Node and user equipment in wireless communication system and method performed by the same

Abstract

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. The present disclosure provides a node and a user equipment in a wireless communication system and methods performed by the same. A method performed by a first node in a wireless communication system includes: receiving a first message from a user equipment (UE), wherein the first message is associated with a request for collection of first data, wherein the first message includes at least one of: configuration information of a reference signal for the collection of the first data suggested by the UE, configuration information of a reference signal for the collection of the first data preferred by the UE, a data volume requested for the collection of the first data, a time requested for the collection of the first data, and information related to a node requested to perform model training based on the first data; transmitting, to the UE, a first reference signal for the collection of the first data based on the first message; and transmitting a third message to a second node.

Description

NODE AND USER EQUIPMENT IN WIRELESS COMMUNICATION SYSTEM AND METHOD PERFORMED BY THE SAME

[0001] The present disclosure relates to a technical field of wireless communication, and more specifically, to a node and a user equipment in a wireless communication system and methods performed by the same.

[0002] 5G mobile communication technologies define broad frequency bands such that high transmission rates and new services are possible, and can be implemented not only in "Sub 6GHz" bands such as 3.5GHz, but also in "Above 6GHz" bands referred to as mmWave including 28GHz and 39GHz. In addition, it has been considered to implement 6G mobile communication technologies (referred to as Beyond 5G systems) in terahertz bands (for example, 95GHz to 3THz bands) in order to accomplish transmission rates fifty times faster than 5G mobile communication technologies and ultra-low latencies one-tenth of 5G mobile communication technologies.

[0003] At the beginning of the development of 5G mobile communication technologies, in order to support services and to satisfy performance requirements in connection with enhanced Mobile BroadBand (eMBB), Ultra Reliable Low Latency Communications (URLLC), and massive Machine-Type Communications (mMTC), there has been ongoing standardization regarding beamforming and massive MIMO for mitigating radio-wave path loss and increasing radio-wave transmission distances in mmWave, supporting numerologies (for example, operating multiple subcarrier spacings) for efficiently utilizing mmWave resources and dynamic operation of slot formats, initial access technologies for supporting multi-beam transmission and broadbands, definition and operation of BWP (BandWidth Part), new channel coding methods such as a LDPC (Low Density Parity Check) code for large amount of data transmission and a polar code for highly reliable transmission of control information, L2 pre-processing, and network slicing for providing a dedicated network specialized to a specific service.

[0004] Currently, there are ongoing discussions regarding improvement and performance enhancement of initial 5G mobile communication technologies in view of services to be supported by 5G mobile communication technologies, and there has been physical layer standardization regarding technologies such as V2X (Vehicle-to-everything) for aiding driving determination by autonomous vehicles based on information regarding positions and states of vehicles transmitted by the vehicles and for enhancing user convenience, NR-U (New Radio Unlicensed) aimed at system operations conforming to various regulation-related requirements in unlicensed bands, NR UE Power Saving, Non-Terrestrial Network (NTN) which is UE-satellite direct communication for providing coverage in an area in which communication with terrestrial networks is unavailable, and positioning.

[0005] Moreover, there has been ongoing standardization in air interface architecture / protocol regarding technologies such as Industrial Internet of Things (IIoT) for supporting new services through interworking and convergence with other industries, IAB (Integrated Access and Backhaul) for providing a node for network service area expansion by supporting a wireless backhaul link and an access link in an integrated manner, mobility enhancement including conditional handover and DAPS (Dual Active Protocol Stack) handover, and two-step random access for simplifying random access procedures (2-step RACH for NR). There also has been ongoing standardization in system architecture / service regarding a 5G baseline architecture (for example, service based architecture or service based interface) for combining Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) technologies, and Mobile Edge Computing (MEC) for receiving services based on UE positions.

[0006] As 5G mobile communication systems are commercialized, connected devices that have been exponentially increasing will be connected to communication networks, and it is accordingly expected that enhanced functions and performances of 5G mobile communication systems and integrated operations of connected devices will be necessary. To this end, new research is scheduled in connection with eXtended Reality (XR) for efficiently supporting AR (Augmented Reality), VR (Virtual Reality), MR (Mixed Reality) and the like, 5G performance improvement and complexity reduction by utilizing Artificial Intelligence (AI) and Machine Learning (ML), AI service support, metaverse service support, and drone communication.

[0007] Furthermore, such development of 5G mobile communication systems will serve as a basis for developing not only new waveforms for providing coverage in terahertz bands of 6G mobile communication technologies, multi-antenna transmission technologies such as Full Dimensional MIMO (FD-MIMO), array antennas and large-scale antennas, metamaterial-based lenses and antennas for improving coverage of terahertz band signals, high-dimensional space multiplexing technology using OAM (Orbital Angular Momentum), and RIS (Reconfigurable Intelligent Surface), but also full-duplex technology for increasing frequency efficiency of 6G mobile communication technologies and improving system networks, AI-based communication technology for implementing system optimization by utilizing satellites and AI (Artificial Intelligence) from the design stage and internalizing end-to-end AI support functions, and next-generation distributed computing technology for implementing services at levels of complexity exceeding the limit of UE operation capability by utilizing ultra-high-performance communication and computing resources.

[0008] In order to meet an increasing demand for wireless data communication services since a deployment of 4G communication system, efforts have been made to develop an improved 5G or pre-5G communication system. Therefore, the 5G or pre-5G communication system is also called "beyond 4G network" or "post LTE system".

[0009] Wireless communication is one of the most successful innovations in modern history. Recently, a number of subscribers of wireless communication services has exceeded 5 billion, and it continues growing rapidly. With the increasing popularity of smart phones and other mobile data devices (such as tablet computers, notebook computers, netbooks, e-book readers and machine-type devices) in consumers and enterprises, a demand for wireless data services is growing rapidly. In order to meet rapid growth of mobile data services and support new applications and deployments, it is very important to improve efficiency and coverage of wireless interfaces.

[0010] The present disclosure relates to node and user equipment in wireless communication system and method performed by the same.

[0011] Embodiment of the present disclosure provide a method performed by a first node in a wireless communication system, including: receiving a first message from a user equipment (UE), wherein the first message is associated with a request for collection of first data, wherein the first message includes at least one of: configuration information of a reference signal for the collection of the first data suggested by the UE, configuration information of a reference signal for the collection of the first data preferred by the UE, a data volume requested for the collection of the first data, a time requested for the collection of the first data, and information related to a node requested to perform model training based on the first data; transmitting, to the UE, a first reference signal for the collection of the first data based on the first message; and transmitting a third message to a second node, wherein the third message includes second configuration information of a reference signal for the collection of the first data, wherein the second node transmits a second reference signal to the UE based on the second configuration information.

[0012] According to embodiments of the present disclosure, the method further includes: transmitting a fifth message to the UE, wherein the fifth message is used for requesting the UE to report the first data; and receiving a seventh message from the UE, wherein the seventh message includes the first data.

[0013] According to embodiments of the present disclosure, the method further includes: receiving an eighth message from the UE, wherein the eighth message includes at least one of: information about whether the UE is capable of model generation, information about whether the UE is capable of model training, information about whether the UE is capable of model inference, information about whether the UE is capable of model fine-tuning, a computing power of the UE.

[0014] According to embodiments of the present disclosure, the method further includes: transmitting a ninth message to the UE, wherein the ninth message includes at least one of: a reference model for model training and / or model generation, an accuracy threshold for determining whether a model can be used.

[0015] According to embodiments of the present disclosure, the method further includes: transmitting a fifteenth message to the UE, wherein the fifteenth message includes at least one of: information related to activation and / or deactivation of a model included in the UE, conditions for activation and / or deactivation and / or fallback and / or transition of a model included in the UE, indications for model training and / or model fine-tuning, valid time of a model included in the UE, valid area of a model included in the UE.

[0016] According to embodiments of the present disclosure, the method further includes: receiving a nineteenth message from the UE and / or a third node, wherein the nineteenth message includes at least one of: performance information related to at least one of model training, model generation, model inference, model fine-tuning, model evaluation, information about that an accuracy of a model is below a threshold, information about that a model needs to be re-trained, information about that a model needs to be fine-tuned.

[0017] According to embodiments of the present disclosure, the first data includes measurement results of the first reference signal and / or the second reference signal.

[0018] Embodiment of the present disclosure provide a method performed by a user equipment (UE) in a wireless communication system, including: transmitting a first message to a first node, wherein the first message is associated with a request for collection of first data, wherein the first message includes at least one of: configuration information of a reference signal for the collection of the first data suggested by the UE, configuration information of a reference signal for the collection of the first data preferred by the UE, a data volume requested for the collection of the first data, a time requested for the collection of the first data, and information related to a node requested to perform model training based on the first data; receiving, from the first node, a first reference signal for the collection of the first data based on the first message; and receiving, from a second node, a second reference signal for the collection of the first data based on second configuration information, wherein a third message is transmitted by the first node to the second node, wherein the third message includes the second configuration information.

[0019] According to embodiments of the present disclosure, the method further includes: receiving a fifth message from the first node, wherein the fifth message is used for requesting the UE to report the first data; and transmitting a seventh message to the first node, wherein the seventh message includes the first data.

[0020] According to embodiments of the present disclosure, the method further includes: transmitting an eighth message to the first node, wherein the eighth message includes at least one of: information about whether the UE is capable of model generation, information about whether the UE is capable of model training, information about whether the UE is capable of model inference, information about whether the UE is capable of model fine-tuning, a computing power of the UE.

[0021] According to embodiments of the present disclosure, the method further includes: receiving a ninth message from the first node, wherein the ninth message includes at least one of: a reference model for model training and / or model generation, an accuracy threshold for determining whether a model can be used.

[0022] According to embodiments of the present disclosure, the method further includes: receiving a fifteenth message from the first node, wherein the fifteenth message includes at least one of: information related to activation and / or deactivation of a model included in the UE, conditions for activation and / or deactivation and / or fallback and / or transition of a model included in the UE, indications for model training and / or model fine-tuning, valid time of a model included in the UE, valid area of a model included in the UE.

[0023] According to embodiments of the present disclosure, the method further includes: transmitting a nineteenth message to the first node, wherein the nineteenth message includes at least one of: performance information related to at least one of model training, model generation, model inference, model fine-tuning, model evaluation, information about that an accuracy of a model is below a threshold, information about that a model needs to be re-trained, information about that a model needs to be fine-tuned.

[0024] According to embodiments of the present disclosure, the first data includes measurement results of the first reference signal and / or the second reference signal.

[0025] Embodiments of the present disclosure provide a method performed by a second node in a wireless communication system, including: receiving a third message from a first node, wherein the third message includes second configuration information of a reference signal for collection of first data; and transmitting a second reference signal to a user equipment (UE) based on the second configuration information; wherein a first message is transmitted from the UE to the first node, wherein the first message is associated with a request for collection of the first data, wherein the first message includes at least one of: configuration information of a reference signal for the collection of the first data suggested by the UE, configuration information of a reference signal for the collection of the first data preferred by the UE, a data volume requested for the collection of the first data, a time requested for the collection of the first data, and information related to a node requested to perform model training based on the first data, and wherein a first reference signal for the collection of the first data is transmitted by the first node to the UE based on the first message.

[0026] According to embodiments of the present disclosure, the first data includes measurement results of the first reference signal and / or the second reference signal.

[0027] Embodiments of the present disclosure provide a node device in a wireless communication system, including: a transceiver configured to transmit and receive signals; and a processor coupled to the transceiver and configured to perform methods performed by any node (e.g., the first node, the second node, etc.) in a wireless communication system according to embodiments of the present disclosure.

[0028] Embodiments of the present disclosure provide a user equipment (UE) in a wireless communication system, including: a transceiver configured to transmit and receive signals; and a processor coupled to the transceiver and configured to perform methods performed by a user equipment (UE) in a wireless communication system according to embodiments of the present disclosure.

[0029] Embodiments of the present disclosure provide a computer-readable medium having stored thereon computer-readable instructions which, when executed by a processor, perform methods performed by any node and / or a user equipment in a wireless communication system according to embodiments of the present disclosure.

[0030] The methods performed by the nodes and / or user equipment (UE) in a wireless communication system provided by the present disclosure can effectively support cooperative transmission of reference signals between nodes by exchanging information related to reference signal configuration and the like between nodes and / or user equipment, for the UE and / or nodes to perform data collection to achieve an effect of intelligent networks.

[0031] The present disclosure provides an efficient method for node and user equipment in wireless communication system and method performed by the same.

[0032] The above and other aspects, features and advantages of certain embodiments of the present disclosure will be more apparent from the following description taken in conjunction with the accompanying drawings, in which:

[0033] FIG. 1 is an exemplary system architecture of System Architecture Evolution (SAE);

[0034] FIG. 2 is an exemplary system architecture according to various embodiments of the present disclosure;

[0035] FIG. 3 shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0036] FIGs. 4a shows schematic diagrams of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0037] FIGs. 4b shows schematic diagrams of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0038] FIGs. 4c shows schematic diagrams of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0039] FIGs. 4d shows schematic diagrams of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0040] FIG. 5 shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0041] FIG. 6 shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0042] FIGs. 7a shows schematic diagrams of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0043] FIGs. 7b shows schematic diagrams of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0044] FIG. 8 shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0045] FIGS. 9a shows schematic diagrams of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0046] FIGS. 9b shows schematic diagrams of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0047] FIGs. 10a shows schematic diagrams of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0048] FIGs. 10b shows schematic diagrams of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0049] FIGs. 11a shows schematic diagrams of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0050] FIGs. 11b shows schematic diagrams of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0051] FIGs. 11c shows schematic diagrams of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0052] FIGs. 11d shows schematic diagrams of an aspect of a method of supporting data collection according to embodiments of the present disclosure;

[0053] FIG. 12 shows a flowchart of a method performed by a first node in a wireless communication system according to embodiments of the present disclosure;

[0054] FIG. 13 shows a flowchart of a method performed by a user equipment (UE) in a wireless communication system according to embodiments of the present disclosure;

[0055] FIG. 14 shows a flowchart of a method performed by a second node in a wireless communication system according to embodiments of the present disclosure;

[0056] FIG. 15 shows a schematic diagram of a node according to embodiments of the present disclosure; and

[0057] FIG. 16 shows a schematic diagram of a user equipment according to embodiments of the present disclosure.

[0058] The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of various embodiments of the present disclosure as defined by the claims and their equivalents. It includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the various embodiments described herein can be made without departing from the scope and spirit of the present disclosure. In addition, descriptions of well-known functions and constructions may be omitted for clarity and conciseness.

[0059] The terms and words used in the following description and claims are not limited to the bibliographical meanings, but, are merely used by the inventor to enable a clear and consistent understanding of the present disclosure. Accordingly, it should be apparent to those skilled in the art that the following description of various embodiments of the present disclosure is provided for illustration purpose only and not for the purpose of limiting the present disclosure as defined by the appended claims and their equivalents.

[0060] It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a component surface” includes reference to one or more of such surfaces.

