Method and device for utilizing artificial intelligence in wireless communication system

Abstract

The present disclosure relates to a 5G or 6G communication system for a supporting higher data transmission rate. In addition, the present disclosure relates to a method performed by a terminal in a wireless communication system, the method comprising the steps of: receiving, from a base station, a logged measurement configuration for data collection; logging measurement information for the data collection on the basis of the logged measurement configuration; stopping the logging of the measurement information for the data collection when a memory for the measurement information for the data collection becomes full; and resuming the logging of the measurement information for the data collection when the memory for the measurement information for the data collection is no longer full.

Description

Method and device for utilizing artificial intelligence in wireless communication systems

[0001] The present disclosure relates to a terminal, a base station, and an upper node supporting the same in a wireless communication system. More specifically, the present disclosure relates to a novel communication method and apparatus for a terminal, a base station, and an upper node utilizing artificial intelligence in a wireless communication system.

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

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

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

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

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

[0007] Furthermore, the advancement of these 5G mobile communication systems encompasses multi-antenna transmission technologies such as new waveforms 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] Meanwhile, as various technologies utilizing artificial intelligence are being developed, there is a demand in the wireless communication field for improved communication methods using artificial intelligence and devices to perform such methods.

[0009] Various embodiments of the present disclosure aim to provide an improved communication method for a terminal, a base station, and an upper node supporting the same in a wireless communication system.

[0010] In addition, various embodiments of the present disclosure aim to provide a new communication method between a terminal, a base station, and an upper node utilizing artificial intelligence in a wireless communication system.

[0011] One embodiment of the present disclosure provides a method performed by a terminal in a wireless communication system, comprising: receiving a logged measurement configuration for data collection from a base station; logging measurement information for data collection based on the logged measurement configuration; stopping the logging of the measurement information for data collection when the memory for the measurement information for data collection is full; and resuming the logging of the measurement information for data collection when the memory for the measurement information for data collection is no longer full.

[0012] Additionally, one embodiment of the present disclosure provides a method performed by a base station in a wireless communication system, comprising: transmitting a logged measurement configuration for data collection to a terminal; and receiving information from the terminal indicating that the memory of the terminal is full for logging measurement information for data collection based on the logged measurement configuration, wherein if the memory for the measurement information for data collection is full, logging of the measurement information for data collection is stopped at the terminal, and if the memory for the measurement information for data collection is no longer full, logging of the measurement information for data collection is resumed at the terminal.

[0013] Additionally, one embodiment of the present disclosure provides a terminal of a wireless communication system comprising: at least one transceiver; at least one processor connected to communicate with the at least one transceiver; and a memory connected to communicate with the at least one processor and executable individually or in any combination thereof, wherein the terminal: receives a logged measurement configuration for data collection from a base station, logs measurement information for data collection based on the logged measurement configuration, stops logging the measurement information for data collection when the memory for the measurement information for data collection is full, and resumes logging the measurement information for data collection when the memory for the measurement information for data collection is no longer full.

[0014] Additionally, one embodiment of the present disclosure provides a base station of a wireless communication system comprising: at least one transceiver; at least one processor connected to communicate with the at least one transceiver; and a memory connected to communicate with the at least one processor and executable individually or in any combination thereof, wherein the base station comprises: a memory storing an instruction to receive from the terminal that the memory of the terminal is full, which logs measurement information for data collection based on the logged measurement configuration, and logs measurement information for data collection based on the logged measurement configuration, and when the memory for the measurement information for data collection is full, logging of the measurement information for data collection at the terminal is stopped, and when the memory for the measurement information for data collection is no longer full, logging of the measurement information for data collection at the terminal is resumed.

[0015] The technical problems to be solved in the embodiments of the present invention are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which the present invention belongs from the description below.

[0016] According to various embodiments of the present disclosure, an improved communication method can be provided for a terminal, a base station, and an upper node supporting the same in a wireless communication system.

[0017] In addition, according to various embodiments of the present disclosure, a new communication method between a terminal, a base station, and an upper node utilizing artificial intelligence in a wireless communication system can be provided.

[0018] The effects obtainable in the present disclosure are not limited to those mentioned in the various embodiments, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure pertains from the description below.

[0019] FIG. 1 is a drawing illustrating the structure of a mobile communication system according to one embodiment of the present disclosure.

[0020] FIG. 2 is a diagram illustrating a wireless connection state transition in a mobile communication system according to one embodiment of the present disclosure.

[0021] FIG. 3 is a diagram illustrating an AI / ML (artificial intelligence and machine learning) model for predicting beam measurement values ​​for beam management according to one embodiment of the present disclosure.

[0022] FIG. 4 is a diagram illustrating a procedure in which a terminal performs a prediction using a UE-side AI / ML model for beam management according to one embodiment of the present disclosure.

[0023] FIG. 5 is a diagram illustrating a procedure in which a network performs a prediction using an NW-side AI / ML model for beam management according to one embodiment of the present disclosure.

[0024] FIG. 6 is a diagram illustrating an embodiment according to one embodiment of the present disclosure in which a terminal instructs a network that memory is full during a procedure to collect measurement data for learning a model.

[0025] FIG. 7 is a drawing illustrating a first embodiment according to one embodiment of the present disclosure in which a terminal stops measurement due to a memory issue during a procedure to collect measurement data for learning a model and then resumes measurement.

[0026] FIG. 8 is a diagram illustrating a second embodiment according to one embodiment of the present disclosure in which a terminal stops measurement due to a memory issue during a procedure to collect measurement data for learning a model and then resumes measurement.

[0027] FIG. 9 is a diagram illustrating a third embodiment according to one embodiment of the present disclosure in which a terminal stops measurement due to a memory issue during a procedure to collect measurement data for learning a model and then resumes measurement.

[0028] FIG. 10 is a diagram illustrating an embodiment according to one embodiment of the present disclosure in which a terminal receives priority for memory occupancy by setting during a procedure to collect measurement data for learning a model.

[0029] FIG. 11 is a diagram illustrating an embodiment according to one embodiment of the present disclosure in which a terminal receives memory occupancy information for each setting during a procedure to collect measurement data for learning a model.

[0030] FIG. 12 is a drawing illustrating the configuration of a terminal according to one embodiment of the present disclosure.

[0031] FIG. 13 is a drawing illustrating the configuration of a base station according to one embodiment of the present disclosure.

[0032] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the attached drawings. In this regard, it should be noted that identical components in the attached drawings are indicated by the same reference numerals whenever possible. Furthermore, detailed descriptions of known functions and configurations that may obscure the essence of the present invention will be omitted.

[0033] In describing the embodiments in this specification, technical details that are well known in the technical field to which the present invention belongs and are not directly related to the present invention are omitted. This is intended to convey the essence of the present invention 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 size of each component does not entirely reflect its actual dimensions. Identical or corresponding components in each drawing have been assigned the same reference numbers.

[0035] The advantages and features of the present invention 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 invention is not limited to the embodiments disclosed below but can be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Throughout the specification, the same reference numerals refer to the same components.

[0036] At this time, 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 the means of instruction 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).

[0037] Additionally, each block may represent a module, segment, or part of code containing one or more executable instructions for executing a specified 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 instance, two blocks described in succession may actually be executed substantially simultaneously, or the blocks may be executed in reverse order according to their corresponding functions.

[0038] In this embodiment, the term "part" refers to a software or hardware component, such as an FPGA or ASIC, 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 operate one or more processors. Accordingly, as an example, the "part" includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and "parts" may be combined into a smaller number of components and "parts" or further separated into additional components and "parts." Furthermore, the components and "parts" may be implemented to operate one or more CPUs within a device or secure multimedia card.

[0039] Hereinafter, the base station is an entity that performs resource allocation for terminals and may be at least one of Node B, BS (Base Station), eNB (eNode B), gNB (gNode B), a wireless access unit, a base station controller, or a node on a network. The terminal may include UE (User Equipment), MS (Mobile Station), a cellular phone, a smartphone, a computer, or a multimedia system capable of performing communication functions. In the present 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.

