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

Abstract

The present disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. Proposed are a method and device in which: a terminal receives, from a source base station, a radio resource control (RRC) reconfiguration message for indicating handover to a target base station, wherein the RRC reconfiguration message includes information about a target cell related to the target base station; when the RRC reconfiguration message includes information indicating to maintain a logged measurement result for data collection, an RRC reconfiguration complete message including information indicating that the terminal has the logged measurement result is transmitted to the target base station; a terminal information request message requesting to report the logged measurement result is received from the target base station; and a terminal information response message including the logged measurement result is transmitted to the target base station.

Description

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

[0001] The present disclosure relates to a method for utilizing artificial intelligence and machine learning in a wireless communication system and an apparatus capable of performing the same.

[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, physical layer standardization 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 that meets 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] The disclosed embodiments aim to provide an apparatus and method capable of effectively providing services in a mobile communication system. The technical problems to be solved by the various embodiments of the present disclosure 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.

[0009] The present disclosure provides a method to prevent the problem of resource and energy waste caused by deleting measurement results performed for data collection related to the source base station when a terminal performs a handover from a source base station to a target base station.

[0010] A method performed by a terminal in a communication system according to an embodiment of the present disclosure for solving the above-mentioned problems, comprising: receiving a radio resource control (RRC) reset message from a source base station instructing a handover to a target base station; wherein the RRC reset message includes information regarding a target cell associated with the target base station; and, if the RRC reset message includes information instructing to maintain a logged measurement result for data collection, transmitting an RRC reset completion message to the target base station including information instructing that the terminal possesses the logged measurement result; receiving a terminal information request message from the target base station requesting to report the logged measurement result; and transmitting a terminal information response message including the logged measurement result to the target base station.

[0011] In addition, a method performed by a source base station in a communication system according to an embodiment of the present disclosure for solving the above-mentioned problems comprises: transmitting a handover request message to a target base station; receiving a handover request acknowledgment message from the target base station in response to the handover request message, the handover request acknowledgment message including a radio resource control (RRC) reset message, wherein the RRC reset message includes information regarding a target cell associated with the target base station; and transmitting the RRC reset message to a terminal to instruct a handover to the target base station, wherein the information instructing to maintain a logged measurement result for data collection included in the RRC reset message instructs the terminal to maintain the logged measurement result.

[0012] In addition, a method performed by a target base station in a communication system according to an embodiment of the present disclosure for solving the above-mentioned problems comprises: receiving a handover request message from a source base station; transmitting a handover request acknowledgment message to the source base station in response to the handover request message, the handover request acknowledgment message including an RRC (radio resource control) reset message, wherein the RRC reset message includes information regarding a target cell associated with the target base station; and, if the RRC reset message includes information instructing to maintain a logged measurement result for data collection, receiving an RRC reset completion message from the terminal including information instructing that the terminal possesses the logged measurement result; transmitting a terminal information request message requesting the terminal to report the logged measurement result; and receiving a terminal information response message including the logged measurement result from the terminal.

[0013] In addition, in a communication system according to an embodiment of the present disclosure for solving the above-mentioned problems, the terminal comprises: at least one transceiver; at least one processor connected to the at least one transceiver so as to be able to communicate with the at least one transceiver; and a memory storing an instruction that is connected to the at least one processor so as to be able to communicate with the at least one processor and is executable individually or in any combination thereof, wherein the terminal receives an RRC (radio resource control) reset message instructing a handover from a source base station to a target base station, wherein the RRC reset message includes information regarding a target cell associated with the target base station; and wherein, if the RRC reset message includes information instructing to maintain a logged measurement result for data collection, the terminal transmits an RRC reset completion message to the target base station including information instructing that the terminal has the logged measurement result, receives a terminal information request message from the target base station requesting to report the logged measurement result, and transmits a terminal information response message including the logged measurement result to the target base station.

[0014] In addition, in a communication system according to an embodiment of the present disclosure for solving the above-mentioned problems, a source base station comprises: at least one transceiver; at least one processor connected to the at least one transceiver so as to be able to communicate with the at least one transceiver; and a memory connected to the at least one processor so as to be able to communicate with the at least one processor and capable of executing the at least one processor individually or in any combination thereof, wherein the source base station transmits a handover request message to a target base station, and receives a handover request acknowledgment message including an RRC (radio resource control) reset message from the target base station in response to the handover request message, wherein the RRC reset message includes information about a target cell associated with the target base station, and stores a command to transmit the RRC reset message to a terminal to instruct a handover to the target base station; wherein the information instructing to maintain a logged measurement result for data collection included in the RRC reset message instructs the terminal to maintain the logged measurement result.

[0015] In addition, in a communication system according to an embodiment of the present disclosure for solving the above-mentioned problems, a target base station comprises: at least one transceiver; at least one processor connected to the at least one transceiver so as to be able to communicate with the at least one transceiver; and a memory storing an instruction that is connected to the at least one processor so as to be able to communicate with the at least one processor and is executable individually or in any combination thereof, wherein the target base station receives a handover request message from a source base station and, in response to the handover request message, transmits a handover request acknowledgment message including an RRC (radio resource control) reset message to the source base station, wherein the RRC reset message includes information regarding a target cell associated with the target base station; wherein, if the RRC reset message includes information instructing to maintain a logged measurement result for data collection, the target base station receives an RRC reset completion message from the terminal including information instructing that the terminal has the logged measurement result; transmits a terminal information request message requesting the terminal to report the logged measurement result; and receives a terminal information response message including the logged measurement result from the terminal.

[0016] The various embodiments of the present disclosure described above are merely some of the preferred embodiments of the present disclosure, and various embodiments reflecting the technical features of the various embodiments of the present disclosure can be derived and understood by those skilled in the art based on the detailed description to be described below.

[0017] The disclosed embodiments provide an apparatus and method capable of effectively providing services in a mobile communication system.

[0018] According to the present disclosure, resource and energy efficiency can be improved by proposing a method to transmit measurement results measured for a source base station to a target base station when a terminal performs a handover.

[0019] The effects obtainable from the various embodiments of the present disclosure are not limited to those mentioned above, and other unmentioned effects can be clearly derived and understood by those skilled in the art based on the following detailed description.

[0020] FIG. 1a is a drawing illustrating the structure of a mobile communication system according to one embodiment of the present disclosure.

[0021] FIG. 1b is a diagram illustrating a wireless connection state transition in a mobile communication system according to one embodiment of the present disclosure.

[0022] FIG. 1c is a diagram illustrating an AI / ML model for predicting beam measurements for beam management according to one embodiment of the present disclosure.

[0023] 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.

[0024] 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.

[0025] FIG. 1f is a diagram illustrating a procedure for a terminal to collect measurement data for learning a model according to one embodiment of the present disclosure.

[0026] FIG. 1g is a drawing illustrating an embodiment according to one embodiment of the present disclosure in which the model learning settings of the terminal are maintained during handover and measurement data that has not been transmitted is deleted.

[0027] FIG. 1h is a diagram illustrating an embodiment according to one embodiment of the present disclosure in which the model learning settings of the terminal are maintained during handover and measurement data that has not been transmitted is transmitted to the target base station.

[0028] FIG. 1i is a drawing illustrating an embodiment according to one embodiment of the present disclosure in which the model learning setting of the terminal is released during handover and measurement data that has not been transmitted is deleted.

[0029] FIG. 1j is a diagram illustrating an embodiment according to one embodiment of the present disclosure in which the model learning setting of the terminal is released during handover and measurement data that has not been transmitted is transmitted to the target base station.

[0030] [Correction pursuant to Rule 91 09.03.2026] FIG. 1k is a drawing illustrating the internal structure of a terminal according to one embodiment of the present disclosure.

[0031] [Correction pursuant to Rule 91 09.03.2026] FIG. 11 is a drawing illustrating the structure of a base station according to one embodiment of the present disclosure.

[0032] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0033] In describing the embodiments, technical details that are well known in the art to which this disclosure belongs and are not directly related to this disclosure are omitted. This is intended to convey the essence of this disclosure more clearly without obscuring it by omitting unnecessary explanations.

[0034] 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.