[0061] The term “include” or “may include” refers to the existence of a corresponding disclosed function, operation or component which can be used in various embodiments of the present disclosure and does not limit one or more additional functions, operations, or components. The terms such as “include” and / or “have” may be construed to denote a certain characteristic, number, step, operation, constituent element, component or a combination thereof, but may not be construed to exclude the existence of or a possibility of addition of one or more other characteristics, numbers, steps, operations, constituent elements, components or combinations thereof.

[0062] The term “or” used in various embodiments of the present disclosure includes any or all of combinations of listed words. For example, the expression “A or B” may include A, may include B, or may include both A and B.

[0063] Unless defined differently, all terms used herein, which include technical terminologies or scientific terminologies, have the same meaning as that understood by a person skilled in the art to which the present disclosure belongs. Such terms as those defined in a generally used dictionary are to be interpreted to have the meanings equal to the contextual meanings in the relevant field of art, and are not to be interpreted to have ideal or excessively formal meanings unless clearly defined in the present disclosure.

[0064] Figures discussed below and various embodiments for describing the principles of the present disclosure in this patent document are only for illustration and should not be interpreted as limiting the scope of the present disclosure in any way. Those skilled in the art will understand that the principles of the present disclosure can be implemented in any suitably arranged system or device.

[0065] FIG. 1 is an exemplary system architecture 100 of system architecture evolution (SAE). User equipment (UE) 101 is a terminal device for receiving data. An evolved universal terrestrial radio access network (E-UTRAN) 102 is a radio access network, which includes a macro base station (eNodeB / NodeB) that provides UE with interfaces to access the radio network. A mobility management entity (MME) 103 is responsible for managing mobility context, session context and security information of the UE. A serving gateway (SGW) 104 mainly provides functions of user plane, and the MME 103 and the SGW 104 may be in the same physical entity. A packet data network gateway (PGW) 105 is responsible for functions of charging, lawful interception, etc., and may be in the same physical entity as the SGW 104. A policy and charging rules function entity (PCRF) 106 provides quality of service (QoS) policies and charging criteria. A general packet radio service support node (SGSN) 108 is a network node device that provides routing for data transmission in a universal mobile telecommunications system (UMTS). A home subscriber server (HSS)109 is a home subsystem of the UE, and is responsible for protecting user information including a current location of the user equipment, an address of a serving node, user security information, and packet data context of the user equipment, etc.

[0066] FIG. 2 is an exemplary system architecture 200 according to various embodiments of the present disclosure. Other embodiments of the system architecture 200 can be used without departing from the scope of the present disclosure.

[0067] User equipment (UE) 201 is a terminal device for receiving data. A next generation radio access network (NG-RAN) 202 is a radio access network, which includes a base station (a gNB or an eNB connected to 5G core network 5GC, and the eNB connected to the 5GC is also called ng-gNB) that provides UE with interfaces to access the radio network. An access control and mobility management function entity (AMF) 203 is responsible for managing mobility context and security information of the UE. A user plane function entity (UPF) 204 mainly provides functions of user plane. A session management function entity SMF 205 is responsible for session management. A data network (DN) 206 includes, for example, services of operators, access of Internet and service of third parties.

[0068] Nodes and / or entities mentioned in the present disclosure may include: gNB, gNB Central Unit (gNB-CU), gNB Distributed Unit (gNB-DU), gNB Central Unit Control Plane (gNB-CU-CP), gNB Central Unit User Plane (gNB CU-UP), en-gNB, eNB, ng-eNB, UE, Positioning Reference Unit (PRU), Access and Mobility Management Function (AMF), Session Management Function (SMF), Mobility Management Entity (MME), Location Management Function (LMF) and other network entities or network logic units, and cells and / or beams managed by them, etc.

[0069] The signal strength and / or signal quality and / or measurement report and / or measurement result and / or signal measurement result and / or channel measurement (result) mentioned in the present disclosure may be a Received Signal Strength Indicator (RSSI), a Reference Signal Receiving Power, RSRP), a Reference Signal Receiving Quality (RSRQ), and a Signal to Interference plus Noise Ratio (SINR), Reference Signal Received Path Power (RSRPP), Observed Time Difference of Arrival, Reference Signal Time Difference, Uplink Angle of Arrival, UL Reference Signal Carrier Phase, Uplink Relative Time of Arrival, Zenith Angles of Arrival, Transmission and Reception Time Difference, etc.

[0070] The network self-optimization decision mentioned in the present disclosure may include network energy-saving, load balancing, coverage optimization, mobility optimization and / or management, network configuration update, etc.

[0071] In the present disclosure, time can be represented by one or more of the following: timestamp, time point, time interval, timer, period of time, time length, time period, time spacing, etc. The time length may be the length of time from a certain time point, which may be the current time. The time may be a relative time or an absolute time. In some implementations, the period of time may be represented by separate fields, for example, by a combination of a start time and an end time, or by a combination of a start time and a time period.

[0072] Furthermore, in the present disclosure, state and mode can refer to each other.

[0073] Information and / or fields described in the present disclosure may be an average value, an instantaneous value, a maximum value, a minimum value, etc., of the corresponding information and / or fields, which is not limited herein.

[0074] The information and / or fields described in the present disclosure may be used to represent one or more of that following situations: uplink, downlink, uplink and downlink, uplink or downlink.

[0075] In the present disclosure, a beam may refer to a synchronization signal and physical broadcast channel (PBCH) block (SSB) beam, or any other beam.

[0076] In the present disclosure, user equipment (UE), user, terminal and the like can refer to each other.

[0077] In the present disclosure, UE may also refer to PRU.

[0078] In the present disclosure, data may include any data used for positioning information generation, trajectory information prediction, location information prediction, LOS and NLOS identifying, identifying of probability of LOS, identifying of probability of NLOS, etc. of the UE, or may include any data used for training and / or generating and / or inferring and / or evaluating a model used for positioning information generation, trajectory information prediction, location information prediction, LOS and NLOS identifying, identifying of probability of LOS, identifying of probability of NLOS, etc. of the UE, or may include any other general data information, etc., which is not limited herein. On the other hand, data described in the present disclosure may also be measurement results of corresponding reference signals. In the present disclosure, these data may be referred to as first data.

[0079] In the present disclosure, a first signal may refer to any signal that may be used to be measured to generate data requested to be collected by the UE. For example, the first signal may include a reference signal. The reference signal may include a Positioning Reference Signal (PRS), a Sounding Reference Signal (SRS), etc. The reference signal may be an uplink and / or downlink reference signal. More generally, the reference signal may also include any other signal that may be used to be measured to generate data requested to be collected by the UE, which is not limited herein.

[0080] In the present disclosure, a reference signal configuration may include one or more of the following related to a reference signal: Resource Set ID, Subcarrier Spacing, Bandwidth, Starting Physical Resource Block, Center Frequency, Comb Size, Cyclic Prefix type, Resource Set Periodicity, Resource Set Slot Offset, Resource Repetition Factor, Resource Time Gap, Resource Number of Symbols, Offset To Carrier, Subcarrier bandwidth, active uplink bandwidth part (Active UL BWP) configuration, resource set configuration, SRS configuration, SRS resource, physical cell identifier, etc.

[0081] Configuration of the active uplink bandwidth part (Active UL BWP) may include one or more of the following: location, bandwidth, subcarrier spacing, Cyclic Prefix, transmitter direct current location (Tx Direct Current Location), whether to move to 7.5 KHz, etc.

[0082] In the present disclosure, for example, an area may be one or more of the following: a cell identifier and / or list of cell identifiers, a beam identifier and / or list of beam identifiers, a node and / or list of nodes, a center point, a radius, etc.

[0083] In the present disclosure, a model may be an artificial intelligence and / or machine learning model, a mathematical model, or any other model.

[0084] In the present disclosure, for example, the artificial intelligence and / or machine learning model may be a 5-layer convolutional neural network, or it may be a neural network of any other number of layers or any other type, etc.

[0085] In the present disclosure, model training may include one or more of the following: generation of a model, training of a model, fine-tuning of a model, etc.

[0086] In the present disclosure, collection may be referred to interchangeably with measurement.

[0087] In the present disclosure, a traditional measurement method may indicate that measurement is not performed by using an artificial intelligence and / or machine learning model.

[0088] In the present disclosure, "related to" or "associated with" and the like may also represent "including" and they may be used interchangeably. For example, a first message related / associated with first information may also represent a first message including the first information, and so on.

[0089] Current positioning technologies are based on traditional algorithms, so an artificial intelligence method is needed to support positioning technologies, so as to improve positioning accuracy.

[0090] Example 1

[0091] A first node may transmit a first message related to a data collection request to a second node. After receiving the first message, the second node may transmit reference signals related to data requested to be collected (e.g., reference signals used to be measured to generate the data requested to be collected) to the first node and / or other nodes according to the first message (e.g., according to a first reference signal configuration determined based on the first message). Or the second node may perform exchanging with other nodes according to the first message, for example, exchanging the configuration of the reference signals (which, for example, may be determined based on the first message) etc., so that the second node and other nodes may cooperatively transmit reference signals related to the data requested to be collected to the first node. Or the second node may perform exchanging with other nodes according to the first message, for example, exchanging the configuration of the reference signals (which, for example, may be determined based on the first message) etc., so that the first node may transmit reference signals related to the data requested to be collected to the second node and other nodes. The data collection triggered by this message may be used for training and / or inference of artificial intelligence and / or machine learning models, or may be used to evaluate artificial intelligence and / or machine learning models. The artificial intelligence and / or machine learning model may be a positioning-related model. This example can support positioning technologies that utilize artificial intelligence and / or machine learning models, which, for example, may improve positioning accuracy.

[0092] In some implementations, the first message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0093] In some implementations, the first message may include one or more of the following fields and / or information:

[0094] > Identification of a request for data collection: used to indicate that the message is used for requesting data collection.

[0095] > Request identification: used to identify the request.

[0096] > Data volume requested to be collected: used to indicate the volume of data requested to be collected, for example, used to indicate how much data is requested to be collected. In some implementations, for example, the message receiving node may perform reference signal configuration according to the information, or may perform reporting and / or collection configuration according to the information, or may perform collection and / or reporting of data according to the information. For example, the message receiving node may configure the number of reference signals to be transmitted according to the information, and / or cooperatively configure the number of reference signals to be transmitted for other nodes.

[0097] > Number of UEs corresponding to data requested to be collected: used to indicate the number of UEs corresponding to the data requested to be collected. For example, it may indicate how many UEs the data thereof is requested to be collected. In some implementations, for example, the message receiving node may perform reference signal configuration according to the information, or may perform reporting and / or collection configuration according to the information, or may perform collection and / or reporting of data according to the information. For example, the message receiving node may configure the number of UEs corresponding to the reference signals to be transmitted according to the information, and / or cooperatively configure the number of UEs corresponding to the reference signals to be transmitted for other nodes.

[0098] > Identifier and / or identifier list of UEs corresponding to data requested to be collected: used to indicate the UEs corresponding to the data requested to be collected. For example, it may indicate which UEs data are requested to be collected. In some implementations, for example, the message receiving node may perform reference signal configuration according to the information, or may perform reporting and / or collection configuration according to the information, or may perform collection and / or reporting of data according to the information. For example, the message receiving node may configure one and / or more UEs corresponding to the reference signals to be transmitted according to the information, and / or cooperatively configure one and / or more UEs corresponding to the reference signals to be transmitted for other nodes.

[0099] > Time requested for data collection: used to indicate the time when data collection is requested, for example, used to indicate the time when data collection is performed. In some implementations, for example, the message receiving node may perform reference signal configuration according to the information, or may perform reporting and / or collection configuration according to the information. For example, the message receiving node may configure the time when the reference signals are to be transmitted based on this information, and / or cooperatively configure the time when reference signals are to be transmitted for other nodes.

[0100] > Suggested reference signal configuration: in some implementations, for example, the suggested reference signal configuration may be a reference signal configuration suggested to be formulated by the message receiving node or other nodes.

[0101] > Preferred reference signal configuration: in some implementations, it may be, for example, a reference signal configuration preferred by the message transmitting node or other nodes. For example, the message transmitting node may transmit a reference signal configuration preferred by itself or other nodes to the message receiving node, and the message receiving node and / or other nodes may refer to the preferred reference signal configuration to perform configuration of reference signals, and / or, cooperatively configure reference signals for other nodes and / or the message transmitting node with reference to the preferred reference signal configuration.

[0102] > Information related to a node requested to perform model training: used to indicate which node is requested to perform model training based on the collected data. This information may be an identifier of a node and / or an identifier list of nodes, or a specific node name or type. The nodes may include one or more of the following: gNB, gNB DU, gNB CU, Location Management Function (LMF), AMF, Operation Administration and Maintenance (OAM), etc.

[0103] > Identification of a request for a node to perform model training: used to indicate that a node is requested to perform model training based on the collected data.

[0104] In some implementations, the second node may transmit a second message related to a data collection acknowledgement and / or response to the first node. The second message may be used to indicate an acknowledgement of the data collection request, or may be used to indicate whether related reference signals can be transmitted according to the data collection request.

[0105] In some implementations, the second message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0106] In some implementations, the second message may include one or more of the following fields and / or information:

[0107] > Request identification: used to identify a request identification corresponding to the acknowledgement and / or response. The identification may be used to associate this message with the request message.

[0108] > Information about whether a data collection request can be accepted

[0109] > Information about whether a data collection request is rejected

[0110] > Cause / reason: it may be used to indicate the reason why a data collection request is rejected or the reason why a data collection request can be accepted.

[0111] > Reference signal configuration: when the second node receives the first message, it can formulate the reference signal configuration. For example, after receiving the data collection request transmitted by the first node, the second node formulates a reference signal configuration and feeds it back to the first node, which is used to transmit the configuration of the second node to transmit reference signals to the first node. In some implementations, for example, the first node may receive and / or measure reference signals based on the reference signal configuration.

[0112] Example 2

[0113] A third node may transmit a third message related to a reference signal transmission request to a fourth node, requesting the fourth node to transmit reference signals (e.g., transmit reference signals to the UE). This message may be used to trigger the transmission of reference signals. In an implementation, for example, measurement data of the transmitted reference signals may be used for training and / or inference of artificial intelligence and / or machine learning models, or may be used for evaluating artificial intelligence and / or machine learning models. The artificial intelligence and / or machine learning model may be a positioning-related model. This example can support positioning technologies that utilize artificial intelligence and / or machine learning models, which, for example, may improve positioning accuracy.

[0114] In some implementations, the third message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0115] In some implementations, the third message may include one or more of the following fields and / or information:

[0116] > Reference signal configuration: the reference signal configuration may be formulated by the third node, and the fourth node is requested to transmit reference signals according to the reference signal configuration. For example, the reference signal configuration may be determined by the third node based on a received first message as described above, and / or determined by the third node based on its own situation and / or the situation of the fourth node.

[0117] > Request identification: used to identify the request.

[0118] In some implementations, the fourth node may transmit a fourth message related to a reference signal transmission acknowledgement and / or response to the third node, to indicate whether the fourth node can transmit reference signals according to the reference signal transmission request transmitted by the third node.

[0119] In some implementations, the fourth message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0120] In some implementations, the fourth message may include one or more of the following fields and / or information:

[0121] > Indication of whether reference signal transmission can be performed in accordance with a reference signal configuration (e.g., a reference signal configuration received through the third message and / or any other pre-configured reference signal configuration, etc.).

