Method and device for measurement prediction of terminal by using artificial intelligence and machine learning in wireless communication system

Abstract

The present disclosure relates to a system for supporting higher data transmission rates by receiving, from a base station, configuration information for measurement and prediction related to an AI / ML that includes at least one piece of information necessary to perform at least one from among measurement, prediction and transmission related to the AI / ML, information about the maximum number of prediction values, information about the interval of the prediction values, and information about cells related to the AI / ML, performing measurement and prediction related to the AI / ML, and then transmitting information about the results of the measurement and / or the prediction to the base station.

Description

Method and device for predicting terminal measurements using artificial intelligence and machine learning in wireless communication systems

[0001] The present disclosure relates to a method and device for obtaining cell measurement information predicted by a terminal in a wireless communication system using artificial intelligence and machine learning models. More specifically, the disclosure relates to measurement prediction using artificial intelligence and machine learning related to terminal mobility.

[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 the sub-6GHz frequency band such as 3.5 gigahertz (3.5GHz), but also in the ultra-high frequency band called millimeter wave (mmWave) such as 28GHz and 39GHz ('Above 6GHz'). In addition, for 6G mobile communication technology, which is called the system after 5G communication (Beyond 5G), implementation in the terahertz (THz) band (for example, 3 THz band at 95GHz) is being considered to achieve a transmission speed that is 50 times faster than 5G mobile communication technology and an ultra-low latency time that is reduced to one-tenth.

[0003] In the early stages of 5G mobile communication technology, the goal is to support services and satisfy performance requirements for enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC). These include beamforming and massive MIMO to mitigate path loss of radio waves in ultra-high frequency bands and increase the transmission distance of radio waves, support for various numerologies (such as operation of multiple subcarrier intervals) and dynamic operation of slot formats for efficient use of ultra-high frequency resources, initial access technology to support multi-beam transmission and wideband, definition and operation of BWP (Bidth Part), new channel coding methods such as LDPC (Low Density Parity Check) codes for large-capacity data transmission and Polar Code for reliable transmission of control information, and L2 pre-processing (L2). Standardization has been made for network slicing, which provides dedicated networks specialized for specific services, and pre-processing.

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

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

[0006] Once these 5G mobile communication systems are commercialized, an explosive increase in connected devices will be connected to the communication network, necessitating enhanced functionality and performance of 5G mobile communication systems and integrated operation of these connected devices. To this end, new research will be conducted on improving 5G performance and reducing complexity, supporting AI services, supporting metaverse services, and drone communications by utilizing eXtended Reality (XR), Artificial Intelligence (AI), and Machine Learning (ML) to efficiently support Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR).

[0007] In addition, the development of these 5G mobile communication systems includes new waveforms to ensure coverage in the terahertz band of 6G mobile communication technology, multi-antenna transmission technologies such as Full Dimensional MIMO (FD-MIMO), Array Antenna, and Large Scale Antenna, metamaterial-based lenses and antennas to improve the coverage of terahertz band signals, high-dimensional spatial multiplexing technology using Orbital Angular Momentum (OAM), Reconfigurable Intelligent Surface (RIS) technology, as well as full duplex technology to improve the frequency efficiency and system network of 6G mobile communication technology, satellite, AI (Artificial Intelligence) from the design stage and AI-based communication technology that realizes system optimization by internalizing end-to-end AI support functions, and ultra-high-performance communication and computing resources to provide services with complexity that exceeds the limits of terminal computing capabilities. It can serve as a basis for the development of next-generation distributed computing technologies that can be realized by utilizing them.

[0008] This can be problematic when moving between high-density microcells or for services such as XR, where the terminal has high mobility. For example, handover failures, radio link failures, ping-pong artifacts, throughput loss, or premature / late handovers can occur.

[0009] The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present invention belongs from the description below.

[0010] According to one embodiment of the present disclosure, a method performed by a terminal of a wireless communication system comprises the steps of: receiving, from a base station, configuration information for measurement and prediction related to artificial intelligence (AI) / machine learning (ML); performing the measurement and the prediction related to the AI / ML based on the configuration information; and transmitting, to the base station, information on a result of at least one of the measurement or the prediction, wherein the configuration information comprises at least one of information necessary to perform at least one of the measurement, the prediction, or the transmission related to the AI / ML, information on a maximum number of prediction values, information on an interval of the prediction values, or information on a cell related to the AI / ML.

[0011] Additionally, it includes a step of transmitting capability information for the measurement and prediction related to the AI / ML to the base station.

[0012] In addition, the information on the result of at least one of the measurement or the prediction is characterized in that it includes at least one of cell unit information, beam unit information, prediction information for an event, or information indicating a change related to the prediction.

[0013] In addition, the information about the cells related to the AI / ML includes at least one of information about the number of cells on which the measurement and the prediction are to be performed, information about the number of cells on which reporting is to be performed, or information about one or more specific cells, and the information necessary to perform at least one of the measurement, the prediction, or the transmission related to the AI / ML includes information about at least one measurement object.