[0035] The advantages and features of the present disclosure, and the methods for achieving them, will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure is complete and to fully inform those skilled in the art of the scope of the disclosure, and the present disclosure is defined only by the scope of the claims. Throughout the specification, the same reference numerals refer to the same components. Furthermore, in describing the present disclosure, if it is determined that a detailed description of a related function or configuration might unnecessarily obscure the essence of the present disclosure, such detailed description is omitted. Additionally, the terms described below are defined considering their functions in the present disclosure, and these may vary depending on the intentions or conventions of the user or operator. Therefore, their definitions should be based on the content throughout the specification.

[0036] 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 the base station to the terminal, and the Uplink (UL) refers to the wireless transmission path of a signal transmitted by the terminal to the base station. Furthermore, although embodiments of this disclosure are described below using a 5G system as an example, embodiments of this disclosure may be applied to other communication systems having similar technical backgrounds or channel types. For example, LTE or LTE-A mobile communication and mobile communication technologies developed after 5G may be included therein. Additionally, this disclosure may be applied to other communication systems with some modifications made at the discretion of a person with skilled technical knowledge, provided that it does not deviate significantly from the scope of this disclosure. The contents of this disclosure are applicable to FDD and TDD systems.

[0037] 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).

[0038] 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.

[0039] In this embodiment, the term "part" refers to a software or hardware component such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit), and the "part" performs certain roles. However, the meaning of "part" is not limited to software or hardware. The "part" may be configured to reside in an addressable storage medium or configured to run one or more processors. Thus, as an example, the "part" includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and "parts" may be combined into a smaller number of components and "parts" or further separated into additional components and "parts." In addition, the components and 'parts' may be implemented to utilize one or more CPUs within the device or secure multimedia card. Also, in the embodiments, 'parts' may include one or more processors.

[0040] FIG. 1a is a drawing illustrating the structure of a mobile communication system according to one embodiment of the present disclosure.

[0041] Referring to FIG. 1a, the wireless access network of a mobile communication system (New Radio, NR) can be composed of a base station (next generation Node B, hereinafter gNB) (1a-10) and an AMF (1a-05, New Radio Core Network).

[0042] 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).

[0043] A mobile communication system can be a next-generation mobile communication system, and a base station can be a next-generation base station.

[0044] 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 an NR UE via a wireless channel and can provide 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 for scheduling by collecting state information such as the buffer status, available transmission power status, and channel status of UEs may be required, 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 implement ultra-high-speed data transmission, it may have a bandwidth greater than the existing maximum bandwidth, and beamforming technology may be additionally incorporated by using Orthogonal Frequency Division Multiplexing (OFDM) as the wireless access technology. In addition, an Adaptive Modulation & Coding (AMC) scheme can be applied to determine the modulation scheme and channel coding rate according to the channel state of the terminal.

[0045] 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 terminals and can be connected to multiple base stations. In addition, the mobile communication system according to one embodiment of the present disclosure can be linked with an LTE system, and for example, the AMF (1a-05) can be connected to the MME (1a-25) through a network interface.

[0046] 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 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).

[0047] FIG. 1b is a diagram illustrating a wireless connection state transition in a mobile communication system according to one embodiment of the present disclosure.

[0048] Referring to Fig. 1b, a mobile communication system may have three radio resource control (RRC) states or RRC modes.

[0049] 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 (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 technology may be the same as that of LTE systems. A mobile communication system according to one embodiment of the present disclosure may be a next-generation mobile communication system.

[0050] 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:

[0051] - Cell re-selection mobility;

[0052] - CN - NR RAN connection (both C / U-planes (control plane / user plane)) has been established for UE;

[0053] - The UE AS(Access Stratum) context is stored in at least one gNB and the UE;

[0054] - Paging is initiated by NR RAN;

[0055] - RAN-based notification area is managed by NR RAN; or

[0056] - NR RAN knows the RAN-based notification area which the UE belongs to.

[0057] 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 including 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).

[0058] FIG. 1c is a diagram illustrating an AI / ML model for predicting beam measurements for beam management according to one embodiment of the present disclosure.

[0059] In one embodiment of the present disclosure, beam management may be one use case in which an AI / ML (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 one 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 one use case in which an AI / ML model can be utilized.

[0060] In one embodiment of the present disclosure, a beam management use case may include sub-use cases called spatial prediction and temporal prediction.

[0061] 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.

[0062] In one embodiment of the present disclosure, during spatial prediction (1c-05) for beam management, the input of an AI / ML model is at a specific time point (e.g., t K One or more beams in ) (e.g., B i)(i=1, 2,..., N), the measured value for (e.g., RSRP and / or RSRQ and / or SINR) (e.g., Meas(B i ,t K )) (1c-15) It may be.

[0063] In one embodiment of the present disclosure, the measurement value for the beam may be one of the following:

[0064] - RSRP and / or RSRQ and / or SINR measured at Layer 1;

[0065] - RSRP and / or RSRQ and / or SINR measured / acquired at Layer 3;

[0066] - 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

[0067] - Values ​​obtained by filtering RSRP and / or RSRQ and / or SINR measured / acquired at Layer 3 (e.g., (weighted) average values ​​using measurements over a specified period).

[0068] 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:

[0069] - SET B related information (e.g., beam-specific ID);

[0070] - Measurement time information;

[0071] - L1-RSRP measurement based on Set B;

[0072] - L1-RSRP measurement based on Set B and assistance information;

[0073] - CIR (Channel Impulse Response) based on Set B; and / or

[0074] - L1-RSRP measurement based on Set B and the corresponding DL Tx and / or Rx beam ID.

[0075] 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 value 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.

[0076] In one embodiment of the present disclosure, the predicted value for the beam may be one of the following:

[0077] - RSRP and / or RSRQ and / or SINR predicted to be measured at Layer 1;

[0078] - RSRP and / or RSRQ and / or SINR predicted to be measured / acquired at Layer 3;

[0079] - 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

[0080] - 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).

[0081] 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:

[0082] - SET A related information (e.g., ID per beam);

[0083] - One beam predicted to have the highest (best) measurement value (e.g., Top-1 beam);

[0084] - N (≥1) beams predicted to have the highest (best) measurements (e.g., Top-N beam); and / or

[0085] - Probability that each beam in SET A is a Top-1 or Top-N beam.

[0086] In one embodiment of the present disclosure, when performing a temporal prediction (1c-10) for beam management, the input to an 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 measurement value for the beam may be one of the following:

[0087] - RSRP and / or RSRQ and / or SINR measured at Layer 1;

[0088] - RSRP and / or RSRQ and / or SINR measured / acquired at Layer 3;

[0089] - 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

[0090] - Values ​​obtained by filtering RSRP and / or RSRQ and / or SINR measured / acquired at Layer 3 (e.g., (weighted) average values ​​using measurements over a specified period).

[0091] 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:

[0092] - SET B related information (e.g., beam-specific ID);

[0093] - Measurement time information;

[0094] - L1-RSRP measurement based on Set B;

[0095] - L1-RSRP measurement based on Set B and assistance information;

[0096] CIR (Channel Impulse Response) based on Set B; and / or

[0097] - L1-RSRP measurement based on Set B and the corresponding DL Tx and / or Rx beam ID.

[0098] In one embodiment of the present disclosure, when making 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 i A single beam (e.g. B) in (i=N+1, N+2,...,N+M)) KPredicted value for ) (e.g., RSRP and / or RSRQ and / or SINR) (e.g., P_Meas(B K ,t i ))(1c-30) may be.

[0099] In one embodiment of the present disclosure, the predicted value for the beam may be one of the following:

[0100] - RSRP and / or RSRQ and / or SINR predicted to be measured at Layer 1;

[0101] - RSRP and / or RSRQ and / or SINR predicted to be measured / acquired at Layer 3;

[0102] - 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

[0103] - 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).

[0104] 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:

[0105] - SET A related information (e.g., beam-specific ID);

[0106] - Prediction time point information;

[0107] - The point in time when the measurement is predicted to be highest (best); and / or

[0108] - N (≥1) time points where the measurement value is predicted to be highest (best).

[0109] In one embodiment of the present disclosure, an AI / ML model for beam management may be an AI / ML model that simultaneously performs the aforementioned spatial prediction and temporal prediction. In this case, the input of the AI / ML model may be a measurement value at one or more time points for each beam for one or more beams. Refer to the foregoing for the input information. Additionally, the output of the AI / ML model may be a predicted value at one or more time points for each beam for one or more beams. Refer to the foregoing for the output information.