[0122] > Reference signal configuration that can be used for reference signal transmission: the reference signal configuration can be formulated by the fourth node, and the third node is requested to transmit reference signals according to the reference signal configuration.

[0123] > Request identification: used to identify a request identification corresponding to the acknowledgement and / or response. The identification is used to associate this message with a request message.

[0124] Example 3

[0125] A fifth node may transmit a fifth message related to a data reporting request to a sixth node. The message may be used for requesting the sixth node to report measured and / or collected data. The data corresponding to this example may be used for training and / or inference of artificial intelligence and / or machine learning models, or may be used to evaluate artificial intelligence and / or machine learning models. The data corresponding to this example may also be data and / or information obtained and / or inferred by artificial intelligence and / or machine learning model. The artificial intelligence and / or machine learning model may be a positioning-related model. This example can support positioning technologies that utilize artificial intelligence and / or machine learning models, which, for example, may improve positioning accuracy.

[0126] In some implementations, the fifth message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0127] In some implementations, the fifth message may include one or more of the following fields and / or information:

[0128] > Request identification: used to identify the request.

[0129] > Reporting type: it may include one or more of the following: on-demand reporting, single reporting, periodic reporting, event-triggered reporting, etc.

[0130] > Reporting periodicity: used to indicate a periodicity of the reporting.

[0131] > Reporting time: used to indicate a requested reporting time. In some implementations, for example, the message receiving node may perform reporting at the reporting time.

[0132] > Collection and / or measurement configuration: used to indicate a collection and / or measurement configuration. The collection and / or measurement configuration may include one or more of the following:

[0133] >> Collection and / or measurement time: used to indicate the time required for collection and / or measurement. The message receiving node performs collection and / or measurement at the collection and / or measurement time.

[0134] >> Collection and / or measurement type: used to indicate the type of collection and / or measurement. The types may include one or more of the following: on-demand collection and / or measurement, single collection and / or measurement, periodic collection and / or measurement, event-triggered collection and / or measurement, etc.

[0135] >> Collection and / or measurement periodicity: used to indicate a periodicity of collection and / or measurement. In some implementations, for example, it may be a periodicity of periodic collection and / or measurement.

[0136] >> Collection and / or measurement type: it may include one or more of the following: Path-based measurement and Sample-based measurement.

[0137] >> Configuration of Sample-based measurement: it may include one or more of the following:

[0138] >>> Sample period of Sample-based measurement: used to indicate the sample period corresponding to a Sample-based measurement.

[0139] >>> Number of samples of Sample-based measurement: it may include one or more of the following: the number of continuous samples, the number of selected samples, etc. Herein the selected samples may be one or more of the continuous samples. For example, the selected samples may be one or more of the continuous samples with the highest power.

[0140] >>> Start time of (continuous) samples

[0141] >>> Time granularity

[0142] >>> Reporting periodicity of Sample-based measurement

[0143] >>> Timing reporting granularity factor

[0144] >>> Basic time unit

[0145] >> Configuration of Path-based measurement: it may include one or more of the following:

[0146] >>> Number of paths of Path-based measurement

[0147] >>> Number of (estimated) channel responses of Path-based measurement: it may include one or more of the following: number of continuous channel responses, number of selected channel responses, etc. Herein the selected channel responses may be one or more of the continuous channel responses. For example, the selected channel responses may be one or more of the continuous channel responses with the highest power.

[0148] >>> Start time of (continuous) measurement

[0149] >>> Time granularity

[0150] >>> Timing reporting granularity factor

[0151] >>> Basic time unit

[0152] > Reporting content and / or data: used to indicate the content requested to be reported. The content may include one or more of the following: UE identifier and / or identifier list, a measurement result of a reference signal, channel measurement, quality indication of reference signals and / or channel measurement results, a reference signal configuration, location information of a UE, an indication that the measurement path of a reference signal is a Line of Sight (LOS) path, an indication that the measurement path of a reference signal is a Non Line of Sight (NLOS) path, a probability that the measurement path of a reference signal is a LOS path, a probability that the measurement path of a reference signal is an NLOS path, power information, phase information, time domain channel measurement type, quality indication, ground truth label and / or its estimated value and / or its predicted value, quality indication of a label, time stamp of a label, start time of collection and / or measurement and / or reporting, time information of data collection and / or measurement and / or reporting, information of whether data and / or information is obtained through an artificial intelligence and / or machine learning model, information of whether data and / or information is obtained through a traditional measurement method, etc. The UE identifier and / or identifier list may include any identification that can identify the UE. The time domain channel measurement type may include one or more of the following: Path-based measurement and Sample-based measurement. The time information in the Path-based measurement is based on the time when a path is detected, which may be not a product of an integer and multiple sample periods. The time in the Path-based measurement is a product of an integer and multiple sample periods. The ground truth label and / or its estimated value and / or its predicted value may include one or more of the following: an indication that the measurement path of a reference signal is a Line of Sight (LOS) path, an indication that the measurement path of a reference signal is a Non Line of Sight (NLOS) path, a probability that the measurement path of a reference signal is a LOS path, a probability that the measurement path of a reference signal is an NLOS path, and any positioning-related ground truth label.

[0153] > Number of UEs corresponding to data requested to be collected and / or reported: used to indicate the number of UEs corresponding to the data requested to be collected and / or reported. For example, it may indicate how many UEs the data thereof is requested to be collected and / or reported. In some implementations, for example, the message receiving node may perform reference signal configuration according to the information, or may perform reporting and / or collection configuration according to the information, or may perform collection and / or reporting of data according to the information. For example, the message receiving node may configure the number of UEs corresponding to the reference signals to be transmitted according to the information, and / or cooperatively configure the number of UEs corresponding to the reference signals to be transmitted for other nodes.

[0154] > Identifier and / or identifier list of UEs corresponding to data requested to be collected and / or reported: used to indicate the UEs corresponding to the data requested to be collected and / or reported. For example, it may indicate which UEs data are requested to be collected. In some implementations, for example, the message receiving node may perform reference signal configuration according to the information, or may perform reporting and / or collection configuration according to the information, or may perform collection and / or reporting of data according to the information. For example, the message receiving node may configure one and / or more UEs corresponding to the reference signals to be transmitted according to the information, and / or cooperatively configure one and / or more UEs corresponding to the reference signals to be transmitted for other nodes.

[0155] > Events that trigger reporting and / or measurement: when an event that triggers reporting and / or measurement is met, reporting and / or measurement is performed. The events may include one or more of the following:

[0156] >> Event 1: signal quality is less than and / or equal to a threshold

[0157] >> Event 2: data volume is greater than and / or equal to a threshold

[0158] >> Event 3: signal quality is greater than and / or equal to a threshold

[0159] >> Event 4: the RRC state of a UE changes, for example, changes from an RRC connected state to an RRC inactive state or an RRC idle state.

[0160] In some implementations, the sixth node may transmit a sixth message related to a data reporting acknowledgement and / or response to the fifth node. This message may be used to inform the fifth node whether the sixth node can perform data reporting according to the data reporting request. In some implementations, for example, this step may be omitted. In some other implementations, for example, this step may be combined with the subsequent seventh message.

[0161] In some implementations, the sixth message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0162] In some implementations, the sixth message may include one or more of the following fields and / or information:

[0163] > Request identification: used to identify a request identification corresponding to the acknowledgement and / or response. The identification is used to associate this message with a request message.

[0164] > Information about whether reporting can be performed

[0165] > Reporting type that can (not) be used for reporting: used to indicate a reporting type that can (not) be used for reporting. Specific information of the reporting type may refer to the reporting type in the fifth message.

[0166] > Reporting periodicity that can (not) be used for reporting: used to indicate a reporting periodicity that can (not) be used for reporting. Specific information of the reporting periodicity may refer to the reporting periodicity in the fifth message.

[0167] > Reporting time that can (not) be used for reporting: used to indicate a reporting time that can (not) be used for reporting. The specific information of the reporting time may refer to the reporting time of the fifth message.

[0168] > Configuration that can (not) be used for collection and / or measurement: used to indicate configuration that can (not) be used for collection and / or measurement. The configuration that can (not) be used for collection and / or measurement may refer to the collection and / or measurement configuration in the fifth message.

[0169] > Content and / or data that can (not) be reported: used to indicate content and / or data that can (not) be reported. The specific information of the content and / or data that can (not) be reported may refer to the reporting content and / or data of the fifth message.

[0170] > Number of UEs corresponding to data that can (not) be collected and / or reported.

[0171] > Identifier and / or identifier list of UEs corresponding to data that can (not) be collected and / or reported.

[0172] > Events that can (not) be used to trigger reporting: the events may refer to the “Events that trigger reporting” in the fifth message.

[0173] In some implementations, the sixth node may transmit a seventh message related to the reported data to the fifth node. For example, after the sixth node receives the fifth message containing a data reporting request transmitted by the fifth node, the sixth node transmits a seventh message related to the reported data to the fifth node. For example, after the sixth node receives the fifth message containing a data reporting request transmitted by the fifth node, and after the sixth node transmits to the fifth node a sixth message (the sixth message may be the aforementioned sixth message containing a data reporting acknowledgement and / or response) containing information about that the sixth node can perform partial or all of the data collection and / or reporting according to the data reporting request transmitted by the fifth node, the sixth node transmits a seventh message related to the reported data to the fifth node. This message may be used to report data. The data corresponding to this example may be used for training and / or inference of artificial intelligence and / or machine learning models, or may be used to evaluate artificial intelligence and / or machine learning models. The data corresponding to this example may also be data and / or information obtained and / or inferred by artificial intelligence and / or machine learning model. The artificial intelligence and / or machine learning model may be a positioning-related model. This example can support positioning technologies that utilize artificial intelligence and / or machine learning models, which, for example, may improve positioning accuracy.

[0174] In some implementations, the seventh message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0175] In some implementations, the seventh message may include one or more of the following fields and / or information:

[0176] > Reported content and / or data: the content and / or data may refer to the “Reporting content and / or data” in the fifth message.

[0177] > Event that triggers the reporting: the event may refer to the “Events that trigger reporting” in the fifth message.

[0178] > Request identification: used to identify a request identification corresponding to the reporting message. The identification is used to associate this message with a request message.

[0179] > Configuration corresponding to the reported content and / or data: the specific information of the configuration may refer to one or more of the following information of the fifth message: reporting type, reporting periodicity, reporting time, collection and / or measurement configuration, number of UEs corresponding to data collected and / or reported, identifier and / or identifier list of UEs corresponding to data collected and / or reported.

[0180] In some implementations, for example, the reported content and / or data transmitted by the sixth node to the fifth node is a part of the reported content and / or data in the seventh message. The fifth node itself has a part of information and / or data stored, for example, the fifth node itself has a part of the reported content and / or data in the seventh message stored. The fifth node can associate the information and / or data stored by the fifth node itself with the information and / or data transmitted by the sixth node to the fifth node through time stamps and / or UE identifiers. When the information and / or data stored by the fifth node itself and the information and / or data transmitted by the sixth node to the fifth node are partially identical, the fifth node can associate the information and / or data stored by the fifth node itself with the information and / or data transmitted by the sixth node to the fifth node through time stamps and / or UE identifiers and / or the same information. The fifth node can obtain complete information and / or data after association.

[0181] The reported information and / or data can also be measured information and / or data or collected information and / or data.

[0182] Example 4

[0183] A seventh node may transmit an eighth message to an eighth node related to information about whether model training and / or inference can be performed. This message may be used to inform the eighth node whether the seventh node and / or other nodes can perform model training and / or reasoning. In some implementations, for example, after obtaining the message, the eighth node may transmit information related to model generation and / or training to the seventh node to inform the seventh node how to generate and / or train a model. For example, when the seventh node and / or other nodes can perform model training, the eighth node may transmit model generation and / or training related information to the seventh node to inform the seventh node how to generate and / or train a model. In other implementations, for example, after obtaining the message, the eighth node may transmit a model to the seventh node. For example, the eighth node may transmit a model to the seventh node when the seventh node and / or other nodes can perform model inference. For example, the model may be further inferred by the seventh node and / or other nodes.

[0184] In some implementations, the eighth message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0185] In some implementations, the eighth message may include one or more of the following fields and / or information:

[0186] > Information about whether a model can be generated and / or trained

[0187] > Information about whether model inference can be performed

[0188] > Information about whether model (re-) training and / or fine-tuning can be performed

[0189] > Computing power of the seventh node and / or other nodes

[0190] Example 5

[0191] A ninth node may transmit a ninth message related to model generation and / or training information to a tenth node. This message may be used to inform the tenth node how to perform model generation and / or training. After receiving the message, the tenth node may perform model generation and / or training according to the content of the ninth message. The artificial intelligence and / or machine learning model may be a positioning-related model. This example can support positioning technologies that utilize artificial intelligence and / or machine learning models, which, for example, may improve positioning accuracy.

[0192] In some implementations, the ninth message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0193] In some implementations, the ninth message may include one or more of the following fields and / or information:

[0194] > Generation and / or training identification: used to identify the generation and / or training.

[0195] > Reference model: it may include one or more of the following: type of a model, structure of a model, weight of a model, bias of a model, etc. The structure of a model may include the number of layers, the number of neurons in each layer, the total number of neurons of the model, etc. The message receiving node may perform model training and / or model generation based on the reference model.

[0196] > Identification of a reference model: it may be used to identify a reference model, based on which the message receiving node may perform model training and / or model generation. In some implementations, for example, when specific information of reference models has been configured to the message receiving node and / or other nodes, the message transmitting node may transmit an identification to indicate to perform model training and / or model generation based on a reference model corresponding to the identification.

[0197] > Accuracy threshold: the accuracy threshold may be used to indicate that when the accuracy of a model is greater than and / or equal to the threshold, the model can be used. The accuracy threshold may also be used to indicate that when the accuracy of a model is less than and / or equal to the threshold, the model cannot be used, e.g., it needs to be (re-) trained and / or fine-tuned.

[0198] > Input of a model: for example, it may include one or more of the following: measurement results, (reference) signal quality, measurement time, etc.

[0199] > Output of a model: for example, it may include one or more of the following: location information of a UE, an indication that the measurement path of a reference signal is a Line of Sight (LOS) path, an indication that the measurement path of a reference signal is a Non Line of Sight (NLOS) path, a probability that the measurement path of a reference signal is a LOS path, a probability that the measurement path of a reference signal is an NLOS path, etc.

[0200] > Download address of raw data: an address from which the message receiving node can download raw data. The message receiving node may perform model training and / or generation based on the raw data downloaded from the address.

[0201] > Model identification: the identification corresponding to a model generated according to the message.

[0202] > Training method of a model: it may include one or more of the following: supervised learning, semi-supervised learning, unsupervised learning, reinforcement learning, etc.