[0040] Terms used in the following description to identify connection nodes, terms referring to network entities or network functions (NFs), terms referring to messages, terms referring to interfaces between network entities, terms referring to various identification information, etc., are examples provided for the convenience of explanation. Accordingly, the present invention is not limited to the terms described below, and other terms referring to objects having equivalent technical meanings may be used.

[0041] Furthermore, the embodiments of the present disclosure may be applied to other communication systems having a technical background or channel type similar to the embodiments of the present disclosure described below. Additionally, the embodiments of the present disclosure may be applied to other communication systems with some modifications, provided that they do not deviate significantly from the scope of the present disclosure, at the judgment of a person with skilled technical knowledge. For example, 5th generation mobile communication technologies (5G, new radio, NR) developed after LTE-A may be included therein, and the 5G below may be a concept that includes existing LTE, LTE-A, and other similar services. Furthermore, various embodiments based on the 5G or wireless communication systems of the present disclosure may include application to wireless communication technologies developed after 5G (6G, 6G wireless communication systems, etc.). Additionally, the present disclosure may be applied to other communication systems with some modifications, provided that they do not deviate significantly from the scope of the present disclosure, at the judgment of a person with skilled technical knowledge.

[0042] For the convenience of the following explanation, some terms and names defined in the 3GPP (3rd generation partnership project) LTE (long term evolution) standards and / or 3GPP NR (new radio) standards may be used. However, the present invention is not limited by the above terms and names and can be applied in the same way to systems conforming to other standards.

[0043] FIG. 1 is a drawing illustrating the structure of a mobile communication system according to one embodiment of the present disclosure.

[0044] Referring to FIG. 1, the wireless access network of a mobile communication system (new radio, NR) can be composed of a base station (next generation Node B, hereinafter gNB) (1-10) and an AMF (1-05, access and mobility management function or new radio core network).

[0045] The user terminal (new radio user equipment, hereinafter NR UE or terminal) (1-15) can connect to an external network through the gNB (1-10) and AMF (1-05).

[0046] A mobile communication system can be a next-generation mobile communication system, and a base station can be a next-generation base station.

[0047] According to one embodiment, the gNB (1-10) in FIG. 1 may correspond to the eNB (Evolved Node B) of an existing LTE system. The gNB (1-10) is connected to an NR UE via a wireless channel and can provide a superior service compared to the existing Node B (1-20). 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 the UEs may be required, and this can be performed by the gNB (1-10). According to one embodiment, a single gNB (1-10) can typically control multiple cells. To implement ultra-high-speed data transmission, it may have a bandwidth greater than the existing maximum bandwidth, and orthogonal frequency division multiplexing (OFDM) may be used as a wireless access technology, and beamforming technology may be additionally incorporated. 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.

[0048] According to one embodiment, in FIG. 1, the AMF (1-05) can perform functions such as mobility support, bearer configuration, and QoS (quality of service) configuration. The AMF (1-05) is a device responsible for various control functions as well as mobility management functions for terminals and can be connected to multiple base stations. In addition, the mobile communication system according to one embodiment of the present disclosure can be linked with an LTE system, and for example, the AMF (1-05) can be connected to an MME (1-25, mobility management function) through a network interface.

[0049] According to one embodiment, the MME (1-25) can be connected to an existing base station eNB (1-30). For example, in FIG. 1, a terminal supporting LTE-NR dual connectivity (DC) can transmit and receive data while maintaining a connection to both the gNB (1-10) and the eNB (1-30) (1-35).

[0050] FIG. 2 is a diagram illustrating a wireless connection state transition in a mobile communication system according to one embodiment of the present disclosure.

[0051] Referring to Fig. 2, the mobile communication system may have three radio resource control (RRC) states or RRC modes.

[0052] Specifically, the connection mode (RRC_CONNECTED, 2-05) may be a wireless connection state in which the terminal can transmit and receive data. The standby mode (RRC_IDLE, 2-30) may correspond to a wireless connection state in which the terminal monitors whether paging is being transmitted to it. The above two modes are wireless connection states applicable to LTE systems, and the detailed description may be the same as that of an LTE system. A mobile communication system according to one embodiment of the present disclosure may be a next-generation mobile communication system.

[0053] According to one embodiment of the present disclosure, a new inactive (RRC_INACTIVE) radio access state (2-15) may be defined in a mobile communication system. In the inactive radio access state (2-15), a UE context may be maintained between the base station and the terminal, and RAN (radio access network) based paging may be supported. The features of the inactive radio access state (2-15) may include at least one of the following:

[0054] - Cell re-selection mobility;

[0055] - CN - NR RAN connection (both C / U-planes (control plane / user plane)) has been established for UE;

[0056] - The UE AS (access stratum) context is stored in at least one gNB and the UE;

[0057] - Paging is initiated by NR RAN;

[0058] - RAN-based notification area is managed by NR RAN; 또는

[0059] - NR RAN knows the RAN-based notification area which the UE belongs to.

[0060] According to one embodiment of the present disclosure, a terminal in an INACTIVE wireless connection state (2-15) may use a specific procedure and transition to a connection mode (2-05) or a standby mode (2-30). It may transition from the INACTIVE mode (2-15) to the connection mode (2-05) according to a Resume procedure, and may transition from the connection mode (2-05) to the INACTIVE mode (2-15) using a Release procedure including suspend setting information (2-10). 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, it may be possible to transition from the INACTIVE mode (2-15) to the standby mode (2-30) through a Release procedure after Resume (2-20). The transition between the connection mode (2-05) and the standby mode (2-30) may follow LTE technology. Also, according to FIG. 2, the transition between the above modes can be made through an establishment or release procedure (2-25).

[0061] FIG. 3 is a diagram illustrating an AI / ML model for predicting beam measurement values ​​for beam management according to one embodiment of the present disclosure.

[0062] 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.

[0063] In one embodiment of the present disclosure, a use case for beam management may include sub-use cases of spatial prediction and temporal prediction.

[0064] In one embodiment of the present disclosure, a set of beams used as input to an AI / ML model for a use case for beam management 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 use case for beam management may be referred to as SET A.

[0065] In one embodiment of the present disclosure, when spatially predicting beam management (3-05), the input of an AI / ML model may be a measurement value (e.g., RSRP (reference signal received power) and / or RSRQ (reference signal received quality) and / or SINR (signal to interference plus noise ratio)) (e.g., Meas(Bi,tK)) (3-15) for one or more beams (e.g., Bi) (i=1, 2, …, N) at a specific time point (e.g., tK).

[0066] In one embodiment of the present disclosure, the measurement value for the beam may be one of the following:

[0067] - RSRP and / or RSRQ and / or SINR measured at Layer 1;

[0068] - RSRP and / or RSRQ and / or SINR measured / acquired at Layer 3;

[0069] - 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

[0070] - 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).

[0071] In one embodiment of the present disclosure, when spatially predicting beam management (3-05), the following information may be considered / used as input to an AI / ML model:

[0072] - SET B related information (e.g., beam-specific ID);

[0073] - Measurement time information;

[0074] - L1-RSRP measurement based on Set B;

[0075] - L1-RSRP measurement based on Set B and assistance information;

[0076] - CIR (channel impulse response) based on Set B; and / or

[0077] - L1-RSRP measurement based on Set B and the corresponding DL Tx and / or Rx beam ID.

[0078] In one embodiment of the present disclosure, when spatially predicting beam management (3-05), the output of the AI / ML model may be a predicted value (e.g., RSRP and / or RSRQ and / or SINR) (e.g., P_Meas(Bi,tK)(i=N+1, N+2,…,N+M)) (3-20) for one or more beams (e.g., Bi) at a specific time point (e.g., tK).