[0014] According to one embodiment of the present disclosure, a method performed by a base station of a wireless communication system comprises the steps of: transmitting, to a terminal, configuration information for measurement and prediction related to artificial intelligence (AI) / machine learning (ML); and receiving, from the terminal, information on a result of at least one of the measurement or the prediction, wherein the configuration information comprises at least one of information necessary for performing at least one of the measurement related to the AI / ML, the prediction, or transmission of information on the result of the terminal, information on a maximum number of prediction values, information on an interval of the prediction values, or information on a cell related to the AI / ML.

[0015] According to one embodiment of the present disclosure, a terminal of a wireless communication system comprises: a transceiver; and a control unit connected to the transceiver, configured to receive, from a base station, configuration information for measurement and prediction related to artificial intelligence (AI) / machine learning (ML), perform the measurement and the prediction related to the AI / ML based on the configuration information, and transmit information on a result of at least one of the measurement or the prediction to the base station, wherein the configuration information includes at least one of information necessary for performing at least one of the measurement, the prediction, or the transmission related to the AI / ML, information on a maximum number of prediction values, information on an interval of the prediction values, or information on a cell related to the AI / ML.

[0016] According to one embodiment of the present disclosure, a base station of a wireless communication system comprises: a transceiver; and a control unit connected to the transceiver and configured to transmit, to a terminal, configuration information for measurement and prediction related to artificial intelligence (AI) / machine learning (ML), and to receive, from the terminal, information on a result of at least one of the measurement or the prediction, wherein the configuration information comprises at least one of information necessary for performing at least one of the measurement related to the AI / ML, the prediction, or transmission of information on the result of the terminal, information on a maximum number of prediction values, information on an interval of the prediction values, or information on a cell related to the AI / ML.

[0017] According to one embodiment of the present disclosure, a terminal can generate predicted cell measurement information for the future using an AI / ML model and report this to a base station (or network). For example, this allows the base station to proactively prepare for a handover, preventing handover delays. Furthermore, by proactively instructing the terminal to perform a handover, the terminal can be handed over to another base station or cell before a problem (e.g., radio link failure) occurs. In other words, by receiving predicted measurement information from the terminal as described above, the base station can take preemptive measures to avoid unintended events.

[0018] The effects that can be obtained from the present disclosure are not limited to the effects mentioned in the various embodiments, and other effects that are not mentioned can be clearly understood by a person having ordinary skill in the art to which the present disclosure belongs from the description below.

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

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

[0021] FIG. 3 is a diagram illustrating a process in which a terminal performs cell measurement and reporting operations according to one embodiment of the present disclosure.

[0022] FIG. 4 is a diagram illustrating an operation of reporting cell measurement results when a terminal satisfies a reporting condition of cell measurement results according to an embodiment of the present disclosure.

[0023] FIG. 5 is a diagram illustrating a signaling procedure between a terminal and a base station for performing AI / ML prediction related to mobility of the terminal according to one embodiment of the present disclosure.

[0024] FIG. 6 is a diagram illustrating the internal structure of a terminal according to an embodiment of the present disclosure.

[0025] FIG. 7 is a diagram illustrating the structure of a base station according to one embodiment of the present disclosure.

[0026] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0027] In describing the embodiments, descriptions of technical contents that are well known in the technical field to which the present disclosure belongs and are not directly related to the present disclosure are omitted.

[0028] This is to convey the gist of the present disclosure more clearly without obscuring it by omitting unnecessary explanations.

[0029] For the same reason, some components in the attached drawings are exaggerated, omitted, or schematically depicted. Furthermore, the dimensions of each component do not entirely reflect the actual size. Identical or corresponding components in each drawing are assigned the same reference numbers.

[0030] The advantages and features of the present disclosure and the methods for achieving them will become apparent with reference to the embodiments described in detail below together with the accompanying drawings.

[0031] However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms. These embodiments are provided only to complete the composition of the present disclosure and to fully inform those skilled in the art of the disclosure of the scope of the disclosure, and the present disclosure is defined only by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

[0032] At this time, it will be understood that each block of the processing flow diagrams and combinations of the flow diagrams can be performed by computer program instructions. These computer program instructions can be installed in a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, so that the instructions executed by the processor of the computer or other programmable data processing equipment create a means for performing the functions described in the flow diagram block(s). These computer program instructions can also be stored in a computer-available or computer-readable memory that can be directed to a computer or other programmable data processing equipment for implementation in a specific manner, so that the instructions stored in the computer-available or computer-readable memory can also produce a manufactured item that includes an instruction means for performing the functions described in the flow diagram block(s). Since the computer program instructions can also be installed on a computer or other programmable data processing device, a series of operational steps can be performed on the computer or other programmable data processing device to create a computer-executable process, and the instructions that cause the computer or other programmable data processing device to perform the steps for performing the functions described in the flowchart block(s) can also provide steps for performing the functions described in the flowchart block(s).

[0033] Additionally, each block may represent a module, segment, or portion of code that contains one or more executable instructions for performing a specific logical function(s). It should also be noted that in some alternative implementation examples, the functions described in the blocks may occur out of order. For example, two blocks depicted in succession may actually be executed substantially concurrently, or the blocks may sometimes be executed in reverse order, depending on their respective functions.

