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 higher data transmission rates. A disclosure of the present specification provides a method comprising the steps of: receiving at least one piece of logged measurement configuration information from a base station; performing measurement logging on a serving cell on the basis of the at least one piece of logged measurement configuration information; and transmitting, to the base station, a first message including logged measurement information, wherein the logged measurement information includes a logging instance corresponding to each of the at least one piece of logged measurement configuration information, and each logging instance includes information about at least one reference signal and at least one measurement result value for the at least one reference signal.

Description

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

[0001] The present disclosure relates to a terminal and a base station of a mobile communication system, and more specifically, the present disclosure relates to a method and apparatus for utilizing artificial intelligence and machine learning in a mobile 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 to guarantee coverage in the terahertz band of 6G mobile communication technology, Full Dimensional MIMO (FD-MIMO), array antennas, and large-scale antennas; metamaterial-based lenses and antennas to improve terahertz band signal coverage; high-dimensional spatial multiplexing technology using OAM (Orbital Angular Momentum); and Reconfigurable Intelligent Surface (RIS) technology; 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 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] As a result of the aforementioned developments and advancements in mobile communication systems, it has become possible to provide a variety of services, and thus measures to effectively provide these services are required.

[0009] One embodiment of the present disclosure aims to provide a method and apparatus for beam management in which a terminal performs a prediction using a UE-side AI / ML model or a network performs a prediction using a NW-side AI / ML model.

[0010] In addition, one embodiment of the present disclosure aims to provide a method and apparatus for a terminal to collect / report / store measurement data for learning a model.

[0011] The technical problems to be solved by the present invention are not limited to those mentioned above, and other unmentioned technical problems may be considered by those skilled in the art from the various embodiments of the present disclosure described below.

[0012] A method performed by a terminal of a wireless communication system according to an embodiment of the present invention for solving the above-mentioned problems comprises: receiving at least one logged measurement configuration information from a base station; performing logging of a measurement for a serving cell based on the at least one logged measurement configuration information; and transmitting a first message including the logged measurement information to the base station, wherein the logged measurement information includes a logging instance corresponding to each of the at least one logged measurement configuration information, and each of the logging instances may include information of at least one reference signal and at least one measurement result value for the at least one reference signal.

[0013] According to an embodiment, the logged measurement information may further include identification information of the serving cell.

[0014] According to an embodiment, the reference signal may include at least one of a CSI-RS (channel state information reference signal) or an SSB (synchronization signal block).

[0015] According to an embodiment, the step of transmitting the first message may include: receiving a second message from the base station requesting the transmission of the logged measurement information; and transmitting the first message containing the logged measurement information to the base station in response to the second message.

[0016] According to an embodiment, the second message may be a UE (user equipment) information request message.

[0017] According to an embodiment, the first message may be a UE information response message.

[0018] According to an embodiment, the method may further include the step of transmitting a terminal capability information message to the base station, the message containing information indicating whether the terminal supports AI / ML-based operations.

[0019] In addition, a method performed by a base station of a wireless communication system according to an embodiment of the present invention for solving the above-mentioned problems comprises: a step of transmitting at least one logged measurement configuration information to a terminal; and a step of receiving from the terminal a first message including measurement information that is measured and logged based on the at least one logged measurement configuration information, wherein the logged measurement information includes a logging instance corresponding to each of the at least one logged measurement configuration information, and each of the logging instances may include information of at least one reference signal and at least one measurement result value for the at least one reference signal.

[0020] According to an embodiment, the step of receiving the first message may include: transmitting a second message to the terminal requesting the transmission of the logged measurement information; and receiving the first message containing the logged measurement information from the terminal in response to the second message.

[0021] According to an embodiment, the second message may be a UE (user equipment) information request message, and the first message may be a UE information response message.

[0022] According to an embodiment, the method may further include the step of receiving a capability information message from the terminal that includes information indicating whether the terminal supports AI / ML-based operations.

[0023] In addition, a terminal of a wireless communication system according to an embodiment of the present invention for solving the above-mentioned problems comprises: a transceiver; and a control unit connected to the transceiver, receiving at least one logged measurement configuration information from a base station, performing logging of a measurement for a serving cell based on the at least one logged measurement configuration information, and transmitting a first message including the logged measurement information to the base station, wherein the logged measurement information includes a logging instance corresponding to each of the at least one logged measurement configuration information, and each of the logging instances may include information of at least one reference signal and at least one measurement result value for the at least one reference signal.

[0024] In addition, a base station of a wireless communication system according to an embodiment of the present invention for solving the above-mentioned problems comprises: a transceiver; and a control unit connected to the transceiver and transmitting at least one logged measurement configuration information to a terminal, and receiving from the terminal a first message including measurement information that is measured and logged based on the at least one logged measurement configuration information, wherein the logged measurement information includes a logging instance corresponding to each of the at least one logged measurement configuration information, and each of the logging instances may include information of at least one reference signal and at least one measurement result value for the at least one reference signal.

[0025] One embodiment of the present disclosure may provide a method and apparatus for beam management in which a terminal performs a prediction using a UE-side AI / ML model or a network performs a prediction using a NW-side AI / ML model.

[0026] In addition, one embodiment of the present disclosure may provide a method and apparatus for a terminal to collect / report / store measurement data for learning a model.

[0027] The effects obtainable in the present disclosure are not limited to those mentioned in the various embodiments, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure pertains from the description below.

[0028] FIG. 1a is a drawing illustrating the structure of a mobile communication system according to one embodiment of the present disclosure.

[0029] FIG. 1b is a diagram illustrating a wireless connection state transition in a mobile communication system according to one embodiment of the present disclosure.

[0030] FIG. 1c is a diagram illustrating an AI / ML model for predicting beam measurement values ​​for beam management according to one embodiment of the present disclosure.

[0031] FIG. 1d 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.

[0032] FIG. 1e 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.

[0033] FIG. 1f 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.

[0034] FIG. 1g is a drawing illustrating a method in which a terminal receives measurement data for model learning or stores it, according to one embodiment of the present disclosure.

[0035] FIG. 1h is a drawing illustrating a method in which a terminal receives measurement data for model learning or stores it, according to one embodiment of the present disclosure.

[0036] FIG. 1i is a drawing illustrating the internal structure of a terminal according to one embodiment of the present disclosure.

[0037] FIG. 1j is a drawing illustrating the structure of a base station according to one embodiment of the present disclosure.

[0038] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0039] In describing the embodiments, technical details that are well known in the technical field 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.

[0040] 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.

[0041] 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 related functions or configurations 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.

[0042] 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 an embodiment of this disclosure is described below using a 5G system as an example, the embodiment 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. In addition, the present disclosure may be applied to other communication systems with some modifications made at the discretion of a person with skilled technical knowledge, without significantly departing from the scope of the present disclosure. The contents of the present disclosure are applicable to FDD and TDD systems.

[0043] 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).

[0044] 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.

[0045] 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 may be configured to run one or more processors. Accordingly, 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. Furthermore, in the embodiment, the 'part' may include one or more processors.

[0046] For the convenience of the following explanation, the present invention uses terms and names defined in the LTE (3rd Generation Partnership Project Long Term Evolution), 5GS, and NR specifications, which are standards defined by the 3GPP (The 3rd Generation Partnership Project) organization among currently existing communication standards. However, the present invention is not limited by the above terms and names and can be applied in the same way to systems conforming to other standards. For example, the present invention can be applied to 3GPP 5GS / NR (5th generation mobile communication standard).

[0047] FIG. 1a is a drawing illustrating the structure of a mobile communication system according to one embodiment of the present disclosure.

[0048] Referring to FIG. 1a, the wireless access network of a mobile communication system (New Radio, NR) may be composed of a base station (next generation Node B, hereinafter gNB) (1a-10) and an AMF (1a-05, New Radio Core Network).

[0049] The user terminal (New Radio User Equipment, hereinafter NR UE or terminal) (1a-15) can connect to an external network through gNB (1a-10) and AMF (1a-05), etc.

[0050] The mobile communication system may be a next-generation mobile communication system, and the base station (1a-10) may be a next-generation base station.

[0051] According to one embodiment, the gNB (1a-10) in FIG. 1a may correspond to the eNB (Evolved Node B) of an existing LTE system. The gNB (1a-10) is connected to the NR UE (1a-15) via a wireless channel and can provide a superior service compared to the existing Node B (1a-20). 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 may be required to collect status information such as the buffer status, available transmission power status, and channel status of the UEs (1a-15) and perform scheduling, and this can be performed by the gNB (1a-10). According to one embodiment, a single gNB (1a-10) can typically control multiple cells. To achieve ultra-high-speed data transmission, the next-generation mobile communication system can have a bandwidth greater than the existing maximum bandwidth, and can additionally incorporate beamforming technology by using Orthogonal Frequency Division Multiplexing (OFDM) as a wireless access technology. In addition, the next-generation mobile communication system can apply an Adaptive Modulation & Coding (AMC) method that determines the modulation scheme and channel coding rate according to the channel state of the terminal (1a-15).

[0052] According to one embodiment, in FIG. 1a, the AMF (1a-05) can perform functions such as mobility support, bearer configuration, and quality of service (QoS) configuration. The AMF (1a-05) is a device responsible for various control functions as well as mobility management functions for the terminal (1a-15) and can be connected to a plurality of base stations (1a-10). 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 (1a-05) can be connected to the MME (1a-25) through a network interface.