[0079] In one embodiment of the present disclosure, the predicted value for the beam may be one of the following:

[0080] - RSRP and / or RSRQ and / or SINR predicted to be measured at Layer 1;

[0081] - RSRP and / or RSRQ and / or SINR predicted to be measured / acquired at Layer 3;

[0082] - 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

[0083] - 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).

[0084] In one embodiment of the present disclosure, when spatially predicting beam management (3-05), the following information may be considered / used as the output of an AI / ML model:

[0085] - SET A related information (e.g., ID per beam);

[0086] - One beam predicted to have the highest (best) measurement value (e.g., Top-1 beam);

[0087] - N (≥1) beams predicted to have the highest (best) measurements (e.g., Top-N beam); and / or

[0088] - Probability that each beam in SET A is a Top-1 or Top-N beam.

[0089] In one embodiment of the present disclosure, when making a temporal prediction for beam management (3-10), the input to the AI / ML model may be a measurement (e.g., RSRP and / or RSRQ and / or SINR) (e.g., Meas(BK,ti)) (3-25) for a beam (e.g., BK) at one or more (past) time points (e.g., ti (i=1, 2,…,N)). In one embodiment of the present disclosure, the measurement for the beam may be one of the following:

[0090] - RSRP and / or RSRQ and / or SINR measured at Layer 1;

[0091] - RSRP and / or RSRQ and / or SINR measured / acquired at Layer 3;

[0092] - 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

[0093] - 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).

[0094] In one embodiment of the present disclosure, when making a temporal prediction (3-10) for beam management, the following information may be considered / used as input to an AI / ML model:

[0095] - SET B related information (e.g., beam-specific ID);

[0096] - Measurement time information;

[0097] - L1-RSRP measurement based on Set B;

[0098] - L1-RSRP measurement based on Set B and assistance information;

[0099] CIR based on Set B; and / or

[0100] - L1-RSRP measurement based on Set B and the corresponding DL Tx and / or Rx beam ID.

[0101] In one embodiment of the present disclosure, when making a temporal prediction for beam management (3-10), the output of the AI / ML model may be a predicted value (e.g., RSRP and / or RSRQ and / or SINR) (e.g., P_Meas(BK,ti)) (3-30) for one beam (e.g., BK) at one or more (future) time points (e.g., ti (i=N+1, N+2,…,N+M)).

[0102] In one embodiment of the present disclosure, the predicted value for the beam may be one of the following:

[0103] - RSRP and / or RSRQ and / or SINR predicted to be measured at Layer 1;

[0104] - RSRP and / or RSRQ and / or SINR predicted to be measured / acquired at Layer 3;

[0105] - 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

[0106] - 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).

[0107] In one embodiment of the present disclosure, when making a temporal prediction (3-10) for beam management, the following information may be considered / used as the output of an AI / ML model:

[0108] - SET A related information (e.g., beam-specific ID);

[0109] - Prediction time point information;

[0110] - The point in time when the measurement is predicted to be highest (best); and / or

[0111] - N (≥1) time points where the measurement value is predicted to be highest (best).

[0112] In one embodiment of the present disclosure, the AI / ML model for beam management may be an AI / ML model that simultaneously performs the spatial and temporal predictions described above. In this case, the input of the AI / ML model may be a measurement value at one or more time points for each beam for one or more beams. Refer to the foregoing for the input information. Additionally, the output of the AI / ML model may be a prediction value at one or more time points for each beam for one or more beams. Refer to the foregoing for the output information.

[0113] 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. For example, the base station may utilize the information received from the terminal for downlink transmission beam management, improving location accuracy, improving CSI feedback, etc.

[0114] In one embodiment of the present disclosure, a network (NW) (a base station or LMF (e.g., location management function)) may drive an AI / ML model to derive predicted values ​​and related information. The AI / ML model driven 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, improving CSI feedback, etc.

[0115] FIG. 4 is a diagram illustrating a procedure in which a terminal performs a prediction using a UE-side AI / ML model for beam management according to one embodiment of the present disclosure.

[0116] In step 4-05, a terminal (e.g., terminal 1) (4-10) may transmit the terminal's capability information to a connected base station (e.g., base station 1 or network 1) (4-15) (e.g., via a UE Capability Information message). To receive this, the base station (4-15) may first request the terminal (4-10) to transmit the terminal's capability information (e.g., via a UE Capability Enquiry message). The capability information transmitted by the terminal (4-10) 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).

[0117] In step 4-25, the base station (4-15) may provide the terminal (4-10) with a measurement / reporting configuration (e.g., training configuration) to collect data necessary for training the UE-side model. Prior to this, the base station (4-15) may receive a request for a configuration regarding model training from the terminal (4-10) or the terminal server (4-20).

[0118] In step 4-30, the terminal (4-10) performs measurements to generate data necessary for training the UE-side model, and then reports the measurement results (e.g., training report) to the base station (4-15) or the terminal server (4-20). If the terminal (4-10) reports the measurement results to the base station (4-15), the base station (4-15) can reprocess the report received from the terminal (4-10) and transmit it to the terminal server (4-20).

[0119] In step 4-35, the terminal server (4-20) can learn the UE-side model using the received report.

[0120] In step 4-50, the terminal (e.g., terminal 2) (4-40) may receive the trained model from the terminal server (4-20). For example, the trained model may be transmitted to the terminal (4-40) via a base station (e.g., base station 2) (4-45). Base station 1 (4-15) and base station 2 (4-45) may be the same base station or different base stations. Additionally, although terminal 1 (4-10) and terminal 2 (4-15) are different terminals, the following procedure may be applied even if the two terminals are the same terminal, without excluding the case where they are the same terminal.

[0121] Before receiving the model, terminal 2 (4-40) may transmit the terminal's capability information to the connected base station 2 (4-45) (e.g., via a UE Capability Information message). To receive this, the base station (4-45) may first request the terminal (4-40) to transmit the terminal's capability information (e.g., via a UE Capability Enquiry message). The capability information transmitted by the terminal (4-40) may include whether the terminal (4-40) supports AI / ML related functions (e.g., UE-side model related functions / operations) (by AI / ML functionality, by use case, or by sub-use case).

[0122] In step 4-55, the terminal (4-40) 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. Step 4-55 can be performed after the terminal (4-40) receives inference-related settings from the base station (4-45).

[0123] In step 4-60, the terminal (4-40) can report the result of the prediction or inference to the base station (4-45).

[0124] In step 4-65, the base station (4-45) can perform downlink beam management for the terminal (4-40) based on the prediction / inference result received from the terminal (4-40) and select an appropriate beam to service the terminal (4-40).

[0125] FIG. 5 is a diagram illustrating a procedure in which a network performs a prediction using an NW-side AI / ML model for beam management according to one embodiment of the present disclosure.

[0126] In step 5-05, a terminal (e.g., terminal 1) (5-10) may transmit the terminal's capability information to a connected base station (e.g., base station 1 or network 1) (5-15) (e.g., via a UE Capability Information message). To receive this, the base station (5-15) may first request the terminal (5-10) to transmit the terminal's capability information (e.g., via a UE Capability Enquiry message). The capability information transmitted by the terminal (5-10) may include whether the terminal (5-10) supports AI / ML related functions (e.g., NW-side model related functions / operations) (e.g., inference and / or learning) (by AI / ML functionality, by use case, or by sub-use case).

[0127] In step 5-25, the base station (5-15) may provide a measurement / reporting configuration (e.g., training configuration) to the terminal (5-10) for data collection necessary for NW-side model training. Prior to this, the base station (5-15) may receive a request for a configuration regarding model training from the terminal or training object (5-20).

[0128] In one embodiment of the present disclosure, the learning object (5-20) may be a base station (e.g., 5-15 or another base station) or an AMF or UPF (user plane function) or OAM (operation administration and maintenance).