[0203] > Structure of a model: including but not limited to perceptrons, feedforward neural networks, radial basis function networks, deep feedforward networks, recurrent neural networks, long / short-term memory networks, gated recurrent units, autoencoders, variational autoencoders, denoising autoencoders, sparse autoencoders, Markov chains, Hoffitt networks, Boltzmann machines, restricted Boltzmann machines, deep belief networks, deep convolutional networks, deconvolutional neural networks, deep convolutional inverse graph networks, generative adversarial networks, liquid state machines, extreme learning machines, echo state networks, deep residual networks, Kohonen networks, support vector machines, neural Turing machines, convolutional neural networks, artificial neural networks, recurrent neural networks, deep neural networks, etc.

[0204] > Model information: the model information may refer to the information in the twelfth message.

[0205] In some implementations, the tenth node may transmit a tenth message related to model generation and / or training information acknowledgement and / or response to the ninth node. This message may be used to inform the ninth node whether the tenth node and / or other nodes can perform model generation and / or training according to the model generation and / or training information. Alternatively, the message may be used to notify the ninth node of an acknowledgement of the model generation and / or training information by the tenth node and / or other nodes.

[0206] In some implementations, the tenth message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0207] In some implementations, the tenth message may include one or more of the following fields and / or information:

[0208] > Generation and / or training identification: used to identify the generation and / or training identification corresponding to the acknowledgement and / or response. This identification is used to associate with the message in which the generation and / or training information is included.

[0209] > Whether model generation and / or training can be performed according to model generation and / or training information

[0210] > Model information corresponding to model generation and / or training which can be performed according to the model generation and / or training information: the model information may refer to the information in the twelfth message.

[0211] > Model information corresponding to model generation and / or training which cannot be performed according to the model generation and / or training information: the model information may refer to the information in the twelfth message.

[0212] > Cause / reason: when model generation and / or training cannot be performed according to the model generation and / or training information, the cause / reason is used to indicate the reason why model generation and / or training cannot be performed according to the model generation and / or training information. The reason may include one or more of the following: computing resources are insufficient, there is no training function, there is no training module, etc.

[0213] In some implementations, the tenth node may transmit an eleventh message related to information that model generation and / or training is complete to the ninth node. This message may be used to inform the ninth node that the tenth node and / or other nodes have completed model generation and / or training.

[0214] In some implementations, the eleventh message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0215] In some implementations, the eleventh message may include one or more of the following fields and / or information:

[0216] > Generation and / or training identification: used to identify the generation and / or training identification corresponding to the completion information. This identification is used to associate with the message in which the generation and / or training information is included.

[0217] > Indication that model generation and / or training is complete: used to indicate that model generation and / or training is completed.

[0218] > Functions of a model: which may include one or more of the following: positioning information generation, trajectory information prediction, location information prediction, LOS and NLOS identifying, identifying of probability of LOS, identifying of probability of NLOS, generation of information of the valid / applicable time corresponding to the predicted and / or generated and / or identified information, generation of accuracy corresponding to the predicted and / or generated and / or identified information, likelihood identifying / prediction, etc.

[0219] > Model identification

[0220] > Model accuracy

[0221] > Model information corresponding to a model the generation and / or training of which is completed: the model information may refer to the information in the twelfth message.

[0222] > Model information corresponding to a model the generation and / or training of which is not completed: the model information may refer to the information in the twelfth message.

[0223] Example 6

[0224] An eleventh node may transmit a twelfth message related to model information to a twelfth node. This information may be used to inform information corresponding to a model contained in the eleventh node and / or other nodes. After receiving this message, the twelfth node may use this information for model management. The artificial intelligence and / or machine learning model may be a positioning-related model. This example can support positioning technologies that utilize artificial intelligence and / or machine learning models, which, for example, may improve positioning accuracy.

[0225] In some implementations, the twelfth message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0226] In some implementations, the twelfth message may include one or more of the following fields and / or information:

[0227] > Functions of a model: which may include one or more of the following: positioning information generation, trajectory information prediction, location information prediction, LOS and NLOS identifying, identifying of probability of LOS, identifying of probability of NLOS, generation of information of the valid / applicable time corresponding to the predicted and / or generated and / or identified information, generation of accuracy corresponding to the predicted and / or generated and / or identified information, likelihood identifying / prediction, etc. And / or, this field may also be called output information of a model.

[0228] > Input information of a model: for example, it may include one or more of the following: measurement result, (reference) signal quality, measurement time, etc.

[0229] > Model size

[0230] > Model type

[0231] > Number of layers of a model

[0232] > Weights of a model

[0233] > Model bias

[0234] > Model identification

[0235] > Available time of a model

[0236] > Available area of a model

[0237] > Model accuracy

[0238] Example 7

[0239] A thirteenth node may transmit a thirteenth message related to model update and / or transfer information to a fourteenth node. This information may be used for model update of the fourteenth node, or may be used to transmit a model to the fourteenth node and / or other nodes for the fourteenth node and / or the other nodes to install the model. The artificial intelligence and / or machine learning model may be a positioning-related model. This example can support positioning technologies that utilize artificial intelligence and / or machine learning models, which, for example, may improve positioning accuracy.

[0240] In some implementations, the thirteenth message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0241] In some implementations, the thirteenth message may include one or more of the following fields and / or information:

[0242] > Update and / or transfer identification: used to identify an update and / or transfer.

[0243] > Updated model information: it may include one or more of the following:

[0244] >> Functions of a model: which may include one or more of the following: positioning information generation, trajectory information prediction, location information prediction, LOS and NLOS identifying, identifying of probability of LOS, identifying of probability of NLOS, generation of information of the valid / applicable time corresponding to the predicted and / or generated and / or identified information, generation of accuracy corresponding to the predicted and / or generated and / or identified information, likelihood identifying / prediction, etc. And / or, this field may also be called output information of a model.

[0245] >> Input information of a model: for example, it may include one or more of the following: measurement result, (reference) signal quality, measurement time, etc.

[0246] >> Model size

[0247] >> Model type

[0248] >> Number of layers of a model

[0249] >> Weights of a model

[0250] >> Model bias

[0251] >> Model identification

[0252] >> Available time of a model

[0253] >> Available area of a model

[0254] >> Model accuracy

[0255] > Conditions and / or events for model installation: when a condition and / or event for model installation is met, model installation is performed. The conditions and / or events may include one or more of the following:

[0256] >> UE enters one or more cells

[0257] >> UE leaves one or more cells

[0258] >> UE enters one or more areas

[0259] >> UE leaves one or more areas

[0260] >> Time: it may represent the time when model installation is performed, for example, when this time is reached, model installation is performed.

[0261] >> A measurement result is within an interval

[0262] >> A measurement result is greater than and / or equal to a threshold

[0263] >> A measurement result is less than and / or equal to a threshold

[0264] >> Model information: the model information may refer to the information in the twelfth message.

[0265] In some implementations, the fourteenth node may transmit a fourteenth message related to an acknowledgement and / or response for model update and / or transfer to the thirteenth node. This message may be used to inform the thirteenth node whether the fourteenth node and / or other nodes can perform model update and / or whether model installation can be performed according to the model transfer information. Alternatively, the message may be used to notify the thirteenth node of an acknowledgement of the model update and / or transfer information of the fourteenth node and / or other nodes.

[0266] In some implementations, the fourteenth message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0267] In some implementations, the fourteenth message may include one or more of the following fields and / or information:

[0268] > Update and / or transfer identification: used to identify the update and / or transfer identification corresponding to the acknowledgement and / or response. This identification is used to associate with the message in which the update and / or transfer information is included.

[0269] > Whether model update can be performed (e.g., according to the received updated model information)

[0270] > Whether model installation can be performed based on the model transfer information

[0271] > Cause / reason: it may indicate the reason why model update cannot be performed, or the reason why model installation cannot be performed according to the model transfer information. The reason may include one or more of the following: insufficient computing power, insufficient storage, and no artificial intelligence and / or machine learning functions.

[0272] Example 8

[0273] In some implementations, a fifteenth node transmits a fifteenth message related to the model management information to a sixteenth node. This information may be used to manage a model contained in the sixteenth node. This example may be used to manage a model so that the model can be used effectively, for example, to guarantee the accuracy of model output. The artificial intelligence and / or machine learning model may be a positioning-related model. This example can support positioning technologies that utilize artificial intelligence and / or machine learning models, which, for example, may improve positioning accuracy.

[0274] In some implementations, the fifteenth message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0275] In some implementations, the fifteenth message may include one or more of the following fields and / or information:

[0276] > Request identification: used to identify a request.

[0277] > Information of model activation and / or deactivation: it may include one or more of the following:

[0278] >> Model identification

[0279] >> Model function

[0280] >> Time of model (de) activation

[0281] >> Model information: the model information may refer to the information in the twelfth message.

[0282] > Conditions and / or events for model activation and / or deactivation and / or rollback: when a condition and / or event is met, the model is activated and / or deactivated and / or rolled back. The condition and / or event may include one or more of the following:

[0283] >> Model identification

[0284] >> Model information: the model information may refer to the information in the twelfth message.

[0285] >> UE enters one or more cells

[0286] >> UE leaves one or more cells

[0287] >> UE enters one or more areas

[0288] >> UE leaves one or more areas

[0289] >> Time: it may represent the time at which model activation and / or deactivation and / or rollback is performed, for example, when this time is reached, model activation and / or deactivation and / or rollback is performed.

[0290] >> A measurement result is within an interval

[0291] >> A measurement result is greater than and / or equal to a threshold

[0292] >> A measurement result is less than and / or equal to a threshold

[0293] >> A measurement path is a LOS path

[0294] >> A measurement path is an NLOS path

[0295] >> The number of LOS paths is greater than and / or equal to and / or less than a threshold

[0296] >> The number of NLOS paths is greater than and / or equal to and / or less than a threshold

[0297] >> Accuracy is greater than and / or equal to and / or less than a threshold: for example, when accuracy is less than and / or equal to a threshold, the model is deactivated and / or rolled back.

[0298] > Conditions and / or events for model transformation: when a condition and / or event is met, the model is transformed. The conditions and / or events may include one or more of the following:

[0299] >> Model identification

[0300] >> Model information: the model information may refer to the information in the twelfth message.

[0301] >> UE enters one or more cells

[0302] >> UE leaves one or more cells

[0303] >> UE enters one or more areas

[0304] >> UE leaves one or more areas

[0305] >> Time: it may represent the time when model installation is performed

[0306] >> A measurement result is within an interval

[0307] >> A measurement result is greater than and / or equal to a threshold

[0308] >> A measurement result is less than and / or equal to a threshold

[0309] >> A measurement path is a LOS path

[0310] >> A measurement path is an NLOS path

[0311] >> The number of LOS paths is greater than and / or equal to and / or less than a threshold

[0312] >> The number of NLOS paths is greater than and / or equal to and / or less than a threshold

[0313] >> Accuracy is greater than and / or equal to and / or less than a threshold: for example, when accuracy is less than and / or equal to a threshold, the model is deactivated and / or rolled back.

[0314] >> Model information before transformation

[0315] >> Model information after transformation

[0316] > Model transformation information: it may include one or more of the following: model information before transformation, model information after transformation, etc. The model information may refer to the information in the twelfth message.

[0317] > Model rollback information: it may include one or more of the following: model information before rollback, model information after rollback, rollback to a traditional mode, etc. The model information may refer to the information in the twelfth message. The traditional mode may be a mode that does not utilize artificial intelligence machine learning models.

[0318] > Indication of model (re-) training and / or fine-tuning: when the message receiving node receives this information, the model is (re-) trained and / or fine-tuned.

[0319] > Model valid time: it may represent the time when the model can be used to perform inference.

[0320] > Model valid area: it may represent the area where the model can be used to perform inference, and / or the area from which the input that the model can use to perform inference comes.

[0321] > Model identification

[0322] In some implementations, the sixteenth node may transmit a sixteenth message related to a model management acknowledgement and / or response to the fifteenth node. This message may be used to inform the fifteenth node whether the sixteenth node and / or other nodes can perform model management according to the model management information. Alternatively, the message may be used to notify the fifteenth node of an acknowledgement of the sixteenth node and / or other nodes for the model management information.

[0323] In some implementations, the sixteenth message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0324] In some implementations, the sixteenth message may include one or more of the following fields and / or information:

[0325] > Request identification: used to identify a request identification corresponding to the acknowledgement and / or response. The identification is used to associate this message with a request message.

[0326] > Whether model management is acceptable: for example, it may indicate whether model management can be performed according to the received model management information.

[0327] > Model identification

[0328] > Information of a model for which model management is acceptable: the model information may refer to the information in the twelfth message.

[0329] > Information of a model for which model management is not acceptable: the model information may refer to the information in the twelfth message.

[0330] > Cause / reason: it may indicate the reason why model management is not accepted. The reason may include one or more of the following: insufficient computing power, insufficient storage, no artificial intelligence and / or machine learning functions, no relevant models, etc.

[0331] Example 9

[0332] A seventeenth node may transmit a seventeenth message related to a performance reporting request to an eighteenth node. This message may be used for requesting performance reporting. The performance may be a performance related to training and / or generation and / or inference and / or fine-tuning and / or evaluation of the model. This performance may be used for training and / or generation and / or inference and / or fine-tuning of the model. This performance may also be used to evaluate an artificial intelligence machine learning model. The artificial intelligence and / or machine learning model may be a positioning-related model. This example can support positioning technologies that utilize artificial intelligence and / or machine learning models, which, for example, may improve positioning accuracy.

[0333] In some implementations, the seventeenth message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0334] In some implementations, the seventeenth message may include one or more of the following fields and / or information:

[0335] > Request identification: used to identify a request.

[0336] > Reporting type: it may include one or more of the following: on-demand reporting, single reporting, periodic reporting, event-triggered reporting, etc.

[0337] > Reporting periodicity: used to indicate the periodicity of reporting.

[0338] > Reporting content: used to indicate the content requested to be reported. The content may include the information and / or content in the nineteenth message. The content may also include one or more of the following: UE identifier and / or identifier list, a measurement result of a reference signal, channel measurement, quality indication of reference signals and / or channel measurement results, a reference signal configuration, location information of a UE, an indication that the measurement path of a reference signal is a Line of Sight (LOS) path, an indication that the measurement path of a reference signal is a Non Line of Sight (NLOS) path, a probability that the measurement path of a reference signal is a LOS path, a probability that the measurement path of a reference signal is an NLOS path, power information, phase information, time domain channel measurement type, quality indication, ground truth label and / or its estimated value and / or its predicted value, quality indication of a label, time stamp of a label, start time of collection and / or measurement and / or reporting, time information of data collection and / or measurement and / or reporting, information of whether data and / or information is obtained through an artificial intelligence and / or machine learning model, information of whether data and / or information is obtained through a traditional measurement method, etc. The UE identifier and / or identifier list may include any identification that can identify the UE. The time domain channel measurement type may include one or more of the following: Path-based measurement and Sample-based measurement. The time information in the Path-based measurement is based on the time when a path is detected, which may be not a product of an integer and multiple sample periods. The time in the Path-based measurement is a product of an integer and multiple sample periods. The ground truth label and / or its estimated value and / or its predicted value may include one or more of the following: an indication that the measurement path of a reference signal is a Line of Sight (LOS) path, an indication that the measurement path of a reference signal is a Non Line of Sight (NLOS) path, a probability that the measurement path of a reference signal is a LOS path, a probability that the measurement path of a reference signal is an NLOS path, and any positioning-related ground truth label.