[0034] Here, the term '~ unit' used in the present embodiment means software or hardware components such as FPGA (field programmable gate array) or ASIC (application-specific integrated circuit), and the '~ unit' performs certain roles. However, the '~ unit' is not limited to software or hardware. The '~ unit' may be configured to be on an addressable storage medium, and may be configured to play one or more processors. Accordingly, as an example, the '~ unit' includes components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, 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 '~ units' may be combined into a smaller number of components and '~ units', or further separated into additional components and '~ units'. Additionally, the components and '~parts' may be implemented to play one or more central processing units (CPUs) within the device or secure multimedia card.

[0035] For the convenience of the following description, some terms and names defined in the 3rd generation partnership project (3GPP) standards (standards for 5G, NR, LTE, or similar systems) may be used. In addition, terms and names newly defined in next-generation communication systems (e.g., 6G, Beyond 5G systems) to which the present disclosure may be applied, or terms and names used in existing communication systems may be used. The use of such terms is not limited to the terms and names of the present disclosure, and may be equally applied to systems conforming to other standards, and may be modified into other forms without departing from the technical spirit of the present disclosure. Embodiments of the present disclosure may be easily modified and applied to other communication systems.

[0036] Additionally, it will be understood that singular expressions such as “a” and “the above” include plural expressions unless they clearly indicate otherwise in one embodiment of the present disclosure.

[0037] Additionally, in one embodiment of the present disclosure, the size in relation to blocks and the like may be expressed equally in length or size.

[0038] Additionally, in one embodiment of the present disclosure, terms including ordinal numbers, such as first, second, etc., may be used to describe various components, but the components are not limited by these terms. These terms are used solely to distinguish one component from another. For example, without departing from the scope of the present disclosure, a first component could be referred to as a second component, and similarly, a second component could also be referred to as a first component.

[0039] Additionally, in one embodiment of the present disclosure, the term “and / or” includes a combination of a plurality of related described items or any one of a plurality of related described items.

[0040] In addition, the terms used in the embodiments of the present disclosure are only used to describe specific embodiments and are not intended to limit the present disclosure. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this specification, it should be understood that the terms "comprise" or "have" are intended to specify the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but do not exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0041] Additionally, the terms “associated with” and “associated therewith” and their derivatives used in one embodiment of the present disclosure may mean include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicated with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, and the like.

[0042] Additionally, in the present disclosure, expressions such as "more than" and "less than" are used to determine whether a specific condition is satisfied or fulfilled. However, this is merely a description to express an example and does not exclude descriptions of more than or less than. Conditions described as "more than" may be replaced with "more than," conditions described as "less than" may be replaced with "less than," and conditions described as "more than and less than" may be replaced with "more than and less than."

[0043] Additionally, although the present disclosure describes embodiments using terms used in certain communication standards (e.g., long term evolution (LTE) and new radio (NR) defined by the 3rd generation partnership project (3GPP)), these are merely examples for illustrative purposes. The embodiments of the present disclosure can be easily modified and applied to other communication systems.

[0044] Before delving into the detailed description of this disclosure, examples of possible interpretations of some terms used herein are provided. However, it should be noted that the interpretations provided below are not limited to these examples.

[0045] In the present disclosure, a terminal (or communication terminal) is an entity that communicates with a base station or another terminal, and may be referred to as a node, UE (user equipment), NG UE (next generation UE), MS (mobile station), device, or terminal. In addition, the terminal may include at least one of a smartphone, a tablet PC, a mobile phone, a video phone, an e-book reader, a desktop PC, a laptop PC, a netbook computer, a PDA, a PMP (portable multimedia player), an MP3 player, a medical device, a camera, or a wearable device. In addition, the terminal may include at least one of a television, a DVD (digital video disk) player, an audio player, a refrigerator, an air conditioner, a vacuum cleaner, an oven, a microwave oven, a washing machine, an air purifier, a set-top box, a home automation control panel, a security control panel, a media box, a game console, an electronic dictionary, an electronic key, a camcorder, or an electronic picture frame.In addition, the terminal may include at least one of various medical devices (e.g., various portable medical measuring devices (such as blood glucose meters, heart rate monitors, blood pressure monitors, or body temperature monitors), magnetic resonance angiography (MRA), magnetic resonance imaging (MRI), computed tomography (CT), cameras, or ultrasound machines), navigation devices, global navigation satellite systems (GNSS), event data recorders (EDR), flight data recorders (FDR), automotive infotainment devices, electronic equipment for ships (e.g., navigation devices for ships, gyrocompasses, etc.), avionics, security devices, head units for vehicles, industrial or home robots, drones, ATMs for financial institutions, POSs (points of sales) for stores, or Internet of Things devices (e.g., light bulbs, various sensors, sprinkler devices, fire alarms, thermostats, streetlights, toasters, exercise equipment, hot water tanks, heaters, boilers, etc.). Additionally, the terminal may include various types of multimedia systems capable of performing communication functions. Meanwhile, the present disclosure is not limited to the above description, and the terminal may also be referred to by terms having the same or similar meaning.

