Method and device for utilizing artificial intelligence and machine learning in wireless communication system

Abstract

The present disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. A method performed by a user equipment (UE) of a wireless communication system, according to one embodiment of the present disclosure, comprises the steps of: receiving, from a base station, a control message including configuration information for network-side data collection; logging a layer 1 (L1) measurement result on the basis of the configuration information for network-side data collection; and transmitting the logged L1 measurement result to the base station. When the UE is in a dual connectivity (DC) state, the configuration information for network-side data collection is associated with a master cell group (MCG), and a configuration by a secondary node (SN) is excluded.

Description

Method and device for utilizing artificial intelligence and machine learning in wireless communication systems

[0001] The present disclosure relates to a wireless communication system. More specifically, the present disclosure relates to a method and apparatus for utilizing artificial intelligence and machine learning in a wireless communication system.

[0002] 5G mobile communication technology defines a wide frequency band to enable fast transmission speeds and new services, and can be implemented not only in frequency bands below 6 GHz ('Sub 6 GHz'), such as 3.5 gigahertz (3.5 GHz), but also in ultra-high frequency bands called millimeter waves (mmWave), such as 28 GHz and 39 GHz ('Above 6 GHz'). In addition, for 6G mobile communication technology, which is referred to as a system beyond 5G, implementation in the terahertz band (e.g., the 3 terahertz (3 THz) band at 95 GHz) is being considered to achieve transmission speeds 50 times faster and ultra-low latency reduced to one-tenth compared to 5G mobile communication technology.

[0003] In the early stages of 5G mobile communication technology, aiming to satisfy service support and performance requirements for enhanced Mobile BroadBand (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC), technologies such as beamforming and Massive MIMO to mitigate path loss and increase transmission distance in ultra-high frequency bands, support for various numerologies (such as the operation of multiple subcarrier spacings) and dynamic operation of slot formats for the efficient utilization of ultra-high frequency resources, initial access techniques to support multi-beam transmission and broadband, definition and operation of Band-Width Parts (BWP), Low Density Parity Check (LDPC) codes for high-volume data transmission, new channel coding methods such as Polar Codes for the reliable transmission of control information, and L2 pre-processing (L2 Standardization has been carried out for pre-processing, network slicing which provides a dedicated network specialized for specific services, and other methods.

[0004] Currently, discussions are underway to improve and enhance the performance of the initial 5G mobile communication technology, taking into account the services that the 5G mobile communication technology was intended to support. Additionally, standardization of the physical layer is in progress for technologies such as V2X (Vehicle-to-Everything), which helps autonomous vehicles make driving decisions and enhance user convenience based on their own location and status information transmitted by the vehicle; NR-U (New Radio Unlicensed), which aims for system operation in unlicensed bands to comply with various regulatory requirements; NR terminal low power consumption technology (UE Power Saving); Non-Terrestrial Network (NTN), which is direct terminal-satellite communication for securing coverage in areas where communication with the terrestrial network is impossible; and positioning.

[0005] In addition, standardization is underway in the field of wireless interface architecture / protocols for technologies such as the Industrial Internet of Things (IIoT) to support new services through linkage and convergence with other industries, Integrated Access and Backhaul (IAB) which provides nodes to expand network service areas by integrating wireless backhaul links and access links, Mobility Enhancement including Conditional Handover and Dual Active Protocol Stack (DAPS) Handover, and 2-step Random Access (2-step RACH for NR) which simplifies random access procedures. Standardization is also underway in the field of system architecture / services for 5G baseline architectures (e.g., Service based Architecture, Service based Interface) to incorporate Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) technologies, and Mobile Edge Computing (MEC), which provides services based on the location of the terminal.

[0006] When such 5G mobile communication systems are commercialized, connected devices, which are increasing explosively, will be connected to communication networks. Accordingly, it is expected that there will be a need to enhance the functionality and performance of 5G mobile communication systems and to integrate the operation of connected devices. To this end, new research is planned to be conducted on 5G performance improvement and complexity reduction, support for AI services, support for metaverse services, and drone communication using eXtended Reality (XR), Artificial Intelligence (AI), and Machine Learning (ML) to efficiently support Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR).

[0007] Furthermore, the advancement of these 5G mobile communication systems encompasses multi-antenna transmission technologies such as new waveforms, Full Dimensional MIMO (FD-MIMO), array antennas, and large-scale antennas to guarantee coverage in the terahertz band of 6G mobile communication technology; metamaterial-based lenses and antennas; high-dimensional spatial multiplexing technology using Orbital Angular Momentum (OAM); and Reconfigurable Intelligent Surface (RIS) technology to improve terahertz band signal coverage; as well as full-duplex technology for enhancing frequency efficiency and system networks in 6G mobile communication technology; AI-based communication technologies that realize system optimization by utilizing satellites and Artificial Intelligence (AI) from the design stage and internalizing end-to-end AI support functions; and the realization of services of complexity exceeding the limits of terminal computing capabilities by utilizing ultra-high-performance communication and computing resources. It could serve as a foundation for the development of next-generation distributed computing technologies.

[0008] The embodiments of the present disclosure aim to provide an apparatus and a method capable of effectively providing services in a wireless communication system.

[0009] According to one embodiment of the present disclosure, a method is provided to be performed by user equipment (UE) of a wireless communication system. The method comprises the steps of: receiving a control message from a base station that includes configuration information for network-side data collection; logging a layer 1 (L1) measurement result based on the configuration information for network-side data collection; and transmitting the logged L1 measurement result to the base station. When the UE is in a dual connectivity (DC) state, the configuration information for network-side data collection is associated with a master cell group (MCG), and a configuration by a secondary node (SN) is excluded.

[0010] According to one embodiment of the present disclosure, a method is provided to be performed by a base station of a wireless communication system. The method comprises the steps of: transmitting a control message containing configuration information for network-side data collection to a UE; and receiving from the UE an L1 measurement result recorded by the UE based on the configuration information for network-side data collection. When the UE is in a DC state, the configuration information for network-side data collection is associated with an MCG, and a configuration by an SN is excluded.

[0011] According to one embodiment of the present disclosure, a UE of a wireless communication system is provided. The UE comprises at least one transceiver; at least one processor connected to communicate with the at least one transceiver; and a memory connected to communicate with the at least one processor and storing instructions that are executable individually or in any combination of the at least one processor. The instructions cause the UE to receive a control message from a base station containing configuration information for network-side data collection, log an L1 measurement result based on the configuration information for network-side data collection, and transmit the logged L1 measurement result to the base station. When the UE is in a DC state, the configuration information for network-side data collection is associated with an MCG, and a configuration by an SN is excluded.

[0012] According to one embodiment of the present disclosure, a base station of a wireless communication system is provided. The base station comprises at least one transceiver; at least one processor connected to communicate with the at least one transceiver; and a memory connected to communicate with the at least one processor and storing instructions that are executable individually or in any combination of the at least one processor. The instructions cause the base station to transmit a control message containing network-side data collection configuration information to a UE, and to receive from the UE an L1 measurement result recorded by the UE based on the network-side data collection configuration information. When the UE is in a DC state, the network-side data collection configuration information is associated with an MCG, and a configuration by an SN is excluded.

[0013] An embodiment of the present disclosure provides an apparatus and a method capable of effectively providing services in a wireless communication system.

[0014] FIG. 1 is a drawing illustrating the structure of a mobile communication system according to one embodiment of the present disclosure.

[0015] FIG. 2 is a diagram illustrating a wireless connection state transition in a mobile communication system according to one embodiment of the present disclosure.

[0016] FIG. 3 is a diagram illustrating an AI / ML model for predicting beam measurements for beam management according to one embodiment of the present disclosure.

[0017] FIG. 4 is a diagram illustrating a procedure in which a terminal performs a prediction using a UE-side AI / ML model for beam management according to one embodiment of the present disclosure.

[0018] FIG. 5 is a diagram illustrating a procedure in which a network performs a prediction using an NW-side AI / ML model for beam management according to one embodiment of the present disclosure.

[0019] FIG. 6 is a diagram illustrating a procedure in which a terminal collects / reports measurement data for learning a model, according to one embodiment of the present disclosure.

[0020] FIG. 7a is a diagram illustrating a procedure in which a terminal in a DC state collects / reports measurement data for learning a model, according to one embodiment of the present disclosure.

[0021] FIG. 7b is a diagram illustrating a procedure in which a terminal in a DC state collects / reports measurement data for learning a model, according to one embodiment of the present disclosure.

[0022] FIG. 8 is a drawing illustrating the internal structure of a terminal according to one embodiment of the present disclosure.

[0023] FIG. 9 is a drawing illustrating the structure of a base station according to one embodiment of the present disclosure.

[0024] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0025] In describing the embodiments, technical details that are well known in the art to which this disclosure belongs and are not directly related to this disclosure are omitted. This is intended to convey the essence of this disclosure more clearly without obscuring it by omitting unnecessary explanations.

[0026] For the same reason, some components in the attached drawings have been exaggerated, omitted, or schematically depicted. Additionally, the dimensions of each component do not entirely reflect their actual dimensions. Identical or corresponding components in each drawing have been assigned the same reference numbers.

[0027] The advantages and features of the present disclosure, and the methods for achieving them, will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure is complete and to fully inform those skilled in the art of the scope of the disclosure, and the present disclosure is defined only by the scope of the claims. Throughout the specification, the same reference numerals refer to the same components. Furthermore, in describing the present disclosure, if it is determined that a detailed description of a related function or configuration might unnecessarily obscure the essence of the present disclosure, such detailed description is omitted. Additionally, the terms described below are defined considering their functions in the present disclosure, and these may vary depending on the intentions or conventions of the user or operator. Therefore, their definitions should be based on the content throughout the specification.