[0129] In step 5-30, the terminal (5-10) performs measurements to generate data necessary for NW-side model training, and then reports the measurement results (e.g., training report) to the base station (5-15) or the training object (5-20). If the terminal (5-10) reports the measurement results to the base station (5-15), the base station (5-15) can reprocess the report received from the terminal (5-10) and transmit it to the training object (5-20).

[0130] In step 5-35, the learning object (5-20) can learn the NW-side model using the received report. Alternatively, the base station (5-15) can learn the NW-side model using the measurement results received in 5-30 (e.g., without delivering them to the learning object). If the base station (5-15) has learned, the base station (5-15) may possess the learned model itself or provide the learned model to the learning object (5-20).

[0131] In step 5-50, the base station (e.g., base station 2) (5-45) may receive a trained model from the training object (5-20). Alternatively, the base station (5-45) may possess a model that it has trained itself. Base station 1 (5-15) and base station 2 (5-45) may be the same base station or different base stations. Additionally, terminal 1 (5-10) and terminal 2 (5-15) are different terminals, but the following procedure may also apply when the two terminals are the same terminal, without excluding the case where they are the same terminal.

[0132] Before or after step 5-50, terminal 2 (5-40) may transmit the terminal's capability information to the connected base station 2 (5-45) (e.g., via a UE Capability Information message). To receive this, the base station (5-45) may first request the terminal (5-40) to transmit the terminal's capability information (e.g., via a UECapabilityEnquiry message). The capability information transmitted by the terminal (5-40) may include whether the terminal (5-40) supports AI / ML related functions (e.g., NW-side model related functions / operations) (by AI / ML functionality, by use case, or by sub-use case).

[0133] In step 5-55, the terminal (5-40) can perform a measurement (e.g., after receiving a measurement setting for the NW-side model from the base station) and then report the measurement result to the base station (5-45).

[0134] In step 5-60, the base station (5-45) can perform inference or prediction using the measurement report received from the terminal (5-40) and the learned model. For example, in the case of beam management, spatial or temporal beam prediction can be performed.

[0135] In step 5-65, the base station (5-45) can perform downlink beam management for the terminal (5-40) based on the prediction / inference result and select an appropriate beam to service the terminal (5-40).

[0136] In one embodiment of the present disclosure, the contents of the disclosure regarding the UE-side model and / or NW-side model have been described assuming a use case for downlink beam management; however, they can be used / applied in the same way to various use cases using the UE-side model and / or NW-side model regardless of the use case.

[0137] In one embodiment of the present disclosure, the contents of the present disclosure may be used in a UE-side model and / or an NW-side model.

[0138] FIG. 6 is a diagram illustrating an embodiment according to one embodiment of the present disclosure in which a terminal instructs a network that memory is full during a procedure to collect measurement data for learning a model.

[0139] In step 6-15, the terminal (6-05) receives a request for terminal capability-related information from the base station (6-10) and can report terminal capability-related information to the base station (6-10) based on the request. Steps 6-10 and 6-15 may cross-reference steps 5-05 and / or step 4-05. The terminal (6-05) can report the terminal's memory capability information (e.g., minimum supported memory) (for storing measurement data for model learning) to the base station (6-10).

[0140] In step 6-20, the terminal (6-05) may receive model training related configuration information (e.g., training configuration) from the base station (6-10) (via an RRC message, e.g., an RRC Reconfiguration message or an RRC Resume message). Step 6-20 may cross-reference step 5-25 and / or step 4-25.

[0141] In step 6-22, the terminal (6-05) can perform measurements according to the measurement / reporting settings (e.g., training configuration) for the received model training and store the resulting data in memory.

[0142] In step 6-25, the terminal (6-05) can report information related to stored data (e.g., availability information) to the base station (6-10).

[0143] In one embodiment of the present disclosure, at step 6-25, the terminal (6-05) may provide information to the base station (6-10) regarding the existence of stored data. For example, if the terminal (6-05) has stored data, it may report to the base station by setting indicator A to true (or setting the value of the 1-bit field to 1, without excluding the opposite setting) or including it. If the terminal (6-05) does not have stored data, it may report to the base station (6-10) by setting indicator A to false (or setting the value of the 1-bit field to 0, without excluding the opposite setting) or omitting it. The information regarding the existence of stored data may be provided through a UE Assistance information message, and the terminal (6-05) may report to the base station (6-10) the existence of stored data through a UE Assistance information message if it has received the relevant UE Assistance information setting in advance (e.g., at 6-20 or thereafter). Immediately after receiving the relevant UE Assistance information settings, the terminal (6-05) may report to the base station (6-10) whether there is a stored data. Alternatively, the terminal (6-05) may report the changed information to the base station (6-10) when the information regarding the existence of the stored data changes (e.g., when storage starts without any stored data). Based on the report from the terminal (6-05), the base station (6-10) may decide whether to request a measurement report (e.g., 6-30) from the terminal or determine the timing of the request.

[0144] In one embodiment of the present disclosure, at step 6-25, the terminal (6-05) may report to the base station (6-10) that the condition is satisfied if at least one of the following conditions is satisfied. Alternatively, the terminal (6-05) may report to the base station (6-10) that the condition is not satisfied if at least one of the following conditions is not satisfied (no longer).

[0145] - 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).

[0146] - 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).

[0147] The above threshold values ​​(threshold values ​​1 and 2) may each be set by the base station (e.g., through UE Assistance information settings), values ​​pre-set in the terminal, or values ​​fixed in the standard. For example, the terminal (6-05) may report the satisfaction of the above condition to the base station (6-10) by setting indicator B to true (or setting the value of the 1-bit field to 1, without excluding the opposite setting) or including it. The terminal may report the dissatisfaction of the above condition to the base station (6-10) by setting indicator B to false (or setting the value of the 1-bit field to 0, without excluding the opposite setting) or omitting it. Additionally, the terminal (6-05) may report specific size information (e.g., available remaining size in memory, size of data stored in memory by the terminal) to the base station (6-10). The above report may be provided via a UE Assistance information message, and the terminal (6-05) may transmit the above report to the base station (6-10) via a UE Assistance information message if it has received the relevant UE Assistance information setting in advance (e.g., at 6-20 or thereafter). The terminal (6-05) may report the above report to the base station (6-10) immediately after receiving the relevant UE Assistance information setting. Additionally, the terminal (6-05) may report the changed information to the base station (6-10) when the information in the above report changes (e.g., when condition 1 is not satisfied but is satisfied). Alternatively, the terminal (6-05) may periodically transmit the above report information to the base station (6-10), and the transmission cycle of the terminal (6-05) may be set by the base station (6-10). Based on the report from the terminal (6-05), the base station (6-10) may determine whether to request a measurement report (e.g., 6-30) from the terminal (6-05) or determine the time of the request.

[0148] In one embodiment of the present disclosure, a prohibit timer may be set for each function to limit the reporting of UE Assistance information messages too frequently for one function of the terminal (e.g., indicating the existence of stored data). Information regarding the prohibit timer may be included in the UE Assistance information setting for the corresponding function. When the terminal (6-05) transmits a UE Assistance information message for reporting on the corresponding function, it may run the prohibit timer for the duration of the set prohibit timer. While the prohibit timer is running, the terminal (6-05) may not be able to transmit a new UE Assistance information message for the same function. The terminal (6-05) may stop running the prohibit timer when the UE Assistance information setting for the corresponding function is deactivated.

[0149] In step 6-30, the base station (6-10) may instruct / request the terminal (6-05) to report the stored measurement data (for model training) (after receiving information regarding the stored data from the terminal). For example, the base station (6-10) may use a UE Information Request message, an RRC reconfiguration message, or a new RRC message for this purpose.

[0150] In step 6-35, the terminal (6-05) may report the stored measurement data (for model training) to the base station (6-10). The above step may cross-reference step 4-30 of 4 and / or step 5-30 of FIG. 5. For example, the terminal (6-05) may use a UE Information Response message, a Measurement report message, or a new RRC message for the report.