[0046] In addition, in the present disclosure, the base station is an entity that communicates with a terminal and performs resource allocation of the terminal, and may have various forms and may be referred to as a BS (base station), a NodeB (NB), an NG RAN (next generation radio access network), an AP (access point), a TRP (transmission reception point), a wireless access unit, a base station controller, or a node on a network. Alternatively, it may be referred to as a CU (central unit) or a DU (distributed unit) depending on functional separation. Meanwhile, the present disclosure is not limited thereto, and the base station may be referred to by a term having the same or similar meaning.

[0047] Additionally, in the present disclosure, an RRC (radio resource control) message may be referred to as a higher level information, a higher level message, a higher level signal, a higher level signaling, a higher layer signaling, or a higher layer signaling, and the present disclosure is not limited thereto and may also be referred to by terms having the same or similar meaning.

[0048] Additionally, in the present disclosure, data may be referred to as user data, user plane (UP) data, or application data, or may be referred to by terms having the same or similar meaning as signals transmitted and received via a data radio bearer (DRB).

[0049] Additionally, in the present disclosure, the direction of data transmitted from a terminal may be referred to as uplink (UL), and the direction of data transmitted to the terminal may be referred to as downlink (DL). Accordingly, in the case of uplink transmission, the transmitter may refer to the terminal, and the receiver may refer to a base station or a specific network entity of the communication system. Alternatively, in the case of downlink transmission, the transmitter may refer to a base station or a specific network entity of the communication system, and the receiver may refer to the terminal.

[0050] FIG. 1 is a diagram illustrating the structure of a mobile communication system according to one embodiment of the present disclosure.

[0051] Referring to FIG. 1, a wireless access network of a mobile communication system (New Radio, NR) according to one embodiment of the present disclosure may be composed of a base station (next generation Node B, hereinafter referred to as gNB) (1-10) and an AMF (1-05, New Radio Core Network).

[0052] According to one embodiment of the present disclosure, a user terminal (New Radio User Equipment, hereinafter referred to as NR UE or terminal) (1-15) can access an external network through a gNB (1-10) and an AMF (1-05).

[0053] According to one embodiment of the present disclosure, the mobile communication system may be a next-generation mobile communication system, and the base station may be a next-generation base station.

[0054] According to one embodiment, the gNB (1-10) in FIG. 1 may correspond to an eNB (Evolved Node B) of an existing LTE system. The gNB (1-10) is connected to an NR UE via a wireless channel and may provide a service superior to that of an existing Node B (1-20). In the 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 that collects status information such as buffer status, available transmission power status, and channel status of UEs and performs scheduling may be required, and this may be performed by the gNB (1-10). According to one embodiment, a single gNB (1-10) may typically control multiple cells. In order to implement ultra-high-speed data transmission, it may have a bandwidth greater than the existing maximum, and an orthogonal frequency division multiplexing (OFDM) scheme may be used as a wireless access technology, and additional beamforming technology may be incorporated. Additionally, an adaptive modulation and coding (AMC) method can be applied, which determines the modulation scheme and channel coding rate according to the channel status of the terminal.

[0055] According to one embodiment, in FIG. 1, the AMF (1-05) may perform functions such as mobility support, bearer setup, and QoS (quality of service) setup. The AMF (1-05) is a device that is responsible for various control functions as well as mobility management functions for terminals and may be connected to multiple base stations. In addition, the mobile communication system according to one embodiment of the present disclosure may also be interoperable with an LTE system, and for example, the AMF (1-05) may be connected to an MME (1-25) via a network interface.

[0056] According to one embodiment, the MME (1-25) may be connected to an existing base station, eNB (1-30). For example, in FIG. 1, a terminal supporting LTE-NR Dual Connectivity can transmit and receive data while maintaining connection to both the gNB (1-10) and the eNB (1-30) (1-35).

[0057] FIG. 2 is a diagram for explaining a wireless connection state transition in a mobile communication system according to an embodiment of the present disclosure.

[0058] According to one embodiment of the present disclosure, a mobile communication system may have three radio access states (RRC (radio resource control) states) or RRC modes.

[0059] According to FIG. 2, the connected mode (RRC_CONNECTED, 2-05) may be a wireless connection state in which the terminal can transmit and receive data. Additionally, 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 itself. The above two modes are wireless connection states also applicable to the LTE system, and the detailed description may be identical to that of the LTE system. The mobile communication system according to an embodiment of the present disclosure may be a next-generation mobile communication system.