[0053] According to one embodiment, the MME (1a-25) can be connected to an existing base station eNB (1a-30). For example, in FIG. 1a, a terminal (1a-15) supporting LTE-NR Dual Connectivity can transmit and receive data while maintaining a connection to both the gNB (1a-10) and the eNB (1a-30) (1a-35).

[0054] FIG. 1b is a diagram illustrating a wireless connection state transition in a mobile communication system according to one embodiment of the present disclosure.

[0055] Referring to Fig. 1b, a mobile communication system may have three radio resource control (RRC) states or RRC modes.

[0056] Specifically, the connection mode (RRC_CONNECTED, 1b-05) may be a wireless connection state in which the terminal can transmit and receive data. The standby mode (idle mode) (RRC_IDLE, 1b-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.

[0057] According to one embodiment of the present disclosure, a new inactive (RRC_INACTIVE) radio access state (1b-15) may be defined in a mobile communication system. In the inactive radio access state (1b-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 radio access state (1b-15) may include at least one of the following:

[0058] - Cell re-selection mobility;

[0059] - CN - NR RAN connection (both C / U-planes (control plane / user plane)) has been established for UE;

[0060] - The UE AS(Access Stratum) context is stored in at least one gNB and the UE;

[0061] - Paging is initiated by NR RAN;

[0062] - RAN-based notification area is managed by NR RAN; or

[0063] - NR RAN knows the RAN-based notification area which the UE belongs to.

[0064] According to one embodiment of the present disclosure, a terminal in an INACTIVE wireless connection state (1b-15) may use a specific procedure and transition to a connection mode (1b-05) or a standby mode (1b-30). The terminal may transition from the INACTIVE mode (1b-15) to the connection mode (1b-05) according to a resume procedure. Additionally, the terminal may transition from the connection mode (1b-05) to the INACTIVE mode (1b-15) using a Release procedure that includes suspend setting information (1b-10). 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. Additionally, according to one embodiment, the terminal may transition from the INACTIVE mode (1b-15) to the standby mode (1b-30) through a Resume followed by a Release procedure (1b-20). The transition between the connection mode (1b-05) and the standby mode (1b-30) may follow LTE technology. Additionally, according to FIG. 1b, the transition between the modes may be performed through an establishment or release procedure (1b-25).

[0065] FIG. 1c is a diagram illustrating an AI / ML model for predicting beam measurement values ​​for beam management (BM) according to one embodiment of the present disclosure.

[0066] In one embodiment of the present disclosure, beam management may be a use case in which an AI / ML (artificial intelligence / machine learning) model can be utilized. For example, a base station may utilize an AI / ML model for beam management of a downlink (DL) transmission beam. In one embodiment of the present disclosure, positioning accuracy enhancements may be a use case in which an AI / ML model can be utilized. In one embodiment of the present disclosure, Channel state information (CSI) feedback enhancement may be a use case in which an AI / ML model can be utilized.

[0067] In one embodiment of the present disclosure, a beam management use case may include a sub-use case called spatial prediction (1c-05) and temporal prediction (1c-10).

[0068] In one embodiment of the present disclosure, a set of beams used as input to an AI / ML model for a beam management use case may be referred to as SET B. In one embodiment of the present disclosure, a set of beams derived as output to an AI / ML model for a beam management use case may be referred to as SET A.

[0069] In one embodiment of the present disclosure, when performing spatial prediction (1c-05) for beam management, the input of the AI / ML model is at a specific time point (time instance K) (e.g., t KOne or more beams in ) (e.g., B i Measurement values ​​for )(i=1, 2, ..., N) (e.g., RSRP (reference signal received power) and / or RSRQ (reference signal received quality) and / or SINR (signal-to-interference-plus-noise ratio))(e.g., Meas(B i ,t K ))(1c-15) may be.

[0070] In one embodiment of the present disclosure, the measured value for the beam may be one of the following:

[0071] - RSRP and / or RSRQ and / or SINR measured at Layer 1;

[0072] - RSRP and / or RSRQ and / or SINR measured / acquired at Layer 3;

[0073] - The filtered RSRP and / or RSRQ and / or SINR values ​​measured at Layer 1 (e.g., a weighted average value using measurements over a specified period); or

[0074] - Filtered RSRP and / or RSRQ and / or SINR values ​​measured / acquired at Layer 3 (e.g., a weighted average value using measurements over a specified period).

[0075] In one embodiment of the present disclosure, when performing spatial prediction (1c-05) for beam management, the following information may be considered (or used) as input to an AI / ML model:

[0076] - SET B related information (e.g., beam ID);

[0077] - Measurement time information;

[0078] - L1-RSRP measurement based on Set B;

[0079] - L1-RSRP measurement based on Set B and assistance information;

[0080] - CIR (Channel Impulse Response) based on Set B; and / or

[0081] - L1-RSRP measurement based on Set B and the corresponding DL Tx and / or Rx beam ID.

[0082] In one embodiment of the present disclosure, during spatial prediction (1c-05) for beam management, the output of the AI / ML model is at a specific time point (e.g., t K One or more beams in ) (e.g., B i Predicted values ​​for ) (e.g., RSRP and / or RSRQ and / or SINR) (e.g., P_Meas(B i ,t K )(i=N+1, N+2, ..., N+M))(1c-20) may be possible.

[0083] In one embodiment of the present disclosure, the predicted value for the beam may be one of the following:

[0084] - RSRP and / or RSRQ and / or SINR predicted to be measured at Layer 1;

[0085] - RSRP and / or RSRQ and / or SINR predicted to be measured / acquired at Layer 3;

[0086] - 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., a (weighted) average value using measured / predicted values ​​over a specified period); or

[0087] - 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., a (weighted) average value using measured / predicted values ​​over a specified period).

[0088] In one embodiment of the present disclosure, when performing spatial prediction (1c-05) for beam management, the following information may be considered (or used) as the output of an AI / ML model:

[0089] - SET A related information (e.g., ID per beam);

[0090] - One beam predicted to have the highest (best) measurement value (e.g., Top-1 beam);

[0091] - N (≥1) beams (e.g., Top-N beam) predicted to have the highest (best) measurements; and / or

[0092] - Probability that each beam in SET A is a Top-1 or Top-N beam.

[0093] In one embodiment of the present disclosure, when performing temporal prediction (1c-10) for beam management, the input to the AI / ML model is one or more (past) time points (e.g., t i A single beam (e.g., B) in )(i=1, 2, ..., N)) K Measured values ​​for ) (e.g., RSRP and / or RSRQ and / or SINR) (e.g., Meas(B K ,t i ))(1c-25) may be. In one embodiment of the present disclosure, the measured value for the beam may be one of the following:

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

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

[0096] - The filtered RSRP and / or RSRQ and / or SINR values ​​measured at Layer 1 (e.g., a weighted average value using measurements over a specified period); or

[0097] - Filtered RSRP and / or RSRQ and / or SINR values ​​measured / acquired at Layer 3 (e.g., a weighted average value using measurements over a specified period).

[0098] In one embodiment of the present disclosure, when performing temporal prediction (1c-10) for beam management, the following information may be considered / used as input to an AI / ML model:

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

[0100] - Measurement time information;

[0101] - L1-RSRP measurement based on Set B;

[0102] - L1-RSRP measurement based on Set B and assistance information;

[0103] - CIR (Channel Impulse Response) based on Set B; and / or

[0104] - L1-RSRP measurement based on Set B and the corresponding DL Tx and / or Rx beam ID.

[0105] In one embodiment of the present disclosure, when performing a temporal prediction (1c-10) for beam management, the output of an AI / ML model is at one or more (future) points in time (e.g., t iA single beam (e.g., B) in )(i=N+1, N+2, ..., N+M)) K Predicted values ​​for ) (e.g., RSRP and / or RSRQ and / or SINR) (e.g., P_Meas(B K ,t i ))(1c-30) may be.

[0106] In one embodiment of the present disclosure, the predicted value for the beam may be one of the following:

[0107] - RSRP and / or RSRQ and / or SINR predicted to be measured at Layer 1;

[0108] - RSRP and / or RSRQ and / or SINR predicted to be measured / acquired at Layer 3;

[0109] - 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., a (weighted) average value using measured / predicted values ​​over a specified period); or

[0110] - 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., a (weighted) average value using measured / predicted values ​​over a specified period).

[0111] In one embodiment of the present disclosure, when performing temporal prediction (1c-10) for beam management, the following information may be considered / used as the output of an AI / ML model:

[0112] - SET A related information (e.g., beam ID);

[0113] - Prediction time point information;

[0114] - The point in time when the measurement is predicted to be highest (best); and / or

[0115] - N (≥1) time points where the measurement value is predicted to be highest (best).

[0116] In one embodiment of the present disclosure, the AI / ML model for beam management may be an AI / ML model that simultaneously performs the aforementioned spatial prediction (1c-05) and temporal prediction (1c-10). 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. Refer to the foregoing for the input information. 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. Refer to the foregoing for the output information.

[0117] In one embodiment of the present disclosure, a terminal (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, improving location accuracy, and improving CSI feedback.

[0118] In one embodiment of the present disclosure, a network (NW) (e.g., a base station or an LMF (e.g., a 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 improving CSI feedback.