[0028] Hereinafter, the base station is an entity that performs resource allocation for terminals and may be at least one of a gNode B, eNode B, Node B, BS (Base Station), wireless access unit, base station controller, or a node on a network. The terminal may include a UE (User Equipment), MS (Mobile Station), cellular phone, smartphone, computer, or a multimedia system capable of performing communication functions. In this disclosure, the Downlink (DL) refers to the wireless transmission path of a signal transmitted by a base station to a terminal, and the Uplink (UL) refers to the wireless transmission path of a signal transmitted by a terminal to a base station. Furthermore, although embodiments of this disclosure are described below using a 5G system as an example, embodiments of this disclosure may be applied to other communication systems having similar technical backgrounds or channel types. For example, LTE or LTE-A mobile communication and mobile communication technologies developed after 5G may be included therein. Additionally, this disclosure may be applied to other communication systems with some modifications made at the discretion of a person with skilled technical knowledge, provided that it does not deviate significantly from the scope of this disclosure. The contents of this disclosure are applicable to FDD and TDD systems.

[0029] At this point, it will be understood that each block of the process flow diagrams and combinations of the flow diagrams can be executed by computer program instructions. Since these computer program instructions can be loaded into the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, the instructions executed through the processor of the computer or other programmable data processing equipment create means to perform the functions described in the flow diagram block(s). Since these computer program instructions can also be stored in computer-available or computer-readable memory that can be directed toward the computer or other programmable data processing equipment to implement the function in a specific way, the instructions stored in computer-available or computer-readable memory can also produce a manufactured item containing instruction means to perform the function described in the flow diagram block(s). Since computer program instructions can be loaded onto a computer or other programmable data processing equipment, instructions that perform a series of operation steps on the computer or other programmable data processing equipment to create a process executed by the computer can also provide steps for executing the functions described in the flowchart block(s).

[0030] Additionally, each block may represent a module, segment, or part of code containing one or more executable instructions for executing a specific logical function(s). It should also be noted that in some alternative execution examples, the functions mentioned in the blocks may occur out of order. For example, two blocks described in succession may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order according to their corresponding functions.

[0031] In this embodiment, the term "part" refers to a software or hardware component such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit), and the "part" performs certain roles. However, the meaning of "part" is not limited to software or hardware. The "part" may be configured to reside in an addressable storage medium or configured to run one or more processors. Thus, as an example, the "part" includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and "parts" may be combined into a smaller number of components and "parts" or further separated into additional components and "parts." In addition, the components and 'parts' may be implemented to utilize one or more CPUs within the device or secure multimedia card. Also, in the embodiments, 'parts' may include one or more processors.

[0032] FIG. 1 is a drawing illustrating the structure of a mobile communication system according to one embodiment of the present disclosure.

[0033] Referring to FIG. 1, the wireless access network of a mobile communication system (New Radio, NR) can be composed of a base station (next generation Node B, hereinafter gNB) (1-10) and an access and mobility management function (AMF) (1-05).

[0034] The user terminal (New Radio User Equipment, hereinafter NR UE or terminal) (1-15) can connect to an external network through gNB (1-10) and AMF (1-05).

[0035] A mobile communication system can be a next-generation mobile communication system, and a base station can be a next-generation base station.

[0036] According to one embodiment, the gNB (1-10) in FIG. 1 may correspond to the eNB (Evolved Node B) of an existing LTE system. The gNB (1-10) is connected to the NR UE (1-15) via a wireless channel (1-20) and can provide superior service compared to the existing Node B. In a next-generation mobile communication system according to one embodiment of the present disclosure, since all user traffic is serviced through a shared channel, a device for scheduling by collecting state information such as the buffer status, available transmission power status, and channel status of the UEs may be required, and this can be performed by the gNB (1-10). According to one embodiment, a single gNB (1-10) can typically control multiple cells. To implement ultra-high-speed data transmission, it may have a bandwidth greater than the existing maximum bandwidth, and beamforming technology may be additionally incorporated by using Orthogonal Frequency Division Multiplexing (OFDM) as the wireless access technology. In addition, an Adaptive Modulation & Coding (AMC) scheme can be applied to determine the modulation scheme and channel coding rate according to the channel state of the terminal.

[0037] According to one embodiment, in FIG. 1, the AMF (1-05) can perform functions such as mobility support, bearer configuration, and quality of service (QoS) configuration. The AMF (1-05) is a device responsible for various control functions as well as mobility management functions for terminals and can be connected to multiple base stations. In addition, the mobile communication system according to one embodiment of the present disclosure can be linked with an LTE system, and for example, the AMF (1-05) can be connected to a mobile management entity (MME) (1-25) through a network interface.

[0038] According to one embodiment, the MME (1-25) can be connected to an existing base station eNB (1-30). For example, in FIG. 1, a terminal (1-15) supporting LTE-NR Dual Connectivity can transmit and receive data by connecting to the eNB (1-30) as well as the gNB (1-10) via a wireless channel (1-35).

[0039] FIG. 2 is a diagram illustrating a wireless connection state transition in a mobile communication system according to one embodiment of the present disclosure.

[0040] Referring to Fig. 2, the mobile communication system may have three radio resource control (RRC) states or RRC modes.

[0041] Specifically, the connection mode (RRC_CONNECTED, 2-05) may be a wireless connection state in which the terminal can transmit and receive data. The idle mode (RRC_IDLE, 2-30) may correspond to a wireless connection state in which the terminal monitors whether paging is being transmitted to it. The above two modes are wireless connection states applicable to LTE systems, and the detailed description may be the same as that of an LTE system. A mobile communication system according to one embodiment of the present disclosure may be a next-generation mobile communication system.

[0042] In a mobile communication system according to one embodiment of the present disclosure, a novel inactive mode (RRC_INACTIVE, 2-15) may be defined. In the inactive mode (2-15), a UE context may be maintained between the base station and the terminal, and RAN (radio access network) based paging may be supported. The features of the inactive mode (2-15) may include at least one of the following:

[0043] - Cell re-selection mobility;

[0044] - The connection between the core network (CN) for the UE and the NR RAN has been established in both the control plane (C-plane) and the user plane (U-plane).

[0045] - The UE AS (Access Stratum) context is stored in at least one gNB and the UE;

[0046] - Paging is initiated by NR RAN;

[0047] - RAN-based notification area is managed by NR RAN; or

[0048] - NR RAN knows the RAN-based notification area to which the UE belongs.

[0049] According to one embodiment of the present disclosure, a terminal in an inactive mode (2-15) may transition to a connected mode (2-05) or an idle mode (2-30) through a specific procedure. For example, the terminal may transition between the connected mode (2-05) and the inactive mode (2-15) through a resume procedure and a release with suspend procedure (2-10), respectively. More specifically, the terminal may transition from the inactive mode (2-15) to the connected mode (2-05) according to the resume procedure. Additionally, the terminal may transition from the connected mode (2-05) to the inactive mode (2-15) upon receiving an RRC Release message containing suspend setting information. The procedure may be performed by transmitting and receiving one or more RRC messages between the terminal and the base station and may consist of one or more steps.

[0050] Additionally, according to one embodiment, the terminal can perform a transition from inactive mode (2-15) to idle mode (2-30) through a Resume-Release procedure. The transition between the connection mode (2-05) and the idle mode (2-30) may follow LTE technology. Also, referring to FIG. 2, the transition from the inactive mode (2-15) to the idle mode (2-30) may be performed through a release procedure (2-20). Additionally, a direct transition between the connection mode (2-05) and the idle mode (2-30) may be performed through an establishment procedure and a release procedure (2-25), respectively.

[0051] FIG. 3 is a diagram illustrating an AI / ML model for predicting beam measurement values ​​for beam management according to one embodiment of the present disclosure.

[0052] In one embodiment of the present disclosure, an artificial intelligence (AI) / machine learning (ML) model may be utilized for beam management (BM). For example, a base station may utilize an AI / ML model for beam management of downlink (DL) transmission beams. In one embodiment of the present disclosure, an AI / ML model may be utilized for positioning accuracy enhancements. In one embodiment of the present disclosure, an AI / ML model may be utilized for Channel State Information (CSI) feedback enhancement.

[0053] In one embodiment of the present disclosure, an application example (use case) of AI / ML for beam management may include sub-use cases such as spatial prediction and temporal prediction.

[0054] In one embodiment of the present disclosure, for a beam management application example, a set of beams used as input to an AI / ML model may be referred to as SET B, and a set of beams derived as output to an AI / ML model may be referred to as SET A.

[0055] Referring to FIG. 3, BM Case 1 (3-05) for spatial prediction and BM Case 2 (3-10) for temporal prediction are illustrated. First, in the case of BM Case 1 (3-05) for spatial prediction, the input to the AI / ML model may be a measurement value (Meas(Bi,tK)) (3-15) for one or more beams (Bi, i=1, 2, …, N) at a specific time point (tK).

[0056] In one embodiment of the present disclosure, the measurement value for the beam may be one of the following:

[0057] - RSRP and / or RSRQ and / or SINR measured at Layer 1;

[0058] - RSRP and / or RSRQ and / or SINR measured / acquired at Layer 3;

[0059] - Filtered RSRP and / or RSRQ and / or SINR values ​​measured at Layer 1 (e.g., average or weighted average of measurements over a specified period); or

[0060] - Filtered RSRP and / or RSRQ and / or SINR values ​​measured / acquired at Layer 3 (e.g., average or weighted average of measurements over a specified period).