[0151] In step 6-40, because the amount of data that can be stored in the buffer or memory of the terminal (6-05) (e.g., memory for storing measurement data for model training) may be limited, the memory of the terminal may become full (or there may be insufficient memory for additional measurements or data collection for model training). For example, the terminal (6-05) continues to perform measurements (for model training) and stores the generated data in memory, but the base station (6-10) does not send a request (e.g., 6-30) to retrieve it, so the terminal (6-05) cannot report the stored data to the base station (6-10) (e.g., 6-35), and the memory may become full.

[0152] In step 6-45, the terminal (6-05) may stop further measurements (for model training) and / or data collection / storage (because the memory is full).

[0153] In step 6-50, the terminal (6-05) may indicate to the base station (6-10) that the memory is full, and / or that additional measurements (for model learning) and / or data collection have been stopped. Upon receiving this, the base station (6-10) may (temporarily) stop providing the measurement resources (e.g., transmission of SSB or CSI-RS resources) that were allocated / provided to the terminal (6-05).

[0154] FIG. 7 is a drawing illustrating a first embodiment according to one embodiment of the present disclosure in which a terminal stops measurement due to a memory issue during a procedure to collect measurement data for learning a model and then resumes measurement.

[0155] Steps 7-15 through 7-50 can cross-reference steps 6-15 through 6-50 for each step.

[0156] In step 7-55, the base station (7-10) may request the terminal (7-05) to transmit the measurement data it has stored. The above step may cross-refer to step 7-30.

[0157] In step 7-60, the terminal (7-05) can transmit measurement data for training the model to the base station (7-10). The above step may cross-reference step 7-35.

[0158] In step 7-65, the terminal (7-05) may resume measurements and data collection for training the model. This may be because the terminal has performed a measurement report or because there is remaining / free space in memory. The terminal (7-05) may free up space in memory by deleting or discarding the measurement-reported data. The terminal (7-05) may delete or discard the measurement-reported data when it successfully transmits the measurement report or when the base station (7-10) indicates that it has successfully received the measurement report. The above operation of the terminal (7-05) may be performed without specific instructions from the base station (7-10). The base station (7-10) may (implicitly) know that the terminal (7-05) has resumed measurements by receiving the terminal's measurement report (e.g., 7-60). If the base station (7-05) has not (temporarily) provided the measurement resources (e.g., transmission of SSB or CSI-RS resources) that were allocated / provided to the terminal (7-05) according to step 7-50, the base station (7-10) can provide the measurement resources again for smooth measurement of the terminal (7-05).

[0159] FIG. 8 is a diagram illustrating a second embodiment according to one embodiment of the present disclosure in which a terminal stops measurement due to a memory issue during a procedure to collect measurement data for learning a model and then resumes measurement.

[0160] Steps 8-15 through 8-60 can cross-reference steps 7-15 through 7-60 for each step.

[0161] In step 8-65, the terminal (8-05) can check whether at least one of the following conditions is satisfied.

[0162] - Condition 1. When the size of the data stored (in memory) by the terminal (for model training) is smaller than a specific threshold (e.g., threshold 1).

[0163] - Condition 2. When the size of the remaining / free space (free space) in the terminal's memory (for model training) is greater than a specific threshold (e.g., threshold 2).

[0164] If the terminal (8-05) satisfies at least one of the above conditions, it can perform step 8-70.

[0165] In one embodiment of the present disclosure, the threshold value may be a fixed value, information set by the network (8-10) (e.g., via step 8-20), a value set in the terminal, or a value defined in a standard. In one embodiment of the present disclosure, the terminal (8-05) may perform step 8-70 without checking the condition (e.g., as a terminal implementation or at a time desired by the terminal).

[0166] In step 8-70, the terminal (8-05) may request the network (8-10) to resume the measurement (for data collection for model training) (e.g., via a UE Assistance information message, a UE information request message, a new RRC message, or MAC CE or L1 signaling). The message for resuming the measurement may include an information field or an indicator for requesting the resumption of the measurement. The request to resume the measurement may mean a request for the network (8-10) to retransmit / provide the measurement resources (e.g., transmission of SSB or CSI-RS resources) that were discontinued in step 8-50. Additionally, the request to resume the measurement may indicate that memory-related issues associated with the discontinuation of the measurement have been resolved. In order to transmit the request, the terminal (8-05) may be instructed by the base station (8-10) to allow the request (prior to this step) (e.g., may be instructed in step 8-20 to allow the transmission of a UE Assistance information message containing the request).

[0167] In step 8-75, the base station (8-10) may instruct or permit the terminal (8-05) to resume the measurement (e.g., via an RRC Reconfiguration message, an RRC Resume message, a UE information response message, a new RRC message, or MAC CE or L1 signaling). This may mean that the network (8-10) retransmits / provides the measurement resources (e.g., transmission of SSB or CSI-RS resources) that were discontinued in step 8-50. Although it has been described that the discontinued measurement procedure is resumed according to steps 8-70 and 8-75, it is not excluded that the discontinued procedure be resumed according to a terminal request in step 8-70, in which case the operation in 8-75 may be omitted.

[0168] In step 8-80, the terminal (8-05) can resume measurements on the resource (e.g., if the base station allowed / instructed the resumption of measurements in 8-75) and collect / store / report data again (for model training). Step 8-80 may cross-reference step 7-65.

[0169] Compared to the embodiment of FIG. 7, in FIG. 7, the interrupted procedure is resumed based on the report of measurement data from the terminal (7-05) in step 7-60 without the intervention of the base station (7-10), but in the embodiment of FIG. 8, the interrupted procedure is not resumed immediately after the terminal (8-05) reports the measurement data, but rather the measurement operation for model learning can be resumed when a request for resumption is received from the base station (8-10) according to steps 8-70 and 8-75.

[0170] FIG. 9 is a diagram illustrating a third embodiment according to one embodiment of the present disclosure in which a terminal stops measurement due to a memory issue during a procedure to collect measurement data for learning a model and then resumes measurement.

[0171] Steps 9-15 through 9-60 can cross-reference steps 8-15 through 8-60 for each step.

[0172] In step 9-65, the base station (9-10) may instruct or permit the terminal (9-05) to resume the measurement. The above step may cross-reference step 8-75. The base station (9-10) may perform the above step without an explicit request for resumption from the terminal (9-05) (e.g., 8-70). The base station (9-10) may decide to resume the measurement by receiving measurement data from the terminal (9-05) (e.g., 9-60) after the terminal (9-05)'s instruction in 9-50 (e.g., instructing that the memory is full or that the measurement has been stopped). The base station (9-10) may decide to resume the measurement and transmit a message or information instructing the terminal (9-05) to resume the measurement. When resuming the measurement, the base station (9-10) may provide resources for the measurement (e.g., SSB or CSI-RS resources, etc.) again for the smooth measurement of the terminal (9-05).

[0173] In one embodiment of the present disclosure, step 9-65 is omitted, and the base station (9-10) may instruct or permit the terminal (9-05) to resume measurement through step 9-55. For example, in step 9-55, the base station (9-10) may instruct or permit the terminal (9-05) to resume measurement (simultaneously with or after the report of measurement data (e.g., 9-60)) while requesting the terminal (9-05) to report measurement data. To this end, the base station (9-10) may include and transmit a separate indicator in step 9-55 that instructs the resumption of measurement.

[0174] In step 9-70, the terminal (9-05) can resume measurements on the resource (e.g., if the base station (9-10) allowed / instructed the resumption of measurements in 9-65) and collect / store / report data again (for model training). The above step 9-70 may cross-refer to step 8-80.