[0110] 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, location accuracy improvement, and CSI feedback improvement.

[0111] In one embodiment of the present disclosure, a network (NW) (base station or LMF (e.g., 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.

[0112] 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.

[0113] In step 1d-05, a terminal (e.g., terminal 1) (1d-10) may transmit the terminal's capability information (e.g., via a UECapabilityInformation message) to a connected base station (e.g., base station 1 or network 1) (1d-15). To receive the terminal capability information, the base station may first request the terminal to transmit the terminal's capability information (e.g., via a UECapabilityEnquiry message). The capability information transmitted by the terminal may include whether the terminal supports AI / ML-related functions (e.g., UE-side model-related functions / operations) (or operations using AI / ML models) (e.g., by AI / ML functionality, by use case, or by sub-use case). Meanwhile, step 1d-05 may be omitted. For example, if the base station has already received the terminal capability information, it does not transmit a terminal capability request to the terminal, and the step of the terminal transmitting the terminal capability information may be omitted.

[0114] In step 1d-25, the base station may transmit model training-related configuration information (e.g., training configuration) to the terminal 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 may receive configuration information regarding model training or a configuration request regarding model training from the terminal or the training object (1d-20). However, this is an embodiment of the present disclosure, and the base station may transmit the training configuration to the terminal even without receiving a configuration request regarding model training. For example, the base station may transmit the training configuration to the terminal based on specific conditions (e.g., based on the judgment of the base station, or when conditions set for transmitting the training configuration are satisfied, or based on a pre-set time or period).

[0115] In one embodiment of the present disclosure, the learning object (1d-20) may be a terminal or a terminal server. The terminal server may be connected to the terminal via a 3GPP network (e.g., a server within the 3GPP network) or connected to the terminal via an external network (e.g., Wi-Fi) (e.g., a server outside the 3GPP network).

[0116] In step 1d-30, the terminal may perform measurements to generate data necessary for training the UE-side model, and then report the measurement results (e.g., training report) to a base station or a training object. If the terminal reports the measurement results to the base station, the base station may reprocess the received report and deliver it to the training object. Alternatively, the base station may deliver the information received from the terminal to the training object as is.

[0117] In step 1d-35, the training object can train the UE-side model using the received reports.

[0118] In step 1d-50, the terminal (e.g., terminal 2) (1d-40) can receive the trained model from the training object. For example, the trained model can be delivered to the terminal via a base station (e.g., base station 2) (1d-45).

[0119] Before receiving the model, Terminal 2 may transmit the terminal's capability information to the connected Base Station 2 (e.g., via a UECapabilityInformation message). To receive the terminal capability information, the Base Station may first request the terminal to transmit the terminal's capability information (e.g., via a UECapabilityEnquiry message). The capability information transmitted by the terminal may include whether the terminal 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, the step of transmitting the terminal capability information as described above may be omitted.

[0120] In step 1d-55, terminal 2 can perform inference or prediction using the received model. For example, in the case of beam management, the terminal can perform spatial or temporal beam prediction. This step can be performed after terminal 2 receives inference-related settings from base station 2.

[0121] In step 1d-60, terminal 2 may report the result of the prediction or inference to base station 2.

[0122] In step 1d-65, base station 2 can perform downlink beam management for a terminal based on the prediction / inference result received from the terminal and select an appropriate beam to service the terminal.

[0123] 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.

[0124] In step 1e-05, a terminal (e.g., terminal 1) (1e-10) may transmit the terminal's capability information to a connected base station (e.g., base station 1 or network 1) (1e-15) (e.g., via a UECapabilityInformation message). To receive the terminal capability information, the base station may first request the terminal to transmit the terminal's capability information (e.g., via a UECapabilityEnquiry message). The capability information transmitted by the terminal may include whether the terminal supports AI / ML-related functions (e.g., NW-side model-related functions / operations) (e.g., inference and / or learning) (by AI / ML functionality, by use case, or by sub-use case). Meanwhile, step 1e-05 may be omitted. For example, if the base station has already received the terminal capability information, it does not transmit a terminal capability request to the terminal, and the step of the terminal transmitting the terminal capability information may be omitted.

[0125] In step 1e-25, the base station may transmit model training-related configuration information (e.g., training configuration) to the terminal for collecting data necessary for NW-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 may receive configuration information regarding model training or a configuration request regarding model training from the terminal or the training object (1e-20). However, this is an embodiment of the present disclosure, and the base station may transmit the training configuration to the terminal even without receiving a configuration request regarding model training. For example, the base station may transmit the training configuration to the terminal based on specific conditions (e.g., based on the judgment of the base station, or when conditions set for transmitting the training configuration are satisfied, or based on a pre-set time or period).

[0126] 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 it may be a server connected to them. It may also be an external server (outside of 3GPP).

[0127] In step 1e-30, the terminal may perform measurements to generate data necessary for training the NW-side model, and then report the measurement results (e.g., training report) to a base station or a training object. If the terminal reports the measurement results to the base station, the base station may reprocess the received report and deliver it to the training object. Alternatively, the base station may deliver the information received from the terminal to the training object as is.

[0128] In step 1e-35, the learning object can learn the NW-side model using the received report. Alternatively, the base station can learn the NW-side model using the measurement results received in 1e-30 (e.g., without delivering them to the learning object).

[0129] In step 1e-50, the base station (e.g., base station 2) (1e-45) may receive a trained model from the training object. Alternatively, the base station may have a model that it has trained itself.

[0130] Before or after step 1e-50, terminal 2 (1e-40) may transmit the terminal's capability information to the connected base station 2 (e.g., via a UECapabilityInformation message). To receive the terminal capability information, the base station may first request the terminal to transmit the terminal's capability information (e.g., via a UECapabilityEnquiry message). The capability information transmitted by the terminal may include whether the terminal 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 the terminal capability information as described above may be omitted.

[0131] In step 1e-55, terminal 2 can perform a measurement (e.g., after receiving measurement settings for the NW-side model from the base station) and then report the measurement results to the base station.

[0132] In step 1e-60, the base station may perform inference or prediction using the measurement report received from the terminal and the learned model. For example, in the case of beam management, spatial or temporal beam prediction may be performed.

[0133] In step 1e-65, the base station can perform downlink beam management for the terminal based on the prediction / inference result and select an appropriate beam to service the terminal.

[0134] 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.

[0135] In 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.

[0136] FIG. 1f is a diagram illustrating a procedure for a terminal to collect measurement data for learning a model according to one embodiment of the present disclosure.

[0137] In step 1f-15, the terminal may report information related to terminal capabilities to the base station after receiving a request for such information from the base station. The above step may cross-reference step 1e-05 or step 1d-05. For example, the terminal may report information related to the terminal's memory capabilities (e.g., minimum supported memory) (for storing measurement data for model training) to the base station. Additionally, as described above, step 1f-15 may be omitted.

[0138] In step 1f-20, the terminal may receive model training configuration information (e.g., training configuration) from the base station. The model training configuration information may be transmitted, for example, via an RRC Reconfiguration message or an RRC Resume message. The step may cross-reference step 1e-25 and / or step 1d-25.

[0139] In step 1f-22, the terminal can perform measurements based on model training configuration information (e.g., training configuration) and store the resulting data in memory.

[0140] In step 1f-25, the terminal may report information related to stored data (e.g., availability information) to the base station.

[0141] In one embodiment of the present disclosure, at step 1f-25, the terminal may provide information to the base station regarding the existence of stored data. For example, if the terminal has stored data, it may report to the base station by setting indicator A to true or including it. If the terminal does not have stored data, it may report to the base station by setting indicator A to false or omitting it. The information regarding the existence of stored data may be provided via a UE Assistance information message, and the terminal may report the existence of stored data to the base station 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 may report the existence of stored data to the base station immediately after receiving the relevant UE Assistance information setting. Alternatively, the terminal may report the changed information to the base station when the information regarding the existence of stored data changes (e.g., when storage begins in a state where there is no stored data). Based on the report from the terminal, the base station may determine whether to request a measurement report from the terminal (e.g., 1f-30) or determine the timing of the request.