[0061] In one embodiment of the present disclosure, when spatially predicting beam management according to BM Case 1 (3-05), at least one of the following information or a combination thereof may be considered (or used) as input to an AI / ML model:

[0062] - SET B related information (e.g., beam-specific ID);

[0063] - Measurement time information;

[0064] - L1-RSRP measurement based on Set B;

[0065] - L1-RSRP measurement based on Set B and assistance information;

[0066] - Channel Impulse Response (CIR) based on Set B; and / or

[0067] - L1-RSRP measurement based on Set B and the corresponding DL Tx and / or Rx beam ID.

[0068] In one embodiment of the present disclosure, when spatially predicting beam management according to BM Case 1 (3-05), the output of the AI / ML model may be a predicted value (P_Meas(Bi,tK), i=N+1, N+2,…,N+M) (3-20) for one or more beams (Bi) at a specific time point (tK).

[0069] In one embodiment of the present disclosure, the predicted value for the beam may be one of the following:

[0070] - RSRP and / or RSRQ and / or SINR predicted to be measured at Layer 1;

[0071] - RSRP and / or RSRQ and / or SINR predicted to be measured / acquired at Layer 3;

[0072] - Predicted value (or filtered value) of the filtered RSRP and / or RSRQ and / or SINR measured (or predicted to be measured) at Layer 1 (e.g., average or weighted average of measured / predicted values ​​over a given period); or

[0073] - Predicted value (or filtered value) of the filtered RSRP and / or RSRQ and / or SINR measured / acquired (or predicted to be measured / acquired) at Layer 3 (e.g., average or weighted average of measured / predicted values ​​over a specified period).

[0074] In one embodiment of the present disclosure, when spatially predicting beam management according to BM Case 1 (3-05), at least one of the following information or a combination thereof may be considered (or used) as the output of an AI / ML model:

[0075] - SET A related information (e.g., beam-specific ID);

[0076] - One beam predicted to have the highest (best) measurement value (e.g., Top-1 beam);

[0077] - N (≥1) beam(s) predicted to have the highest (best) measurements (e.g., Top-N beam(s)); and / or

[0078] - Probability that each beam in SET A is a Top-1 or Top-N beam(s).

[0079] Next, for BM Case 2 (3-10) for temporal prediction, the input to the AI / ML model may be a measurement (Meas(BK,ti)) (3-25) for a beam (e.g., BK) at one or more (past) time points (ti, i=1, 2,…,N).

[0080] In one embodiment of the present disclosure, the measurement value for the beam may be one of the following:

[0081] - RSRP and / or RSRQ and / or SINR measured at Layer 1;

[0082] - RSRP and / or RSRQ and / or SINR measured / acquired at Layer 3;

[0083] - Filtered RSRP and / or RSRQ and / or SINR values ​​measured at Layer 1 (e.g., average or weighted average of measurements over a specified period); or

[0084] - Filtered RSRP and / or RSRQ and / or SINR values ​​measured / acquired at Layer 3 (e.g., average or weighted average of measurements over a specified period).

[0085] In one embodiment of the present disclosure, when making a temporal prediction for beam management according to BM Case 2 (3-10), at least one of the following information or a combination thereof may be considered / used as input to an AI / ML model:

[0086] - SET B related information (e.g., beam-specific ID);

[0087] - Measurement time information;

[0088] - L1-RSRP measurement based on Set B;

[0089] - L1-RSRP measurement based on Set B and assistance information;

[0090] - Channel Impulse Response (CIR) based on Set B; and / or

[0091] - L1-RSRP measurement based on Set B and the corresponding DL Tx and / or Rx beam ID.

[0092] In one embodiment of the present disclosure, when making a temporal prediction for beam management according to BM Case 2 (3-10), the output of the AI / ML model may be a predicted value (P_Meas(BK,ti)) (3-30) for one beam (BK) at one or more (future) time points (ti, i=N+1, N+2,…,N+M).

[0093] In one embodiment of the present disclosure, the predicted value for the beam may be one of the following:

[0094] - RSRP and / or RSRQ and / or SINR predicted to be measured at Layer 1;

[0095] - RSRP and / or RSRQ and / or SINR predicted to be measured / acquired at Layer 3;

[0096] - Predicted value (or filtered value) of the filtered RSRP and / or RSRQ and / or SINR measured (or predicted to be measured) at Layer 1 (e.g., average or weighted average of measured / predicted values ​​over a given period); or

[0097] - Predicted value (or filtered value) of the filtered RSRP and / or RSRQ and / or SINR measured / acquired (or predicted to be measured / acquired) at Layer 3 (e.g., average or weighted average of measured / predicted values ​​over a specified period).

[0098] In one embodiment of the present disclosure, when making a temporal prediction for beam management according to BM Case 2 (3-10), at least one of the following information or a combination thereof may be considered / used as the output of an AI / ML model:

[0099] - SET A related information (e.g., beam-specific ID);

[0100] - Prediction time point information;

[0101] - The point in time when the measurement is predicted to be highest (best); and / or

[0102] - N (≥1) time points where the measurement value is predicted to be highest (best).

[0103] In one embodiment of the present disclosure, an AI / ML model for beam management may be an AI / ML model that simultaneously performs the aforementioned spatial prediction and temporal prediction. In this case, the input of the AI / ML model may be a measurement value at one or more time points for each beam for one or more beams. Specific details regarding the input of the AI / ML model may be referenced to the foregoing. Additionally, the output of the AI / ML model may be a prediction value at one or more time points for each beam for one or more beams. Specific details regarding the output of the AI / ML model may be referenced to the foregoing.

[0104] In one embodiment of the present disclosure, a terminal (or UE) may run an AI / ML model to derive predicted values ​​and related information. The AI / ML model run by the terminal may be referred to as a terminal-side AI / ML model or a UE-side AI / ML model. The terminal may transmit the derived information and related information to a base station through the AI / ML model. For example, the base station may utilize the information received from the terminal for downlink transmission beam management, location accuracy improvement, and / or CSI feedback improvement.

[0105] In one embodiment of the present disclosure, a network (NW) (base station or LMF (location management function)) may derive predicted values ​​and related information using an AI / ML model. The AI / ML model used by the network may be referred to as a network-side AI / ML model or an NW-side AI / ML model. For example, a base station may perform predictions based on measurement information received from a terminal and utilize them for downlink transmission beam management, improving location accuracy, and / or improving CSI feedback.

[0106] With reference to FIGS. 4 through 7b, a specific procedure for utilizing the AI / ML model of FIG. 3 in a wireless network system according to various embodiments of the present disclosure will be described below.

[0107] FIG. 4 is a diagram illustrating a procedure in which a terminal performs a prediction using a UE-side AI / ML model for beam management according to one embodiment of the present disclosure.

[0108] In step 4-10, terminal 1 (4-01) may transmit terminal capability information (e.g., via a UECapabilityInformation message) to base station 1 (or network 1) (4-02) connected to it. To receive terminal capability information, base station 1 (4-02) may first request terminal 1 (4-01) to transmit terminal capability information (e.g., via a UECapabilityEnquiry message). The terminal capability information may include information indicating whether terminal 1 (4-01) supports AI / ML related functions (e.g., UE-side model related functions / operations) (by AI / ML functionality, by use case, or by sub-use case). Meanwhile, step 4-10 may be omitted. For example, if base station 1 (4-02) receives terminal capability information in advance, it may not transmit a terminal capability request to terminal 1 (4-01), and in such cases, the step of terminal 1 (4-01) transmitting terminal capability information may be omitted.

[0109] In step 4-15, base station 1 (4-02) may transmit model training related configuration information (e.g., training configuration information) to terminal 1 (4-01) for collecting data necessary for UE-side model training. The training configuration information may include at least one of configuration information for measurement and configuration information for reporting.

[0110] Prior to this, base station 1 (4-02) may receive configuration information regarding model learning or a configuration request regarding model learning from terminal 1 (4-01) or another terminal or learning object (4-03). However, this process is optional, and base station 1 (4-02) may transmit learning configuration information to terminal 1 (4-01) even without receiving a configuration request regarding model learning. For example, based on specific conditions (e.g., according to the judgment of the base station, or when conditions set to transmit learning configuration information are satisfied, or when a pre-set time or period is reached), base station 1 (4-02) may transmit learning configuration information to terminal 1 (4-01).

[0111] In one embodiment of the present disclosure, the learning object (4-03) may be terminal 1 (4-01) or another terminal or terminal server. The terminal server may be connected to the terminal via a 3GPP network (e.g., a server within the 3GPP network) or may be connected to the terminal via an external network (e.g., a server outside the 3GPP network or Wi-Fi).

[0112] In step 4-20, terminal 1 (4-01) performs measurements to generate data necessary for learning the UE-side model, and then reports the measurement results to base station 1 (4-02) or a learning object (4-03). If terminal 1 (4-01) reports the measurement results to base station 1 (4-02), base station 1 (4-02) may reprocess the received report and transmit it to the learning object (4-03). Alternatively, base station 1 (4-02) may transmit the information received from terminal 1 (4-01) to the learning object (4-03) as is.

[0113] In step 4-25, the learning object (4-03) can train a UE-side model using the received report.

[0114] In step 4-30, terminal 2 (4-04) can receive the trained model from the training object (4-03). For example, the trained model can be transmitted to terminal 2 (4-04) via base station 2 (4-05).