[0175] In various embodiments of the present disclosure, a prohibit timer may be set per function to limit reporting via UE Assistance information messages that are too frequent for each function of the terminal (e.g., reporting that a buffer is full or instructing to stop measurement and / or requesting to resume measurement). 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 for reporting on the corresponding function, the prohibit timer may be activated for the duration of the set prohibit timer, and the terminal may be unable to transmit a new UE Assistance information message for the same function while the prohibit timer is activated. The terminal may stop the activation of the prohibit timer when the UE Assistance information setting for the corresponding function is deactivated.

[0176] In various embodiments of the present disclosure, when a terminal requests the resumption of measurement from a base station (e.g., 8-70), it may also transmit information about measurement data stored by the terminal (e.g., size) or information about remaining / free space in memory (e.g., size).

[0177] In various embodiments of the present disclosure, the terminal may receive one or more settings regarding data collection for model learning (e.g., 6-20). For each of the settings, the terminal may collect measurements and data (e.g., 6-22), report storage-related information to the network (e.g., 6-25), receive a request to report measurement data to the base station (e.g., 6-30), and perform a report (e.g., 6-35).

[0178] When a terminal receives one or more settings (regarding data collection for model training), considering the terminal's limited memory size (for data collection for model training), the network may consider measurement data (e.g., Data 1) for a specific setting (e.g., Setting 1) to be relatively more important than measurement data (e.g., Data 2) for another setting (e.g., Setting 2). For example, if the terminal's memory is full of Data 1 and Data 2, the network may want the terminal to stop measuring for Setting 2 but not stop measuring for Setting 1, and to delete (i.e., overwrite) the existing Data 2 to save the additional Data 1 generated as a result.

[0179] FIG. 10 is a diagram illustrating an embodiment according to one embodiment of the present disclosure in which a terminal receives priority for memory occupancy by setting during a procedure to collect measurement data for learning a model.

[0180] Steps 10-15 through 10-50 can cross-reference steps 6-15 through 6-50 for each step.

[0181] In step 10-20, the terminal (10-05) can receive multiple (e.g., 3) (independent) settings from the network (10-10) for the purpose of collecting measurement data for training the model.

[0182] At this time, the network (10-10) can set priority information for each setting to the terminal (10-05). For example, it can instruct priority 1 for setting 1, priority 2 for setting 2, and priority 3 for setting 3. A lower priority value may indicate a higher priority. For example, setting 1 may be the highest priority setting and setting 3 may be the lowest priority setting.

[0183] In step 10-22, the terminal (10-05) may perform separate measurements and data collection for each setting and store the related data in a single common memory. The common memory may mean that the memory in which measured data for different settings (setting 1, setting 2, setting 3) is stored is identical, common, or managed in common.

[0184] In step 10-25, the terminal (10-05) can instruct the base station (10-10) to provide information related to the measurement data stored for each setting.

[0185] In step 10-40, the common memory of the terminal (10-05) may be filled with measurement data for a plurality of settings. For example, measurement data for setting 1 (e.g., data 1) may occupy 10% of the memory, measurement data for setting 2 (e.g., data 2) may occupy 20% of the memory, and measurement data for setting 3 (e.g., data 3) may occupy 70% of the memory.

[0186] In step 10-45, the terminal (10-05) can select the setting with the lowest priority (e.g., setting 3) and (temporarily) suspend measurement and data collection for that setting.

[0187] In step 10-50, the terminal (10-05) can instruct the network (10-10) to provide information regarding the setting for which the measurement was stopped (e.g., setting ID=3).

[0188] In step 10-55, the terminal (10-05) can (still) perform measurements and data collection for other settings (e.g., setting 1 and setting 2). When data 1 and / or data 2 are additionally generated according to setting 1 and setting 2, the terminal (10-05) can delete data 3 that was stored in memory (partially, entirely, or equal to the size of the generated data 1 and / or data 2) and store data 1 and / or data 2 in the corresponding memory space. That is, to store the (additionally generated) (relatively higher priority) data 1 and / or data 2 in memory, the additionally generated data 1 and / or data 2 can be overwritten onto data 3 (relatively lower priority).

[0189] In step 10-60, the terminal (10-05) may delete and overwrite all data 3 as the data 1 and / or data 2 are generated. As a result, for example, 30% of the memory size may be occupied by data 1 and 70% of the memory size may be occupied by data 2.

[0190] In step 10-65, the terminal (10-05) may select the setting with relatively lower priority (e.g., setting 2) among the uninterrupted settings (e.g., setting 1 and setting 2) and (temporarily) suspend measurement and data collection for it.

[0191] In step 10-70, the terminal (10-05) can instruct the network (10-10) to provide information regarding the setting for which the measurement was stopped (e.g., setting ID=2).

[0192] In step 10-75, the terminal (10-05) can (still) perform measurements and data collection for another setting (e.g., setting 1). When data 1 is additionally generated according to setting 1, the terminal (10-05) can delete data 2 that was stored in memory (partially, entirely, or by the size of the generated data 1) and store data 1 in that memory space. That is, to store the (additionally generated) (relatively higher priority) data 1 in memory, the additionally generated data 1 can be overwritten on data 2 (relatively lower priority).

[0193] In step 10-80, the terminal (10-05) can delete and overwrite all data 2 as the data 1 continues to be generated. As a result, all memory can be occupied by data 1 (100%).

[0194] In steps 10-85, the terminal can now (temporarily) suspend measurement and data collection for setting 1.

[0195] In step 10-90, the terminal may instruct the network (10-10) to provide information regarding the setting for which the measurement was stopped (e.g., setting ID=1).

[0196] As an example of the present disclosure, the steps of FIG. 7, FIG. 8, or FIG. 9 may be performed for each interrupted setting.

[0197] As an example of the present disclosure, when the method of FIG. 7 is applied to interrupted measurements, the terminal in FIG. 7-65 may first resume measurements for some settings (e.g., measurements for the interrupted setting with the highest priority) or simultaneously resume measurements for all settings.

[0198] As an example of the present disclosure, when the method of FIG. 8 is applied to interrupted measurements, in FIG. 8-70, the terminal may first request the resumption of a measurement for some settings (e.g., a measurement for the interrupted setting with the highest priority) or request the resumption of measurements for all settings simultaneously. In FIG. 8-75, the base station may first allow the resumption of a measurement for some settings (e.g., a measurement for the interrupted setting with the highest priority) or allow the resumption of measurements for all settings simultaneously.

[0199] As an example of the present disclosure, when the method of FIG. 9 is applied to interrupted measurements in FIG. 9-65, the base station may first instruct the resumption of measurements for some settings (e.g., measurements for the interrupted setting with the highest priority) or simultaneously instruct the resumption of measurements for all settings.

[0200] FIG. 11 is a diagram illustrating an embodiment according to one embodiment of the present disclosure in which a terminal receives memory occupancy information for each setting during a procedure to collect measurement data for learning a model.

[0201] Steps 11-15 through 11-50 can cross-reference steps 10-15 through 10-50 for each step.

[0202] In step 11-20, the terminal (11-05) can receive multiple (e.g., three) (independent) settings from the network (11-10) for the purpose of collecting measurement data for training the model.

[0203] At this time, the network (11-10) may set / limit the (max) memory occupancy (e.g., unit is %) or (max) storage size (e.g., unit is KB) for each setting to the terminal. For example, it may instruct the (max) memory occupancy 1 or (max) storage size 1 for setting 1, the (max) memory occupancy 2 or (max) storage size 2 for setting 2, and the (max) memory occupancy 3 or (max) storage size 3 for setting 3. The sum of the memory occupancy for all settings set by the base station (11-10) may be 100% or less. The sum of the storage sizes for all settings set by the base station (11-10) may be less than or equal to the memory size supported by the terminal (11-05) (e.g., the value reported by the terminal in 11-20). For example, the network (11-10) can limit / set the (max) memory usage (e.g., unit is %) for setting 1 to 50%, limit / set the (max) memory usage (e.g., unit is %) for setting 2 to 20%, and limit / set the (max) memory usage (e.g., unit is %) for setting 3 to 30%.