[0339] > Events that trigger reporting: the events may refer to the events that trigger reporting in the fifth message.

[0340] In some implementations, the eighteenth node transmits an eighteenth message related to a performance reporting acknowledgment and / or response to the seventeenth node. This message may be used to inform the seventeenth node whether the eighteenth node can perform performance reporting according to the performance reporting request.

[0341] In some implementations, the eighteenth message may be one or more of the following: an RRC message, a MAC CE, a physical layer signal and / or signalling, an Xn message, an X2 message, an F1 message, an NG message, an E1 message, an NR Positioning Protocol A message, an LTE Positioning Protocol message, an NAS message, etc.

[0342] In some implementations, the eighteenth message may include one or more of the following fields and / or information:

[0343] > Request identification: used to identify a request identification corresponding to the acknowledgement and / or response. The identification is used to associate this message with a request message.

[0344] > Whether reporting can be performed

[0345] > Reporting type that can (not) be used for reporting: it may include one or more of the following: on-demand reporting, single reporting, periodic reporting, event-triggered reporting, etc.

[0346] > Reporting periodicity that can (not) be used for reporting

[0347] > Contents that can (not) be reported: the content may include the information and / or content in the nineteenth message. The content may include one or more of the following: UE identifier and / or identifier list, a measurement result of a reference signal, channel measurement, quality indication of reference signals and / or channel measurement results, a reference signal configuration, location information of a UE, an indication that the measurement path of a reference signal is a Line of Sight (LOS) path, an indication that the measurement path of a reference signal is a Non Line of Sight (NLOS) path, a probability that the measurement path of a reference signal is a LOS path, a probability that the measurement path of a reference signal is an NLOS path, power information, phase information, time domain channel measurement type, quality indication, ground truth label and / or its estimated value and / or its predicted value, quality indication of a label, time stamp of a label, start time of collection and / or measurement and / or reporting, time information of data collection and / or measurement and / or reporting, information of whether data and / or information is obtained through an artificial intelligence and / or machine learning model, information of whether data and / or information is obtained through a traditional measurement method, etc. The UE identifier and / or identifier list may include any identification that can identify the UE. The time domain channel measurement type may include one or more of the following: Path-based measurement and Sample-based measurement. The time information in the Path-based measurement is based on the time when a path is detected, which may be not a product of an integer and multiple sample periods. The time in the Path-based measurement is a product of an integer and multiple sample periods. The ground truth label and / or its estimated value and / or its predicted value may include one or more of the following: an indication that the measurement path of a reference signal is a Line of Sight (LOS) path, an indication that the measurement path of a reference signal is a Non Line of Sight (NLOS) path, a probability that the measurement path of a reference signal is a LOS path, a probability that the measurement path of a reference signal is an NLOS path, and any positioning-related ground truth label.

[0348] > Events that can (not) be used to trigger reporting: the events may refer to the “Events that trigger reporting” in the fifth message.

[0349] In some implementations, the eighteenth node may transmit a nineteenth message related to the reported performance to the seventeenth node. This message may be used to report performance. This performance may be used for training and / or generation and / or inference and / or fine-tuning of the model. This performance may also be used to evaluate an artificial intelligence machine learning model.

[0350] In some implementations, the nineteenth message may include one or more of the following fields and / or information:

[0351] > Request identification: used to identify the request corresponding to the reporting. The identification is used to associate this message with a request message.

[0352] > Reported performance: for example, it may be performance information related to at least one of model training, model generation, model inference, model fine-tuning, model evaluation.

[0353] > Indication that the accuracy of a model is too low: e.g., it may be information about that the accuracy of a model is below a certain threshold.

[0354] > Indication that model retraining is required: e.g., it may be information about that a model needs to be retrained.

[0355] > Indication that model fine-tuning is required: e.g., it may be information about that a model needs to be fine-tuned.

[0356] Exemplary embodiments of the present disclosure are further described below with reference to the accompanying drawings.

[0357] The text and drawings are provided as examples only to help understand the present disclosure. They should not be construed as limiting the scope of the present disclosure in any way. Although certain embodiments and examples have been provided, based on the disclosure herein, it is obvious to those skilled in the art that changes can be made to the illustrated embodiments and examples without departing from the scope of the present disclosure.

[0358] FIG. 3 shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 3 shows a relevant process of exchanging a data collection request between nodes, so that a second node can transmit relevant information and / or reference signals according to the data collection request, so that the first node can collect relevant data. The collected relevant data may be used for training and / or inference of artificial intelligence and / or machine learning models. Alternatively, the collected relevant data may be used to evaluate artificial intelligence and / or machine learning models.

[0359] For example, in some implementations, the first node may be a UE and / or a PRU, and the second node may be a gNB and / or AMF and / or LMF, for example. In other implementations, for example, the first node may be a gNB and / or gNB CU, and the second node may be an AMF and / or LMF. In yet other implementations, for example, the first node may be a gNB DU and the second node may be a gNB CU. In yet other implementations, for example, the first node may be an AMF and the second node may be an LMF. In yet other implementations, for example, the first node may be a gNB and / or AMF and / or LMF, and the second node may be a UE and / or a PRU. In yet other implementations, for example, the first node may be an AMF and / or an LMF, and the second node may be a gNB and / or a gNB CU. In yet other implementations, for example, the first node may be a gNB CU and the second node may be a gNB DU. In yet other implementations, for example, the first node may be an LMF and the second node may be an AMF.

[0360] Step 301A: the first node transmits a data collection request to the second node. The data collection request may be the aforementioned first message.

[0361] Step 302A: optionally, the second node transmits a data collection acknowledgement and / or response to the first node. The data collection acknowledgement and / or response may be the aforementioned second message.

[0362] The second node may transmit relevant information and / or reference signals according to the data collection request, so that the first node can collect relevant data. The collected relevant data may be used for training and / or inference of artificial intelligence and / or machine learning models. Alternatively, the collected relevant data may be used to evaluate artificial intelligence and / or machine learning models.

[0363] FIG. 4a shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 4a shows a relevant process of exchanging a reference signal transmission request between nodes, to request a fourth node to transmit relevant reference signals. The third node may measure on the reference signals to collect relevant data. The collected data may be forwarded to other nodes. The collected relevant data may be used by the third node and / or other nodes for training and / or inference of artificial intelligence and / or machine learning models. Alternatively, the collected relevant data may be used by the third node and / or other nodes to evaluate artificial intelligence and / or machine learning models.

[0364] For example, in some implementations, the third node may be a UE and / or a PRU, and the fourth node may be a gNB and / or AMF and / or LMF, for example. In other implementations, for example, the third node may be a gNB and / or gNB CU, and the fourth node may be an AMF and / or LMF. In yet other implementations, for example, the third node may be a gNB DU and the fourth node may be a gNB CU. In yet other implementations, for example, the third node may be an AMF and the fourth node may be an LMF. In yet other implementations, for example, the third node may be a gNB and / or AMF and / or LMF, and the fourth node may be a UE and / or a PRU. In yet other implementations, for example, the third node may be an AMF and / or an LMF, and the fourth node may be a gNB and / or a gNB CU. In yet other implementations, for example, the third node may be a gNB CU and the fourth node may be a gNB DU. In yet other implementations, for example, the third node may be an LMF and the fourth node may be an AMF.

[0365] Step 401A: the third node transmits a reference signal transmission request to the fourth node. The reference signal transmission request may be the aforementioned third message.

[0366] Step 402A: the fourth node transmits a reference signal transmission acknowledgement and / or response to the third node. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0367] The fourth node may transmit relevant information and / or reference signals according to the reference signal transmission request, so that the third node and / or UE and / or PRU can collect relevant data. The collected relevant data may be used for training and / or inference of artificial intelligence and / or machine learning models. Alternatively, the collected relevant data may be used to evaluate artificial intelligence and / or machine learning models.

[0368] FIG. 4b shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 4b shows a relevant process of exchanging a reference signal transmission request and / or a data collection request and / or data reporting between nodes. The collected relevant data may be used by the nodes for training and / or inference of artificial intelligence and / or machine learning models. Alternatively, the collected relevant data may be used to evaluate artificial intelligence and / or machine learning models.

[0369] The exchange of the data collection request and / or the reference signal transmission request may be implemented through option 4B-A or option 4B-B.

[0370] Option 4B-A:

[0371] Step 401B.a: UE transmits a data collection request to gNB1 DU. The data collection request may be the aforementioned first message. The data collection request may be transmitted by a Medium Access Control (MAC) Control Element (CE) and / or physical layer signalling and / or physical layer signals. As described above, the first message may include a first reference signal configuration and / or information for determining the first reference signal configuration. The gNB1 (e.g., gNB1 DU and / or gNB1 CU) may transmit a reference signal (e.g., it may be referred to as a first reference signal) for data collection to the UE based on the first reference signal configuration. The gNB1 may also determine a second reference signal configuration for gNB2 (e.g., gNB2 DU and / or gNB2 CU) based on the first message and / or the first reference signal configuration. The gNB1 may transmit the second reference signal configuration to gNB2, and gNB2 may transmit a reference signal (e.g., it may be referred to as a second reference signal) for data collection to the UE based on the second reference signal configuration, which may be achieved by the following steps.

[0372] Step 402B.a: gNB1 DU transmits a reference signal transmission request to gNB1 CU. The reference signal transmission request may be the aforementioned third message.

[0373] Step 403B.a: gNB1 CU transmits a reference signal transmission request to gNB2 CU. The reference signal transmission request may be the aforementioned third message.

[0374] Step 404B.a: gNB2 CU transmits a reference signal transmission request to gNB2 DU. The reference signal transmission request may be the aforementioned third message.

[0375] Step 405B.a: gNB2 DU transmits a reference signal transmission acknowledgement and / or response to gNB2 CU. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0376] Step 406B.a: the gNB2 CU transmits a reference signal transmission acknowledgement and / or response to the gNB1 CU. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0377] Step 407B.a: gNB1 CU transmits a reference signal transmission acknowledgement and / or response to gNB1 DU. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0378] Step 408B.a: gNB1 DU transmits a data collection acknowledgement and / or response to UE. The data collection acknowledgement and / or response may be the aforementioned second message. The data collection acknowledgement and / or response may be transmitted via MAC CE and / or physical layer signalling and / or physical layer signals.

[0379] Option 4B-B:

[0380] Step 401B.b: UE transmits a data collection request to gNB1 CU. The data collection request may be the aforementioned first message. The data collection request may be transmitted through radio resource control (RRC) signalling.

[0381] Step 402B.b: gNB1 CU transmits a reference signal transmission request to gNB1 DU. The reference signal transmission request may be the aforementioned third message.

[0382] Step 403B.b: gNB1 DU transmits a reference signal transmission acknowledgement and / or response to gNB1 CU. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0383] Step 404B.b: gNB1 CU transmits a reference signal transmission request to gNB2 CU. The reference signal transmission request may be the aforementioned third message.

[0384] Step 405B.b: gNB2 CU transmits a reference signal transmission request to gNB2 DU. The reference signal transmission request may be the aforementioned third message.

[0385] Step 406B.b: gNB2 DU transmits a reference signal transmission acknowledgement and / or response to gNB2 CU. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0386] Step 407B.b: gNB2 CU transmits a reference signal transmission acknowledgement and / or response to gNB1 CU. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0387] Step 408B.b: gNB1 CU transmits data collection acknowledgement and / or response to UE. The data collection acknowledgement and / or response may be the aforementioned second message. The data collection acknowledgement and / or response may be transmitted through RRC signalling.

[0388] 408B.ba: gNB1 CU transmits a reference signal transmission request to gNB1 DU. The reference signal transmission request may be the aforementioned third message.

[0389] Step 408B.bb: gNB1 DU transmits a reference signal transmission acknowledgement and / or response to gNB1 CU. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0390] It should be noted that in the above step 402B.b, the gNB1 CU can ask whether the gNB1 DU can perform reference signal transmission according to the configuration in step 402B.b. While in step 408B.ba, after 407B.b ends, and after the gNB1 CU confirms that the neighboring cell (e.g., a cell of gNB2) can perform reference signal transmission according to the corresponding configuration, the gNB1 CU may transmit acknowledgement / confirmation information to the gNB1 DU, to inform the gNB1 DU that reference signal transmission can be performed according to the configuration in step 408B.ba.

[0391] Step 409B.1: gNB1 DU transmits a reference signal to UE.

[0392] Step 409B.2: gNB2 DU transmits a reference signal to UE.

[0393] Step 410B: UE performs data collection based on reference signals received from gNB1 DU and / or gNB2 DU.

[0394] Data reporting may be achieved through Option 4B-C or Option 4B-D or Option 4C-D or Option 4C-E.

[0395] Option 4B-C:

[0396] Step 411B.c: gNB1 CU transmits a data reporting request to UE. The data reporting request may be the aforementioned fifth message.

[0397] Step 412B.c: UE transmits a data reporting acknowledgement and / or response to gNB1 CU. The data reporting acknowledgement and / or response may be the aforementioned sixth message.

[0398] Step 413B.c: UE transmits reported data to gNB1 CU. The reported data may be the aforementioned seventh message.

[0399] Option 4B-D:

[0400] Step 411B.d: gNB1 DU transmits a data reporting request to UE. The data reporting request may be the aforementioned fifth message.

[0401] Step 412B.d: UE transmits a data reporting acknowledgement and / or response to gNB1 DU. The data reporting acknowledgement and / or response may be the aforementioned sixth message.

[0402] Step 413B.d: UE transmits reported data to gNB1 DU. The reported data may be the aforementioned seventh message.

[0403] Options 4C-D are described later.

[0404] Options 4C-E are described later.

[0405] In the example shown in FIG. 4b, the data collection requested by the UE is completed by the radio access network node and the UE. This example can reduce signalling exchange between the radio access network node and the core network.

[0406] FIGS. 4c-4d show schematic diagrams of an aspect of a method of supporting data collection according to embodiments of the present disclosure (FIG. 4d continues FIG. 4c). Specifically, FIGs. 4c-4d show related processes of exchanging a reference signal transmission request and / or a data collection request and / or data reporting between nodes. The collected relevant data may be used by the nodes for training and / or inference of artificial intelligence and / or machine learning models. Alternatively, the collected relevant data may be used to evaluate artificial intelligence and / or machine learning models.

[0407] The exchange of the data collection request and / or the reference signal transmission request may be implemented through option 4C-A or option 4C-B or option 4C-C or option 4C-F.

[0408] Option 4C-A:

[0409] Step 401C.a: UE transmits a data collection request to gNB1 DU. The data collection request may be the aforementioned first message. The data collection request may be transmitted via MAC CE and / or physical layer signalling and / or physical layer signals.

[0410] Step 402C.a: gNB1 DU transmits a data collection request to gNB1 CU. The data collection request may be the aforementioned first message.

[0411] Step 403C.a: gNB1 CU transmits a data collection request to AMF and / or LMF. It may also be that gNB1 CU transmits a data collection request to LMF through AMF. The data collection request may be the aforementioned first message.