[0115] Meanwhile, before terminal 2 (4-04) receives the model that has been trained, terminal 2 (4-04) may transmit terminal capability information to base station 2 (4-05) connected to it (e.g., via a UECapabilityInformation message). Base station 2 (4-05) may first request terminal 2 (4-04) to transmit terminal capability information (e.g., via a UECapabilityEnquiry message) in order to receive terminal capability information. The terminal capability information may include information indicating whether terminal 2 (4-04) supports AI / ML related functions (e.g., UE-side model related functions / operations) (by AI / ML functionality, by use case, or by sub-use case). Meanwhile, as described above, if base station 2 (4-05) has received terminal capability information in advance, the step of transmitting terminal capability information may be omitted.

[0116] In step 4-35, terminal 2 (4-04) can perform inference or prediction using the received model. For example, in the case of beam management, terminal 2 (4-04) can perform spatial or temporal beam prediction. Step 4-35 can be performed after terminal 2 (4-04) receives inference-related settings from base station 2 (4-05).

[0117] In step 4-40, terminal 2 (4-04) can report the result of the prediction or inference to base station 2 (4-05).

[0118] In step 4-45, base station 2 (4-05) can perform downlink beam management for terminal 2 (4-04) based on the prediction / inference result received from terminal 2 (4-04) and select an appropriate beam to service terminal 2 (4-04).

[0119] Meanwhile, terminal 1 (4-01) and terminal 2 (4-04) may be different terminals or the same terminal. Likewise, base station 1 (4-02) and base station 2 (4-05) may be different base stations or the same base station.

[0120] FIG. 5 is a diagram illustrating a procedure in which a network performs a prediction using an NW-side AI / ML model for beam management according to one embodiment of the present disclosure.

[0121] In step 5-10, terminal 1 (5-01) may transmit terminal capability information (e.g., via a UECapabilityInformation message) to base station 1 (or network 1) (5-02) connected to it. To receive terminal capability information, base station 1 (5-02) may first request terminal 1 (5-01) to transmit terminal capability information (e.g., via a UECapabilityEnquiry message). The terminal capability information may include information indicating whether terminal 1 (5-01) supports AI / ML related functions (e.g., NW-side model related functions / operations) (e.g., inference and / or learning) (by AI / ML functionality, by use case, or by sub-use case). Meanwhile, step 5-10 may be omitted. For example, if base station 1 (5-02) receives terminal capability information in advance, it may not transmit a terminal capability request to terminal 1 (5-01), and in this case, the step of terminal 1 (5-01) transmitting terminal capability information may be omitted.

[0122] In step 5-15, base station 1 (5-02) may transmit model training related configuration information (e.g., training configuration information) to terminal 1 (5-01) for collecting data necessary for NW-side model training. The training configuration information may include at least one of configuration information for measurement and configuration information for reporting.

[0123] Prior to this, base station 1 (5-02) may receive configuration information regarding model learning or a configuration request regarding model learning from terminal 1 (5-01) or another terminal or learning object (5-03). However, this process is optional, and base station 1 (5-02) may transmit learning configuration information to terminal 1 (5-01) even without receiving a configuration request regarding model learning. For example, base station 1 (5-02) may transmit learning configuration information to terminal 1 (5-01) based on specific conditions (e.g., according to the judgment of the base station, or when conditions set to transmit learning configuration information are satisfied, or when a pre-set time or period is reached).

[0124] In one embodiment of the present disclosure, the learning object (5-03) may be base station 1 (5-02) or another base station or an AMF or a UPF (user plane function) or an OAM (operations, administration, and maintenance) or a TCE (Trace collection entity) or an MCE (Measurement collection entity), or a server connected thereto. Additionally, the learning object (5-03) may be a server outside the 3GPP network.

[0125] In step 5-20, terminal 1 (5-01) performs measurements to generate data necessary for NW-side model learning, and then reports the measurement results to base station 1 (5-02) or learning object (5-03). If terminal 1 (5-01) reports the measurement results to base station 1 (5-02), base station 1 (5-02) may reprocess the received report and transmit it to the learning object (5-03). Alternatively, base station 1 (5-02) may transmit the information received from terminal 1 (5-01) to the learning object (5-03) as is.

[0126] In step 5-25, the learning object (5-03) can learn the NW-side model using the received report. Alternatively, base station 1 (5-02) can learn the NW-side model using the measurement results received in step 5-20 (e.g., without transmitting them to the learning object (5-03)).

[0127] In step 5-30, base station 2 (5-05) may receive a trained model from a training object (5-03). Alternatively, base station 2 (5-05) may possess a model that it has trained itself.

[0128] Meanwhile, before or after step 5-30, terminal 2 (5-04) may transmit terminal capability information to base station 2 (5-05) connected to it (e.g., via a UECapabilityInformation message). To receive terminal capability information, base station 2 (5-05) may first request terminal 2 (5-04) to transmit terminal capability information (e.g., via a UECapabilityEnquiry message). The capability information may include information indicating whether terminal 2 (5-04) supports AI / ML related functions (e.g., NW-side model related functions / operations) (by AI / ML functionality, by use case, or by sub-use case). Meanwhile, as described above, if base station 2 (5-05) has already received terminal capability information, the step of transmitting terminal capability information may be omitted.

[0129] In step 5-35, terminal 2 (5-04) can perform a measurement (e.g., after receiving measurement settings for the NW-side model from the base station) and report the measurement results to base station 2 (5-05).

[0130] In step 5-40, base station 2 (5-05) can perform inference or prediction using the measurement report received from terminal 2 (5-04) and the learned model. For example, in the case of beam management, spatial or temporal beam prediction can be performed.

[0131] In step 5-45, base station 2 (5-05) can perform downlink beam management for terminal 2 (5-04) based on the prediction / inference result and select an appropriate beam to service terminal 2 (5-04).

[0132] Meanwhile, terminal 1 (5-01) and terminal 2 (5-04) may be different terminals or the same terminal. Likewise, base station 1 (5-02) and base station 2 (5-05) may be different base stations or the same base station.

[0133] In addition, the description of FIGS. 4 and 5 described above was written assuming that the UE-side model and / or NW-side model are used for downlink beam management, but this is merely an example and can be understood in the same way for various use cases using the UE-side model and / or NW-side model as well as beam management.

[0134] FIG. 6 is a diagram illustrating a procedure in which a terminal collects / reports measurement data for learning a model, according to one embodiment of the present disclosure.

[0135] In step 6-10, the terminal (6-01) receives a request for terminal capability information from the base station (6-02) and may report its terminal capability information to the base station (6-02). Step 6-10 may cross-reference the above-described step 5-10 or step 4-10. For example, the terminal (6-01) may report the terminal (6-01)'s memory-related capability information (e.g., minimum support memory) (for storing measurement data for model learning) to the base station (6-02). Additionally, as described above, if the base station (6-02) has received the terminal capability information in advance, step 6-10 may be omitted.

[0136] In step 6-15, the terminal (6-01) may receive model training configuration information (e.g., training configuration information) from the base station (6-02). For example, the model training configuration information may be received by including it in CSI measurement configuration information (e.g., CSI-MeasConfig). For example, the model training configuration information may include configuration information (e.g., resource information) for the terminal (6-01) to collect / measure (e.g., measurement of the downlink beam) data required for model training (e.g., layer 1 RSRP value for the downlink beam). For example, the model training configuration information may include information for the terminal (6-01) to report the data collected / measured for model training.

[0137] Apart from or in addition to the above-mentioned model learning configuration information, the terminal (6-01) may receive data availability reporting configuration information (e.g., Availability configuration or UE Assistance information configuration) from the base station (6-02). For example, the data availability reporting configuration information may be received by including it in OtherConfig.

[0138] The above-mentioned configuration information related to model training and / or configuration information related to data availability reporting may be transmitted via RRC messages (e.g., RRC Reconfiguration messages or RRC Resume messages). The above-mentioned RRC messages may be transmitted to SRB1 (signaling radio bearer 1). Step 6-15 may cross-reference Step 5-15 and / or Step 4-15.

[0139] In step 6-17, the terminal (6-01) may perform a measurement based on configuration information related to model learning and store (log) the resulting data in memory. In one embodiment of the present disclosure, the terminal (6-01) may periodically perform a measurement (e.g., for a beam resource) and store the result (e.g., L1-RSRP). For example, this may be referred to as periodic logging. In one embodiment of the present disclosure, the terminal (6-01) may perform a measurement (e.g., for a beam resource) only when a specific event is satisfied and store the result (e.g., L1-RSRP). For example, this may be referred to as event-based logging. If the event is not satisfied, the terminal (6-01) may stop the measurement and stop storing the result. In one embodiment of the present disclosure, as one of the events, if the signal strength of the serving cell measured by the terminal (6-01) is better than a specific threshold (e.g., a threshold set by the network) (e.g., for a certain period of time set by the network), the event may be satisfied. In one embodiment of the present disclosure, as one of the events, if the signal strength of the serving cell measured by the terminal (6-01) is worse than a specific threshold (e.g., a threshold set by the network) (e.g., for a certain period of time set by the network), the event may be satisfied.

[0140] In step 6-20, the terminal (6-01) may report information (e.g., Availability information) indicating that the stored data is available to the base station (6-02). At this time, the terminal (6-01) may report information indicating that the stored data is available to the base station (6-02) based on the configuration information related to the data availability report received in step 6-15. The information indicating that the stored data is available may be included in a UE Assistance information message and received via SRB1.