[0142] In one embodiment of the present disclosure, at step 1f-25, if the terminal satisfies at least one of the following conditions, it may report to the base station that the condition is satisfied. Alternatively, if the terminal does not (no longer) satisfy at least one of the following conditions, it may report to the base station that the condition is (no longer) satisfied.

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

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

[0145] The above thresholds (thresholds 1 and 2) may each be set by the base station (e.g., through UE Assistance information settings) or are fixed values ​​in the standard. For example, the terminal may report the satisfaction of the above condition to the base station by setting indicator B to true or including it. The terminal may report the dissatisfaction of the above condition to the base station by setting indicator B to false or omitting it. Additionally, the terminal may report specific size information (e.g., available remaining size in memory, size of data stored in memory by the terminal). The above report may be provided via UE Assistance information messages, and the terminal may transmit the above report to the base station via UE Assistance information messages if it has received the relevant UE Assistance information settings in advance (e.g., in or after 1f-20). The terminal may report the above report to the base station immediately after receiving the relevant UE Assistance information settings. If the information in the above report changes (e.g., if condition 1 is not satisfied but is satisfied), the terminal may report the changed information to the base station. Alternatively, the terminal may periodically transmit the above-mentioned report information to the base station, and the transmission cycle of the terminal may be set by the base station. Based on the report from the terminal, the base station may determine whether to request a measurement report (e.g., 1f-30) from the terminal or determine the time of the request.

[0146] In one embodiment of the present disclosure, a prohibit timer may be set per function to limit the reporting of UE Assistance information messages too frequently for a single function of the terminal (e.g., indicating the existence of stored data). Information regarding the prohibit timer may be included within the UE Assistance information setting for the corresponding function. When the terminal transmits a UE Assistance information message to report on the corresponding function, it may operate the prohibit timer for the duration of the set prohibit timer, and the terminal may not be able to transmit a new UE Assistance information message for the same function while the prohibit timer is operating. The terminal may stop the operation of the prohibit timer when the UE Assistance information setting for the corresponding function is deactivated.

[0147] In step 1f-30, the base station may instruct or request that the stored measurement data (for model training) be reported (after receiving information regarding the stored data from the terminal). For example, the base station may instruct or request that the measurement data be reported using a UE Information Request message, an RRC reconfiguration message, or a new RRC message.

[0148] In step 1f-35, the terminal may report the stored measurement data (for model training) to the base station. The step may cross-reference step 1d-30 of FIG. 1d and / or step 1e-30 of FIG. 1e. For example, the terminal may use a UE Information Response message, a Measurement report message, or a new RRC message for the report.

[0149] FIG. 1g is a drawing illustrating an embodiment according to one embodiment of the present disclosure in which the model learning settings of the terminal are maintained during handover and measurement data that has not been transmitted is deleted.

[0150] Steps 1g-05 through 1g-35 can cross-reference steps 1f-05 through 1f-35 for each step.

[0151] In step 1g-20, a base station (e.g., base station 1) (1g-10) may transmit a configuration for model training to a terminal. Prior to this, the base station may receive configuration information regarding model training or a configuration request regarding model training from a training object (1g-12). However, this is an embodiment of the present disclosure, and the base station may transmit the training configuration to the terminal even without receiving a configuration request regarding model training. For example, the base station may transmit the training configuration to the terminal based on specific conditions (e.g., based on the judgment of the base station, or when conditions set for transmitting the training configuration are satisfied, or based on a pre-set time or period).

[0152] In step 1g-22, the terminal can perform measurements and collect data (e.g., data 1) according to the model learning settings while connected to a base station (e.g., base station 1) (1g-10).

[0153] In step 1g-24, the terminal may store data (e.g., data 1) measured (for model training) when connected to a base station (e.g., base station 1) (1g-10).

[0154] In step 1g-25, the terminal may report information related to stored data (e.g., availability information) (for data 1) to the base station. The terminal may provide the base station with information regarding the existence of stored data by reporting the information related to data to the base station.

[0155] In step 1g-30, the base station may request the terminal to provide a measurement report (for data 1).

[0156] In step 1g-35, the terminal can report the measured data (e.g., data 1) (for model training) to the base station (e.g., base station 1).

[0157] In step 1g-36, a base station (e.g., base station 1) may transmit measured data (e.g., data 1) to a learning object (for model training). As described above, the base station may reprocess the measured data and transmit it to the learning object, or transmit the data received from the terminal to the learning object as is.

[0158] In step 1g-40, the terminal may store data that it failed to report to the base station. The data may be data (e.g., Data 1) measured when the terminal was connected to base station 1 (for model training).

[0159] In step 1g-50, Base Station 1 may request a handover to Base Station 2 (e.g., 1g-45). For example, Base Station 1 may request a handover by sending a HANDOVER REQUEST message to Base Station 2.

[0160] In step 1g-55, Base Station 2 may instruct Base Station 1 to accept a handover request. For example, Base Station 2 may accept the handover request by transmitting a HANDOVER REQUEST ACKNOWLEDGEMENT message. The HANDOVER REQUEST ACKNOWLEDGEMENT message may include configuration information (e.g., target cell configuration information) that the target cell (e.g., a cell within Base Station 2) or the target base station (e.g., Base Station 2) sets for the terminal. The target base station (e.g., Base Station 2) may instruct the terminal (via Base Station 1) through the target cell configuration information to maintain (or change some configuration information) the model learning settings (e.g., 1g-20) set by the source base station (e.g., Base Station 1). In addition, the target base station (e.g., base station 2) can instruct the terminal (via base station 1) to delete (or discard) data (e.g., data 1) (1g-40) that was generated by the model learning setting (e.g., 1g-20) set by the source base station (e.g., base station 1) but has not yet been reported, through the target cell setting information (via base station 1).

[0161] In step 1g-60, a source base station (e.g., base station 1) may instruct a terminal to perform a handover including the target cell configuration information received from a target base station (e.g., base station 2). For example, the source base station (e.g., base station 1) may instruct the terminal to perform a handover via an RRC Reconfiguration message including reconfigurationWithSync, and the target cell configuration information may be included in the RRC Reconfiguration message.

[0162] In step 1g-62, the terminal can delete (or discard) data (e.g., data 1) (1g-40) that was generated by the model learning settings (e.g., 1g-20) set by the source base station (e.g., base station 1) but has not completed reporting.

[0163] In step 1g-65, the terminal can successfully perform a handover to a target base station (e.g., base station 2). For example, the terminal can successfully perform a handover to a target base station by performing a random access and sending an RRC Reconfiguration Complete message.

[0164] In step 1g-70, the terminal (while connected to base station 2) can perform measurements and collect data (e.g., data 2) according to the model learning settings maintained (or changed) at handover.

[0165] In step 1g-75, the terminal can transmit stored data-related information regarding the measured and collected data (e.g., data 2) (while connected to base station 2). The above step may cross-reference 1f-25.

[0166] In step 1g-80, the base station may request the terminal to transmit or report on the measured, collected, or stored data (e.g., data 2). The above step may cross-refer to 1f-30.

[0167] In step 1g-85, the terminal may transmit or report measured, collected, or stored data (e.g., data 2) to the base station. The above step may cross-refer to 1f-35.

[0168] In step 1g-90, the base station can transmit data (for model training) and related information received from the terminal to the training object (1g-12).

[0169] In one embodiment of the present disclosure, when a terminal deletes data (which it failed to transmit to a previous base station) as in step 1g-62, it may result in a waste of resources and energy used by the base station to allocate or set resources to generate said data and by the terminal to measure it. To avoid such waste of resources and energy, the terminal may need a method to report said data to the network after the handover, in which case said data may be used for model training of a learning object.