[0412] Step 404C.a: LMF or AMF transmits a reference signal transmission request to gNB1 CU and / or gNB2 CU. The reference signal transmission request may be the aforementioned third message.

[0413] Step 405C.a: gNB1 CU transmits a reference signal transmission request to gNB1 DU. And / or gNB2 CU transmits a reference signal transmission request to gNB2 DU. The reference signal transmission request may be the aforementioned third message.

[0414] Step 406C.a: gNB1 DU transmits a reference signal transmission acknowledgement and / or response to gNB1 CU. And / or gNB2 DU transmits a reference signal transmission acknowledgement and / or response to gNB2 CU. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0415] Step 407C.a: gNB1 CU transmits reference signal transmission acknowledgement and / or response to AMF and / or LMF. And / or gNB2 CU transmits a reference signal transmission acknowledgement and / or response to AMF and / or LMF. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0416] Step 408C.a: gNB1 DU transmits a data collection acknowledgement and / or response to the gNB1 DU. The data collection acknowledgement and / or response may be the aforementioned second message.

[0417] Step 409C.a: gNB1 DU transmits data collection acknowledgement and / or response to UE. The data collection acknowledgement and / or response may be the aforementioned second message. The data collection acknowledgement and / or response may be transmitted via MAC CE and / or physical layer signalling and / or physical layer signals.

[0418] Option 4C-B:

[0419] Step 401C.b: UE transmits a data collection request to gNB1 CU. The data collection request may be the aforementioned first message.

[0420] Step 402C.b: gNB1 CU transmits a data collection request to AMF and / or LMF. It may also be that gNB1 CU transmits a data collection request to LMF through AMF. The data collection request may be the aforementioned first message.

[0421] Step 403C.b: LMF or AMF transmits a reference signal transmission request to gNB1 CU and / or gNB2 CU. The reference signal transmission request may be the aforementioned third message.

[0422] Step 404C.b: gNB1 CU transmits a reference signal transmission request to gNB1 DU. And / or gNB2 CU transmits a reference signal transmission request to gNB2 DU. The reference signal transmission request may be the aforementioned third message.

[0423] Step 405C.b: gNB1 DU transmits a reference signal transmission acknowledgement and / or response to gNB1 CU. And / or gNB2 DU transmits a reference signal transmission acknowledgement and / or response to gNB2 CU. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0424] Step 406C.b: gNB1 CU transmits reference signal transmission acknowledgement and / or response to AMF and / or LMF. And / or gNB2 CU transmits a reference signal transmission acknowledgement and / or response to AMF and / or LMF. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0425] Step 407C.b: gNB1 CU transmits data collection acknowledgement and / or response to UE. The data collection acknowledgement and / or response may be the aforementioned second message.

[0426] Option 4C-C:

[0427] Step 401C.c: UE transmits a data collection request to AMF and / or LMF. The data collection request may be the aforementioned first message.

[0428] Step 402C.c: LMF or AMF transmits a reference signal transmission request to gNB1 CU and / or gNB2 CU. The reference signal transmission request may be the aforementioned third message.

[0429] Step 403C.c: gNB1 CU transmits a reference signal transmission request to gNB1 DU. And / or gNB2 CU transmits a reference signal transmission request to gNB2 DU. The reference signal transmission request may be the aforementioned third message.

[0430] Step 404C.c: gNB1 DU transmits a reference signal transmission acknowledgement and / or response to gNB1 CU. And / or gNB2 DU transmits a reference signal transmission acknowledgement and / or response to gNB2 CU. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0431] Step 405C.c: gNB1 CU transmits a reference signal transmission acknowledgement and / or response to AMF and / or LMF. And / or gNB2 CU transmits a reference signal transmission acknowledgement and / or response to AMF and / or LMF. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0432] Step 406C.c: AMF and / or LMF transmits data collection acknowledgement and / or response to UE. The data collection acknowledgement and / or response may be the aforementioned second message.

[0433] Option 4C-F:

[0434] Step 401C.f: LMF or AMF transmits a reference signal transmission request to gNB1 CU and / or gNB2 CU. The reference signal transmission request may be the aforementioned third message.

[0435] Step 402C.f: gNB1 CU transmits a reference signal transmission request to gNB1 DU. And / or gNB2 CU transmits a reference signal transmission request to gNB2 DU. The reference signal transmission request may be the aforementioned third message.

[0436] Step 403C.f: gNB1 DU transmits a reference signal transmission acknowledgement and / or response to gNB1 CU. And / or gNB2 DU transmits a reference signal transmission acknowledgement and / or response to gNB2 CU. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0437] Step 404C.f: gNB1 CU transmits a reference signal transmission acknowledgement and / or response to AMF and / or LMF. And / or gNB2 CU transmits a reference signal transmission acknowledgement and / or response to AMF and / or LMF. The reference signal transmission acknowledgement and / or response may be the aforementioned fourth message.

[0438] Step 410C.1: gNB1 DU transmits a reference signal to UE.

[0439] Step 401C.2: gNB2 DU transmits a reference signal to UE.

[0440] Step 411C: UE performs data collection based on reference signals received from gNB1 DU and / or gNB2 DU.

[0441] Data reporting may be achieved through Option 4B-C or Option 4B-D or Option 4C-D or Option 4C-E.

[0442] Options 4B-C are the same as described above.

[0443] Options 4B-D are the same as described above.

[0444] Option 4C-D:

[0445] Step 412C.d: AMF and / or LMF transmits a data reporting request to gNB CU. The data reporting request may be the aforementioned fifth message.

[0446] Step 413C.d: gNB1 CU transmits a data reporting request to UE. The data reporting request may be the aforementioned fifth message.

[0447] Step 414C.d: UE transmits a data reporting acknowledgement and / or response to gNB1 CU. The data reporting acknowledgement and / or response may be the aforementioned sixth message.

[0448] Step 415C.d: gNB1 CU transmits a data reporting acknowledgement and / or response to AMF and / or LMF. The data reporting acknowledgement and / or response may be the aforementioned sixth message.

[0449] Step 416C.d: UE transmits reported data to gNB1 CU. The reported data may be the aforementioned seventh message.

[0450] Step 417C.d: gNB1 CU transmits reported data to AMF and / or LMF. The reported data may be the aforementioned seventh message.

[0451] Option 4C-E:

[0452] Step 412C.e: AMF and / or LMF transmits a data reporting request to gNB CU. The data reporting request may be the aforementioned fifth message.

[0453] Step 413C.e: gNB1 CU transmits data reporting request to gNB1 DU. The data reporting request may be the aforementioned fifth message.

[0454] Step 414C.e: gNB1 DU transmits a data reporting request to the UE. The data reporting request may be the aforementioned fifth message.

[0455] Step 415C.e: UE transmits a data reporting acknowledgement and / or response to gNB1 DU. The data reporting acknowledgement and / or response may be the aforementioned sixth message.

[0456] Step 416C.e: gNB1 DU transmits a data reporting acknowledgement and / or response to gNB1 CU. The data reporting acknowledgement and / or response may be the aforementioned sixth message.

[0457] Step 417C.e: gNB1 CU transmits a data reporting acknowledgement and / or response to AMF and / or LMF. The data reporting acknowledgement and / or response may be the aforementioned sixth message.

[0458] Step 418C.e: UE transmits reported data to gNB1 DU. The reported data may be the aforementioned seventh message.

[0459] Step 419C.e: gNB1 DU transmits reported data to gNB1 CU. The reported data may be the aforementioned seventh message.

[0460] Step 420C.e: gNB1 CU transmits reported data to AMF and / or LMF. The reported data may be the aforementioned seventh message.

[0461] In the examples shown in FIGs. 4C and 4D, the data collection requested by the UE is overall planned by the core network node. The examples may enhance the management functions of the core network.

[0462] FIG. 5 shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 5 shows a relevant process of reporting data between nodes. The fifth node collects relevant data. The collected data may be forwarded to other nodes. The collected relevant data may be used by the fifth node and / or other nodes for training and / or inference of artificial intelligence and / or machine learning models. Alternatively, the collected relevant data may be used by the fifth node and / or other nodes to evaluate artificial intelligence and / or machine learning models.

[0463] For example, in some implementations, the fifth node may be a UE and / or a PRU, and the sixth node may be a gNB and / or AMF and / or LMF, for example. In other implementations, for example, the fifth node may be a gNB and / or gNB CU, and the sixth node may be an AMF and / or LMF. In yet other implementations, for example, the fifth node may be a gNB DU and the sixth node may be a gNB CU. In yet other implementations, for example, the fifth node may be an AMF and the sixth node may be an LMF. In yet other implementations, for example, the fifth node may be a gNB and / or AMF and / or LMF, and the sixth node may be a UE and / or a PRU. In yet other implementations, for example, the fifth node may be an AMF and / or an LMF, and the sixth node may be a gNB and / or a gNB CU. In yet other implementations, for example, the fifth node may be a gNB CU and the sixth node may be a gNB DU. In yet other implementations, for example, the fifth node may be an LMF and the sixth node may be an AMF.

[0464] Step 501A: optionally, the fifth node transmits a data reporting request to the sixth node. The data reporting request may be the aforementioned fifth message and / or first message.

[0465] Step 502A: optionally, the sixth node transmits a data reporting acknowledgement and / or response to the fifth node. The data reporting acknowledgement and / or response may be the aforementioned sixth message and / or second message. In some implementations, for example, this step may be omitted. In some other implementations, for example, this step may be combined with the subsequent seventh message.

[0466] In some implementations, for example, if the sixth node needs to perform data collection, the sixth node can trigger a relevant process to perform data collection. For example, a positioning related process can be performed to perform positioning related data collection.

[0467] In some implementations, for example, after receiving the first message, the sixth node may transmit reference signals related to data requested to be collected (e.g., reference signals used to be measured to generate the data requested to be collected) to the fifth node and / or other nodes according to the first message (e.g., according to a first reference signal configuration determined based on the first message).

[0468] Alternatively, the sixth node may perform exchanging with other nodes according to the first message, for example, exchanging the configuration of the reference signals (which, for example, may be determined based on the first message) etc., so that the sixth node and other nodes may cooperatively transmit reference signals related to the data requested to be collected to the fifth node.

[0469] Alternatively, the sixth node may perform exchanging with other nodes according to the first message, for example, exchanging the configuration of the reference signals (which, for example, may be determined based on the first message) etc., so that the fifth node and other nodes may cooperatively transmit reference signals related to the data requested to be collected to the UE. The process of transmitting the reference signal configuration may refer to FIG. 4a. Herein, the sixth node in FIG. 5 is the third node in FIG. 4a, and the fifth node in FIG. 5 is the fourth node in FIG. 4a.

[0470] Alternatively, the sixth node may perform exchanging with other nodes according to the first message, for example, exchanging the configuration of the reference signals (which, for example, may be determined based on the first message) etc., so that the fifth node may transmit reference signals related to the data requested to be collected to the sixth node and other nodes.

[0471] Alternatively, the sixth node may perform exchanging with other nodes according to the first message, for example, exchanging the configuration of the reference signals (which, for example, may be determined based on the first message) etc., so that the UE may transmit reference signals related to the data requested to be collected to the fifth node and other nodes. The process of transmitting the reference signal configuration may refer to FIG. 4a. Herein, the sixth node in FIG. 5 is the third node in FIG. 4a, and the fifth node in FIG. 5 is the fourth node in FIG. 4a. Herein, the exchanging with the UE may refer to FIG. 4a, the fifth node in FIG. 4a may be the third node in FIG. 4a, and the fourth node in FIG. 4a may be the UE.

[0472] Step 503A: the sixth node transmits the reported data to the fifth node. The reported data may be the aforementioned seventh message.

[0473] The fifth node collects relevant data. The collected data may be forwarded to other nodes. The collected relevant data may be used by the fifth node and / or other nodes for training and / or inference of artificial intelligence and / or machine learning models. Alternatively, the collected relevant data may be used by the fifth node and / or other nodes to evaluate artificial intelligence and / or machine learning models. The artificial intelligence and / or machine learning model may be a positioning-related model. This example can support positioning technologies that utilize artificial intelligence and / or machine learning models, which, for example, may improve positioning accuracy.

[0474] FIG. 6 shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 6 shows a relevant process of exchanging information about whether model training and / or inference can be performed between nodes. In some implementations, for example, after obtaining the message, the eighth node may transmit information related to model generation and / or training to the seventh node to inform the seventh node how to generate and / or train a model. For example, when the seventh node and / or other nodes can perform model training, the eighth node may transmit model generation and / or training related information to the seventh node to inform the seventh node how to generate and / or train a model. In other implementations, for example, after obtaining the message, the eighth node may transmit a model to the seventh node. For example, the eighth node may transmit a model to the seventh node when the seventh node and / or other nodes can perform model inference. For example, the model may be further inferred by the seventh node and / or other nodes.

[0475] For example, in some implementations, for example, the seventh node may be a UE and / or a PRU, and the eighth node may be a gNB and / or AMF and / or LMF. In other implementations, for example, the seventh node may be a gNB and / or gNB CU, and the eighth node may be an AMF and / or LMF. In yet other implementations, for example, the seventh node may be a gNB DU and the eighth node may be a gNB CU. In yet other implementations, for example, the seventh node may be an AMF and the eighth node may be an LMF. In yet other implementations, for example, the seventh node may be a gNB and / or AMF and / or LMF, and the eighth node may be a UE and / or a PRU. In yet other implementations, for example, the seventh node may be an AMF and / or an LMF, and the eighth node may be a gNB and / or a gNB CU. In yet other implementations, for example, the seventh node may be a gNB CU and the eighth node may be a gNB DU. In yet other implementations, for example, the seventh node may be an LMF and the eighth node may be an AMF.

[0476] Step 601A: the seventh node transmits information about whether model training and / or inference can be performed to the eighth node. The information about whether model training and / or inference can be performed may be the aforementioned eighth message.

[0477] In some implementations, for example, after obtaining the message, the eighth node may transmit information related to model generation and / or training to the seventh node to inform the seventh node how to generate and / or train a model. For example, when the seventh node and / or other nodes can perform model training, the eighth node may transmit model generation and / or training related information to the seventh node to inform the seventh node how to generate and / or train a model. In other implementations, for example, after obtaining the message, the eighth node may transmit a model to the seventh node. For example, the eighth node may transmit a model to the seventh node when the seventh node and / or other nodes can perform model inference. For example, the model may be further inferred by the seventh node and / or other nodes.

[0478] FIG. 7a shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 7a shows a relevant process of exchanging of model generation and / or training information between nodes. After receiving the message, the tenth node may perform model generation and / or training according to the content of the ninth message.

[0479] For example, in some implementations, the ninth node may be a UE and / or a PRU, and the tenth node may be a gNB and / or AMF and / or LMF, for example. In other implementations, for example, the ninth node may be a gNB and / or gNB CU, and the tenth node may be an AMF and / or LMF. In yet other implementations, for example, the ninth node may be a gNB DU and the tenth node may be a gNB CU. In yet other implementations, for example, the ninth node may be an AMF and the tenth node may be an LMF. In yet other implementations, for example, the ninth node may be a gNB and / or AMF and / or LMF, and the tenth node may be a UE and / or a PRU. In yet other implementations, for example, the ninth node may be an AMF and / or an LMF, and the tenth node may be a gNB and / or a gNB CU. In yet other implementations, for example, the ninth node may be a gNB CU and the tenth node may be a gNB DU. In yet other implementations, for example, the ninth node may be an LMF and the tenth node may be an AMF.