[0060] According to one embodiment of the present disclosure, a new inactive (RRC_INACTIVE) radio connection state (2-15) may be defined in a mobile communication system. In this radio connection state (2-15), UE context may be maintained between a base station and a terminal, and RAN (radio access network)-based paging may be supported. The characteristics of this new radio connection state (2-15) may include at least one of the following:

[0061] - Cell re-selection mobility;

[0062] - CN - NR RAN connection (both C / U-planes (control plane / user plane)) has been established for UE;

[0063] - The UE AS(Access Stratum) context is stored in at least one gNB and the UE;

[0064] - Paging is initiated by NR RAN;

[0065] - RAN-based notification area is managed by NR RAN;

[0066] - NR RAN knows the RAN-based notification area which the UE belongs to;

[0067] According to one embodiment of the present disclosure, a terminal in an INACTIVE wireless connection state (2-15) can use a specific procedure to transition to a connected mode (2-05) or a standby mode (2-30). The terminal can transition from INACTIVE mode (2-15) to a connected mode (2-05) using a Resume procedure, and can transition from connected mode (2-05) to INACTIVE mode (2-15) using a Release procedure including suspend configuration information (2-10). The procedure can be performed by transmitting and receiving one or more RRC messages between the terminal and the base station, and can consist of one or more steps. In addition, according to one embodiment, transitioning from INACTIVE mode (2-15) to a standby mode (2-30) can be possible through a Release procedure after Resume (2-20). The transition between connected mode (2-05) and standby mode (2-30) can be based on LTE technology. Also, according to FIG. 2, switching between the above modes can be achieved through an establishment or release procedure (2-25).

[0068] FIG. 3 is a diagram illustrating a process in which a terminal performs cell measurement and reporting operations according to one embodiment of the present disclosure.

[0069] According to one embodiment of the present disclosure, in step 3-15, the terminal (3-05) may report its capability information to the base station (3-10). In addition, in step 3-20, the base station (3-10) may transmit a message (e.g., an RRCReconfiguration message, etc.) including configuration information (e.g., measConfig IE) related to the cell measurement operation to the terminal (3-05).

[0070] According to one embodiment, the configuration information (e.g., measConfig IE) may include information necessary for the terminal to report the measurement results to the base station, depending on the type of measurement report (e.g., periodical, event-triggered, event-triggered periodical, etc.). For example, in the case of event-triggered reporting or event-triggered periodical, the terminal may report the measurement results when a specific event configured based on the configuration information is satisfied. For example, in an NR system, the following events may be configured.

[0071] In relation to one embodiment, Events related to intra- / inter-RAT measurements can be exemplified in [Table 1] below.

[0072] Event A1: Serving becomes better than absolute threshold;Event A2: Serving becomes worse than absolute threshold;Event A3: Neighbour becomes amount of offset better than PCell / PSCell;Event A4: Neighbour becomes better than absolute threshold;Event A5: PCell / PSCell becomes worse than absolute threshold1 AND Neighbour / SCell becomes better than another absolute threshold2;Event A6: Neighbour becomes amount of offset better than SCell;Event D1: Distance between UE and a reference location referenceLocation1 becomes larger than configured threshold distanceThreshFromReference1 and distance between UE and a reference location referenceLocation2 becomes shorter than configured threshold distanceThreshFromReference2;Event B1: Neighbour becomes better than absolute threshold;Event B2: PCell becomes worse than absolute threshold1 AND Neighbour becomes better than another absolute threshold2;

[0073] According to one embodiment, similar to condition-based measurement reporting, in condition-based handover, when a specific event is satisfied, the terminal (3-05) can perform a handover based on condition-based handover configuration information. Events related to condition-based handover can be exemplified in [Table 2] below.

[0074] CondEvent A3: Conditional reconfiguration candidate becomes amount of offset better than PCell / PSCell;CondEvent A4: Conditional reconfiguration candidate becomes better than absolute threshold;CondEvent A5: PCell / PSCell becomes worse than absolute threshold1 AND Conditional reconfiguration candidate becomes better than another absolute threshold2;CondEvent D1: Distance between UE and a reference location referenceLocation1 becomes larger than configured threshold distanceThreshFromReference1 and distance between UE and a reference location referenceLocation2 of conditional reconfiguration candidate becomes shorter than configured threshold distanceThreshFromReference2;CondEvent T1: Time measured at UE becomes more than configured threshold t1-Threshold but is less than t1-Threshold + duration;

[0075] According to one embodiment, when a specific event is satisfied in the Sidelink Relay, the terminal (3-05) may perform a specific action. Events related to the Relay, related to one embodiment, may be exemplified in [Table 3].

[0076] Event X1: Serving L2 U2N Relay UE becomes worse than absolute threshold1 AND NR Cell becomes better than another absolute threshold2;Event

[0077] According to one embodiment, even in the case of NR-U (Unlicensed), when a specific event is satisfied, the terminal (3-05) can perform a specific action. Events related to NR-U in relation to one embodiment can be exemplified in [Table 4].

[0078] Event I1: Interference becomes higher than absolute threshold.

[0079] According to one embodiment of the present disclosure, if the Events exemplified above are continuously satisfied with a specific condition during a specific time period (time-to-trigger), the terminal (3-05) may determine that the Event is satisfied. According to FIG. 3, in step 3-20, when the set condition is satisfied, the terminal (3-05) may report a message (e.g., MeasurementReport) containing the measurement result to the base station (3-10). Alternatively, the terminal may perform a terminal operation (e.g., condition-based handover) corresponding to the condition. The base station (3-10) that has received the measurement result may utilize the result for a specific purpose. For example, in step 3-35, based on the result, the base station (3-10) may determine whether to handover the terminal (3-05). According to one embodiment, when the base station (3-10) triggers a handover, the handover may be requested to the target cell in step 3-40. In addition, in step 3-45, the base station (3-10) may transmit handover configuration information configured based on the configuration information received from the target cell to the terminal (3-05). According to one embodiment, in step 3-50, the terminal (3-05) that received the configuration information may perform a handover based on the configuration information. FIG. 4 is a diagram illustrating an operation of reporting cell measurement results when a terminal satisfies a reporting condition of cell measurement results according to one embodiment of the present disclosure.