[0170] For example, as shown in FIG. 1h described below, after completing the handover (e.g., after 1g-65), the terminal may transmit or report to the target base station (e.g., base station 2) data (e.g., data 1) (e.g., 1h-40) that was measured at the source base station (e.g., base station 1) but could not be transmitted (to base station 1) (e.g., 1h-85). However, at this time, the terminal may have data (e.g., data 2) (1h-70) measured based on configuration information received from base station 2 in addition to data 1, and may report data 2 to the base station along with data 1. Data 1 and data 2 reported to the base station may be transmitted as a learning object (1h-90). However, the training object may not be able to distinguish each data and may not know the "data collection environment information" (e.g., information regarding which base station or under what settings / environments / conditions the terminal measured and collected the data) (e.g., Data collection environment information or DCEI) for each data. For example, if the measurement environment information of Data 1 and Data 2 is the same, the training object may use Data 1 and Data 2 together as training data without distinguishing between them to train a single AI / ML model. However, if the data collection environment information (DCEI) of Data 1 and Data 2 is different, the training object may distinguish Data 1 and Data 2 to train different AI / ML models according to DCEI, using Data 1 to train AI / ML Model 1 and Data 2 to train AI / ML Model 2. Therefore, when providing model training data to the training object, a procedure (e.g., 1h-90) may be required to provide the data along with the related data collection environment information (DCEI) separately.

[0171] FIG. 1h is a diagram illustrating an embodiment according to one embodiment of the present disclosure in which the model learning settings of the terminal are maintained during handover and measurement data that has not been transmitted is transmitted to the target base station.

[0172] Steps 1h-05 through 1h-90 can cross-reference steps 1f-05 through 1f-90 for each step.

[0173] In step 1h-20, the base station may transmit (measurement) configuration information for model learning to the terminal. The configuration information may include data collection environment information (DCEI). For example, the base station (e.g., base station 1) may include DCEI 1 in the configuration information and transmit it to the terminal. In one embodiment of the present disclosure, the data collection environment information (DCEI) may mean at least one of the following information.

[0174] - Information 1. Network-side additional condition (where the terminal measures or collects data)

[0175] ■ A single network-side additional condition may be information used, configured, or implemented by a base station or cell. For example, it may be information indicating (mapped to information) at least one of the following: downlink spatial domain transmission filter, Quasi-colocation (QCL) information, Transmission Configuration Indication (TCI) information, input and output order of a model, downlink transmission beam information (e.g., direction, number, indexing), settings related to downlink transmission beam operation, beam codebook, settings related to antenna operation, transmission power information, terminal distribution information, antenna height information, cell or base station installation environment information (e.g., indoor, outdoor, urban, rural, road).

[0176] ■ (One or more) network-side additional conditions can be represented by a single ID, which can be referred to, for example, as a network-side additional condition ID or an associated ID.

[0177] - Information 2. (One or more) PLMN and / or base station and / or cell-related information (where the terminal measures / collects data)

[0178] ■ For example, it may be PCI (Physical Cell ID) or PCI set, PCI list, PCI range, PLMN ID (list) information, CGI (Cell Global Identifier) ​​(list) information, Cell (ID) (list) information, Area (ID) (list) information.

[0179] - Information 3. Frequency information (of which the terminal measures / collects data)

[0180] In step 1h-24, the terminal may store data (e.g., data 1) measured (for model training) when connected to a base station (e.g., base station 1) (1g-10). The terminal may store DCEI 1 together with or separately from data 1 (e.g., within a UE variable).

[0181] In step 1h-25, the terminal may report stored data-related information (e.g., availability information) to the base station. By reporting the data-related information to the base station, the terminal may provide the base station with information regarding the existence of the stored data. Additionally, the terminal may indicate (or report) DCEI 1 along with the stored data-related information (e.g., data 1). In one embodiment of the present disclosure, DCEI may be omitted in step 1h-30. This is because information regarding DCEI 1 may not be necessary since the base station receiving the stored data-related information is also Base Station 1. When the base station (e.g., Base Station 1) receives stored data-related information (e.g., 1h-25) that does not include DCEI, it may know that it is a transmission regarding its own DCEI (e.g., DCEI 1).

[0182] In step 1h-35, the terminal may report data (e.g., Data 1) measured or collected at Base Station 1 (for model training) to the Base Station. At this time, the terminal may instruct (or report) to the Base Station a DCEI (e.g., DCEI 1) for Base Station 1 along with the measured or collected data. In one embodiment of the present disclosure, the DCEI may be omitted in step 1h-35. This is because information regarding DCEI 1 may not be necessary since the Base Station receiving the data is also Base Station 1. When the Base Station (e.g., Base Station 1) receives a report (e.g., 1h-35) that does not include the DCEI, it may know that it is a report regarding its own DCEI (e.g., DCEI 1).

[0183] In step 1h-36, the base station (e.g., base station 1) may transmit DCEI 1 along with data 1 received from the terminal to the learning object.

[0184] In step 1h-40, the terminal may store data (e.g., data 1) and / or related DCEI (e.g., DCEI 1) that it failed to report to the base station.

[0185] In step 1h-55, the target base station (base station 2) may instruct the source base station (base station 1) to accept the handover request. For example, base station 2 may accept the handover request by sending a HANDOVER REQUEST ACKNOWLEDGEMENT message. The HANDOVER REQUEST ACKNOWLEDGEMENT message may include a target cell (e.g., a cell within base station 2) or configuration information (e.g., target cell configuration information) that the target base station (e.g., base station 2) sets for the terminal. The target base station (e.g., base station 2) may instruct the terminal (via base station 1) through the target cell configuration information to maintain (or change some configuration information) the model learning settings (e.g., 1g-20) set by the source base station (e.g., base station 1). Additionally, the target base station (e.g., base station 2) can instruct the terminal (via base station 1) through the target cell configuration information to retain (not delete) data (e.g., data 1) (1g-40) that was generated by the model learning configuration (e.g., 1g-20) set by the source base station (e.g., base station 1) but has not yet been reported (for future transmission). Additionally, the target base station (e.g., base station 2) can transmit the DCEI (e.g., DCEI 2) for base station 2 to the source base station (e.g., base station 1) through the target cell configuration information.

[0186] In step 1h-60, the source base station (e.g., base station 1) may instruct the terminal to perform a handover, including the target cell configuration information received from the target base station (e.g., base station 2). For example, the source base station (e.g., base station 1) may instruct the terminal to perform a handover via an RRC Reconfiguration message including reconfigurationWithSync, and the target cell configuration information may be included in the RRC Reconfiguration message. Upon receiving this, the terminal may continue to store (in memory) data (e.g., data 1) (1h-40) that was generated by the model learning configuration (e.g., 1h-20) set by the source base station (e.g., base station 1) but has not completed reporting, without deleting it. Additionally, the terminal may store the received DCEI 2.

[0187] In step 1h-65, the terminal can successfully perform a handover to the target base station (e.g., base station 2). For example, the terminal can successfully perform a handover to the target base station by performing a random access and sending an RRC Reconfiguration Complete message. The terminal can transmit stored data-related information (for data 1) in the RRC Reconfiguration Complete message. The terminal can transmit DCEI 1 along with the stored data-related information (for data 1).

[0188] In step 1h-70, the terminal (while connected to base station 2) can perform measurements and collect data (e.g., data 2) according to the model training settings maintained (or changed) at handover. The terminal may store DCEI 2 together with or separately from data 2 (e.g., within a UE variable).

[0189] In step 1h-75, the terminal may transmit stored data-related information (for data 1 and / or data 2). The stored data-related information for data 1 and data 2 may be transmitted separately. The terminal may transmit DCEI 1 along with the stored data-related information for data 1 and DCEI 2 along with the stored data-related information for data 2.

[0190] In step 1h-80, the base station may request the terminal to measure, collect, or transmit or report data (e.g., data 1 and / or data 2). Requests for data 1 and data 2 may be transmitted separately.

[0191] In step 1h-85, the terminal may transmit or report to the base station measured, collected, or stored data (e.g., data 1 and / or data 2). Data 1 and data 2 may be transmitted separately. The terminal may transmit DCEI 1 with data 1 and DCEI 2 with data 2.

[0192] In step 1h-90, the base station may transmit data (for model training), DCEI, and related information received from the terminal to the training object (1h-12). At this time, data 1 and data 2 may be transmitted separately. The base station may transmit DCEI 1 together with data 1 and DCEI 2 together with data 2.