[0141] In one embodiment of the present disclosure, at step 6-20, the terminal (6-01) may provide information to the base station (6-02) regarding the existence of stored data. For example, if the terminal (6-01) has stored data, it may report to the base station (6-02) by setting indicator A to 'true' or including it. If the terminal (6-01) does not have stored data, it may report to the base station (6-02) by setting indicator A to 'false' or omitting it. The information regarding the existence of stored data may be provided through a UE Assistance information message, and the terminal (6-01) may report to the base station (6-02) the existence of stored data through the UE Assistance information message if it has received the relevant UE Assistance information setting in advance (e.g., at step 6-15 or thereafter). For example, the terminal (6-01) may report to the base station (6-02) the existence of stored data immediately after receiving the relevant UE Assistance information setting. Alternatively, the terminal (6-01) may report the changed information to the base station (6-02) when the information regarding the existence of stored data changes (e.g., when storage starts in a state where there is no stored data). Based on the report from the terminal (6-01), the base station (6-02) may decide whether to request a measurement report from the terminal (6-01) and / or determine the time of the request.

[0142] In one embodiment of the present disclosure, at step 6-20, if the terminal (6-01) satisfies at least one of the following conditions, it may report to the base station (6-02) that the condition is satisfied. Alternatively, if the terminal (6-01) does not (no longer) satisfy at least one of the following conditions, it may report to the base station (6-02) that the condition is (no longer) satisfied.

[0143] - Condition 1. When the remaining available size in memory (for storing measurement results for model training) is smaller than a specific threshold (e.g., threshold 1).

[0144] - Condition 2. When the size of the data stored by the terminal for the measurement results used for model training is larger than a specific threshold (e.g., threshold 2).

[0145] The above threshold values ​​(threshold values ​​1 and 2) may each be set by the base station (6-02) (e.g., through UE Assistance information settings) or may be fixed values ​​in the standard. For example, the terminal (6-01) may report to the base station (6-02) that the above condition is satisfied by setting or including indicator B to 'true'. The terminal (6-01) may report to the base station (6-02) that the above condition is not satisfied by setting or omitting indicator B to 'false'. Additionally, the terminal (6-01) may report specific size information (e.g., available remaining size in memory, size of data stored in memory by the terminal (6-01)). The above report may be provided through a UE Assistance information message, and the terminal (6-01) may transmit the above report to the base station (6-02) through a UE Assistance information message if it has received the relevant UE Assistance information settings in advance (e.g., at step 6-15 or thereafter). For example, the terminal (6-01) may report the above report to the base station (6-02) immediately after receiving the relevant UE Assistance information setting. Alternatively, the terminal (6-01) may report the changed information to the base station (6-02) when the information in the above report changes (e.g., when condition 1 is not satisfied but is satisfied). Alternatively, the terminal (6-01) may periodically transmit the above report information to the base station (6-02), and the transmission cycle may be set by the base station (6-02). Based on the report from the terminal (6-01), the base station (6-02) may decide whether to request a measurement report from the terminal (6-01) and / or determine the time of the request.

[0146] In one embodiment of the present disclosure, a prohibit timer may be set for each function to limit the reporting of UE Assistance information messages too frequently for one function of the terminal (6-01) (e.g., indicating the existence of stored data). Information regarding the prohibit timer may be included in the UE Assistance information setting for the corresponding function. For example, when the terminal (6-01) transmits a UE Assistance information message for reporting on the corresponding function, it may start a prohibit timer set to a set value. While the prohibit timer is running, the terminal (6-01) may not transmit a new UE Assistance information message for the same function. The terminal (6-01) may stop the prohibit timer when the UE Assistance information setting for the corresponding function is deactivated.

[0147] In step 6-25, the base station (6-02) may instruct or request the terminal (6-01) to report measurement data (for model learning) stored in the terminal (6-01). For example, the base station (6-02) may instruct or request the terminal (6-01) to report the measurement data being stored using a UE Information Request message, an RRC reconfiguration message, a new RRC message, or control information. For example, the request may be transmitted via SRB1.

[0148] In step 6-30, the terminal (6-01) may report stored measurement data (for model learning) to the base station (6-02). Step 6-30 may cross-reference step 4-20 and / or step 5-20. For example, the terminal (6-01) may use a UE Information Response message, a Measurement report message, a new RRC message, or control information for the report. For example, the report may be transmitted via SRB1 or SRB2.

[0149] The following describes how to receive data collection settings (or training settings) for model training when the terminal is in a DC (Dual connectivity) state. In the DC state, the terminal can be simultaneously connected to an MN (Master node) (or Master gNB) and an SN (Secondary node) (or Secondary gNB). Here, the MN corresponds to the MCG (master cell group) and the SN corresponds to the SCG (secondary cell group).

[0150] In one embodiment of the present disclosure, when a base station collects data for model learning (e.g., through instructions from an OAM), it may provide data collection settings (or learning settings) for model learning to a terminal that is not in a DC state, or provide data collection settings for model learning to a terminal in a DC state where the base station itself is an MN. The process of providing the data collection settings (or learning settings) for model learning may be performed in steps 6-15 described above. Since the base station is connected to multiple terminals, it may not be essential to select a terminal in a DC state where it is an SN to provide data collection settings for model learning. That is, it may be preferable for the base station to select a terminal that is not in a DC state or a terminal in a DC state where the base station itself is an MN to provide the settings and collect data.

[0151] Accordingly, in one embodiment of the present disclosure, a conditional presence (e.g., MCG) may be defined in the data collection settings (or learning settings) for model learning that a base station sets for a terminal. For example, a conditional presence (e.g., MCG) as shown in Table 1 below may be defined for (one or more) data collection settings (or learning settings) for model learning (e.g., csi-LoggedMeasurementConfigToAddModList) provided by a base station to a terminal. That is, the base station may provide the one or more data collection settings (or learning settings) for model learning to the terminal through an MCG setting (e.g., when the base station is a MN), but there may be a constraint that prevents the base station from providing the settings to the terminal through an SCG setting (e.g., when the base station is a SN or when the MN passes through a SN). More specifically, the one or more data collection settings (or learning settings) for model learning and the CSI measurement setting information (CSI-MeasConfig) including them may be expressed as shown in Table 1 below.

[0152] CSI-MeasConfigThe IECSI-MeasConfigis used to configure CSI-RS (reference signals) belonging to the serving cell in whichCSI-MeasConfigis included, channel state information reports to be transmitted on PUCCH on the serving cell in whichCSI-MeasConfigis included and channel state information reports on PUSCH triggered by DCI received on the serving cell in whichCSI-MeasConfigis included. See also TS 38.214, clause 5.2.CSI-MeasConfiginformation element-- ASN1START-- TAG-CSI-MEASCONFIG-STARTCSI-MeasConfig ::= SEQUENCE {nzp-CSI-RS-ResourceToAddModList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-Resources)) OF NZP-CSI-RS-Resource OPTIONAL, -- Need Nnzp-CSI-RS-ResourceToReleaseList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-Resources)) OF NZP-CSI-RS-ResourceId OPTIONAL, -- Need Nnzp-CSI-RS-ResourceSetToAddModList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-ResourceSets)) OF NZP-CSI-RS-ResourceSetOPTIONAL, -- Need Nnzp-CSI-RS-ResourceSetToReleaseList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-ResourceSets)) OF NZP-CSI-RS-ResourceSetIdOPTIONAL, -- Need N<...>[[csi-LoggedMeasurementConfigToAddModList-r19 SEQUENCE (SIZE (1..maxNrofLoggedMeasurementConfigurations-r19)) OF CSI-LoggedMeasurementConfig-r19OPTIONAL, -- Need MCGcsi-LoggedMeasurementConfigToReleaseList-r19 SEQUENCE (SIZE (1..maxNrofLoggedMeasurementConfigurations-r19)) OF CSI-LoggedMeasurementConfigId-r19 OPTIONAL -- Need N]]}-- TAG-CSI-MEASCONFIG-STOP-- ASN1STOPConditional PresenceExplanationMCGThis field is optionally present, Need N, inmasterCellGroup. It is absent insecondaryCellGroup.

[0153] In one embodiment of the present disclosure, regarding a data collection setting (e.g., csi-LoggedMeasurementConfigToAddModList) for model learning (one or more) provided by a base station to a terminal, the base station may provide said setting (e.g., csi-LoggedMeasurementConfigToAddModList) to the terminal through an MCG setting (e.g., when the base station is an MN), but there may be a constraint not to provide said setting to the terminal through an SCG setting (e.g., when the base station is an SN or when the MN goes through the SN). For example, said data collection setting for model learning (one or more) may be defined as shown in Table 2 below.

[0154] csi-LoggedMeasurementConfigToAddModListConfigured CSI report settings for the logging of L1 radio measurement results as specified in TS 38.214. NW only configures it within MCG configuration (ie, inmasterCellGroup).

[0155] In one embodiment of the present disclosure, a data collection setting (or learning setting) for model learning (one or more) provided by a base station to a terminal (e.g., csi-LoggedMeasurementConfigToAddModList) may be defined as a setting included only in an MCG setting (e.g., separately from CSI-MeasConfig), rather than a setting included in a setting that can be included in both an MCG setting and an SCG setting (e.g., CSI-MeasConfig).

[0156] Meanwhile, as an embodiment of the present disclosure, the terminal may receive data collection settings (or training settings) for model training from not only the MN but also the SN, or through not only the MCG settings but also the SCG settings. A detailed explanation thereof will be described in more detail below with reference to FIGS. 7a and 7b.