[0119] FIG. 1d 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.

[0120] Referring to FIG. 1d, in step 1d-05, a terminal (e.g., terminal 1) (UE1) (1d-10) may transmit capability information of the terminal (1d-10) to a connected base station (e.g., base station 1 or network 1) (gNB1) (1d-15) (e.g., via a UECapabilityInformation message). To receive the terminal capability information, the base station (1d-15) may first request the terminal (1d-10) to transmit the capability information of the terminal (1d-10) (e.g., via a UECapabilityEnquiry message). The capability information transmitted by the terminal (1d-10) may include information indicating (indicating, associated with) whether the terminal (1d-10) 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, depending on the embodiment, step 1d-05 may be omitted. For example, if the base station (1d-15) has already received terminal capability information, the base station (1d-15) does not transmit a terminal capability request to the terminal (1d-10), and the step of the terminal (1d-10) transmitting terminal capability information may be omitted.

[0121] In step 1d-25, the base station (1d-15) may transmit model training-related configuration information (e.g., training configuration) to the terminal (1d-10) for collecting data necessary for UE-side model training. The training configuration may include at least one of configuration information for measurement and configuration information for reporting. Prior to this, the base station (1d-15) may receive configuration information regarding model training or a configuration request regarding model training from the terminal (1d-10) or a training entity (1d-20). However, this is an example of the present disclosure, and the base station (1d-15) may transmit the training configuration to the terminal (1d-10) even without receiving a configuration request regarding model training. For example, based on specific conditions (for example, based on the judgment of the base station (1d-15), or when conditions set to transmit the training configuration are satisfied, or based on a pre-set time or period), the base station (1d-15) can transmit the training configuration to the terminal (1d-10).

[0122] In one embodiment of the present disclosure, the learning object (1d-20) may be a terminal (1d-10) or a terminal server. The terminal server may be connected to the terminal (1d-10) via a 3GPP network (e.g., a server within the 3GPP network) or connected to the terminal (1d-10) via an external network (e.g., Wi-Fi) (e.g., a server outside the 3GPP network).

[0123] In step 1d-30, the terminal (1d-10) may perform measurements to generate data necessary for training the UE-side model, and then report the measurement results (e.g., training report) to the base station (1d-15) or the training object (1d-20). If the terminal (1d-10) reports the measurement results to the base station (1d-15), the base station (1d-15) may, according to an embodiment, reprocess the received report and transmit it to the training object (1d-20). Alternatively, according to an embodiment, the base station (1d-15) may transmit the information received from the terminal (1d-10) to the training object (1d-20) as is.

[0124] In step 1d-35, the training object (1d-20) can train a UE-side model using the received reports (model training).

[0125] In step 1d-50, a terminal (e.g., terminal 2) (UE2) (1d-40) may receive a trained model from a training object (1d-20). For example, the trained model may be transmitted to the terminal (1d-40) via a base station (e.g., base station 2) (gNB2) (1d-45). According to an embodiment, the terminal 2 (1d-40) may be the same terminal as the terminal 1 (1d-10), and the base station 2 (1d-45) may be the same base station as the base station 1 (1d-15).

[0126] Although not illustrated, prior to receiving the model, terminal 2 (1d-40) may transmit capability information of terminal 2 (1d-40) to the connected base station 2 (1d-45) (e.g., via a UECapabilityInformation message). To receive terminal capability information, the base station (1d-45) may first request terminal 2 (1d-40) to transmit capability information of terminal 2 (1d-40) (e.g., via a UECapabilityEnquiry message). The capability information transmitted by terminal 2 (1d-40) may include information indicating (indicating, associated with) whether terminal 2 (1d-40) 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, the step of transmitting terminal capability information may be omitted.

[0127] In step 1d-55, terminal 2 (1d-40) can perform inference or prediction using the received model. For example, in the case of beam management, terminal 2 (1d-40) can perform spatial or temporal beam prediction. This step can be performed after terminal 2 (1d-40) receives inference-related settings from base station 2 (1d-45).

[0128] In step 1d-60, terminal 2 (1d-40) may report the result of the prediction or inference to base station 2 (1d-45) (prediction report).

[0129] In step 1d-65, base station 2 (1d-45) can perform downlink beam management for terminal 2 (1d-40) based on the prediction / inference result received from terminal 2 (1d-40) and select an appropriate beam to service the terminal.

[0130] FIG. 1e 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.

[0131] Referring to FIG. 1e, in step 1e-05, a terminal (e.g., terminal 1) (UE1) (1e-10) may transmit capability information of the terminal (1e-10) to a connected base station (e.g., base station 1 or network 1) (gNB1) (1e-15) (e.g., via a UECapabilityInformation message). To receive the terminal capability information, the base station (1e-15) may first request the terminal (1e-10) to transmit the capability information of the terminal (1e-10) (e.g., via a UECapabilityEnquiry message). The capability information transmitted by the terminal (1e-10) may include information indicating (indicating, associated with) whether the terminal (1e-10) 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, depending on the embodiment, step 1e-05 may be omitted. For example, if the base station (1e-15) has already received terminal capability information, the base station (1e-15) does not transmit a terminal capability request to the terminal (1e-10), and the step of the terminal (1e-10) transmitting terminal capability information may be omitted.

[0132] In step 1e-25, the base station (1e-15) may transmit model learning-related configuration information (e.g., training configuration) to the terminal (1e-10) for collecting data necessary for NW-side model learning. The training configuration may include at least one of configuration information for measurement and configuration information for reporting. Prior to this, the base station (1e-15) may receive configuration information regarding model learning or a configuration request regarding model learning from the terminal (1e-10) or the learning object (1e-20). However, this is an embodiment of the present disclosure, and the base station (1e-15) may transmit the training configuration to the terminal (1e-10) even without receiving a configuration request regarding model learning. For example, the base station (1e-15) may transmit the training configuration to the terminal (1e-10) based on specific conditions (e.g., based on the judgment of the base station (1e-15), or when conditions set for transmitting the training configuration are satisfied, or based on a pre-set time or period).

[0133] In one embodiment of the present disclosure, the learning object (1e-20) may be a base station (e.g., 1e-15 or another base station) or an AMF or UPF or OAM or TCE (Trace collection entity) or MCE (Measurement collection entity). Or the learning object (1e-20) may be a server connected to them. Additionally, the learning object (1e-20) may be an external server (outside of 3GPP).

[0134] In step 1e-30, the terminal (1e-10) may perform measurements to generate data necessary for NW-side model training, and then report the measurement results (e.g., training report) to the base station (1e-15) or the training object (1e-20). If the terminal (1e-10) reports the measurement results to the base station (1e-15), the base station (1e-15) may, according to an embodiment, reprocess the received report and transmit it to the training object (1e-20). Alternatively, according to an embodiment, the base station (1e-15) may transmit the information received from the terminal (1e-10) to the training object (1e-20) as is.

[0135] In step 1e-35, the training object (1e-20) can train the NW-side model using the received report. Alternatively, the base station (1e-15) can train the NW-side model (model training) using the measurement results received from 1e-30 (e.g., without transmitting them to the training object (1e-20)).

[0136] In step 1e-50, the base station (e.g., base station 2) (gNB2) (1e-45) may receive a trained model from the training object (1e-20). Alternatively, according to an embodiment, the base station (1e-45) may possess a model that it has trained itself.

[0137] Although not illustrated, before or after step 1e-50, terminal 2 (UE2) (1e-40) may transmit capability information of terminal 2 (1e-40) to the connected base station 2 (1e-45) (e.g., via a UECapabilityInformation message). To receive terminal capability information, the base station (1e-45) may first request terminal 2 (1e-40) to transmit capability information of terminal 2 (1e-40) (e.g., via a UECapabilityEnquiry message). The capability information transmitted by terminal 2 (1e-40) may include information indicating (indicating, associated with) whether terminal 2 (1e-40) 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, the step of transmitting terminal capability information as described above may be omitted.

[0138] In step 1e-55, terminal 2 (1e-40) can perform a measurement (e.g., after receiving measurement settings for the NW-side model from the base station (1e-45)) and then report the measurement result (e.g., measurement report) to the base station (1e-45).

[0139] In step 1e-60, base station 2 (1e-45) can perform inference or prediction using the measurement report received from terminal 2 (1e-40) and the learned model. For example, in the case of beam management, spatial or temporal beam prediction can be performed.

[0140] In step 1e-65, base station 2 (1e-45) can perform downlink beam management for terminal 2 (1e-40) based on prediction / inference results and select an appropriate beam to service the terminal (1e-40).

[0141] According to an embodiment, the terminal 2 (1e-40) may be the same terminal as the terminal 1 (1e-10), and the base station 2 (1e-45) may be the same base station as the base station 1 (1e-15).

[0142] In one embodiment of the present disclosure, the content of the invention related to the UE-side model and / or NW-side model has been described assuming a downlink beam management use case; however, it can be used or applied in the same way to various use cases using the UE-side model and / or NW-side model regardless of the use case.

[0143] As an example of one embodiment of the present disclosure, the contents of the present invention may be used in a UE-side model and / or an NW-side model.

[0144] FIG. 1f 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.