[0080] FIG. 4 may relate to an operation of a terminal reporting a cell measurement result when a reporting condition of a cell measurement result is satisfied according to an embodiment of the present disclosure.

[0081] According to one embodiment of the present disclosure, the terminal (4-10) can evaluate the signal strength or quality of a signal of the base station (4-05) based on a signal (e.g., a synchronization signal block (SSB) or a channel state information reference signal (CSI-RS)) that can be transmitted from the base station (4-05).

[0082] For convenience of explanation, the explanation may be centered on SSB, which is an example of the terminal's cell measurement result reporting operation, but the same can be applied to CSI-RS, etc.

[0083] In one embodiment, for SSB, the transmission cycle of SSB may be determined according to the settings of the base station (4-05). For example, the transmission cycle of SSB may be set to 20 ms, and the base station (4-05) may transmit SSB at a cycle of up to 160 ms.

[0084] According to one embodiment, when a base station (4-05) sets a specific event (e.g., Event A2) to a terminal (4-10) according to FIG. 4, the terminal (4-10) can continuously evaluate whether the RSRP value measured based on SSB is lower than the threshold during a specific time period (time-to-trigger, TTT) from the time point (4-15) when the RSRP value measured based on SSB becomes lower than the set absolute threshold.

[0085] According to one embodiment, if the RSRP value measured based on SSB is lower than the set absolute threshold from the initial time point (4-15) when the RSRP value is lower than the set absolute threshold until the time point (4-20) when the time interval has elapsed, the terminal (4-10) may determine that the Event A2 described above is satisfied and may report a measurement report triggered by Event A2 to the base station (4-05).

[0086] In one embodiment, the reason why TTT is considered in determining whether the conditions for performing a measurement report are satisfied may be to compensate for the variability of the measurement signal. In addition, the TTT value may be set by the base station (4-05) for each specific Event that is configured. According to one embodiment, if the Events that can be exemplified continuously satisfy a specific condition for a specific time period (TTT), a terminal operation corresponding to the purpose of the configured Event may be performed.

[0087] According to one embodiment, if the measurement-related configuration information received by the terminal is periodical or event-triggered periodical, the terminal (4-10) may perform periodic measurement reporting.

[0088] In one embodiment, as a Layer 3 (L3) handover mechanism, handovers can be triggered and executed by the network or base station based on previously reported historical cell measurement results and / or cell measurement event(s). For example, this may correspond to a type of reactive approach. This reactive handover approach may work well for existing services when the UE moves between macro cells or when the UE has low mobility. However, it may be problematic when the UE has high mobility or moves between dense micro cells, or for future services such as XR. For example, handover failures, radio link failures, ping-pong phenomenon, loss of throughput, or too early / late handovers may occur. Conditional handovers were introduced in Rel-16 to improve handover robustness, and LTM handovers were introduced in Rel-18 to reduce service interruption time due to frequent handovers between small cells. However, these two handover mechanisms may still be reactive and thus insufficient.

[0089] In one embodiment, a mechanism based on AI / ML (Artificial Intelligence and Machine Learning) algorithms can enable a proactive approach. For example, a terminal can generate predicted cell measurement information for the future using an AI / ML model and report this to a base station (or network). This allows the base station to proactively prepare for a handover, preventing handover delays. By proactively instructing the terminal to perform a handover, the terminal can be handed over to another base station or cell before a problem (e.g., radio link failure) occurs.

[0090] Additionally, according to one embodiment, by receiving cell measurement information from a terminal, a base station can achieve improved handover and / or radio resource management (RRM) performance compared to a reactive approach. For example, it can make better network operation / configuration decisions or take proactive measures to avoid unintended events.

[0091] FIG. 5 is a diagram illustrating a signaling procedure between a terminal and a base station for performing AI / ML prediction related to mobility of the terminal according to one embodiment of the present disclosure.

[0092] According to FIG. 5, in step 5-15, the terminal (5-05) may report mobility-related AI / ML prediction capabilities (UE capabilities) and / or cell measurement-related capabilities to the base station or network (5-10). The capabilities may be conveyed via a UE capability message (e.g., a UECapabilityInformation message, etc.).

[0093] According to one embodiment, the base station (5-10) may transmit a message (e.g., a UECapabilityEnquiry message) requesting transmission of related terminal capabilities to the terminal (5-05). The mobility-related AI / ML predictive capabilities may be exemplified by at least one of the following.

