measurements based on the configuration; generate positioning related data based on the performed positioning measurements; and provide the positioning related data to a network entity or function managing endpoint terminal location.

12. The apparatus according to claim 11, wherein the apparatus is further caused to perform the positioning measurements and / or provide the positioning related data during different network-connection status, comprising RRC IDLE / INACTIVE / CONNECTED mode; and provide the location information message to the network entity or function managing endpoint terminal location.

13. An apparatus, comprising, at least one processor; and at least one memory storing instructions for a trace collection management function, wherein the instructions, when executed by the at least one processor, cause the apparatus at least to: receive trace records comprising positioning related data comprising location information related to an endpoint terminal, the location information obtained based on a minimization of drive test, MDT, configuration for collecting data for training orvalidating an artificial intelligence and machine learning, AIML, model for positioning endpoint terminal; and make the positioning related data available for usage as input in AIML positioning model training.

14. A method, comprising generating a minimization of drive test, MDT, configuration for collecting data for training or validating an artificial intelligence and machine learning, AIML, model for positioning endpoint terminal; and providing a trace session activation message comprising the MDT configuration to a network entity or function managing data, wherein the MDT configuration comprises at least one of a job type field comprising an indication to collect data for the training of the AIML model for positioning, a field for list of positioning related data to be obtained for positioning, a field for configuration details, a field for assistance data, a field for a reporting trigger for collecting positioning related data, or AIML-specific fields comprising at least one of label type or label value for the positioning related data.

15. The method according to claim 14, wherein the indication in the job type field is used to route a data collection request for collecting positioning reference data to a network entity or function managing a location of an endpoint terminal;the field for list of measurements for positioning comprises measurement configurations that support collection of different types of measurements for positioning, the different types of measurements for positioning comprising at least one of reference symbol received power, RSRP, reference symbol received path power, RSRPP, time of arrival, ToA, angle of arrival, AoA, or time / angle / power / phase-based measurements; the field for a reporting trigger for collecting positioning related data is indicative of at least one of a periodic trigger, an aperiodic trigger, an immediate trigger, a deferred trigger, or an event-based trigger to support a configuration in an endpoint terminal to transmit positioning related data; and the label type and / or label value are useable to configure the endpoint terminal to collect labels associated with positioning related data collected by the endpoint terminal to assist in training or validating the AIML model for positioning.

16. A method, comprising receiving a trace session activation message comprising a minimization of drive test, MDT, configuration for artificial intelligence and machine learning, AIML, positioning; verifying user consent for data collection for positioning based on the MDT configuration; and providing the trace session activation message to a network entity or function managing endpoint terminal mobility.

17. A method, comprising, receiving a trace session activation message comprising a minimization of drive test, MDT, configuration for artificial intelligence and machine learning, AIML, positioning; storing trace control parameters and configuration parameters related to the MDT configuration; andproviding the trace session activation message to a network entity or function managing endpoint terminal location.

18. A method, comprising receiving a trace session activation message comprising a minimization of drive test, MDT, configuration for collecting positioning related data for training or validating an artificial intelligence and machine learning, AIML, model for positioning; configuring, through the LTE Positioning Protocol, LPP, an endpoint terminal to collect and send positioning related data to be used for training or validating the AIML model for positioning based on the MDT configuration; receiving, from an endpoint or at least one gNodeB, positioning related data ; inserting the positioning related data into payloads of trace records; and providing the trace records to a network entity or function managing trace collection.

19. The method according to claim 18, further comprising receiving from an endpoint terminal, through at least one LPP message, logged positioning measurement reports, comprising of positioning related data, taken over a predetermined period of time while the endpoint terminal is in RRC IDLE mode or RRC INACTIVE mode.

20. The method according to claim 18 or 19, further comprising, wherein the endpoint is configured based on the MDT configuration, configuring, through the LTE Positioning Protocol, LPP, an endpoint terminal to include in its positioning related data the positioning related measurements used to estimate the position of the endpoint terminal.

21. The method according to any of claims 18 to 20, wherein the MDT configuration includes an indication of at least one positioning method to be used by the location management function to collect positioning related data.

22. The method according to any of claims 18 to 21, wherein the method is applied at a network entity or function managing endpoint terminal location, and wherein the trace records are provided: in a first data format comprising an encoded LPP message, or in a second data format comprising a structured message generated by the network entity or function managing endpoint terminal location, the structured message type comprising the positioning related data.

23. The method according to any of claims 18 to 22, further comprising inserting, in the payloads of the trace records, configuration details and assistance data provided when configuring, through the LTE Positioning Protocol, LPP, the endpoint terminal to collect and send positioning related data, wherein the configuration details comprise a positioning reference signal, PRS, configuration related to the endpoint terminal, and wherein the assistance data comprise transmission-reception point, TRP, information related to the endpoint terminal.

24. A method, comprising receiving, at an endpoint terminal, through an LTE Positioning Protocol, LPP, a configuration for configuring the endpoint terminal for collecting positioning related data for training or validating an artificial intelligence and machine learning, AIML, for positioning; performing positioning measurements based on the configuration;generating positioning related data based on the performed positioning measurements; and providing the positioning related data to a network entity or function managing endpoint terminal location.

25. The method according to claim 24, further comprising, performing the positioning measurements and / or providing the positioning related data during different network-connection status of the endpoint terminal, comprising RRC IDLE / INACTIVE / CONNECTED mode; and providing the location information message to the network entity or function managing endpoint terminal location.

26. A method, comprising receiving trace records comprising positioning related data comprising location information related to an endpoint terminal, the location information obtained based on a minimization of drive test, MDT, configuration for collecting data for training or validating an artificial intelligence and machine learning, AIML, model for positioning endpoint terminal; and making the positioning related data available for usage as input in AIML positioning model training.

27. A computer program product for a computer, including software code portions for performing the steps of any of claims 14 to 15, 16, 17, 18 to 23, 24 to 25, or 26, when said product is run on the computer.

28. The computer program product according to claim 27, wherein the computer program product includes a computer-readable medium on which said software code portions are stored, and / orthe computer program product is directly loadable into the internal memory of the computer and / or transmittable via a network by means of at least one of upload, download and push procedures.

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