[0145] Referring to FIG. 1f, in step 1f-15, the terminal (1f-05) may receive information related to terminal capabilities from the base station (1f-10) and then report said information to the base station (1f-10). Step 1f-15 may cross-reference step 1e-05 or step 1d-05. For example, the terminal (1f-05) may report information related to memory capabilities (e.g., minimum supported memory) of the terminal (1f-05) (for storing measurement data for model learning) to the base station (1f-10). Additionally, as described above, step 1f-15 may be omitted.

[0146] In step 1f-20, the terminal (1f-05) may receive model training related configuration information (e.g., training configuration) from the base station (1f-10). For example, the model training related configuration information may include configuration information (e.g., resource information) for the terminal (1f-05) 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). Additionally, for example, the model training related configuration information may include information for the terminal (1f-05) to report the data collected / measured for model training.

[0147] Apart from or in addition to the above model learning related setting information, the terminal (1f-05) can receive data related information (e.g., availability) and reporting related settings (e.g., availability configuration or UE Assistance information settings) (e.g., via OtherConfig) from the base station (1f-10).

[0148] The above training configuration and / or Availability configuration may be transmitted via an RRC Reconfiguration message or an RRC Resume message. The message may be transmitted to SRB1 (signaling radio bearer 1). Step 1f-20 may cross-reference Step 1e-25 and / or Step 1d-25.

[0149] In step 1f-22, the terminal (1f-05) may perform measurements based on model training configuration information (e.g., training configuration) and store (log) the resulting data in memory. In one embodiment of the present disclosure, the terminal (1f-05) may periodically perform measurements (e.g., regarding beam resources) and store the results (e.g., L1-RSRP). For example, this may be referred to as periodic logging. In one embodiment of the present disclosure, the terminal may perform measurements (e.g., regarding beam resources) and store the results (e.g., L1-RSRP) only when a specific event is satisfied. For example, this may be referred to as event-based logging. If the event is not satisfied, the terminal (1f-05) may stop the measurements and stop storing the results. In one embodiment of the present disclosure, as one of the events, if the signal strength of the serving cell of the terminal (1f-05) is better (higher) than a specific threshold value (e.g., a threshold value set by the network) (for a certain period of time set by the network), the event may be determined to be satisfied. In one embodiment of the present disclosure, as one of the events, if the signal strength of the serving cell of the terminal (1f-05) is worse (lower) than a specific threshold value (e.g., a threshold value set by the network) (for a certain period of time set by the network), the event may be determined to be satisfied.

[0150] In step 1f-25, the terminal (1f-05) can report stored data-related information (e.g., availability information) to the base station (1f-10) (e.g., via SRB1). At this time, the terminal (1f-05) can report data-related information (e.g., availability information) based on the availability configuration received from the base station (1f-10).

[0151] In one embodiment of the present disclosure, at step 1f-25, the terminal (1f-05) may provide information to the base station (1f-10) regarding the existence of stored data. For example, if the terminal (1f-05) has stored data, it may report to the base station (1f-10) by setting indicator A to true or including it. If the terminal (1f-05) does not have stored data, it may report to the base station (1f-10) 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 (1f-05) may report to the base station (1f-10) the existence of stored data through a UE Assistance information message if it has received the relevant UE Assistance information setting in advance (e.g., at 1f-20 or thereafter). The terminal (1f-05) may report to the base station (1f-10) the existence of stored data immediately after receiving the relevant UE Assistance information setting. Alternatively, the terminal (1f-05) may report the changed information to the base station (1f-10) 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 (1f-05), the base station (1f-10) may determine whether to request a measurement report (e.g., 1f-30) from the terminal (1f-05) or determine the timing of the request.

[0152] In one embodiment of the present disclosure, at step 1f-25, if the terminal (1f-05) satisfies at least one of the following conditions, it may report to the base station (1f-10) that the condition is satisfied. Alternatively, if the terminal (1f-05) does not (further) satisfy at least one of the following conditions, it may report to the base station (1f-10) that the condition is (further) not satisfied.

[0153] - 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).

[0154] - Condition 2. When the size of the data stored by the terminal (1f-05) for the measurement results for model training is greater than a specific threshold value (e.g., threshold value 2).

[0155] The above threshold values ​​(threshold value 1, threshold value 2) may each be set by the base station (1f-10) (e.g., through UE Assistance information settings) or may be fixed values ​​in the standard. For example, the terminal (1f-05) may report to the base station (1f-10) that the above condition is satisfied by setting indicator B to true or including it. The terminal (1f-05) may report to the base station (1f-10) that the above condition is not satisfied by setting indicator B to false or omitting it. In addition, the terminal (1f-05) may report specific size information (e.g., available remaining size in memory, size of data stored in memory by the terminal (1f-05)) to the base station (1f-10). The above report may be provided via a UE Assistance information message, and the terminal (1f-05) may transmit the above report to the base station (1f-10) via a UE Assistance information message if it has received the relevant UE Assistance information setting in advance (e.g., at 1f-20 or thereafter). The terminal (1f-05) may report the above report to the base station (1f-10) immediately after receiving the relevant UE Assistance information setting. The terminal (1f-05) may report the changed information to the base station (1f-10) when the information in the above report changes (e.g., when condition 1 is not satisfied but is satisfied). Alternatively, the terminal (1f-05) may periodically transmit the above report information to the base station (1f-10), and the transmission cycle of the terminal (1f-05) may be set by the base station (1f-10). Based on the report of the terminal (1f-05), the base station (1f-10) may determine whether to request a measurement report (e.g., 1f-30) from the terminal (1f-05) or determine the time of the request.

[0156] 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 (1f-05) (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. When the terminal (1f-05) transmits a UE Assistance information message for reporting on the corresponding function, it may operate the prohibit timer for the duration of the set prohibit timer, and the terminal (1f-05) may not be able to transmit a new UE Assistance information message for the same function while the prohibit timer is operating. The terminal (1f-05) may stop the operation of the prohibit timer when the UE Assistance information setting for the corresponding function is deactivated.

[0157] In step 1f-30, the base station (1f-10) may instruct or request the terminal (1f-05) to report the stored measurement data (for model training) (after receiving information regarding the stored data from the terminal (1f-05)). For example, the base station (1f-10) may instruct or request the terminal (1f-05) to report the stored measurement data using a UE Information Request message, an RRC reconfiguration message, or a new RRC message (e.g., via SRB1).

[0158] In step 1f-35, the terminal (1f-05) can report the stored measurement data (for model learning) to the base station (1f-10) (e.g., via SRB1). Step 1f-35 may cross-reference step 1d-30 of FIG. 1d and / or step 1e-30 of FIG. 1e. For example, the terminal (1f-05) may use a UE Information Response message, a Measurement report message, or a new RRC message for the report.

[0159] FIG. 1g is a drawing illustrating a method in which a terminal receives measurement data for model learning or stores it, according to one embodiment of the present disclosure.

[0160] Referring to FIG. 1g, in step 1g-05, the terminal may generate an RRC message (e.g., UEInformationResponse message) to receive measurement data for model training. After receiving the measurement data, the terminal may transmit the message, which can be referenced in step 1f-35.

[0161] In step 1g-10, the terminal may include parameter 1 (e.g., csi-LogMeasReport) within a single RRC message (e.g., UEInformationResponse message) to contain measurement data for model training.

[0162] In step 1g-15, the terminal may include parameter 2 (e.g., csi-LogMeasInfoList) in parameter 1.

[0163] In step 1g-20, the terminal may store the measurement data in a terminal variable (UE variable) (e.g., VarCSI-LogMeasReport) to store / record / log (e.g., step 1f-22) before containing it in a single RRC message (e.g., UEInformationResponse message) (e.g., step 1f-35) to report the measurement data for model training to the base station (e.g., see step 1f-35). The terminal variable (e.g., VarCSI-LogMeasReport) may include the parameter 2 (1g-15).

[0164] In step 1g-25, the terminal may include one or more parameters 3 (e.g., csi-LogMeasInfo) within parameter 2. One of the multiple parameters 3 may be used to distinguish and store measured data for model training for each (serving) cell (and / or each configuration (ID)).

[0165] In step 1g-30, the terminal can accommodate at least one of the following parameters within parameter 3.

[0166] - Parameter 4-1 (e.g., cellId): Parameter 4-1 may indicate the (serving) cell or cell ID that measured the received measurement data.

[0167] - Parameter 4-2 (e.g., refCSI-LoggedMeasurementConfigId): Parameter 4-2 may indicate a measurement configuration or configuration ID for model training related to the measurement data being stored.

[0168] - Parameter 4-3 (e.g., csi-RS-MeasResultList): Parameter 4-3 may include data measured based on CSI-RS.

[0169] - Parameter 4-4 (e.g., csi-SSB-MeasResultList): Parameter 4-4 may contain data measured based on the SSB (synchronization signal block).

[0170] In step 1g-35, the terminal may accommodate one or more parameter sets (e.g., parameter set A) within parameter 4-3 (e.g., csi-RS-MeasResultList), and one parameter set (e.g., parameter set A) may include at least one of the following parameters.

[0171] - Parameter 5-1 (e.g., rs-Index): Parameter 5-1 may indicate the RS (Reference signal) index. Parameter 5-1 may indicate / receive the parameter CSI-RS index (e.g., parameter 6-1) or the SSB index (e.g., parameter 6-2).

[0172] - Parameter 5-2 (e.g., l1-RSRP): Parameter 5-2 may indicate L1-RSRP (RSRP measured at Layer 1).