[0094] - Whether the terminal supports performing actions using AI / ML (e.g., mobility-related)

[0095] - Whether the terminal supports prediction and / or reporting using AI / ML (e.g., mobility-related)

[0096] - Whether the terminal supports prediction and / or reporting of cell-level measurements (e.g., RSRP / RSRQ (reference signal received quality) / SINR (signal to interference plus noise ratio)) using AI / ML (e.g., mobility-related)

[0097] - Whether the terminal supports prediction and / or reporting of beam-level measurements (e.g., RSRP / RSRQ / SINR, etc.) using AI / ML (e.g., mobility-related)

[0098] - Whether the terminal supports handover failure prediction and / or reporting using AI / ML (e.g., mobility-related)

[0099] - Whether the terminal supports RLF (radio link failure) prediction and / or reporting using AI / ML (e.g., mobility-related)

[0100] - Whether the terminal supports prediction and / or reporting of specific events (e.g., event A3, etc.) using AI / ML (e.g., mobility-related)

[0101] In one embodiment, when multiple AI / ML models are defined / indicated, the presence or absence of the exemplified support may be defined / indicated on a model-by-model basis (e.g., by model ID).

[0102] According to one embodiment, the above event may be exemplified by [Table 5].

[0103] Event A1: Serving becomes better than absolute threshold;Event A2: Serving becomes worse than absolute threshold;Event A3: Neighbour becomes amount of offset better than PCell / PSCell;Event A4: Neighbour becomes better than absolute threshold;Event A5: PCell / PSCell becomes worse than absolute threshold1 AND Neighbour / SCell becomes better than another absolute threshold2;Event A6: Neighbour becomes amount of offset better than SCell;Event D1: Distance between UE and a reference locationreferenceLocation1becomes larger than configured thresholddistanceThreshFromReference1and distance between UE and a reference locationreferenceLocation2becomes shorter than configured thresholddistanceThreshFromReference2;CondEvent A3: Conditional reconfiguration candidate becomes amount of offset better than PCell / PSCell;CondEvent A4: Conditional reconfiguration candidate becomes better than absolute threshold wherecondEventA4can also be used for current PSCell (i.e., in case it is configured as candidate PSCell for CondEvent A4 evaluation) for CHO with candidate SCG(s) case;CondEvent A5: PCell / PSCell becomes worse than absolute threshold1 AND Conditional reconfiguration candidate becomes better than another absolute threshold2;CondEvent D1: Distance between UE and a reference locationreferenceLocation1becomes larger than configured thresholddistanceThreshFromReference1and distance between UE and a reference locationreferenceLocation2of conditional reconfiguration candidate becomes shorter than configured thresholddistanceThreshFromReference2;CondEvent D2: Distance between UE and a moving reference location determined based onreferenceLocation1becomes larger than configured thresholddistanceThreshFromReference1and distance between UE and a moving reference location determined based onreferenceLocation2of conditional reconfiguration candidate becomes shorter than configured thresholddistanceThreshFromReference2;CondEvent T1: Time measured at UE becomes more than configured thresholdt1-Thresholdbut is less thant1-Threshold + duration;Event X1: Serving L2 U2N Relay UE becomes worse than absolute threshold1 AND NR Cell becomes better than another absolute threshold2;Event X2: Serving L2 U2N Relay UE becomes worse than absolute threshold;For event I1, measurement reporting event is based on CLI measurement results, which can either be derived based on SRS-RSRP or CLI-RSSI.Event I1: Interference becomes higher than absolute threshold.The reporting events concerning Aerial UE altitude are labelled HNwithNequal to 1 and 2. Additionally, the reporting events concerning Aerial UE altitude and the neighboring cell measurements simultaneously are labelled AMHNwithMequal to 3, 4, 5 andNequal to 1, 2.Event H1: Aerial UE altitude becomes higher than a threshold;Event H2: Aerial UE altitude becomes lower than a threshold.Event A3H1: Neighbour becomes offset better than SpCell and the Aerial UE altitude becomes higher than a threshold.Event A3H2: Neighbour becomes offset better than SpCell and the Aerial UE altitude becomes lower than a threshold.Event A4H1: Neighbour becomes better than threshold1 and the Aerial UE altitude becomes higher than a threshold2.Event A4H2: Neighbour becomes better than threshold1 and the Aerial UE altitude becomes lower than a threshold2.Event A5H1: SpCell becomes worse than threshold1 and neighbour becomes better than threshold2 and the Aerial UE altitude becomes higher than a threshold3.Event A5H2: SpCell becomes worse than threshold1 and neighbour becomes better than threshold2 and the Aerial UE altitude becomes lower than a threshold3.;

[0104] In one embodiment, if the terminal (5-05) supports the aforementioned capability, it may indicate to the base station (5-10) support of the aforementioned capability by including a related indicator in a message (e.g., a UE capability message) or setting it to a specific value (e.g., true). In one embodiment, if the terminal (5-05) does not support the aforementioned capability, it may indicate to the base station (5-10) that it does not support the aforementioned capability by not including the related indicator in the UE capability message or setting it to a specific value (e.g., false). In step 5-20, the base station (5-10) may provide the terminal (5-05) with settings related to cell measurement and / or AI / ML prediction and / or reporting, taking into account the capabilities of the received terminal. In one embodiment, the settings may include an AI / ML model (UE-sided model) that the terminal (5-05) can perform. In one embodiment, the transmission of an AI / ML model that the terminal can perform may be provided to the terminal (5-05) from an external server (e.g., over the top, OTT) without being defined in the standard. In step 5-20, the terminal (5-05) may receive cell measurement configuration information (e.g., MeasConfig) from the base station (5-10).