[0204] In step 11-22, the terminal (11-05) can perform separate measurements and data collection for each setting and store the related data in a single common memory.

[0205] In step 11-25, the terminal (11-05) can instruct the base station to provide information related to the measurement data stored for each setting.

[0206] In step 11-40, the size of the data stored for one setting of the terminal (11-05) may reach the (maximum) memory share (e.g., unit is %) or (maximum) storable size (e.g., unit is KB) set by the base station (11-10) (in 11-20). For example, the measurement data for setting 3 (e.g., data 3) may reach the set (maximum) memory share of 30% by occupying 30% of the total memory size.

[0207] In step 11-45, the terminal (11-05) may (temporarily) suspend measurement and data collection for a setting (e.g., setting 3) that has reached a set (max) memory occupancy or (max) storage capacity.

[0208] In step 11-50, the terminal (11-05) can instruct the network (11-10) to provide information regarding the setting for which the measurement was stopped (e.g., setting ID=3).

[0209] In step 11-55, the terminal (11-05) can perform measurements and data collection (e.g., Data 1 and Data 2, respectively) for other settings (e.g., setting 1 and setting 2).

[0210] In step 11-60, the size of the data (e.g., data 2) stored for another setting (e.g., setting 2) of the terminal (11-05) may reach the (maximum) memory occupancy (e.g., 20%) or (maximum) storable size set by the base station (11-10) (in 11-20). For example, 8% of the memory of the terminal (11-05) may be occupied by data 1, 20% by data 2, and 30% by data 3.

[0211] In step 11-65, the terminal (11-05) may (temporarily) suspend measurement and data collection for a setting (e.g., setting 2) that has reached a set (max) memory occupancy or (max) storage capacity.

[0212] In step 11-70, the terminal (11-05) can instruct the network (11-10) to provide information regarding the setting for which the measurement was stopped (e.g., setting ID=2).

[0213] In step 11-75, the terminal (11-05) can (still) perform measurements and data collection for another setting (e.g., setting 1).

[0214] In step 11-80, the size of the data (e.g., data 1) stored for another setting (e.g., setting 1) of the terminal (11-05) can reach the (maximum) memory occupancy (e.g., 50%) or (maximum) storable size set by the base station (11-10) (in 11-20).

[0215] In step 11-85, the terminal (11-05) may (temporarily) suspend measurement and data collection for a setting (e.g., setting 1) that has reached a set (max) memory occupancy or (max) storage capacity.

[0216] In step 11-90, the terminal (11-05) may instruct the network (11-10) to provide information regarding the setting for which the measurement was stopped (e.g., setting ID=1).

[0217] For the method of resuming suspended settings, refer to the method of Fig. 10.

[0218] In addition, in the embodiments of FIGS. 10 and 11, the terminal does not perform measurement and data collection operations in relation to the interrupted setting, and the base station may not provide resources for measurement for the setting in which the terminal has instructed the interruption of measurement.

[0219] FIG. 12 is a drawing illustrating the configuration of a terminal according to one embodiment of the present disclosure.

[0220] Referring to FIG. 12, the terminal may include an RF (Radio Frequency) processing unit (12-10), a baseband processing unit (12-20), a storage unit (12-30), and a control unit (12-40).

[0221] The RF processing unit (12-10) 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 (12-10) can up-convert a baseband signal provided by the baseband processing unit (12-20) 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 (12-10) may include a transmit filter, a receive filter, an amplifier, a mixer, an oscillator, a DAC (digital to analog converter), an ADC (analog to digital converter), etc. Although only one antenna is shown in FIG. 12, the terminal may be equipped with multiple antennas. Additionally, the RF processing unit (12-10) may include multiple RF chains. Furthermore, the RF processing unit (12-10) may perform beamforming. For the above beamforming, the RF processing unit (12-10) can adjust the phase and magnitude of each of the signals transmitted and received through multiple antennas or antenna elements. In addition, the RF processing unit can perform MIMO (multi-input multi-output) and can receive multiple layers when performing MIMO operation.

[0222] The baseband processing unit (12-20) 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 (12-20) can generate complex symbols by encoding and modulating the transmitted bit sequence. Additionally, when receiving data, the baseband processing unit (12-20) can restore the received bit sequence by demodulating and decoding the baseband signal provided by the RF processing unit (12-10). For example, in the case of following the orthogonal frequency division multiplexing (OFDM) method, when transmitting data, the baseband processing unit (12-20) 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 (12-20) can divide the baseband signal provided from the RF processing unit (12-10) 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.

[0223] The baseband processing unit (12-20) and the RF processing unit (12-10) can transmit and receive signals as described above. Accordingly, the baseband processing unit (12-20) and the RF processing unit (12-10) 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 (12-20) and the RF processing unit (12-10) 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 (12-20) and the RF processing unit (12-10) 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.

[0224] The storage unit (12-30) can store data such as basic programs, application programs, and setting information for the operation of the terminal. In particular, the storage unit (12-30) can store information related to a second connection node that performs wireless communication using wireless connection technology. Additionally, the storage unit (12-30) can provide the stored data upon a request from the control unit (12-40).

[0225] The control unit (12-40) can control the overall operations of the terminal. For example, the control unit (12-40) can control the terminal to perform the embodiments and / or methods of the present disclosure described above. For example, the control unit (12-40) can transmit and receive signals through the baseband processing unit (12-20) and the RF processing unit (12-10). Additionally, the control unit (12-40) can write and read data to and from the storage unit (12-30). To this end, the control unit (12-40) may include at least one processor. For example, the control unit (12-40) 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 (12-42) as illustrated in the drawing.

[0226] FIG. 13 is a drawing illustrating the configuration of a base station according to one embodiment of the present disclosure.

[0227] Referring to FIG. 13, according to one example of the present disclosure, a base station may be configured to include an RF processing unit (13-10), a baseband processing unit (13-20), a backhaul communication unit (13-30), a storage unit (13-40), and a control unit (13-50).

[0228] The RF processing unit (13-10) 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 (13-10) can up-convert a baseband signal provided by the baseband processing unit (13-20) 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 (13-10) may include a transmit filter, a receive filter, an amplifier, a mixer, an oscillator, a DAC, an ADC, etc. Although only one antenna is shown in the drawing, the base station may be equipped with multiple antennas. In addition, the RF processing unit (13-10) may include multiple RF chains. Furthermore, the RF processing unit (13-10) may perform beamforming. For the above beamforming, the RF processing unit (13-10) 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.

[0229] The baseband processing unit (13-20) 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 (13-20) can generate complex symbols by encoding and modulating the transmitted bit sequence. Additionally, when receiving data, the baseband processing unit (13-20) can restore the received bit sequence by demodulating and decoding the baseband signal provided by the RF processing unit (13-10). For example, in the case of an OFDM method, when transmitting data, the baseband processing unit (13-20) 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 (13-20) can divide the baseband signal provided by the RF processing unit (13-10) 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 (13-20) and the RF processing unit (13-10) can transmit and receive signals as described above. Accordingly, the baseband processing unit (13-20) and the RF processing unit (13-10) may be referred to as a transmitting unit, a receiving unit, a transmitting and receiving unit, a communication unit, or a wireless communication unit.

[0230] The backhaul communication unit (13-30) can provide an interface for communicating with other nodes within the network. That is, the backhaul communication unit (13-30) 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.

[0231] The storage unit (13-40) can store data such as basic programs, application programs, and configuration information for the operation of the main station. In particular, the storage unit (13-40) can store information regarding a bearer assigned to a connected terminal, measurement results reported from the connected terminal, etc. Additionally, the storage unit (13-40) can store information that serves as a criterion for determining whether to provide or disconnect multiple connections to the terminal. Furthermore, the storage unit (13-40) can provide the stored data upon a request from the control unit (13-50).