[0173] One of the multiple parameter sets (e.g., parameter set A) may be used to distinguish and store data measured for model training for each RS (index).

[0174] In step 1g-40, the terminal may contain parameter 6-1 (e.g., csi-RS-Index) within parameter 5-1. Parameter 6-1 may indicate a CSI RS (resource) index.

[0175] In step 1g-45, the terminal can store a CSI RS (resource) index (e.g., NZP-CSI-RS-ResourceId) within parameter 6-1.

[0176] In step 1g-50, parameter 5-2 can indicate an integer between 0 and 127, each indicating (L1-)RSRP in units of 1 dBm.

[0177] In step 1g-55, the terminal may accommodate one or more parameter sets (e.g., parameter set B) within parameter 4-4 (e.g., csi-SSB-MeasResultList), and one parameter set (e.g., parameter set B) may include at least one of the following parameters.

[0178] - Parameter 5-3 (e.g., rs-Index): Parameter 5-3 may indicate the RS (Reference signal) index. Parameter 5-3 may indicate / receive the parameter CSI-RS index (e.g., parameter 6-1) or the SSB index (e.g., parameter 6-2).

[0179] - Parameter 5-4 (e.g., l1-RSRP): Parameter 5-4 may indicate L1-RSRP (RSRP measured at Layer 1). For Parameter 5-4, refer to the description of Parameter 5-2.

[0180] One of the multiple parameter sets (e.g., parameter set B) may be used to distinguish and store data measured for model training for each RS (index).

[0181] In step 1g-60, the terminal may contain parameter 6-2 (e.g., ssb-Index) within parameter 5-3. Parameter 6-2 may indicate an SSB (resource) index. In step 1g-65, the terminal may contain an SSB (resource) index (e.g., SSB-Index) within parameter 6-2.

[0182] The terminal can store a CSI RS (resource) index (e.g., NZP-CSI-RS-ResourceId) within parameter 6-1.

[0183] In step 1g-50, parameter 5-2 can indicate an integer between 0 and 127, each indicating (L1-)RSRP in units of 1 dBm.

[0184] In one embodiment of the present disclosure, a method for storing measurement data for model learning, such as the above 1g, may have the following issues.

[0185] - First issue: For model training, the terminal must be able to store measurement results from one or more points in time or time for one beam (resource) within one measurement setup within one (serving) cell. Otherwise, the terminal may only store / report measurement results for one point in time (e.g., the most recent point in time), which may result in frequent measurement reporting and inefficient use of wireless resources and energy of the terminal / base station. Furthermore, when a training object (e.g., base station or OAM) performs model training regarding temporal prediction, measurement data for multiple points in time may be essential for each beam (resource). However, with the storage method of FIG. 1g above, the terminal may not be able to store / report measurement results for one or more points in time. Therefore, in one embodiment of the present disclosure, to solve the above issue, a measurement result list capable of storing / reporting measurement results for multiple points in time for each beam (resource) may be defined / used.

[0186] - Second issue: When a terminal performs event-based measurements and data storage (e.g., 1f-22) (i.e., in the case of event-based logging), the terminal's measurements may not be continuous, and consequently, the stored data may not be temporally continuous. For example, the terminal may perform measurements when an event is satisfied, perform N1 measurements on a single beam (resource), and store N1 measurement data (e.g., L1-RSRP). In this case, the N1 data may be the result of continuously (periodically) measuring a periodic resource provided by the base station. For example, if the period of the beam (resource) is T, the N1 data may each be data measured at time T. Subsequently, the terminal may stop measurements for a certain period (e.g., interruption period 1) when the event is not satisfied. Afterward, the terminal may resume measurements when the event is satisfied again and perform N2 measurements on the same beam (resource) to store N2 additional measurement data (e.g., L1-RSRP). Since the results are measured for the same beam (resource), N2 measurement data may also be data measured at each time interval T. In this case, the N2 measurement data may not be data measured continuously from the preceding N1 measurement data. That is, they may not be data measured consecutively, but data measured with a gap of 1 interval. However, if the terminal stores / reports N2 measurement data together with the preceding N1 measurement data (for example, if it stores / reports all N1+N2 measurement data in a single common measurement data list), the receiving learning object may perceive all N1+N2 measurement data as data measured continuously (all with a period T). In this case, when the learning object performs model training regarding temporal prediction, it may fail to consider the time gap (e.g., gap 1) between the N1 measurement data and the N2 measurement data, and incorrect model training may be performed.Accordingly, in one embodiment of the present disclosure, to resolve the above issue, the terminal can distinguish between N1 measurement data and N2 measurement data and store / report them in different measurement data lists.

[0187] - Third issue: In Fig. 1g, the terminal can indicate a beam (resource) index (e.g., parameter 5-1 or 5-3) (e.g., rs-Index) for each measurement data (e.g., parameter 5-2 or 5-4) (e.g., l1-RSRP). For example, considering that one measurement data (e.g., parameter 5-2 or 5-4) (e.g., l1-RSRP) may be information with a size of 8 bits, the method of the terminal allocating an additional 9 bits (e.g., csi-RS-Index) or 6 bits (e.g., ssb-Index) for each measurement data to indicate the beam (resource) index (e.g., parameter 5-1 or 5-3) (e.g., rs-Index) associated with one measurement data to store / report data may be inefficient. Considering that a terminal can measure multiple measurement data (e.g., parameter 5-2 or 5-4) (e.g., l1-RSRP) for a single beam (resource), this method may be an inefficient receiving / reporting method and may result in wasted memory of the terminal for receiving, wasted wireless resources for reporting, and wasted energy of the terminal / base station. Therefore, in one embodiment of the present disclosure, to solve the above issue, when receiving / reporting multiple measurement data (e.g., parameter 5-2 or 5-4) (e.g., l1-RSRP) for a single beam (resource), the terminal may receive / report a single common beam (resource) index only once.

[0188] - Fourth Issue: When a terminal stores multiple measurement data (e.g., parameter 5-2 or 5-4) (e.g., l1-RSRP) for a single beam (resource), storing the absolute value of the measurement data (e.g., Absolute l1-RSRP) may be inefficient. This is because it is highly likely that the difference in values ​​between data measured consecutively by the terminal for a single beam (resource) will not be significant. Therefore, in one embodiment of the present disclosure, to resolve the above issue, when the terminal stores / reports multiple consecutively measured data values ​​in a single measurement data list, it may use a method of indicating the relative difference value with respect to a single fixed data value rather than indicating the absolute value for each data. In one embodiment of the present disclosure, the terminal may store / report the first measurement data in each measurement data list using the absolute value of the measurement data (e.g., Absolute l1-RSRP). For measurement data other than the first in each measurement data list (i.e., starting from the second measurement data), the difference value between the absolute value of the first measurement data and the absolute value of the corresponding data may be calculated and stored / reported. Alternatively, measurement data other than the first (e.g., the Nth) within each measurement data list (i.e., starting from the second measurement data) can be received / reported by calculating the difference between the absolute value of the preceding data (e.g., the N-1th) and the absolute value of the corresponding data.

[0189] FIG. 1h is a drawing illustrating a method in which a terminal receives measurement data for model learning or stores it, according to one embodiment of the present disclosure.

[0190] Referring to FIG. 1h, in step 1h-05, the terminal may generate an RRC message (e.g., UEInformationResponse message) to receive measurement data for model training. After receiving the measurement data, the terminal may transmit the message, and refer to step 1f-35.

[0191] In step 1h-10, the terminal may include parameter 7 (e.g., csi-LogMeasReport) within a single RRC message (e.g., UEInformationResponse message) to contain measurement data for model training.

[0192] In step 1h-15, the terminal may include parameter 8 (e.g., csi-LogMeasInfoList) in parameter 7.

[0193] In step 1h-20, the terminal may store the measurement data in a terminal variable (UE variable) (e.g., VarCSI-LogMeasReport) to store / record / log (e.g., step 1f-22) before containing it in a single RRC message (e.g., UEInformationResponse message) (e.g., step 1f-35) to report the measurement data for model training to the base station (e.g., see step 1f-35). The terminal variable (e.g., VarCSI-LogMeasReport) may include the parameter 8 (1h-15).

[0194] In step 1h-25, the terminal may include one or more parameters 9 (e.g., csi-LogMeasInfo) within parameter 8. One of the multiple parameters 9 may be used to distinguish and store measured data for model training for each (serving) cell (and / or each setting (ID)).

[0195] In step 1h-30, the terminal may accommodate at least one of the following parameters within parameter 9.

[0196] - Parameter 10-1 (e.g., cellId): Parameter 10-1 may indicate the (serving) cell or cell ID that measured the received measurement data.

[0197] - Parameter 10-2 (e.g., refCSI-LoggedMeasurementConfigId): Parameter 10-2 may indicate a measurement configuration or configuration ID for model training related to the measurement data being stored.

[0198] - Parameter 10-3 (e.g., csi-RS-MeasResultListPerRS): Parameter 10-3 may include data measured based on CSI-RS for each beam (resource) (e.g., CSI-RS). In one embodiment of the present disclosure, through this parameter, the terminal can resolve the aforementioned third issue. That is, when receiving / reporting multiple measurement data (e.g., Parameter 14-1) (e.g., l1-RSRP) for a single CSI-RS beam (resource), a single common beam (resource) index (e.g., Parameter 11-1) can be received / reported only once.