[0193] In one embodiment of the present disclosure, the DCEI may not be transmitted to the terminal. For example, in steps 1h-20 and 1h-60, the base station may not transmit the DCEI to the terminal. Instead, in step 1h-50, base station 1 may transmit DCEI 1 to base station 2, and base station 2 may store it. In step 1h-85, if the terminal transmits data (e.g., data 1) while indicating that the data was measured at a previous base station (e.g., base station 1), base station 2 may transmit data 1 to a learning object including the previously stored DCEI 1 in step 1h-90. In step 1h-85, if the terminal transmits data (e.g., data 2) (without indicating that the data was measured at a previous base station (e.g., base station 1)), base station 2 may transmit data 2 to a learning object including its own DCEI (e.g., DCEI 2).

[0194] FIG. 1i is a drawing illustrating an embodiment according to one embodiment of the present disclosure in which the model learning setting of the terminal is released during handover and measurement data that has not been transmitted is deleted.

[0195] Steps 1i-05 through 1i-65 may cross-reference steps 1g-05 through 1g-65 or steps 1h-05 through 1h-65 for each step.

[0196] In step 1i-55, the target base station (base station 2) may instruct the source base station (base station 1) to accept the handover request. For example, base station 2 may accept the handover request by sending a HANDOVER REQUEST ACKNOWLEDGEMENT message. The HANDOVER REQUEST ACKNOWLEDGEMENT message may include a target cell (e.g., a cell within base station 2) or configuration information (e.g., target cell configuration information) that the target base station (e.g., base station 2) sets for the terminal. The target base station (e.g., base station 2) may instruct the terminal (via base station 1) to release the model learning configuration (e.g., 1i-20) set by the source base station (e.g., base station 1) through the target cell configuration information. In addition, the target base station (e.g., base station 2) can instruct the terminal (via base station 1) to delete data (e.g., data 1) (1i-40) that was generated by the model learning settings (e.g., 1i-20) set by the source base station (e.g., base station 1) but has not yet been reported, through the target cell setting information (via base station 1).

[0197] In step 1i-60, the source base station (e.g., base station 1) may instruct the terminal to perform a handover including the target cell configuration information received from the target base station (e.g., base station 2). For example, the source base station (e.g., base station 1) may instruct the terminal to perform a handover via an RRC Reconfiguration message including reconfigurationWithSync, and the target cell configuration information may be included in the RRC Reconfiguration message.

[0198] In step 1i-62, the terminal can delete data (e.g., data 1) (1i-40) that was generated by the model learning settings (e.g., 1i-20) set by the source base station (e.g., base station 1) but has not completed reporting.

[0199] In step 1i-63, the terminal may disable the model learning settings (e.g., 1i-20) set by the source base station (e.g., base station 1) and stop the measurements and data collection (for model learning) that were being performed accordingly.

[0200] In one embodiment of the present disclosure, when a terminal deletes data (which it failed to transmit to a previous base station) as in step 1i-62, it may result in a waste of resources and energy used by the base station to allocate or set resources and by the terminal to measure the data to generate said data. To avoid such waste of resources and energy, the terminal may need a method to report said data to the network after the handover, and in that case, said data may be used for training a model of a learning object.

[0201] However, as shown in FIG. 1i, if the (target) base station instructs the terminal to disable the model training-related settings, this may imply that the (target) base station also disables the model training-related settings. Since the model training-related settings held by the base station may contain information about the training object, if these settings are disabled, the base station may not be able to deliver the data to the training object even if it receives the data from the terminal. Therefore, in order for the base station to transmit the terminal's measurement data to the training object while the model training-related settings are disabled after the handover, the base station must be able to obtain "Training Entity Information" (Training Entity Information or TEI). For example, as described in 1j-85 below, TEI information may be included along with the data in the measurement report transmitted by the terminal. Additionally, as shown in FIG. 1h, when providing model training data to the training object, a procedure may be required to provide the related data collection environment information (DCEI) separately along with the data.

[0202] FIG. 1j is a diagram illustrating an embodiment according to one embodiment of the present disclosure in which the model learning setting of the terminal is released during handover and measurement data that has not been transmitted is transmitted to the target base station.

[0203] Steps 1j-05 through 1j-90 may cross-reference steps 1g-05 through 1g-90, steps 1h-05 through 1h-90, or steps 1i-05 through 1i-65 for each step.

[0204] In step 1j-21, the training object may include its TEI information in the (measurement) settings (related to model training). The said TEI information may mean at least one of the following information.

[0205] - Information 1. External server address (e.g., IP address)

[0206] - Information 2. OAM (or server associated with OAM) address or ID

[0207] - Information 3. TCE (Trace collection entity) or MCE (Measurement collection entity) address or ID or TCE reference or MCE reference

[0208] - Information 4. UPF or AMF (or servers associated with them) address or ID

[0209] - Information 5. Base station (or server connected to the base station) address or base station ID or cell ID or PCI or CGI

[0210] For example, if the training object of the NW-side model is OAM, information 2 may be included. For example, if the training object of the NW-side model is a base station, information 5 may be included.

[0211] For example, if the training object of the NW-side model or UE-side model is an external server, information 1 may be included.

[0212] In step 1j-20, a base station (e.g., base station 1) may provide (measurement) configuration information for model learning to a terminal, and said configuration information may include data collection environment information (e.g., DCEI1) and / or TEI.

[0213] In step 1j-24, the terminal may store data (e.g., data 1) measured (for model training) when connected to a base station (e.g., base station 1) (1j-10). The terminal may store DCEI 1 and / or TEI together with or separately from data 1 (e.g., within a UE variable).

[0214] In step 1j-25, the terminal may report stored data-related information (Availability information) (for Data 1) to the base station. The terminal may provide the base station with information regarding the existence of stored data by reporting the data-related information to the base station. The terminal may indicate (or report) DCEI 1 and / or TEI to the base station along with the stored data-related information (for Data 1). In one embodiment of the present disclosure, DCEI and / or TEI may be omitted in step 1j-30. This is because base station 1 may already know DCEI 1 and TEI.

[0215] In step 1j-35, the terminal may report to the base station data (e.g., data 1) measured or collected at base station 1 (for model training). At this time, the terminal may report to the base station the DCEI (e.g., DCEI 1) and / or TEI for base station 1 along with the measured or collected data. In one embodiment of the present disclosure, the DCEI and / or TEI may be omitted in step 1j-35. This is because base station 1 may already know the DCEI 1 and TEI.

[0216] In step 1j-36, the base station (e.g., base station 1) may transmit DCEI 1 along with data 1 received from the terminal to the learning object.

[0217] In step 1j-40, the terminal may store data (e.g., data 1) and / or related DCEI (e.g., DCEI 1) and / or related TEI that was not reported to the base station.

[0218] In step 1j-55, the target base station (base station 2) may instruct the source base station (base station 1) to accept the handover request. For example, base station 2 may accept the handover request by sending a HANDOVER REQUEST ACKNOWLEDGEMENT message. The HANDOVER REQUEST ACKNOWLEDGEMENT message may include a target cell (e.g., a cell within base station 2) or configuration information (e.g., target cell configuration information) that the target base station (e.g., base station 2) sets for the terminal. The target base station (e.g., base station 2) may instruct the terminal (via base station 1) to release the model learning configuration (e.g., 1g-20) set by the source base station (e.g., base station 1) through the target cell configuration information. In addition, the target base station (e.g., base station 2) can instruct the terminal (via base station 1) through the target cell setting information to retain (not delete) data (e.g., data 1) (1g-40) that was generated by the model learning setting (e.g., 1g-20) set by the source base station (e.g., base station 1) but has not yet been reported (for future transmission).

[0219] In step 1j-60, the source base station (e.g., base station 1) may instruct the terminal to perform a handover, including the target cell configuration information received from the target base station (e.g., base station 2). For example, the source base station (e.g., base station 1) may instruct the terminal to perform a handover via an RRC Reconfiguration message including reconfigurationWithSync, and the target cell configuration information may be included in the RRC Reconfiguration message. Upon receiving this, the terminal may continue to store (in memory) data (e.g., data 1) (1j-40) that was generated by the model learning configuration (e.g., 1j-20) set by the source base station (e.g., base station 1) but has not completed reporting, without deleting it.

[0220] In step 1i-63, the terminal may disable the model learning settings (e.g., 1i-20) set by the source base station (e.g., base station 1) and stop the measurements and data collection (for model learning) that were being performed accordingly.