[0480] Step 701A: the ninth node transmits model generation and / or training information to the tenth node. The model generation and / or training information may be the aforementioned ninth message.

[0481] Step 702A: optionally, the tenth node transmits a model generation and / or training information acknowledgement and / or response to the ninth node. The model generation and / or training information acknowledgement and / or response may be the aforementioned tenth message.

[0482] Step 703A: the tenth node performs model generation and / or training. When the model generation and / or training is completed, the tenth node transmits a completion of model generation and / or training to the ninth node. The completion of model generation and / or training may be the aforementioned eleventh message.

[0483] FIG. 7b shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 7b shows a relevant process of exchanging of model generation and / or training information between nodes. After receiving the message, the tenth node may perform model generation and / or training according to the content of the ninth message.

[0484] For example, in some implementations, the ninth node may be a UE and / or a PRU, and the tenth node may be a gNB and / or AMF and / or LMF, for example. In other implementations, for example, the ninth node may be a gNB and / or gNB CU, and the tenth node may be an AMF and / or LMF. In yet other implementations, for example, the ninth node may be a gNB DU and the tenth node may be a gNB CU. In yet other implementations, for example, the ninth node may be an AMF and the tenth node may be an LMF. In yet other implementations, for example, the ninth node may be a gNB and / or AMF and / or LMF, and the tenth node may be a UE and / or a PRU. In yet other implementations, for example, the ninth node may be an AMF and / or an LMF, and the tenth node may be a gNB and / or a gNB CU. In yet other implementations, for example, the ninth node may be a gNB CU and the tenth node may be a gNB DU. In yet other implementations, for example, the ninth node may be an LMF and the tenth node may be an AMF.

[0485] Step 701B: the tenth node transmits information about whether model training and / or inference can be performed to the ninth node. The information about whether model training and / or inference can be performed may be the aforementioned eighth message.

[0486] Step 702B: the ninth node transmits model generation and / or training information to the tenth node. The model generation and / or training information may be the aforementioned ninth message.

[0487] Step 703B: optionally, the tenth node transmits a model generation and / or training information acknowledgement and / or response to the ninth node. The model generation and / or training information acknowledgement and / or response may be the aforementioned tenth message.

[0488] Step 704B: optionally, the tenth node performs model generation and / or training. When the model generation and / or training is completed, the tenth node transmits a completion of model generation and / or training to the ninth node. The completion of model generation and / or training may be the aforementioned eleventh message.

[0489] FIG. 8 shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 8 shows a relevant process of exchanging model information between nodes. After receiving this message, the twelfth node may use this information for model management.

[0490] For example, in some implementations, the eleventh node may be a UE and / or a PRU, and the twelfth node may be a gNB and / or AMF and / or LMF, for example. In other implementations, for example, the eleventh node may be a gNB and / or gNB CU, and the twelfth node may be an AMF and / or LMF. In yet other implementations, for example, the eleventh node may be a gNB DU and the twelfth node may be a gNB CU. In yet other implementations, for example, the eleventh node may be an AMF and the twelfth node may be an LMF. In yet other implementations, for example, the eleventh node may be a gNB and / or AMF and / or LMF, and the twelfth node may be a UE and / or a PRU. In yet other implementations, for example, the eleventh node may be an AMF and / or an LMF, and the twelfth node may be a gNB and / or a gNB CU. In yet other implementations, for example, the eleventh node may be a gNB CU and the twelfth node may be a gNB DU. In yet other implementations, for example, the eleventh node may be an LMF and the twelfth node may be an AMF.

[0491] Step 801A: the eleventh node transmits model information to the twelfth node. The model information may be the aforementioned twelfth message.

[0492] After receiving this message, the twelfth node may use this information for model management.

[0493] FIG. 9a shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 9a shows a relevant process of exchanging model update and / or transfer information between nodes. This information may be used for model update of the fourteenth node, or may be used to transmit a model to the fourteenth node and / or other nodes for the fourteenth node and / or the other nodes to install the model.

[0494] For example, in some implementations, for example, the thirteenth node may be a UE and / or a PRU, and the fourteenth node may be a gNB and / or AMF and / or LMF. In other implementations, for example, the thirteenth node may be a gNB and / or gNB CU, and the fourteenth node may be an AMF and / or LMF. In yet other implementations, for example, the thirteenth node may be a gNB DU and the fourteenth node may be a gNB CU. In yet other implementations, for example, the thirteenth node may be an AMF and the fourteenth node may be an LMF. In yet other implementations, for example, the thirteenth node may be a gNB and / or AMF and / or LMF, and the fourteenth node may be a UE and / or a PRU. In yet other implementations, for example, the thirteenth node may be an AMF and / or an LMF, and the fourteenth node may be a gNB and / or a gNB CU. In yet other implementations, for example, the thirteenth node may be a gNB CU and the fourteenth node may be a gNB DU. In yet other implementations, for example, the thirteenth node may be an LMF and the fourteenth node may be an AMF.

[0495] Step 901A: the thirteenth node transmits model update and / or transfer information to the fourteenth node. The model update and / or transfer information may be the aforementioned thirteenth message.

[0496] Step 902A: optionally, the fourteenth node transmits an acknowledgement and / or response for model update and / or transfer to the thirteenth node. The acknowledgement and / or response for model update and / or transfer may be the aforementioned fourteenth message.

[0497] This information may be used for model update of the fourteenth node, or may be used to transmit a model to the fourteenth node and / or other nodes for the fourteenth node and / or the other nodes to install the model.

[0498] FIG. 9b shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 9b shows a relevant process of exchanging model update and / or transfer information between nodes. This information may be used for model update of the fourteenth node, or may be used to transmit a model to the fourteenth node and / or other nodes for the fourteenth node and / or the other nodes to install the model.

[0499] For example, in some implementations, for example, the thirteenth node may be a UE and / or a PRU, and the fourteenth node may be a gNB and / or AMF and / or LMF. In other implementations, for example, the thirteenth node may be a gNB and / or gNB CU, and the fourteenth node may be an AMF and / or LMF. In yet other implementations, for example, the thirteenth node may be a gNB DU and the fourteenth node may be a gNB CU. In yet other implementations, for example, the thirteenth node may be an AMF and the fourteenth node may be an LMF. In yet other implementations, for example, the thirteenth node may be a gNB and / or AMF and / or LMF, and the fourteenth node may be a UE and / or a PRU. In yet other implementations, for example, the thirteenth node may be an AMF and / or an LMF, and the fourteenth node may be a gNB and / or a gNB CU. In yet other implementations, for example, the thirteenth node may be a gNB CU and the fourteenth node may be a gNB DU. In yet other implementations, for example, the thirteenth node may be an LMF and the fourteenth node may be an AMF.

[0500] Step 901B: the fourteenth node transmits information about whether model training and / or inference can be performed to the thirteenth node. The information about whether model training and / or inference can be performed may be the aforementioned eighth message.

[0501] Step 902B: the thirteenth node transmits model update and / or transfer information to the fourteenth node. The model update and / or transfer information may be the aforementioned thirteenth message.

[0502] Step 903B: optionally, the fourteenth node transmits an acknowledgement and / or response for model update and / or transfer to the thirteenth node. The acknowledgement and / or response for model update and / or transfer may be the aforementioned fourteenth message.

[0503] This information may be used for model update of the fourteenth node, or may be used to transmit a model to the fourteenth node and / or other nodes for the fourteenth node and / or the other nodes to install the model.

[0504] FIG. 10a shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 10a shows a relevant process of exchanging model management information between nodes. This information may be used to manage models contained in the sixteenth node.

[0505] For example, in some implementations, for example, the fifteenth node may be a UE and / or a PRU and the sixteenth node may be a gNB and / or AMF and / or LMF. In other implementations, for example, the fifteenth node may be a gNB and / or gNB CU, and the sixteenth node may be an AMF and / or LMF. In yet other implementations, for example, the fifteenth node may be a gNB DU and the sixteenth node may be a gNB CU. In yet other implementations, for example, the fifteenth node may be an AMF and the sixteenth node may be an LMF. In yet other implementations, for example, the fifteenth node may be a gNB and / or AMF and / or LMF, and the sixteenth node may be a UE and / or a PRU. In yet other implementations, for example, the fifteenth node may be an AMF and / or an LMF, and the sixteenth node may be a gNB and / or a gNB CU. In yet other implementations, for example, the fifteenth node may be a gNB CU and the sixteenth node may be a gNB DU. In yet other implementations, for example, the fifteenth node may be an LMF and the sixteenth node may be an AMF.

[0506] Step 1001A: the fifteenth node transmits information related to model management to the sixteenth node. The information related to model management may be the aforementioned fifteenth message.

[0507] Step 1002A: optionally, the sixteenth node transmits a model management acknowledgement and / or response to the fifteenth node. The model management acknowledgement and / or response may be the aforementioned sixteenth message.

[0508] FIG. 10b shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 10b shows a relevant process of exchanging model management information between nodes. This information may be used to manage models contained in the sixteenth node.

[0509] For example, in some implementations, for example, the fifteenth node may be a UE and / or a PRU and the sixteenth node may be a gNB and / or AMF and / or LMF. In other implementations, for example, the fifteenth node may be a gNB and / or gNB CU, and the sixteenth node may be an AMF and / or LMF. In yet other implementations, for example, the fifteenth node may be a gNB DU and the sixteenth node may be a gNB CU. In yet other implementations, for example, the fifteenth node may be an AMF and the sixteenth node may be an LMF. In yet other implementations, for example, the fifteenth node may be a gNB and / or AMF and / or LMF, and the sixteenth node may be a UE and / or a PRU. In yet other implementations, for example, the fifteenth node may be an AMF and / or an LMF, and the sixteenth node may be a gNB and / or a gNB CU. In yet other implementations, for example, the fifteenth node may be a gNB CU and the sixteenth node may be a gNB DU. In yet other implementations, for example, the fifteenth node may be an LMF and the sixteenth node may be an AMF.

[0510] Step 1001B: the sixteenth node transmits model information to the fifteenth node. The model information may be the aforementioned twelfth message.

[0511] Step 1002B: the fifteenth node transmits information related to model management to the sixteenth node. The information related to model management may be the aforementioned fifteenth message.

[0512] Step 1003B: optionally, the sixteenth node transmits a model management acknowledgement and / or response to the fifteenth node. The model management acknowledgement and / or response may be the aforementioned sixteenth message.

[0513] FIG. 11a shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 11a shows a relevant process of exchanging performance information between nodes. This performance may be used for training and / or generation and / or inference and / or fine-tuning of the model. This performance may also be used to evaluate an artificial intelligence machine learning model.

[0514] For example, in some implementations, for example, the seventeenth node may be a UE and / or a PRU, and the eighteenth node may be a gNB and / or AMF and / or LMF. In other implementations, for example, the seventeenth node may be a gNB and / or gNB CU, and the eighteenth node may be an AMF and / or LMF. In yet other implementations, for example, the seventeenth node may be a gNB DU and the eighteenth node may be a gNB CU. In yet other implementations, for example, the seventeenth node may be an AMF and the eighteenth node may be an LMF. In yet other implementations, for example, the seventeenth node may be a gNB and / or AMF and / or LMF, and the eighteenth node may be a UE and / or a PRU. In yet other implementations, for example, the seventeenth node may be an AMF and / or an LMF, and the eighteenth node may be a gNB and / or a gNB CU. In yet other implementations, for example, the seventeenth node may be a gNB CU and the eighteenth node may be a gNB DU. In yet other implementations, for example, the seventeenth node may be an LMF and the eighteenth node may be an AMF.

[0515] Step 1101A: optionally, the seventeenth node transmits a performance reporting request to the eighteenth node. The performance reporting request may be the aforementioned seventeenth message.

[0516] Step 1102A: optionally, the eighteenth node transmits a performance reporting acknowledgement and / or response to the seventeenth node. The performance reporting acknowledgement and / or response may be the aforementioned eighteenth message.

[0517] Step 1103A: the eighteenth node transmits performance information to the seventeenth node. The performance information may be the aforementioned nineteenth message.

[0518] FIG. 11b shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 11b shows a relevant process of exchanging performance information between nodes. This performance may be used for training and / or generation and / or inference and / or fine-tuning of the model. This performance may also be used to evaluate an artificial intelligence machine learning model.

[0519] Step 1101B: AMF and / or LMF transmits performance information to gNB and / or gNB CU. The performance information may be the aforementioned nineteenth message. In some implementations, it may also be that the UE transmits performance information to the gNB and / or gNB CU.

[0520] Step 1102B: gNB and / or gNB CU may formulate model management based on the received performance information.

[0521] Step 1103B: gNB and / or gNB CU transmits information related to model management to UE. The information related to model management may be the aforementioned fifteenth message.

[0522] Step 1104B: optionally, the UE transmits a model management acknowledgement and / or response to the gNB and / or gNB CU. The model management acknowledgement and / or response may be the aforementioned sixteenth message.

[0523] FIG. 11c shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 11c shows a relevant process of exchanging performance information between nodes. This performance may be used for training and / or generation and / or inference and / or fine-tuning of the model. This performance may also be used to evaluate an artificial intelligence machine learning model.

[0524] Step 1101C: AMF and / or LMF transmits performance information to gNB and / or gNB CU. The performance information may be the aforementioned nineteenth message.

[0525] Step 1102C: gNB CU transmits performance information to gNB DU. The performance information may be the aforementioned nineteenth message.

[0526] Step 1103C: gNB DU may formulate model management based on the received performance information.

[0527] Step 1104C: gNB DU transmits information related to model management to UE. The information related to model management may be the aforementioned fifteenth message.

[0528] Step 1105C: optionally, the UE transmits a model management acknowledgement and / or response to the gNB DU. The model management acknowledgement and / or response may be the aforementioned sixteenth message.

[0529] FIG. 11d shows a schematic diagram of an aspect of a method of supporting data collection according to embodiments of the present disclosure. Specifically, FIG. 11d shows a relevant process of exchanging performance information between nodes. This performance may be used for training and / or generation and / or inference and / or fine-tuning of the model. This performance may also be used to evaluate an artificial intelligence machine learning model.

[0530] For example, in some implementations, for example, the seventeenth node may be a UE and / or a PRU, and the eighteenth node may be a gNB and / or AMF and / or LMF. In other implementations, for example, the seventeenth node may be a gNB and / or gNB CU, and the eighteenth node may be an AMF and / or LMF. In yet other implementations, for example, the seventeenth node may be a gNB DU and the eighteenth node may be a gNB CU. In yet other implementations, for example, the seventeenth node may be an AMF and the eighteenth node may be an LMF. In yet other implementations, for example, the seventeenth node may be a gNB and / or AMF and / or LMF, and the eighteenth node may be a UE and / or a PRU. In yet other implementations, for example, the seventeenth node may be an AMF and / or an LMF, and the eighteenth node may be a gNB and / or a gNB CU. In yet other implementations, for example, the seventeenth node may be a gNB CU and the eighteenth node may be a gNB DU. In yet other implementations, for example, the seventeenth node may be an LMF and the eighteenth node may be an AMF.