[0199] - Parameter 10-4 (e.g., csi-SSB-MeasResultList): Parameter 10-4 may include data measured based on SSB for each beam (resource) (e.g., SSB). In one embodiment of the present disclosure, through this parameter, the terminal can resolve the aforementioned third issue. That is, when receiving / reporting multiple measurement data (e.g., Parameter 14-2) (e.g., l1-RSRP) for a single SSB beam (resource), a single common beam (resource) index (e.g., Parameter 11-3) can be received / reported only once.

[0200] In step 1h-35, the terminal may accommodate one or more parameter sets (e.g., parameter set C) within parameter 10-3 (e.g., csi-RS-MeasResultListPerRS), and one parameter set (e.g., parameter set C) may include at least one of the following parameters.

[0201] - Parameter 11-1 (e.g., csi-RS-Index): Parameter 11-1 may indicate a CSI-RS (resource) index. For example, a terminal may contain a CSI-RS (resource) index (e.g., NZP-CSI-RS-ResourceId) within parameter 11-1.

[0202] - Parameter 11-2 (e.g., loggingType): Parameter 11-2 may be an indicator that indicates whether the data stored by the terminal is data measured / stored according to event-based logging or data measured / stored according to periodic logging.

[0203] One of the multiple parameter sets (e.g., parameter set C) may be used to distinguish and store data measured for model training for each RS (e.g., CSI-RS) (resource) (index).

[0204] In step 1h-40, the terminal can accommodate parameter 12-1 (e.g., periodic) within parameter 11-2 (if the data stored by the terminal is data measured / stored according to periodic logging).

[0205] In step 1h-45, the terminal may include parameter 13-1 (e.g., l1-RSRPList) within parameter 12-1.

[0206] In step 1h-50, the terminal may include one or more parameters 14-1 (e.g., l1-RSRP) within parameter 13-1. This may be for storing / reporting measurement data (e.g., L1-RSRP) for each measurement time point. In one embodiment of the present disclosure, the first issue described above may be resolved. That is, (for each beam (resource)) parameter 13-1 may be a list of measurement results capable of storing / reporting measurement results for multiple time points. In one embodiment of the present disclosure, each entry value (e.g., parameter 14-1) included in parameter 13-1 may be stored in chronological order (e.g., in order of oldest measurement / retrieval time or vice versa).

[0207] In step 1h-55, the terminal may select and include parameter 15-1 (e.g., l1-RSRP) or parameter 15-2 (e.g., l1-RSRP-diff) within parameter 14-1. The terminal may include parameter 15-1 (e.g., l1-RSRP) within parameter 14-1. Parameter 15-1 may indicate an integer between 0 and 127 (1h-60), which may indicate (L1-)RSRP (absolute value) in units of 1 dBm. The terminal may include parameter 15-1 (e.g., l1-RSRP) within parameter 14-1 if it is the first entry (or the first parameter 14-1) belonging to parameter 13-1.

[0208] In step 1h-62, the terminal may select and include parameter 15-1 (e.g., l1-RSRP) or parameter 15-2 (e.g., l1-RSRP-diff) within parameter 14-1. The terminal may include parameter 15-2 (e.g., l1-RSRP-diff) within parameter 14-1. For example, parameter 15-2 may indicate an integer between 0 and 15 (1h-63), which may indicate the difference between two (L1-)RSRP values ​​in units of 1 dBm. The terminal may include parameter 15-2 (e.g., l1-RSRP-diff) within parameter 14-1 unless it is the first entry (or the first parameter 14-1) belonging to parameter 13-1. For example, parameter 15-2 (e.g., l1-RSRP-diff) can store the difference between the measurement value corresponding to the corresponding entry (i.e., the non-first entry) (e.g., L1-RSRP value) and the measurement value corresponding to the first entry (e.g., parameter 15-1) (e.g., L1-RSRP value). For example, parameter 15-2 (e.g., l1-RSRP-diff) can store the difference between the measurement value corresponding to the corresponding entry (i.e., the Nth entry, not the first) (e.g., L1-RSRP value) and the measurement value corresponding to the previous entry (i.e., the N-1th) (e.g., L1-RSRP value). That is, this can resolve the aforementioned fourth issue.

[0209] In step 1h-70, the terminal can accommodate parameter 12-2 (e.g., event) within parameter 11-2 (if the data stored by the terminal is data measured / stored according to event-based logging).

[0210] In step 1h-75, the terminal may include one or more parameters 13-2 (e.g., l1-RSRPListPerEventInstance) within parameter 12-2. This may be intended to distinguish and store / report measurement data (e.g., L1-RSRP) for each consecutive event (e.g., Event instance). That is, data measured in one consecutive event (e.g., event instance) (e.g., the aforementioned N1 measurement data) may be stored / stored through one parameter 13-2, and if the event is interrupted and a new event occurs, the corresponding measurement data (e.g., the aforementioned N2 measurement data) may be stored / stored through a separate (another) parameter 13-2. In one embodiment of the present disclosure, this may resolve the aforementioned second issue. In one embodiment of the present disclosure, each entry value (e.g., parameter 13-2) included in parameter 12-2 may be stored in chronological order (e.g., in order of oldest measurement / store time or vice versa).

[0211] In step 1h-80, the terminal may include one or more parameters 14-2 (e.g., l1-RSRP) within parameter 13-2. This may be for receiving / reporting measurement data (e.g., L1-RSRP) for each measurement time point (within each event instance). In one embodiment of the present disclosure, this may resolve the first issue described above. That is, (for each beam (resource)) parameter 13-2 may be a list of measurement results capable of receiving / reporting measurement results for multiple time points (within the corresponding event instance). For a description of parameter 14-2, refer to the description of parameter 14-1 (1h-50, 1h-55, 1h-60, 1h-62, and / or 1h-63). In one embodiment of the present disclosure, each entry value included in parameter 13-2 (e.g., parameter 14-2) may be stored in chronological order (e.g., in order of oldest measurement / retrieval time or vice versa).

[0212] In step 1h-85, the terminal may accommodate one or more parameter sets (e.g., parameter set D) within parameter 10-4 (e.g., csi-SSB-MeasResultListPerRS), and one parameter set (e.g., parameter set D) may include at least one of the following parameters.

[0213] - Parameter 11-3 (e.g., ssb-Index): Parameter 11-3 may indicate an SSB (resource) index. For example, a terminal may contain a CSI RS (resource) index (e.g., SSB Index) within Parameter 11-3. For a description of Parameter 11-3, refer to 1g-60 and / or 1g-65.

[0214] - Parameter 11-4 (e.g., loggingType): Parameter 11-4 may be an indicator indicating whether the data stored by the terminal is data measured / stored according to event-based logging or data measured / stored according to periodic logging. For a description of Parameter 11-4, refer to Parameter 11-2 (e.g., 1h-35 through 1h-80).

[0215] One of the multiple parameter sets (e.g., parameter set D) may be used to distinguish and store data measured for model training for each RS (e.g., SSB) (resource) (index).

[0216] In one embodiment of the present disclosure, the operation of a terminal receiving a terminal variable (e.g., VarCSI-LogMeasReport) may be the same as all or part of [Table 1] below, and the terminal may receive the value received in the terminal variable by moving it to a measurement report message (e.g., UEInformationResponse) when reporting a measurement (step 1f-35).

[0217] 1> set UE variable to log L1 measurement report for NW-side data collection (e.g.,VarCSI-LogMeasReport) as follows: 2> for each serving cell: 3> for each logged L1 measurement configuration for NW-side data collection (e.g.,CSI-LoggedMeasurementConfig): 4> setcellIdas the concerned serving cell ID; 4> setrefCSI-LoggedMeasurementConfigIdas ID of the concerned logged L1 measurement configuration for NW-side data collection (e.g.,csi-LoggedMeasurementConfigId); 4> for each CSI-RS resource (e.g.,NZP-CSI-RS-ResourceId) based on which L1 serving cell measurement is available (or has been received from lower layer): 5> set CSI resource index (e.g.,csi-RS-Index) in an entry in the list of L1 measurement results per CSI-RS resource (e.g.,csi-RS-MeasResultListPerRS) to the CSI-RS resource ID (e.g.,NZP-CSI-RS-ResourceId); 5> if event-based logging is configured for the concerned logged L1 measurement configuration: 6> for each event instance (i.e., time period instance during which the configured event is fulfilled continuously): 7> set L1 RSRPs (e.g., l1-RSRP) in the list of L1 measurements during the concerned event instance (i.e.,l1-RSRPListPerEventInstance), where the 1. st entry is set to the newest (or oldest) L1-RSRP and the 2 nd entry is set to the 2 nd newest (or 2 nd oldest) L1-RSRP, and so on. The 1st entry is set to absolute L1 RSRP (e.g.,l1-RSRP-1st), and the rest of entries are set to differential L1 RSRPs (e.g.,l1-RSRP-diff) compared to the value of the previous entry; 5> else: 6> set L1 RSRPs (e.g., l1-RSRP) in the list of L1 measurements (e.g.,l1-RSRPList), where the 1 st entry is set to the newest (or oldest) L1-RSRP and the 2 nd entry is set to the 2 nd newest (or 2 ndoldest) L1-RSRP, and so on. The 1st entry is set to absolute L1 RSRP (e.g.,l1-RSRP-1st), and the rest of entries are set to differential L1 RSRPs (e.g.,l1-RSRP-diff) compared to the value of the previous entry; 4> for each SSB resource (e.g.,SSB-index) based on which L1 serving cell measurement is available (or has been received from lower layer): 5> set SSB index (e.g.,ssb-Index) in an entry in the list of L1 measurement results per SSB resource (e.g.,csi-SSB-MeasResultListPerRS) to the SSB resource index (e.g.,SSB-index); 5> if event-based logging is configured for the concerned logged L1 measurement configuration: 6> for each event instance (i.e., time period instance during which the configured event is fulfilled continuously): 7> set L1 RSRPs (e.g., l1-RSRP) in the list of L1 measurements during the concerned event instance (i.e.,l1-RSRPListPerEventInstance), where the 1 st entry is set to the newest (or oldest) L1-RSRP and the 2 ndentry is set to the 2 nd newest (or 2 nd oldest) L1-RSRP, and so on. The 1st entry is set to absolute L1 RSRP (eg,l1-RSRP-1st), and the rest of entries are set to differential L1 RSRPs (eg,l1-RSRP-diff) compared to the value of the previous entry; 5> else: 6> set L1 RSRPs (eg, l1-RSRP) in the list of L1 measurements (eg,l1-RSRPList), where the 1 st entry is set to the newest (or oldest) L1-RSRP and the 2 nd entry is set to the 2 nd newest (or 2 nd oldest) L1-RSRP, and so on. The 1st entry is set to absolute L1 RSRP (eg,l1-RSRP-1st), and the rest of entries are set to differential L1 RSRPs (eg,l1-RSRP-diff) compared to the value of the previous entry;