[0157] FIGS. 7a and 7b are drawings illustrating a procedure for a terminal in a DC state to collect / report measurement data for learning a model, according to one embodiment of the present disclosure.

[0158] In step 7-10, the terminal (7-01) may report terminal capability information to the base station or MN (7-02). For a description of step 7-10, refer to the description of step 6-10.

[0159] In step 7-15, the terminal (7-01) may receive model learning related settings from a base station or MN (7-02). For a description of step 7-15, refer to the description of step 6-15. Here, the model learning related settings may be received as MCG settings.

[0160] In step 7-17, the terminal (7-01) may perform measurements according to model learning related settings received from the MN (7-02) or received as MCG settings, and may store / log measurement results or data. At this time, the measurement results or data may be measurement results or data for the MN or MCG. For a description of step 7-17, refer to the description of step 6-17.

[0161] In step 7-18, the terminal (7-01) can determine whether there are measurement results or data for model training that are being stored / logged (for MN or MCG). Or the terminal (7-01) can determine whether the measurement results or data that are being stored / logged (for MN or MCG) satisfy certain conditions.

[0162] If there are measurement results or data for model training that are being stored / logged (for the MN or MCG) or if certain conditions are satisfied for measurement results or data that are being stored / logged (for the MN or MCG), in step 7-20, the terminal (7-01) may report information (e.g., availability) regarding the data stored by the terminal (7-01) to the base station or MN (7-02) via a UE Assistance Information message. For a description of steps 7-18 and / or 7-20, refer to the description of step 6-20.

[0163] In step 7-22, (immediately before or immediately after transmitting the UE Assistance Information message in step 7-20) the terminal (7-01) may start a prohibit timer for the MN or MCG setting. While the prohibit timer is running, the terminal (7-01) may not start transmitting a new UE Assistance Information message to report information regarding data (e.g., availability) for the MN or MCG setting. This action may be intended to prevent the terminal (7-01) from transmitting UE Assistance Information messages too frequently for the MN or for (each) MCG setting. For example, the prohibit timer may be defined as shown in Table 3 below.

[0164] TimerStartStopAt expiryT3xx (The UE maintains one instance of this timer per cell group)Upon transmitting UEAssistanceInformation message with loggedDataCollectionAssistance.Upon releasing loggedDataCollectionAssistance Config during the connection re-establishment / resume procedures, upon receiving loggedDataCollectionAssistance Config set to release, or upon performing MR-DC release.No action.

[0165] Here, loggedDataCollectionAssistance may mean information about data stored by the terminal (7-01) (e.g., availability), and loggedDataCollectionAssistanceConfig may mean configuration information related to the data availability report described in step 6-15.

[0166] In step 7-25, the base station or MN (7-02) may send a UE Information Request message to the terminal (7-01) to request the transmission of model training data stored by the terminal (7-01). For a description of step 7-25, refer to the description of step 6-25.

[0167] In step 7-30, the terminal (7-01) can transmit the stored model training data to the base station or MN (7-02) via a UE Information Response message.

[0168] In step 7-35, the terminal (7-01) may receive an RRC message (such as an RRC Reconfiguration message or an RRC Resume message) for the SN or SCG from the base station or SN (7-03). For example, the RRC message for the SN or SCG may be received via SRB3. Alternatively, the RRC message for the SN or SCG may be included within an RRC message for the MN or MCG (such as an RRC Reconfiguration message or an RRC Resume message) received from the MN (7-02) via SRB1. The RRC message for the SN or SCG may include configuration information related to model learning (for the SN or SCG) and / or configuration information related to data reporting (e.g., Availability) (for the SN or SCG). Configuration information related to data reporting (e.g., Availability) (e.g., loggedDataCollectionAssistanceConfig) may be included within otherConfig, which may be defined as shown in Table 4 below.

[0169] otherConfigContains configuration related to other configurations. When configured for the SCG, only fields drx-PreferenceConfig, maxBW-PreferenceConfig, maxBW-PreferenceConfigFR2-2, maxCC-PreferenceConfig, maxMIMO-LayerPreferenceConfig, maxMIMO-LayerPreferenceConfigFR2-2, minSchedulingOffsetPreferenceConfig, minSchedulingOffsetPreferenceConfigExt, rln-RelaxationReportingConfig, bfd-RelaxationReportingConfig, btNameList, wlanNameList, sensorNameList, obtainCommonLocation, idc-AssistanceConfig, multiTx-PreferenceReportingConfigFR2, ul-TrafficInfoReportingConfig, n3c-RelayUE-InfoReportConfig, successPSCell-Config, sn-InitiatedPSCellChange, and loggedDataCollectionAssistanceConfig can be included.

[0170] For a description of Step 7-35, refer to the description of Step 6-15.

[0171] In step 7-37, the terminal (7-01) may perform measurements according to model learning related settings received from the SN (7-03) or received as SCG settings, and may store / log measurement results or data. At this time, the measurement results or data may be measurement results or data for the SN or SCG. For a description of step 7-37, refer to the description of step 6-17.

[0172] In step 7-38, the terminal (7-01) can determine whether there are measurement results or data for model training that are being stored / logged (for SN or SCG). Or the terminal (7-01) can determine whether the measurement results or data that are being stored / logged (for SN or SCG) satisfy a predetermined condition.

[0173] If there are measurement results or data for model training that are being stored / logged (for the SN or SCG) or if certain conditions are satisfied for measurement results or data that are being stored / logged (for the SN or SCG), in step 7-40, the terminal (7-01) may report information (e.g., availability) regarding the data stored by the terminal (7-01) to the base station or SN (7-03) via a UE Assistance Information message. For example, the UE Assistance Information message may be transmitted via SRB3 or may be included in an RRC message (e.g., ULInformationTransferMRDC) transmitted to the MN (7-02) via SRB1. For a description of steps 7-38 and / or 7-40, refer to the description of step 6-20.

[0174] In step 7-42, (immediately before or immediately after transmitting the UE Assistance Information message in step 7-40), the terminal (7-01) may start a prohibit timer for the SN or SCG setting. While the prohibit timer is running, the terminal (7-01) may not start transmitting a new UE Assistance Information message to report information regarding data (e.g., availability) for the SN or SCG setting. This action may be intended to prevent the terminal (7-01) from transmitting UE Assistance Information messages too frequently for the SN or for (each) SCG setting. For example, the prohibit timer may be defined as shown in Table 5 below.

[0175] TimerStartStopAt expiryT3xx (The UE maintains one instance of this timer per cell group)Upon transmitting UEAssistanceInformation message with loggedDataCollectionAssistance.Upon releasing loggedDataCollectionAssistance Config during the connection re-establishment / resume procedures, upon receiving loggedDataCollectionAssistance Config set to release, or upon performing MR-DC release.No action.

[0176] Here, loggedDataCollectionAssistance may mean information about data stored by the terminal (7-01) (e.g., availability), and loggedDataCollectionAssistanceConfig may mean configuration information related to the data availability report described in step 6-15.

[0177] In one embodiment of the present disclosure, a prohibit timer for the MN or MCG setting according to Table 3 and a prohibit timer for the SN or SCG setting according to Table 5 may each (or together) be set through the Availability configuration of step 7-15 or step 7-35. Additionally, the two timers may be defined / set with a value of a common length or defined / set with a value of different lengths.

[0178] A method for a base station or SN (7-03) to request the transmission of model training data (for SN or SCG) stored in a terminal (7-01) may follow at least one of the following methods.

[0179] - Alt 1: In step 7-45, the base station or SN (7-03) may transmit relevant parameters as Xn to the MN (7-02) to request the transmission of model training data stored by the terminal (7-01). Accordingly, in step 7-55, the MN (7-02) may transmit a UE Information Request message to the terminal (7-01). Here, the MN (7-02) may request the transmission of model training data stored (for the SN or SCG) from the terminal (7-01) based on the Xn interface parameters received from the SN (7-03). At this time, the MN (7-02) may include an indicator (e.g., indicator C) to indicate that the request is for data for the SN or SCG (not for data for the MN or MCG). In this method, in step 7-25, indicator C may be omitted or may indicate that the request is for data for the MN or MCG. For a description of Step 7-55, you can refer to the description of Step 6-25.

[0180] - Alt 2: In step 7-50, MN (7-02) may receive a request from OAM (7-04) to retrieve measurement data for SN or SCG. Accordingly, in step 7-55, MN (7-02) may request the terminal (7-01) to transmit the model training data stored (for SN or SCG). Here, MN (7-02) may request the terminal (7-01) to transmit the model training data stored (for SN or SCG) based on the request received from OAM (7-04) received from SN (7-03). At this time, MN (7-02) may include an indicator (e.g., indicator C) to indicate that the request is for data for SN or SCG (not data for MN or MCG). In this method, in step 7-25, indicator C may be omitted or may indicate that the request is for data for MN or MCG.

[0181] - Alt 3: In step 7-60, the base station or SN (7-03) may send a UE Information Request message to the terminal (7-01) to request the transmission of model training data stored (for the SN or SCG). For a description of step 7-60, refer to the description of step 6-25. The UE Information Request message may be transmitted via SRB3.