[0221] In step 1j-65, the terminal can successfully perform a handover to a target base station (e.g., base station 2). For example, the terminal can successfully perform a handover to the target base station by performing a random access and sending an RRC Reconfiguration Complete message. The terminal can transmit stored data-related information (for data 1) in the RRC Reconfiguration Complete message. The terminal can transmit DCEI 1 and / or TEI along with the stored data-related information (for data 1).

[0222] In step 1j-80, the base station may request the terminal to transmit / report the measured / collected / stored data (e.g., data 1).

[0223] In step 1j-85, the terminal may measure, collect, or transmit or report data (e.g., Data 1) to the base station. The terminal may transmit DCEI 1 and / or TEI along with Data 1.

[0224] In step 1j-90, the base station can transmit to the learning object based on the TEI, and can transmit the data (for model learning) received from the terminal, the DCEI, and related information to the learning object (1j-12).

[0225] In one embodiment of the present disclosure, the TEI is not transmitted to the terminal (e.g., the TEI is not included in steps 1j-20 and 1j-85), and base station 1 can transmit the TEI to base station 2 in step 1j-50. Base station 2, having received this, stores it, and when it receives a transmission of measurement data from the terminal (e.g., 1j-85), it can use the stored TEI to transmit the measurement data to a learning object.

[0226] In one embodiment of the present disclosure, the learning object (e.g., 1g-12, 1h-12, 1i-12, 1j-12) may be a source base station (e.g., base station 1). In this case, measurement data reported to base station 2 may be transmitted to base station 1.

[0227] In one embodiment of the present disclosure, the learning object prior to the handover may be a source base station (e.g., base station 1), and the learning object after the handover may be a target base station (e.g., base station 2). In this case, each base station may perform model learning. In one embodiment of the present disclosure, the learning object prior to the handover may be a source base station (e.g., base station 1), and the learning object after the handover may be a source base station and / or a target base station (e.g., base station 2). For example, among the data reported to base station 2 in step 1h-85 after the handover in FIG. 1h, data 1 may be transmitted to base station 1, and data 2 may be used for model learning of the base station.

[0228] In one embodiment of the present disclosure, a measurement setting for model learning received by a terminal may include an indicator indicating whether the setting is a setting for NW-side model learning where OAM is the learning object, or a setting for NW-side model learning where the base station is the learning object. In this case, for example, if the terminal is indicated that the setting is for NW-side model learning where OAM is the learning object, it may perform an operation of transmitting data that could not be transmitted to base station 1 to base station 2 according to FIG. 1h or FIG. 1j. For example, if the terminal is indicated that the setting is for NW-side model learning where the base station is the learning object, it may perform an operation of deleting data that could not be transmitted to base station 1 without transmitting it to base station 2 according to FIG. 1g or FIG. 1i.

[0229] In one embodiment of the present disclosure, when a terminal receives a full configuration from a target base station or target cell that has performed a handover (and the target cell configuration does not contain any configuration information related to model learning), the terminal may disable the previous model learning related settings and delete the stored measurement data.

[0230] In one embodiment of the present disclosure, when a memory storing measurement data becomes full while a terminal is storing measurement data (e.g., data 1) that was measured at a previous base station but not reported, the terminal may overwrite data 1 using new data (e.g., data 2) (generated at the current base station).

[0231] In one embodiment of the present disclosure, the terminal may delete the stored data after a fixed time or a time set by the network (e.g., via 1j-20) has elapsed, based on the time when the data was stored or based on the time when information related to the stored data (Availability information) was transmitted (e.g., 1j-25).

[0232] In one embodiment of the present disclosure, a terminal that has received measurement settings for model training may transition to RRC_IDLE or RRC_INACTIVE during measurement (e.g., 1f-22) (e.g., upon receiving an RRC Release). At this time, the terminal may (self-deactivate) the measurement settings for model training. When transitioning to RRC_INACTIVE, the terminal may not save the settings to the UE Inactive AS Context. The terminal may delete data that has been measured and saved (which has not been reported to the base station) regarding the settings.

[0233] In one embodiment of the present disclosure, a terminal that has received measurement settings for model training may transition to RRC_INACTIVE during measurement (e.g., 1f-22) (e.g., upon receiving an RRC Release). At this time, the terminal may save the settings without releasing them (e.g., within the UE Inactive AS Context). Subsequently, the terminal may resume measurement for model training by restoring the settings after performing an RRC Resume procedure with a new base station. If there is data that the terminal measured and saved before transitioning to RRC_INACTIVE (which was not reported to the previous base station), the terminal may instruct / transmit information related to the saved data (e.g., availability information) to the new base station via an RRC Resume Complete message. Subsequently, the terminal may receive a request from the new base station to transmit the saved data, and the terminal may transmit the saved data to the base station.

[0234] In one embodiment of the present disclosure, if there is data (related to model learning) that was previously measured and stored (which was not reported to the previous base station) during the RRC Reestablishment procedure, the terminal may instruct or transmit information related to the stored data (e.g., availability information) to the (new) base station via an RRC Reestablishment Complete message. Subsequently, the terminal may receive a request from the base station to transmit the stored data, and the terminal may transmit the stored data to the base station.

[0235] [Correction pursuant to Rule 91 09.03.2026] Referring to Fig. 1k, the terminal may include an RF (Radio Frequency) processing unit (1l-10), a baseband processing unit (1l-20), a storage unit (1l-30), and a control unit (1l-40).

[0236] [Correction pursuant to Rule 91 09.03.2026] The RF processing unit (1l-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 (1l-10) can up-convert a baseband signal provided by the baseband processing unit (1l-20) into an RF band signal and then 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 (1l-10) may include a transmission filter, a reception 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. 1k, the terminal may be equipped with multiple antennas. In addition, the RF processing unit (1l-10) may include multiple RF chains. Additionally, the RF processing unit (1l-10) can perform beamforming. For beamforming, the RF processing unit (1l-10) can adjust the phase and magnitude of each of the signals transmitted and received through multiple antennas or antenna elements. Additionally, the RF processing unit can perform MIMO (multi-input multi-output) and can receive multiple layers when performing MIMO operation.

[0237] The baseband processing unit (1l-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 (1l-20) can generate complex symbols by encoding and modulating the transmitted bit sequence. Additionally, when receiving data, the baseband processing unit (1l-20) can restore the received bit sequence by demodulating and decoding the baseband signal provided by the RF processing unit (1l-10). For example, in the case of following the orthogonal frequency division multiplexing (OFDM) method, when transmitting data, the baseband processing unit (1l-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 (1l-20) can divide the baseband signal provided from the RF processing unit (1l-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.

[0238] The baseband processing unit (1l-20) and the RF processing unit (1l-10) can transmit and receive signals as described above. Accordingly, the baseband processing unit (1l-20) and the RF processing unit (1l-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 (1l-20) and the RF processing unit (1l-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 (1l-20) and the RF processing unit (1l-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.

[0239] The storage unit (1l-30) can store data such as basic programs, application programs, and setting information for the operation of the terminal. In particular, the storage unit (1l-30) can store information related to a second connection node that performs wireless communication using wireless connection technology. Additionally, the storage unit (1l-30) can provide the stored data upon a request from the control unit (1l-40).

[0240] The control unit (1l-40) can control the overall operations of the terminal. For example, the control unit (1l-40) can control the terminal to perform the embodiments and / or methods of the present disclosure described above. For example, the control unit (1l-40) can transmit and receive signals through the baseband processing unit (1l-20) and the RF processing unit (1l-10). Additionally, the control unit (1l-40) can write and read data to and from the storage unit (1l-30). To this end, the control unit (1l-40) may include at least one processor. For example, the control unit (1l-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 (1l-42) as illustrated in the drawing.

[0241] [Correction pursuant to Rule 91 09.03.2026] FIG. 11 is a drawing illustrating the structure of a base station according to one embodiment of the present disclosure.

[0242] [Correction pursuant to Rule 91 09.03.2026] Referring to FIG. 11, according to one example of the present disclosure, a base station may be configured to include an RF processing unit (1m-10), a baseband processing unit (1m-20), a backhaul communication unit (1m-30), a storage unit (1m-40), and a control unit (1m-50).