[0218] In one embodiment of the present disclosure, when a terminal transmits an RRC Reconfiguration Complete message after receiving an RRC Reconfiguration message (e.g., when transmitting to a target base station during a handover), it may indicate that the memory used for the terminal's model training is full. This may be intended to indicate to the (target) base station that the terminal's memory is full and that measurements and / or data collection for model training can no longer be performed.

[0219] In one embodiment of the present disclosure, when a terminal transmits an RRC Reconfiguration Complete message after receiving an RRC Reconfiguration message (e.g., when transmitting to a target base station during a handover), it may indicate a situation in which measurements for model training cannot be performed due to insufficient available battery or power of the terminal. This may mean requesting the (target) base station to release existing settings regarding measurements and / or data collection for model training because the terminal's available battery or power is insufficient (e.g., if measurements for model training have already been set), or it may mean a request not to provide new settings (e.g., if measurements for model training have not been set).

[0220] In one embodiment of the present disclosure, (to resolve the first and / or second issues described above) the terminal may store or log data (e.g., in a terminal variable (e.g., VarCSI-LogMeasReport)) as shown in [Table 2] below (e.g., step 1f-22) and report to the base station (e.g., step 1f-35) by containing it in a terminal report message (e.g., UEInformationResponse).

[0221] UEInformationResponseTheUEInformationResponsemessage is used by the UE to transfer information requested by the network.Signalling radio bearer: SRB1 or SRB2 (when logged measurement information is included) or SRBx (when logged measurement information for network-side data collection is included)RLC-SAP: AMLogical channel: DCCHDirection: UE to networkUEInformationResponse messageUEInformationResponse-v19xy-IEs ::= SEQUENCE {csi-LogMeasReport-r19 CSI-LogMeasReport-r19 OPTIONAL,nonCriticalExtension SEQUENCE {} OPTIONAL}CSI-LogMeasReport-r19 ::= SEQUENCE {csi-LogMeasInfoCellList-r19 CSI-LogMeasInfoCellList-r19,csi-MoreLogMeasAvailable-r19 ENUMERATED {true} OPTIONAL,...}CSI-LogMeasInfoCellList-r19 ::= SEQUENCE (SIZE (1..maxNrofServingCells)) OF CSI-LogMeasInfoCell-r19CSI-LogMeasInfoCell-r19 ::= SEQUENCE {cellId-r19 CHOICE {cellGlobalId-r19 CGI-Info-Logging-r16,pci-arfcn-r19 PCI-ARFCN-NR-r16},csi-LogMeasInfoMeasConfigList-r19 SEQUENCE (SIZE (1..maxNrofLoggedMeasurementConfigurations-r19)) OF CSI-LogMeasInfoMeasConfig-r19,...}CSI-LogMeasInfoMeasConfig-r19 ::= SEQUENCE {refCSI-LoggedMeasurementConfigId-r19 CSI-LoggedMeasurementConfigId-r19,csi-RS-MeasResultList-r19 SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-Resources)) OF CSI-RS-MeasResult-r19 OPTIONAL,ssb-MeasResultList-r19 SEQUENCE (SIZE (1..maxNrofSSBs-r16)) OF SSB-MeasResult-r19 OPTIONAL,...}CSI-RS-MeasResult-r19 ::= SEQUENCE {resourceId-r19 NZP-CSI-RS-ResourceId,rsrp-Results-r19 SEQUENCE (SIZE (1..maxLogCSI-MeasReport-r19)) OF RSRP-Result-r19}SSB-MeasResult-r19 ::= SEQUENCE {ssb-Id-r19 SSB-Index,rsrp-Results-r19 SEQUENCE (SIZE (1..maxLogCSI-MeasReport-r19)) OF RSRP-Result-r19}RSRP-Result-r19 ::= SEQUENCE {l1-RSRP-r19 RSRP-RangetimeGap-r19 ENUMERATED {true} OPTIONAL, ...}-VarCSI-LogMeasReportThe UE variableVarCSI-LogMeasReportincludes the logged CSI measurements information for network-side data collection in accordance withCSI-LoggedMeasurementConfig.VarCSI-LogMeasReportUE variable-- ASN1START-- TAG-VARCSI-LOGMEASREPORT-STARTVarCSI-LogMeasReport-r19 ::= SEQUENCE {csi-LogMeasInfoCellList CSI-LogMeasInfoCellList-r19}-- TAG-VARCSI-LOGMEASREPORT-STOP-- ASN1STOP.

[0222] - CSI-LogMeasInfoCell may be information that stores data measured / collected for training (network-side) AI / ML models by (serving) cell (e.g., cellId).

[0223] - CSI-LogMeasInfoMeasConfig may be information that stores data measured / collected for (network-side) AI / ML model training, per (serving) cell, per measurement / collection setting (ID) (e.g., CSI-LoggedMeasurementConfigId).

[0224] - CSI-RS-MeasResult may be information containing data measured / collected for (network-side) AI / ML model training, per (serving) cell, per measurement / collection setting (ID) (e.g., CSI-LoggedMeasurementConfigId), per CSI-RS resource or beam.

[0225] - SSB-MeasResult may be information containing data measured / collected for training (network-side) AI / ML models per (serving) cell, per measurement / collection setting (ID) (e.g., CSI-LoggedMeasurementConfigId), per SSB resource or beam.

[0226] - RSRP-Result may be information containing (L1-)RSRP and / or timeGap per (serving) cell, per measurement / collection setting (ID) (e.g., CSI-LoggedMeasurementConfigId) for training a (network-side) AI / ML model, per CSI-RS or SSB resource or beam, and per resource transmission time or resource measurement time.

[0227] - The timeGap may be an indicator for representing a point in time for data that is not measured continuously, as in the second issue mentioned above. By including the timeGap, the terminal may indicate that the data at that point in time (e.g., L1-RSRP) is not measured with a specific period (e.g., a resource period or storage period set by the base station) compared to the data at the immediately preceding or subsequent point in time, but is measured with a time gap (a time difference greater than the specific period). By omitting the timeGap, the terminal may indicate that the data at that point in time (e.g., L1-RSRP) is measured with a specific period (e.g., a resource period or storage period set by the base station) compared to the data at the immediately preceding or subsequent point in time.

[0228] FIG. 1i is a drawing illustrating the internal structure of a terminal according to one embodiment of the present disclosure.

[0229] Referring to FIG. 1i, the terminal may include an RF (Radio Frequency) processing unit (1i-10), a baseband processing unit (1i-20), a storage unit (1i-30), a control unit (1i-40), etc.

[0230] The RF processing unit (1i-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 (1i-10) can up-convert a baseband signal provided by the baseband processing unit (1i-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 (1i-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. 1i, the terminal may be equipped with multiple antennas. Additionally, the RF processing unit (1i-10) may include multiple RF chains. Furthermore, the RF processing unit (1i-10) may perform beamforming. For the above beamforming, the RF processing unit (1i-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.

[0231] The baseband processing unit (1i-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 (1i-20) can generate complex symbols by encoding and modulating the transmitted bit sequence. Additionally, when receiving data, the baseband processing unit (1i-20) can restore the received bit sequence by demodulating and decoding the baseband signal provided by the RF processing unit (1i-10). For example, in the case of following the orthogonal frequency division multiplexing (OFDM) method, when transmitting data, the baseband processing unit (1i-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 (1i-20) can divide the baseband signal provided from the RF processing unit (1i-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.

[0232] The baseband processing unit (1i-20) and the RF processing unit (1i-10) can transmit and receive signals as described above. Accordingly, the baseband processing unit (1i-20) and the RF processing unit (1i-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 (1i-20) and the RF processing unit (1i-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 (1i-20) and the RF processing unit (1i-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.