[0182] - Alt 4: In step 7-65, SN (7-03) may generate a UE Information Request message (for SN or SCG) to request the terminal (7-01) to transmit measurement data for SN or SCG. SN (7-03) may place the generated UE Information Request message in a container and transmit it to MN (7-02) via Xn or an inter-node message in a transparent manner to MN (7-02). In step 7-70, MN (7-02) may include the UE Information Request message (for SN or SCG) in an RRC message (e.g., a new message or a DLInformationTransfer message) and transmit it to the terminal (7-01). The UE Information Request message may be transmitted via SRB1.

[0183] The method by which the terminal (7-01) transmits model training data (for SN or SCG) that it has stored to the base station or SN (7-03) may follow at least one of the following methods.

[0184] - Alt 1: In step 7-75, the terminal (7-01) may transmit a UE Information Response message containing model training data (for SN or SCG) to a base station or MN (7-02). The UE Information Response message may be transmitted via SRB1 or SRB2. In step 7-80, the MN (7-02) may transmit the UE Information Response message itself or the model training data included in the UE Information Response message to the SN (7-03) via the Xn interface.

[0185] - Alt 2: In step 7-75, the terminal (7-01) may transmit a UE Information Response message containing model training data (for SN or SCG) to a base station or MN (7-02). The UE Information Response message may be transmitted via SRB1 or SRB2. In step 7-85, the MN (7-02) may directly transmit the UE Information Response message itself or the model training data included in the UE Information Response message to the OAM (7-04).

[0186] - Alt 3: In step 7-90, the terminal (7-01) may (directly) transmit a UE Information Response message containing data for model training (for the SN or SCG) to the base station or SN (7-03). The UE Information Response message may be transmitted via SRB3 or a new SRB (SRBx).

[0187] - Alt 4: In step 7-95, the terminal (7-01) may generate a UE Information Response message containing data for model training (for SN or SCG) and then transmit an RRC message containing it (e.g., a new message or a ULInformationTransferMRDC message) to the MN (7-02). The RRC message may be transmitted via SRB1 or SRB2. In step 7-100, the MN (7-02) may extract a UE Information Response message containing data for model training (for SN or SCG) from the RRC message and transmit it to the SN (7-03) via the Xn interface.

[0188] In the procedure described above from FIGS. 4 to FIGS. 7b, when the base station, MN, or SN requests the transmission of data for model training from the terminal, it may not separately distinguish whether the request is for the SN or SCG or for the MN or MCG. The terminal that receives the request may report both / together the measurement data for the SN or SCG and the measurement data for the MN or MCG.

[0189] In the procedure described above from FIG. 4 to FIG. 7b, when a base station, MN, or SN requests the transmission of data for model training from a terminal, the data may be requested by training setting (or by training setting ID). The terminal that receives the request may report only the measurement data for the requested training setting (or training setting ID).

[0190] In the procedure described above from FIGS. 4 to FIGS. 7b, the base station may instruct the terminal to the node to which the terminal must transmit / report model training data and / or availability within or per training setting (e.g., per training setting ID) and / or per availability configuration setting or per availability configuration setting (ID). Alternatively, the base station may instruct the terminal to the SRB to be used for reporting. In accordance with the instructions, the terminal may determine which node to report to or which SRB to use when transmitting the stored model training data and / or availability.

[0191] In the procedure described above from FIGS. 4 to FIGS. 7b, when the terminal stores data for model training (for MN or MCG), if it is instructed to perform a handover (or MCG change) (e.g., via an RRC Reconfiguration message), it may continue to retain the data without deleting it. Additionally, when the terminal transmits an RRC Reconfiguration Complete message after performing random access to a target base station (target MN) or target cell for a handover, it may indicate that the terminal retains data for model training (for MN or MCG).

[0192] In the procedure described above from FIGS. 4 to FIGS. 7b, when the terminal stores data for model training (for SN or SCG), if it is instructed to change the SCG (or SCG change) (e.g., via an RRC Reconfiguration message), it may continue to retain the data without deleting it. Additionally, when the terminal transmits an RRC Reconfiguration Complete message after performing random access to a target base station (target SN) or target cell for SCG change, it may indicate that the terminal retains data for model training (for SN or SCG).

[0193] In the procedure described above from FIG. 4 to FIG. 7b, when the terminal stores model training data (for MN or MCG, and / or, SN or SCG), when it transmits RRC Reconfiguration Complete after receiving an RRC Reconfiguration message, the terminal may indicate that it possesses model training data (for MN or MCG, and / or, SN or SCG).

[0194] In the procedure described above from FIGS. 4 to FIGS. 7b, when the terminal indicates that it possesses data for model training or provides availability information (e.g., via a UE assistance Information message or an RRC Reconfiguration Complete message), it may indicate information regarding MN or MCG and information regarding SN or SCG separately. Alternatively, it may indicate information separately for training settings or availability settings.

[0195] FIG. 8 is a drawing illustrating the internal structure of a terminal according to one embodiment of the present disclosure.

[0196] Referring to FIG. 8, the terminal may include an RF (Radio Frequency) processing unit (8-10), a baseband processing unit (8-20), a storage unit (8-30), and a control unit (8-40). The terminal may correspond to the terminals from FIG. 1 to FIG. 7b (1-15, 4-01, 4-04, 5-01, 5-04, 6-01, 7-01).

[0197] The RF processing unit (8-10) can perform functions for transmitting and receiving signals through a wireless channel, such as signal band conversion and amplification. For example, the RF processing unit (8-10) can up-convert a baseband signal provided by the baseband processing unit (8-20) into an RF band signal and transmit it through an antenna, and can down-convert an RF band signal received through the antenna into a baseband signal. For example, the RF processing unit (8-10) may include a transmit filter, a receive filter, an amplifier, a mixer, an oscillator, a DAC (digital to analog converter), an ADC (analog to digital converter), etc. Although only one antenna is shown in FIG. 8, the terminal may be equipped with multiple antennas. Additionally, the RF processing unit (8-10) may include multiple RF chains. Furthermore, the RF processing unit (8-10) may perform beamforming. For the above beamforming, the RF processing unit (8-10) can adjust the phase and magnitude of each of the signals transmitted and received through multiple antennas or antenna elements. In addition, the RF processing unit can perform MIMO (multi-input multi-output) and can receive multiple layers when performing MIMO operation.

[0198] The baseband processing unit (8-20) can perform a conversion function between a baseband signal and a bit sequence according to the physical layer specifications of the system. For example, when transmitting data, the baseband processing unit (8-20) can generate complex symbols by encoding and modulating the transmitted bit sequence. Additionally, when receiving data, the baseband processing unit (8-20) can restore the received bit sequence by demodulating and decoding the baseband signal provided by the RF processing unit (8-10). For example, in the case of following the orthogonal frequency division multiplexing (OFDM) method, when transmitting data, the baseband processing unit (8-20) can generate complex symbols by encoding and modulating the transmitted bit sequence, and after mapping the complex symbols to subcarriers, can construct OFDM symbols through inverse fast Fourier transform (IFFT) operations and cyclic prefix (CP) insertion. Additionally, upon receiving data, the baseband processing unit (8-20) can divide the baseband signal provided from the RF processing unit (8-10) into OFDM symbol units, restore the signals mapped to subcarriers through a fast Fourier transform (FFT) operation, and then restore the received bit sequence through demodulation and decoding.

[0199] The baseband processing unit (8-20) and the RF processing unit (8-10) can transmit and receive signals as described above. Accordingly, the baseband processing unit (8-20) and the RF processing unit (8-10) may be referred to as a transmitting unit, a receiving unit, a transmitting and receiving unit, or a communication unit. Furthermore, at least one of the baseband processing unit (8-20) and the RF processing unit (8-10) may include a plurality of communication modules to support a plurality of different wireless access technologies. Additionally, at least one of the baseband processing unit (8-20) and the RF processing unit (8-10) may include different communication modules to process signals of different frequency bands. For example, the different wireless access technologies may include wireless LAN (e.g., IEEE 802.11), cellular network (e.g., LTE), etc. In addition, the above different frequency bands may include super high frequency (SHF) bands (e.g., 2 NRHz, NRHz) and millimeter wave (e.g., 60 GHz) bands.

[0200] The storage unit (8-30) can store data such as basic programs, application programs, and setting information for the operation of the terminal. In particular, the storage unit (8-30) can store information related to a second connection node that performs wireless communication using wireless connection technology. Additionally, the storage unit (8-30) can provide the stored data upon request from the control unit (8-40).

[0201] The control unit (8-40) can control the overall operations of the terminal. For example, the control unit (8-40) can control the terminal to perform the embodiments and / or methods of the present disclosure described above. For example, the control unit (8-40) can transmit and receive signals through the baseband processing unit (8-20) and the RF processing unit (8-10). Additionally, the control unit (8-40) can write and read data to and from the storage unit (8-30). To this end, the control unit (8-40) may include at least one processor. For example, the control unit (8-40) may include a communication processor (CP) that performs control for communication and an application processor (AP) that controls upper layers such as applications, and may include a multiple connection processing unit (8-42) as illustrated in the drawing.

[0202] FIG. 9 is a drawing illustrating the structure of a base station according to one embodiment of the present disclosure.

[0203] Referring to FIG. 9, according to one example of the present disclosure, a base station may be configured to include an RF processing unit (9-10), a baseband processing unit (9-20), a backhaul communication unit (9-30), a storage unit (9-40), and a control unit (9-50). The base station may correspond to the base stations of FIG. 1 through FIG. 7b (1-10, 1-30, 4-02, 4-05, 5-02, 5-05, 6-02, 7-02, 7-03).