[0243] The RF processing unit (1m-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 (1m-10) can up-convert a baseband signal provided by the baseband processing unit (1m-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 (1m-10) may include a transmit filter, a receive filter, an amplifier, a mixer, an oscillator, a DAC, an ADC, etc. Although only one antenna is shown in the drawing, the base station may be equipped with multiple antennas. Additionally, the RF processing unit (1m-10) may include multiple RF chains. Furthermore, the RF processing unit (1m-10) may perform beamforming. For the above beamforming, the RF processing unit (1m-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.

[0244] The baseband processing unit (1m-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 (1m-20) can generate complex symbols by encoding and modulating the transmitted bit sequence. Additionally, when receiving data, the baseband processing unit (1m-20) can restore the received bit sequence by demodulating and decoding the baseband signal provided by the RF processing unit (1m-10). For example, in the case of an OFDM method, when transmitting data, the baseband processing unit (1m-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 (1m-20) can divide the baseband signal provided by the RF processing unit (1m-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 (1m-20) and the RF processing unit (1m-10) can transmit and receive signals as described above. Accordingly, the baseband processing unit (1m-20) and the RF processing unit (1m-10) may be referred to as a transmitting unit, a receiving unit, a transceiver unit, a communication unit, or a wireless communication unit.

[0245] The backhaul communication unit (1m-30) can provide an interface for communicating with other nodes within the network. That is, the backhaul communication unit (1m-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.

[0246] The storage unit (1m-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 (1m-40) can store information regarding bearers assigned to connected terminals, measurement results reported from connected terminals, etc. Additionally, the storage unit (1m-40) can store information that serves as a criterion for determining whether to provide or disconnect multiple connections to the terminals. Furthermore, the storage unit (1m-40) can provide the stored data upon a request from the control unit (1m-50).

[0247] The control unit (1m-50) can control the overall operations of the main base station. For example, the control unit (1m-50) can control the base station to perform the embodiments and / or methods of the present disclosure described above. For example, the control unit (1m-50) can transmit and receive signals through the baseband processing unit (1m-20) and the RF processing unit (1m-10) or through the backhaul communication unit (1m-30). Additionally, the control unit (1m-50) can write and read data to and from the storage unit (1m-40). To this end, the control unit (1m-50) may include at least one processor and may include a multiple connection processing unit (1m-52) as illustrated in the drawing.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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

In a method performed by a terminal in a communication system, A step of receiving an RRC (radio resource control) reset message from a source base station instructing a handover to a target base station, wherein the RRC reset message includes information about a target cell associated with the target base station; If the above RRC reset message includes information instructing to maintain logged measurement results for data collection, the step of transmitting an RRC reset completion message to the target base station including information instructing that the terminal has the logged measurement results; A step of receiving a terminal information request message requesting the reporting of the logged measurement result from the target base station; and A method characterized by including the step of transmitting a terminal information response message, including the above-mentioned logged measurement result, to the target base station. In paragraph 1, A method characterized by further including the step of discarding the logged measurement results when the above RRC reset message does not contain information instructing to retain the above logged measurement results. In paragraph 1, A method characterized in that the above-mentioned logged measurement result includes CGI (cell global identity); or PCI (physical cell identity) and frequency information. In paragraph 1, A method characterized by deleting the logged measurement result and the measurement setting set on the terminal when the terminal receives an RRC release message from the target base station. In a method performed by a source base station in a communication system, A step of transmitting a handover request message to a target base station; A step of receiving a handover request acknowledgment message including an RRC (radio resource control) reset message from the target base station in response to the handover request message, wherein the RRC reset message includes information about a target cell associated with the target base station; and The method includes the step of transmitting the RRC reset message to the terminal to instruct a handover to the target base station, A method characterized by the information instructing to maintain the logged measurement results for data collection included in the above RRC reset message instructing the terminal to maintain the logged measurement results. In paragraph 5, The RRC reset message that does not include information instructing to retain the above-mentioned logged measurement results instructs the terminal to discard the above-mentioned logged measurement results, and A method characterized in that the above-mentioned logged measurement result includes CGI (cell global identity); or PCI (physical cell identity) and frequency information. In a method performed by a target base station in a communication system, A step of receiving a handover request message from a source base station; A step of transmitting a handover request acknowledgment message including an RRC (radio resource control) reset message to the source base station in response to the handover request message, wherein the RRC reset message includes information about a target cell associated with the target base station; If the above RRC reset message includes information instructing to maintain logged measurement results for data collection, the step of receiving an RRC reset completion message from the terminal including information instructing that the terminal has said logged measurement results; A step of transmitting a terminal information request message requesting the terminal to report the logged measurement result; and A method characterized by including the step of receiving a terminal information response message including the above-mentioned logged measurement result from the terminal. In Paragraph 7, The RRC reset message that does not include information instructing to retain the above-mentioned logged measurement results instructs the terminal to discard the above-mentioned logged measurement results, and A method characterized in that the above-mentioned logged measurement result includes CGI (cell global identity); or PCI (physical cell identity) and frequency information. In a terminal in a communication system, At least one transceiver; At least one processor connected to the above at least one transceiver so as to be able to communicate; and The terminal is connected to communicate with at least one processor and is capable of executing individually or in any combination of the at least one processor, so that the terminal, A radio resource control (RRC) reset message is received from a source base station instructing a handover to a target base station, and the RRC reset message includes information about a target cell associated with the target base station; If the above RRC reset message includes information instructing to maintain logged measurement results for data collection, the terminal transmits an RRC reset completion message including information instructing that it has the logged measurement results to the target base station, and Receive a terminal information request message requesting the reporting of the logged measurement results from the above target base station, and A terminal characterized by including a memory that stores an instruction to include the step of transmitting a terminal information response message, including the above-mentioned logged measurement result, to the target base station. In Paragraph 9, A terminal characterized by an instruction executable individually or in any combination of at least one processor, wherein the terminal discards the logged measurement result when the RRC reset message does not contain information instructing to retain the logged measurement result. In Paragraph 9, The above-mentioned logged measurement results include CGI (cell global identity); or PCI (physical cell identity) and frequency information, and A terminal characterized by deleting the logged measurement result and the measurement setting configured on the terminal when the terminal receives an RRC release message from the target base station. In a communication system, regarding a source base station, At least one transceiver; At least one processor connected to the above at least one transceiver so as to be able to communicate; and The source base station is connected to communicate with at least one processor and is capable of executing individually or in any combination of the at least one processor, and, Send a handover request message to the target base station, and In response to the above handover request message, a handover request acknowledgment message including an RRC (radio resource control) reset message is received from the target base station, and the RRC reset message includes information about a target cell associated with the target base station, and It includes a memory that stores a command to transmit the RRC reset message to the terminal to instruct a handover to the target base station; A source base station characterized by information instructing to maintain logged measurement results for data collection included in the above RRC reset message, which instructs the terminal to maintain the above logged measurement results. In Paragraph 12, The RRC reset message that does not include information instructing to retain the above-mentioned logged measurement results instructs the terminal to discard the above-mentioned logged measurement results, and A source base station characterized by the above-mentioned logged measurement result including CGI (cell global identity); or PCI (physical cell identity) and frequency information. In a communication system, regarding a target base station, At least one transceiver; At least one processor connected to the above at least one transceiver so as to be able to communicate; and The target base station is connected to communicate with at least one processor and is capable of executing individually or in any combination of the at least one processor, so that the target base station, Receive a handover request message from the source base station, and In response to the above handover request message, a handover request acknowledgment message including an RRC (radio resource control) reset message is transmitted to the source base station, and the RRC reset message includes information about a target cell associated with the target base station; If the above RRC reset message includes information instructing to maintain logged measurement results for data collection, the terminal receives an RRC reset completion message from the terminal including information instructing that the terminal has the above logged measurement results; Sending a terminal information request message requesting the terminal to report the logged measurement result, and A target base station characterized by including: a memory that stores a command to receive a terminal information response message including the above-mentioned logged measurement result from the terminal. In Paragraph 14, The RRC reset message that does not include information instructing to retain the above-mentioned logged measurement results instructs the terminal to discard the above-mentioned logged measurement results, and A target base station characterized by the above-mentioned logged measurement result including CGI (cell global identity); or PCI (physical cell identity) and frequency information.

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