[0233] The storage unit (1i-30) can store data such as basic programs, application programs, and setting information for the operation of the terminal. In particular, the storage unit (1i-30) can store information related to a second connection node that performs wireless communication using wireless connection technology. Additionally, the storage unit (1i-30) can provide the stored data upon a request from the control unit (1i-40).

[0234] The control unit (1i-40) can control the overall operations of the terminal. For example, the control unit (1i-40) can control the terminal to perform the embodiments and / or methods of the present disclosure described above. For example, the control unit (1i-40) can transmit and receive signals through the baseband processing unit (1i-20) and the RF processing unit (1i-10). Additionally, the control unit (1i-40) can write and read data to and from the storage unit (1i-30). To this end, the control unit (1i-40) may include at least one processor. For example, the control unit (1i-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 (1i-42) as illustrated in the drawing.

[0235] FIG. 1j is a drawing illustrating the structure of a base station according to one embodiment of the present disclosure.

[0236] Referring to FIG. 1j, according to one example of the present disclosure, a base station may be configured to include an RF processing unit (1j-10), a baseband processing unit (1j-20), a backhaul communication unit (1j-30), a storage unit (1j-40), a control unit (1j-50), etc.

[0237] The RF processing unit (1j-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 (1j-10) can up-convert a baseband signal provided by the baseband processing unit (1j-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 (1j-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. In addition, the RF processing unit (1j-10) may include multiple RF chains. Furthermore, the RF processing unit (1j-10) may perform beamforming. For the above beamforming, the RF processing unit (1j-10) can adjust the phase and magnitude of each of the signals transmitted and received through a plurality of antennas or antenna elements. The RF processing unit can perform down-to-down MIMO operation by transmitting one or more layers.

[0238] The baseband processing unit (1j-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 (1j-20) can generate complex symbols by encoding and modulating the transmitted bit sequence. Additionally, when receiving data, the baseband processing unit (1j-20) can restore the received bit sequence by demodulating and decoding the baseband signal provided by the RF processing unit (1j-10). For example, in the case of an OFDM method, when transmitting data, the baseband processing unit (1j-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 (1j-20) can divide the baseband signal provided by the RF processing unit (1j-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 (1j-20) and the RF processing unit (1j-10) can transmit and receive signals as described above. Accordingly, the baseband processing unit (1j-20) and the RF processing unit (1j-10) may be referred to as a transmitting unit, a receiving unit, a transmitting and receiving unit, a communication unit, or a wireless communication unit.

[0239] The backhaul communication unit (1j-30) can provide an interface for communicating with other nodes within the network. That is, the backhaul communication unit (1j-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.

[0240] The storage unit (1j-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 (1j-40) can store information regarding a bearer assigned to a connected terminal, measurement results reported from the connected terminal, etc. Additionally, the storage unit (1j-40) can store information that serves as a criterion for determining whether to provide or disconnect multiple connections to the terminal. Furthermore, the storage unit (1j-40) can provide the stored data upon a request from the control unit (1j-50).

[0241] The control unit (1j-50) can control the overall operations of the main base station. For example, the control unit (1j-50) can control the base station to perform the embodiments and / or methods of the present disclosure described above. For example, the control unit (1j-50) can transmit and receive signals through the baseband processing unit (1j-20) and the RF processing unit (1j-10) or through the backhaul communication unit (1j-30). Additionally, the control unit (1j-50) can write and read data to and from the storage unit (1j-40). To this end, the control unit (1j-50) may include at least one processor and may include a multiple connection processing unit (1j-52) as illustrated in the drawing.

[0242] When implemented in software, a computer-readable storage medium may be provided for storing one or more programs (software modules). One or more programs stored in the computer-readable storage medium are configured for execution by one or more processors within an electronic device. One or more programs include instructions that cause the electronic device to execute methods according to embodiments described in the claims or specification of the present invention.

[0243] Such programs (software modules, software) may be stored in random access memory, non-volatile memory including flash memory, ROM (Read Only Memory), Electrically Erasable Programmable Read Only Memory (EEPROM), magnetic disc storage devices, Compact Disc-ROM (CD-ROM), Digital Versatile Discs (DVDs), or other forms of optical storage devices, magnetic cassettes. Alternatively, they may be stored in memory composed of some or all of these. Additionally, each constituent memory may include multiple units.

[0244] In addition, the above program may be stored on an attachable storage device that can be accessed via a communication network such as the Internet, Intranet, Local Area Network (LAN), Wide LAN (WLAN), or Storage Area Network (SAN), or a combination thereof. Such a storage device may be connected to a device performing an embodiment of the present invention through an external port. Additionally, a separate storage device on a communication network may be connected to a device performing an embodiment of the present invention.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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 terminal of a wireless communication system, A step of receiving at least one logged measurement configuration information from a base station; A step of performing logging of a measurement for a serving cell based on at least one logging measurement setting information; and The method includes the step of transmitting a first message containing logged measurement information to the base station, The above-mentioned logged measurement information includes a logging instance corresponding to each of the at least one logging measurement setting information, and A method characterized in that each of the above logging instances includes information of at least one reference signal and at least one measurement result value for the at least one reference signal.

2. In Paragraph 1, The above-mentioned logged measurement information further includes identification information of the above-mentioned serving cell, and A method characterized in that the above reference signal includes at least one of a CSI-RS (channel state information reference signal) or an SSB (synchronization signal block).

3. In claim 1, the step of transmitting the first message is, A step of receiving a second message from the base station requesting the transmission of the above-mentioned logged measurement information; and In response to the second message, the method includes the step of transmitting the first message containing the logged measurement information to the base station. The above second message is a UE (user equipment) information request message, and A method characterized in that the first message above is a UE information response message.

4. In Paragraph 1, A method further comprising the step of transmitting to the base station a terminal capability information message containing information indicating whether the terminal supports AI / ML-based operations.

5. A method performed by a base station of a wireless communication system, A step of transmitting at least one logged measurement configuration information to a terminal; and The method includes the step of receiving a first message from the terminal containing measurement information that is measured and logged based on at least one logging measurement setting information. The above-mentioned logged measurement information includes a logging instance corresponding to each of the at least one logging measurement setting information, and A method characterized in that each of the above logging instances includes information of at least one reference signal and at least one measurement result value for the at least one reference signal.

6. In Paragraph 5, The above-mentioned logged measurement information further includes identification information of the above-mentioned serving cell, and A method characterized in that the above reference signal includes at least one of a CSI-RS (channel state information reference signal) or an SSB (synchronization signal block).

7. In claim 5, the step of receiving the first message is, A step of transmitting a second message to the terminal requesting the transmission of the above-mentioned logged measurement information; and In response to the second message, the method includes the step of receiving the first message containing the logged measurement information from the terminal. The above second message is a UE (user equipment) information request message, and A method characterized in that the first message above is a UE information response message.

8. In Paragraph 5, A method characterized by further including the step of receiving a capability information message from the terminal containing information indicating whether the terminal supports AI / ML-based operations.

9. In a terminal of a wireless communication system, Transmitter / receiver; and Connected to the above-mentioned transmitting and receiving unit, Receive at least one logged measurement configuration information from a base station, and Logging of measurements for a serving cell is performed based on the above at least one logging measurement setting information, and It includes a control unit that transmits a first message containing logged measurement information to the base station, and The above-mentioned logged measurement information includes a logging instance corresponding to each of the at least one logging measurement setting information, and A terminal characterized in that each of the above logging instances includes information of at least one reference signal and at least one measurement result value for the at least one reference signal.

10. In Paragraph 9, The above-mentioned logged measurement information further includes identification information of the above-mentioned serving cell, and A terminal characterized in that the above reference signal includes at least one of CSI-RS (channel state information reference signal) or SSB (synchronization signal block).

11. In claim 9, the control unit is, A second message requesting the transmission of the above-mentioned logged measurement information is received from the base station, and In response to the second message above, the first message including the logged measurement information is transmitted to the base station, and The above second message is a UE (user equipment) information request message, and A terminal characterized in that the first message above is a UE information response message.

12. In claim 9, the control unit is, A terminal characterized by transmitting a capability information message to the base station that includes information indicating whether the terminal supports AI / ML-based operations.

13. In a base station of a wireless communication system, Transmitter / receiver; and Connected to the above-mentioned transmitting and receiving unit, Transmit at least one logged measurement configuration information to the terminal, and A control unit that receives from the terminal a first message including measurement information that is measured and logged based on at least one logging measurement setting information, and The above-mentioned logged measurement information includes a logging instance corresponding to each of the at least one logging measurement setting information, and A base station characterized in that each of the above logging instances includes information of at least one reference signal and at least one measurement result value for the at least one reference signal.

14. In Paragraph 13, The above-mentioned logged measurement information further includes identification information of the above-mentioned serving cell, and A base station characterized by the above reference signal including at least one of CSI-RS (channel state information reference signal) or SSB (synchronization signal block).

15. In Paragraph 13, The control unit transmits a second message requesting the transmission of the logged measurement information to the terminal, and in response to the second message, receives the first message containing the logged measurement information from the terminal, and The second message above is a UE (user equipment) information request message, and the first message above is a UE information response message. A base station characterized by the above-described control unit receiving a terminal capability information message from the terminal that includes information indicating whether the terminal supports AI / ML-based operations.

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