[0531] Step 1101D: the eighteenth node transmits performance information to the seventeenth node. The performance information may be the aforementioned nineteenth message.

[0532] In the present disclosure, the embodiments are only given as examples, and the above embodiments can be combined, etc.

[0533] In the above examples, for example, under a non gNB CU - gNB DU split architecture, the gNB CU and gNB DU can be combined into one node, for example, a gNB. In this case, the exchange between gNB CU and gNB DU can be regarded as a node internal behavior.

[0534] It should be understood that depending on the application scenario, the various examples, aspects, methods, steps, processes, etc. described herein can be implemented separately or in combination in any manner, and are not limited herein. In the present disclosure, to make the description more simply, generally, a description made in a certain step in one example or embodiment is not repeated again in a corresponding step in another example or embodiment. However, it should be understood that a description made in a certain step in one example or embodiment may apply to the corresponding step of any other example.

[0535] Next, FIG. 12 shows a flowchart of a method 1200 performed by a first node in a wireless communication system according to embodiments of the present disclosure.

[0536] As shown in FIG. 12, a method 1200 performed by a first node in a wireless communication system according to embodiments of the present disclosure may include: in step S1201, receiving a first message from a user equipment (UE), wherein the first message is associated with a request for collection of first data, wherein the first message includes at least one of: configuration information of a reference signal for the collection of the first data suggested by the UE, configuration information of a reference signal for the collection of the first data preferred by the UE, a data volume requested for the collection of the first data, a time requested for the collection of the first data, and information related to a node requested to perform model training based on the first data; in step S1202, transmitting, to the UE, a first reference signal for the collection of the first data based on the first message; and in step S1203, transmitting a third message to a second node, wherein the third message includes second configuration information of a reference signal for the collection of the first data, wherein the second node transmits a second reference signal to the UE based on the second configuration information.

[0537] According to embodiments of the present disclosure, the method further includes: transmitting a fifth message to the UE, wherein the fifth message is used for requesting the UE to report the first data; and receiving a seventh message from the UE, wherein the seventh message includes the first data.

[0538] According to embodiments of the present disclosure, the method further includes: receiving an eighth message from the UE, wherein the eighth message includes at least one of: information about whether the UE is capable of model generation, information about whether the UE is capable of model training, information about whether the UE is capable of model inference, information about whether the UE is capable of model fine-tuning, a computing power of the UE.

[0539] According to embodiments of the present disclosure, the method further includes: transmitting a ninth message to the UE, wherein the ninth message includes at least one of: a reference model for model training and / or model generation, an accuracy threshold for determining whether a model can be used.

[0540] According to embodiments of the present disclosure, the method further includes: transmitting a fifteenth message to the UE, wherein the fifteenth message includes at least one of: information related to activation and / or deactivation of a model included in the UE, conditions for activation and / or deactivation and / or fallback and / or transition of a model included in the UE, indications for model training and / or model fine-tuning, valid time of a model included in the UE, valid area of a model included in the UE.

[0541] According to embodiments of the present disclosure, the method further includes: receiving a nineteenth message from the UE and / or a third node, wherein the nineteenth message includes at least one of: performance information related to at least one of model training, model generation, model inference, model fine-tuning, model evaluation, information about that an accuracy of a model is below a threshold, information about that a model needs to be re-trained, information about that a model needs to be fine-tuned.

[0542] According to embodiments of the present disclosure, the first data includes measurement results of the first reference signal and / or the second reference signal.

[0543] FIG. 13 shows a flowchart of a method 1300 performed by a user equipment (UE) in a wireless communication system according to embodiments of the present disclosure.

[0544] As shown in FIG. 13, a method 1300 performed by a user equipment (UE) in a wireless communication system according to embodiments of the present disclosure may include: in step S1301, transmitting a first message to a first node, wherein the first message is associated with a request for collection of first data, wherein the first message includes at least one of: configuration information of a reference signal for the collection of the first data suggested by the UE, configuration information of a reference signal for the collection of the first data preferred by the UE, a data volume requested for the collection of the first data, a time requested for the collection of the first data, and information related to a node requested to perform model training based on the first data; in step S1302, receiving, from the first node, a first reference signal for the collection of the first data based on the first message; and in a step S1303, receiving, from a second node, a second reference signal for the collection of the first data based on second configuration information, wherein a third message is transmitted by the first node to the second node, wherein the third message includes the second configuration information.

[0545] According to embodiments of the present disclosure, the method further includes: receiving a fifth message from the first node, wherein the fifth message is used for requesting the UE to report the first data; and transmitting a seventh message to the first node, wherein the seventh message includes the first data.

[0546] According to embodiments of the present disclosure, the method further includes: transmitting an eighth message to the first node, wherein the eighth message includes at least one of: information about whether the UE is capable of model generation, information about whether the UE is capable of model training, information about whether the UE is capable of model inference, information about whether the UE is capable of model fine-tuning, a computing power of the UE.

[0547] According to embodiments of the present disclosure, the method further includes: receiving a ninth message from the first node, wherein the ninth message includes at least one of: a reference model for model training and / or model generation, an accuracy threshold for determining whether a model can be used.

[0548] According to embodiments of the present disclosure, the method further includes: receiving a fifteenth message from the first node, wherein the fifteenth message includes at least one of: information related to activation and / or deactivation of a model included in the UE, conditions for activation and / or deactivation and / or fallback and / or transition of a model included in the UE, indications for model training and / or model fine-tuning, valid time of a model included in the UE, valid area of a model included in the UE.

[0549] According to embodiments of the present disclosure, the method further includes: transmitting a nineteenth message to the first node, wherein the nineteenth message includes at least one of: performance information related to at least one of model training, model generation, model inference, model fine-tuning, model evaluation, information about that an accuracy of a model is below a threshold, information about that a model needs to be re-trained, information about that a model needs to be fine-tuned.

[0550] According to embodiments of the present disclosure, the first data includes measurement results of the first reference signal and / or the second reference signal.

[0551] FIG. 14 shows a flowchart of a method 1400 performed by a second node in a wireless communication system according to embodiments of the present disclosure.

[0552] As shown in FIG. 14, a method 1400 performed by a second node in a wireless communication system according to embodiments of the present disclosure may include: in step S1401, receiving a third message from a first node, wherein the third message includes second configuration information of a reference signal for collection of first data; and in step S1402, transmitting a second reference signal to a user equipment (UE) based on the second configuration information. In some implementations, a first message is transmitted from the UE to the first node, wherein the first message is associated with a request for collection of the first data, wherein the first message includes at least one of: configuration information of a reference signal for the collection of the first data suggested by the UE, configuration information of a reference signal for the collection of the first data preferred by the UE, a data volume requested for the collection of the first data, a time requested for the collection of the first data, and information related to a node requested to perform model training based on the first data. In some implementations, a first reference signal for the collection of the first data is transmitted by the first node to the UE based on the first message.

[0553] According to embodiments of the present disclosure, the first data includes measurement results of the first reference signal and / or the second reference signal.

[0554] It should be understood that methods 1200, 1300, 1400, etc. according to embodiments of the present disclosure may also include any steps described in conjunction with various examples, aspects, drawings, etc. of the present disclosure.

[0555] Next, FIG. 15 shows a schematic diagram of a node 1500 according to embodiments of the present disclosure.

[0556] As shown in FIG. 15, a node (or node device) 1500 according to embodiments of the present disclosure may include a transceiver 1510 and a processor 1520. The transceiver 1510 may be configured to transmit and receive signals. The processor 1520 may be coupled to transceiver 1510 and may be configured to (e.g., control transceiver 1510 to) perform methods performed by any node (e.g., first node, second node, etc.) according to embodiments of the present disclosure.

[0557] FIG. 16 shows a schematic diagram of a user equipment 1600 according to embodiments of the present disclosure.

[0558] As shown in FIG. 16, a user equipment 1600 according to embodiments of the present disclosure may include a transceiver 1610 and a processor 1620. The transceiver 1610 may be configured to transmit and receive signals. The processor 1620 may be coupled to transceiver 1610 and may be configured to (e.g., control transceiver 1610 to) perform methods performed by a user equipment according to embodiments of the present disclosure. In the present disclosure, a processor may also be referred to as a controller.

[0559] Embodiments of the present disclosure also provide a computer-readable medium having stored thereon computer-readable instructions which, when executed by a processor, implement any method according to embodiments of the present disclosure.

[0560] Various embodiments of the present disclosure may be implemented as computer-readable codes embodied on a computer-readable recording medium from a specific perspective. A computer-readable recording medium is any data storage device that can store data readable by a computer system. Examples of computer-readable recording media may include read-only memory (ROM), random access memory (RAM), compact disk read-only memory (CD-ROM), magnetic tape, floppy disk, optical data storage device, carrier wave (e.g., data transmission via the Internet), etc. Computer-readable recording media can be distributed by computer systems connected via a network, and thus computer-readable codes can be stored and executed in a distributed manner. Furthermore, functional programs, codes and code segments for implementing various embodiments of the present disclosure can be easily explained by those skilled in the art to which the embodiments of the present disclosure are applied.

[0561] It will be understood that the embodiments of the present disclosure may be implemented in the form of hardware, software, or a combination of hardware and software. The software may be stored as program instructions or computer-readable codes executable on a processor on a non-transitory computer-readable medium. Examples of non-transitory computer-readable recording media include magnetic storage media (such as ROM, floppy disk, hard disk, etc.) and optical recording media (such as CD-ROM, digital video disk (DVD), etc.). Non-transitory computer-readable recording media may also be distributed on computer systems coupled to a network, so that computer-readable codes are stored and executed in a distributed manner. The medium can be read by a computer, stored in a memory, and executed by a processor. Various embodiments may be implemented by a computer or a portable terminal including a controller and a memory, and the memory may be an example of a non-transitory computer-readable recording medium suitable for storing program (s) with instructions for implementing embodiments of the present disclosure. The present disclosure may be realized by a program with code for concretely implementing the apparatus and method described in the claims, which is stored in a machine (or computer)-readable storage medium. The program may be electronically carried on any medium, such as a communication signal transmitted via a wired or wireless connection, and the present disclosure suitably includes its equivalents.

[0562] What has been described above is only the specific implementation of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Anyone who is familiar with this technical field may make various changes or substitutions within the technical scope disclosed in the present disclosure, and these changes or substitutions should be covered within the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1.A method performed by a first node in a wireless communication system, including:receiving a first message from a user equipment (UE), wherein the first message is associated with a request for collection of first data, wherein the first message includes at least one of: configuration information of a reference signal for the collection of the first data suggested by the UE, configuration information of a reference signal for the collection of the first data preferred by the UE, a data volume requested for the collection of the first data, a time requested for the collection of the first data, and information related to a node requested to perform model training based on the first data;transmitting, to the UE, a first reference signal for the collection of the first data based on the first message; andtransmitting a third message to a second node, wherein the third message includes second configuration information of a reference signal for the collection of the first data, wherein the second node transmits a second reference signal to the UE based on the second configuration information.2.The method of claim 1, further including:transmitting a fifth message to the UE, wherein the fifth message is used for requesting the UE to report the first data; andreceiving a seventh message from the UE, wherein the seventh message includes the first data.3.The method of claim 1, further including:receiving an eighth message from the UE, wherein the eighth message includes at least one of:information about whether the UE is capable of model generation, information about whether the UE is capable of model training, information about whether the UE is capable of model inference, information about whether the UE is capable of model fine-tuning, a computing power of the UE.4.The method of claim 1, further including:transmitting a ninth message to the UE, wherein the ninth message includes at least one of:a reference model for model training or model generation, an accuracy threshold for determining whether a model can be used.5.The method of claim 1, further including:transmitting a fifteenth message to the UE, wherein the fifteenth message includes at least one of:information related to activation or deactivation of a model included in the UE, conditions for activation or deactivation or fallback or transition of a model included in the UE, indications for model training or model fine-tuning, valid time of a model included in the UE, valid area of a model included in the UE.6.The method of claim 1, further including:receiving a nineteenth message from the UE or a third node, wherein the nineteenth message includes at least one of:performance information related to at least one of model training, model generation, model inference, model fine-tuning, model evaluation, information about that an accuracy of a model is below a threshold, information about that a model needs to be re-trained, information about that a model needs to be fine-tuned.7.The method of claim 1, wherein the first data includes measurement results of the first reference signal or the second reference signal.8.A method performed by a user equipment (UE) in a wireless communication system, including:transmitting a first message to a first node, wherein the first message is associated with a request for collection of first data, wherein the first message includes at least one of: configuration information of a reference signal for the collection of the first data suggested by the UE, configuration information of a reference signal for the collection of the first data preferred by the UE, a data volume requested for the collection of the first data, a time requested for the collection of the first data, and information related to a node requested to perform model training based on the first data;receiving, from the first node, a first reference signal for the collection of the first data based on the first message; andreceiving, from a second node, a second reference signal for the collection of the first data based on second configuration information,wherein a third message is transmitted by the first node to the second node, wherein the third message includes the second configuration information.9.The method of claim 8, further including:receiving a fifth message from the first node, wherein the fifth message is used for requesting the UE to report the first data; andtransmitting a seventh message to the first node, wherein the seventh message includes the first data.10.The method of claim 8, further including:transmitting an eighth message to the first node, wherein the eighth message includes at least one of:information about whether the UE is capable of model generation, information about whether the UE is capable of model training, information about whether the UE is capable of model inference, information about whether the UE is capable of model fine-tuning, a computing power of the UE.11.The method of claim 8, further including:receiving a ninth message from the first node, wherein the ninth message includes at least one of:a reference model for model training or model generation, an accuracy threshold for determining whether a model can be used.12.The method of claim 8, further including:receiving a fifteenth message from the first node, wherein the fifteenth message includes at least one of:information related to activation or deactivation of a model included in the UE, conditions for activation or deactivation or fallback or transition of a model included in the UE, indications for model training or model fine-tuning, valid time of a model included in the UE, valid area of a model included in the UE.13.The method of claim 8, further including:transmitting a nineteenth message to the first node, wherein the nineteenth message includes at least one of:performance information related to at least one of model training, model generation, model inference, model fine-tuning, model evaluation, information about that an accuracy of a model is below a threshold, information about that a model needs to be re-trained, information about that a model needs to be fine-tuned.14.A LMF(Location Management Function) in a wireless communication system, the LMF comprising:a transceiver; anda processor coupled to the transceiver, wherein the processor is configured to:transmit a first message to a gNB; andtransmit a second message to the gNB;wherein the first message comprises configuration of sample-based measurement, configuration information for collection of data and timing reporting;wherein the second message comprises reporting type and reporting periodicity.15.A method performed by a LMF(Location Management Function) in a wireless communication system, including:transmitting a first message to a gNB; andtransmitting a second message to the gNB;wherein the first message comprises configuration of sample-based measurement, configuration information for collection of data and timing reporting;wherein the second message comprises reporting type and reporting periodicity.

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