[0204] The RF processing unit (9-10) can perform functions for transmitting and receiving signals through a wireless channel, such as signal band conversion and amplification. That is, the RF processing unit (9-10) can up-convert a baseband signal provided by the baseband processing unit (9-20) into an RF band signal and transmit it through an antenna, and can down-convert an RF band signal received through the antenna into a baseband signal. For example, the RF processing unit (9-10) may include a transmit filter, a receive filter, an amplifier, a mixer, an oscillator, a DAC, an ADC, etc. Although only one antenna is shown in the drawing, the base station may be equipped with multiple antennas. Additionally, the RF processing unit (9-10) may include multiple RF chains. Furthermore, the RF processing unit (9-10) may perform beamforming. For beamforming, the RF processing unit (9-10) can adjust the phase and magnitude of each of the signals transmitted and received through multiple antennas or antenna elements. The above RF processing unit can perform down-to-down MIMO operation by transmitting one or more layers.

[0205] The baseband processing unit (9-20) can perform a conversion function between a baseband signal and a bit sequence according to the physical layer specifications of the wireless access technology. For example, when transmitting data, the baseband processing unit (9-20) can generate complex symbols by encoding and modulating the transmitted bit sequence. Additionally, when receiving data, the baseband processing unit (9-20) can restore the received bit sequence by demodulating and decoding the baseband signal provided by the RF processing unit (9-10). For example, in the case of an OFDM method, when transmitting data, the baseband processing unit (9-20) can generate complex symbols by encoding and modulating the transmitted bit sequence, and after mapping the complex symbols to subcarriers, can construct OFDM symbols through IFFT operation and CP insertion. Additionally, upon receiving data, the baseband processing unit (9-20) can divide the baseband signal provided by the RF processing unit (9-10) into OFDM symbol units, restore the signals mapped to subcarriers through FFT operations, and then restore the received bit sequence through demodulation and decoding. The baseband processing unit (9-20) and the RF processing unit (9-10) can transmit and receive signals as described above. Accordingly, the baseband processing unit (9-20) and the RF processing unit (9-10) may be referred to as a transmitting unit, a receiving unit, a transceiver unit, a communication unit, or a wireless communication unit.

[0206] The backhaul communication unit (9-30) can provide an interface for communicating with other nodes within the network. That is, the backhaul communication unit (9-30) can convert a bit sequence transmitted from the main base station to another node, e.g., an auxiliary base station, a core network, etc., into a physical signal, and can convert a physical signal received from the other node into a bit sequence.

[0207] The storage unit (9-40) can store data such as basic programs, application programs, and configuration information for the operation of the main station. In particular, the storage unit (9-40) can store information regarding bearers assigned to connected terminals, measurement results reported from connected terminals, etc. Additionally, the storage unit (9-40) can store information that serves as a criterion for determining whether to provide or disconnect multiple connections to the terminals. Furthermore, the storage unit (9-40) can provide the stored data upon a request from the control unit (9-50).

[0208] The control unit (9-50) can control the overall operations of the main base station. For example, the control unit (9-50) can control the base station to perform the embodiments and / or methods of the present disclosure described above. For example, the control unit (9-50) can transmit and receive signals through the baseband processing unit (9-20) and the RF processing unit (9-10) or through the backhaul communication unit (9-30). Additionally, the control unit (9-50) can write and read data to and from the storage unit (9-40). To this end, the control unit (9-50) may include at least one processor and may include a multiple connection processing unit (9-52) as illustrated in the drawing.

[0209] Meanwhile, the embodiments of the present disclosure disclosed in this specification and drawings are merely specific examples provided to facilitate the explanation of the technical content of the present disclosure and to aid in understanding the present disclosure, and are not intended to limit the scope of the present disclosure. That is, it is obvious to those skilled in the art that other variations based on the technical concept of the present disclosure are possible.

[0210] In addition, each of the above embodiments may be combined and operated as needed. For example, parts of one embodiment of the present disclosure and another embodiment may be combined to operate a base station and a terminal.

[0211] In addition, the embodiments of the present disclosure are applicable to other communication systems, and other variations based on the technical concept of the embodiments may also be implemented. For example, the embodiments may be applied to LTE systems, 5G, NR systems, or 6G systems.

[0212] Therefore, the scope of the present disclosure should not be limited to the described embodiments, but should be defined by the claims set forth below as well as equivalents thereof.

Claims

1. A method performed by a UE (user equipment) of a wireless communication system, A step of receiving a control message from a base station that includes configuration information for collecting network-side data; A step of logging L1 (layer 1) measurement results based on the above-mentioned configuration information for network-side data collection; and The method includes the step of transmitting the recorded L1 measurement result to the base station, A method characterized by the fact that when the above UE is in a DC (dual connectivity) state, the configuration information for network-side data collection is associated with the MCG (master cell group), and the configuration by the SN (secondary node) is excluded.

2. In Paragraph 1, The step of transmitting the L1 measurement results recorded above is, If the amount of data of the recorded L1 measurement result is greater than or equal to a threshold, the step of transmitting a UE assistance information message to the base station, the message including information indicating the availability of the recorded L1 measurement result; A step of receiving a UE information request message from the base station containing information requesting a report of the recorded L1 measurement result; and A method characterized by including the step of transmitting a UE information response message containing the L1 measurement result recorded above to the base station.

3. In Paragraph 2, A method characterized in that the above control message further includes information indicating the above threshold value.

4. In Paragraph 1, A method further comprising the step of transmitting to the base station a UE capability information message containing information indicating whether the UE supports measurement records for network-side data collection.

5. A method performed by a base station of a wireless communication system, A step of transmitting a control message containing configuration information for network-side data collection to a UE (user equipment); and The method includes the step of receiving L1 (layer 1) measurement results recorded by the UE based on the network-side data collection configuration information from the UE, and A method characterized by the fact that when the above UE is in a DC (dual connectivity) state, the configuration information for network-side data collection is associated with the MCG (master cell group), and the configuration by the SN (secondary node) is excluded.

6. In Paragraph 5, The step of receiving the L1 measurement result recorded above is, A step of receiving a UE assistance information message from the UE, which includes information indicating the availability of the recorded L1 measurement result when the amount of data of the recorded L1 measurement result is greater than or equal to a threshold; The step of transmitting a UE information request message to the UE, the message including information requesting the reporting of the L1 measurement results recorded above; and A method characterized by including the step of receiving a UE information response message from the above UE that includes the recorded L1 measurement result.

7. In Paragraph 6, A method characterized in that the above control message further includes information indicating the above threshold value.

8. In Paragraph 6, A method further comprising the step of receiving a UE capability information message from the above UE, the message including information indicating whether the UE supports measurement records for network-side data collection.

9. In the UE (user equipment) of a wireless communication system, At least one transceiver; At least one processor connected to the above at least one transceiver so as to be able to communicate; and Connected to communicate with at least one processor and capable of executing individually or in any combination of the at least one processor, the UE, Receive a control message from a base station containing configuration information for network-side data collection, and Based on the above network-side data collection configuration information, L1 (layer 1) measurement results are logged, and It includes a memory that stores a command causing the above-mentioned recorded L1 measurement result to be transmitted to the base station, and A UE characterized in that, when the above UE is in a DC (dual connectivity) state, the configuration information for network-side data collection is associated with the MCG (master cell group), and the configuration by the SN (secondary node) is excluded.

10. In Paragraph 9, The instruction executable by the above at least one processor individually or in any combination thereof is for the UE to transmit the recorded L1 measurement result, If the amount of data of the recorded L1 measurement result is greater than or equal to a threshold, a UE assistance information message containing information indicating the availability of the recorded L1 measurement result is transmitted to the base station, and Receive a UE information request message from the base station containing information requesting a report of the recorded L1 measurement result, and A UE characterized by causing a UE information response message containing the above-mentioned recorded L1 measurement result to be transmitted to the base station.

11. In Paragraph 10, A UE characterized by further including information indicating the threshold value in the above control message.

12. In Paragraph 9, The instruction that is executable individually or in any combination of the above-mentioned at least one processor is, the UE, A UE characterized by further causing the transmission to the base station of a UE capability information message containing information indicating whether the UE supports measurement records for network-side data collection.

13. In a base station of a wireless communication system, At least one transceiver; At least one processor connected to the above at least one transceiver so as to be able to communicate; and The base station is connected to communicate with at least one processor and is capable of executing individually or in any combination of the at least one processor, and, A control message containing configuration information for network-side data collection is transmitted to the UE (user equipment), and It includes a memory that stores a command causing the receiving of an L1 (layer 1) measurement result recorded by the UE based on the network-side data collection configuration information from the UE, and A base station characterized by the fact that when the above UE is in a DC (dual connectivity) state, the network-side data collection configuration information is associated with the MCG (master cell group), and the configuration by the SN (secondary node) is excluded.

14. In Paragraph 13, The instruction executable by at least one processor, individually or in any combination, for the base station to receive the recorded L1 measurement result, From the above UE, if the amount of data of the recorded L1 measurement result is greater than or equal to a threshold, a UE assistance information message is received that includes information indicating the availability of the recorded L1 measurement result, and Transmit a UE information request message to the UE, which includes information requesting the reporting of the L1 measurement results recorded above, and Causing to receive a UE information response message from the above UE that includes the recorded L1 measurement result, and A base station characterized by further including information indicating the threshold value in the above control message.

15. In Paragraph 13, The instruction that can be executed individually or in any combination of the above-mentioned at least one processor is, the base station, A base station characterized by further causing to receive a UE capability information message from the above UE, the message including information indicating whether the UE supports measurement records for network-side data collection.

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