[0232] The control unit (13-50) can control the overall operations of the main base station. For example, the control unit (13-50) can control the base station to perform the embodiments and / or methods of the present disclosure described above. For example, the control unit (13-50) can transmit and receive signals through the baseband processing unit (13-20) and the RF processing unit (13-10) or through the backhaul communication unit (13-30). Additionally, the control unit (13-50) can write and read data to and from the storage unit (13-40). To this end, the control unit (13-50) may include at least one processor and may include a multiple connection processing unit (13-52) as illustrated in the drawing.

[0233] It should be noted that the aforementioned configuration diagrams, exemplary diagrams of control / data signal transmission methods, exemplary diagrams of operation procedures, and configuration diagrams are not intended to limit the scope of the rights of the present disclosure. That is, all components, entities, or steps of operation described in the embodiments of the present disclosure should not be interpreted as essential components for the implementation of the disclosure, and may be implemented within a scope that does not impair the essence of the disclosure even if only some components are included. Furthermore, each embodiment may be combined and operated as needed. For example, parts of the methods proposed in the present disclosure may be combined to operate network entities and terminals.

[0234] The operations of the base station or terminal described above can be realized by providing a memory device storing the corresponding program code in any component within the base station or terminal device. That is, the control unit of the base station or terminal device can execute the operations described above by reading the program code stored in the memory device using a processor or CPU (Central Processing Unit) and executing it.

[0235] Various components of entities, base stations, or terminal devices and modules described in this disclosure may be operated using hardware circuits, such as, for example, complementary metal oxide semiconductor-based logic circuits, firmware, software, and / or a combination of hardware and firmware and / or software embedded in a machine-readable medium. For example, various electrical structures and methods may be implemented using electrical circuits such as transistors, logic gates, and application-specific semiconductors.

[0236] When implemented in software, a computer-readable storage medium may be provided for storing one or more programs (software modules). One or more programs stored in the computer-readable storage medium are configured for execution by one or more processors within an electronic device. One or more programs include instructions that cause the electronic device to execute methods according to the embodiments described in the claims or specification of this disclosure.

[0237] Such programs (software modules, software) may be stored in random access memory, non-volatile memory including flash memory, ROM (Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), magnetic disc storage devices, CD-ROM (Compact Disc-ROM), Digital Versatile Discs (DVDs), or other forms of optical storage devices, magnetic cassettes. Alternatively, they may be stored in memory composed of some or all of these. Additionally, each constituent memory may include multiple units.

[0238] Additionally, the program may be stored on an attachable storage device accessible via a communication network such as the Internet, Intranet, Local Area Network (LAN), Wide LAN (WLAN), or Storage Area Network (SAN), or a combination thereof. Such a storage device may be connected to a device performing an embodiment of the present disclosure through an external port. Additionally, a separate storage device on a communication network may be connected to a device performing an embodiment of the present disclosure.

[0239] In the specific embodiments of the present disclosure described above, the components included in the present disclosure are expressed in a singular or plural form according to the specific embodiments presented. However, the singular or plural expression is selected to suit the situation presented for convenience of explanation, and the present disclosure is not limited to singular or plural components; even if a component is expressed in the plural, it may be composed of a singular form, and even if a component is expressed in the singular form, it may be composed of a plural form.

[0240] 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. Furthermore, 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. Additionally, 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 possible.

Claims

1. A method performed by a terminal in a wireless communication system, A step of receiving a logged measurement configuration for data collection from a base station; A step of logging measurement information for data collection based on the above log measurement settings; If the memory for the measurement information for the above data collection is full, the step of stopping the logging of the measurement information for the above data collection; and A method comprising the step of resuming logging of measurement information for data collection when the memory for measurement information for data collection is no longer full.

2. In Paragraph 1, A step of receiving a terminal information request message from the base station requesting a report of the measurement information for the above data collection; and The method includes the step of transmitting a terminal information response message containing the measurement information for the above data collection to the base station. A method for discarding the measurement information for data collection stored in the memory based on the successful transmission of the terminal information response message.

3. In Paragraph 1, A method further comprising the step of transmitting information to the base station indicating that the memory for the measurement information for the above data collection is full.

4. In Paragraph 1, The above log measurement settings include a plurality of settings, and The measurement information for the above data collection is logged for each of the above settings, and The method of reporting the measurement information for the above data collection according to the above settings.

5. In a method performed by a base station in a wireless communication system, A step of transmitting a logged measurement configuration for data collection to a terminal; and Based on the above log measurement settings, the method includes the step of logging measurement information for data collection and receiving information from the terminal indicating that the memory of the terminal is full. If the memory for the measurement information for the above data collection is full, logging of the measurement information for the above data collection is stopped at the terminal, and, A method in which logging of the measurement information for the above data collection is resumed at the terminal when the memory for the above measurement information for the above data collection is no longer full.

6. In Paragraph 5, A step of transmitting a terminal information request message to the terminal requesting a report of the measurement information for the above data collection; and The method includes the step of receiving a terminal information response message from the terminal that includes the measurement information for the above data collection, A method for discarding the measurement information for data collection stored in the memory based on the successful transmission of the terminal information response message.

7. In Paragraph 6, A method for resuming the logging of the measurement information for data collection without instructions from the base station, based on the terminal information response message.

8. In Paragraph 5, The above log measurement settings include a plurality of settings, and The measurement information for the above data collection is logged for each of the above settings, and The method of reporting the measurement information for the above data collection according to the above settings.

9. In a terminal of a wireless communication system, At least one transceiver; At least one processor connected to the above at least one transceiver so as to be able to communicate; and Connected to communicate with at least one processor and capable of executing individually or in any combination of the at least one processor, the terminal: Receive a logged measurement configuration for data collection from the base station, and Based on the above log measurement settings, measurement information for the above data collection is logged, and If the memory for the measurement information for the above data collection is full, the logging of the measurement information for the above data collection is stopped, and A memory storing a command to resume logging of the measurement information for data collection when the memory for the measurement information for data collection is no longer full; A terminal including 10. In Paragraph 9, The above memory is the terminal, Receive a terminal information request message from the base station requesting a report of the measurement information for the above data collection, and It further includes a command to transmit a terminal information response message containing the measurement information for the above data collection to the base station, and A terminal that discards the measurement information for data collection stored in the memory based on the successful transmission of the above terminal information response message.

11. In Paragraph 9, The above memory is the terminal, A terminal further comprising a command to transmit information to the base station indicating that the memory for the measurement information for the above data collection is full.

12. In Paragraph 10, The above log measurement settings include a plurality of settings, and The measurement information for the above data collection is logged for each of the above settings, and The above measurement information for collecting the above data is a terminal reported according to the above settings.

13. In a base station of a wireless communication system, At least one transceiver; At least one processor connected to the above at least one transceiver so as to be able to communicate; and Connected to communicate with at least one processor and capable of executing individually or in any combination of the at least one processor, the base station: Transmit the logged measurement configuration for data collection to the terminal, and Based on the above log measurement settings, the memory includes a command to receive from the terminal information indicating that the terminal's memory is full, logging measurement information for data collection, and If the memory for the measurement information for the above data collection is full, logging of the measurement information for the above data collection is stopped at the terminal, and, A base station in which logging of the measurement information for the above data collection is resumed at the terminal when the memory for the above data collection for the above measurement information is no longer full.

14. In Paragraph 13, The above memory is the terminal, Sending a terminal information request message to the terminal requesting a report of the measurement information for the above data collection, and It further includes a command to receive a terminal information response message containing the measurement information for the above data collection from the terminal, and A base station in which the measurement information for data collection stored in the memory is discarded based on the successful transmission of the above terminal information response message.

15. In Paragraph 14, Based on the terminal information response message above, logging of the measurement information for data collection is resumed without instructions from the base station, and The above log measurement settings include a plurality of settings, and The measurement information for the above data collection is logged for each of the above settings, and The above measurement information for the above data collection is a base station reported for each of the above settings.

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