[0105] According to one embodiment, the configuration information may be received via a message (e.g., an RRC Reconfiguration message or an RRC Resume message) from the base station (5-10).

[0106] According to one embodiment, the configuration information may include information related to "measurement objects" that may indicate radio resource information on which the terminal (5-05) is to perform measurement. For example, the information related to each measurement object may indicate frequency / time position and subcarrier spacing information of a reference signal (e.g., SSB or CSI-RS) on which the terminal (5-05) is to perform measurement. According to one embodiment, the information related to each measurement object (e.g., MeasObjectNR) may be indicated / identified with a specific ID (e.g., MeasObjectId).

[0107] According to one embodiment, the configuration information may include information related to "Reporting configurations" that indicate settings related to measurement reporting of the terminal (5-05). For example, the information related to reporting configurations may indicate whether the terminal (5-05) should report periodically or whether to trigger when a specific event occurs, and may indicate a reference signal used by the terminal for measurement / reporting. Information related to reporting configurations (e.g., ReportConfigNR) may be indicated / identified by a specific ID (e.g., ReportConfigId).

[0108] According to one embodiment, cell measurement configuration information (e.g., MeasConfig) may include information related to multiple measurement objects (e.g., MeasObjectNR) and information related to multiple report configurations (e.g., ReportConfigNR), and information related to a particular measurement object may be linked to information related to a particular report configuration.

[0109] According to one embodiment, the terminal (5-05) may set information requiring measurement through the terminal (5-05) through information related to the measurement object and information related to the report setting, or may set conditions or methods related to the report.

[0110] According to one embodiment, the cell measurement configuration information may include a linkage ID (e.g., MeasId) to indicate / identify the linkage between the ID of the linked measurement object and the ID of the report configuration. In this case, in steps 5-25 and 5-30, the terminal (5-05) may perform measurements and reports according to the corresponding measurement object and the linked report configuration.

[0111] In step 5-25, the terminal (5-05) can perform cell measurement and perform AI / ML prediction and / or reporting according to the settings related to the received cell measurement and / or AI / ML prediction and / or reporting.

[0112] In step 5-30, the terminal (5-05) may transmit information or reports generated as a result of cell measurement and / or AI / ML prediction performance (AI / ML model operation results) to the base station (5-10). According to one embodiment, the information or reports may be transmitted via a message (e.g., a MeasurementReport message) transmitted from the base station (5-10) to the terminal (5-05).

[0113] According to one embodiment, the terminal (5-05) can transmit a cell measurement value (e.g., RSRP, RSRQ, SINR, etc.) predicted in the future to the base station (5-10). The predicted cell measurement value may correspond to a cell-level measurement value or a beam-level measurement value, etc. The predicted cell measurement value may be a value generated by combining prediction values ​​using measurement result values ​​received from a physical layer and / or AI / ML prediction values ​​(e.g., using Layer 3 filtering). Alternatively, the predicted cell measurement value may be a value generated using only AI / ML prediction values ​​(e.g., using Layer 3 filtering).

[0114] According to one embodiment, the terminal (5-05) may indirectly indicate to the base station (5-10) which measurement object (e.g., as indicated by MeasObjectId) and which report configuration (e.g., as indicated by ReportConfigId) the measurement report was generated through by including an associated ID (e.g., MeasId) in each measurement report (e.g., MeasResults).

[0115] According to one embodiment, as a result of AI / ML model operation, the terminal (5-05) may report to the base station (5-10) events that can be predicted (e.g., Event A3, RLF, handover failure, etc.) in addition to predicted cell measurement values ​​(e.g., RSRP, RSRQ, SINR, etc.).

[0116] Additionally, according to one embodiment, the terminal (5-05) may indicate to the network the prediction accuracy and / or occurrence probability for the predicted cell measurement value or event, thereby indicating how accurate the prediction is and / or with what probability it will occur.

[0117] In one embodiment, if a terminal transmits predicted cell measurement values, etc. by running a UE-sided model to the base station, the base station can utilize this to prepare for a handover of the terminal in advance, or handover the terminal to another cell or base station in advance before an RLF occurs. In addition, the handover ping-pong phenomenon can also be prevented. However, from the terminal's perspective, running a UE-sided AI / ML model may be a task that requires a large amount of computing resources and energy consumption. In addition, if the predicted information generated by running the AI / ML model is excessive, this may result in a large signaling overhead between the terminal and the network, which may result in a waste of radio resources. Therefore, the network needs to instruct / configure AI / ML prediction-related settings to the terminal appropriately to the situation (e.g., under the network's judgment).

[0118] According to one embodiment of the present disclosure, in step 5-20, the base station can optionally set AI / ML prediction settings to the terminal. The base station can link the cell measurement settings and the AI / ML prediction settings. In step 5-20, the terminal can receive a plurality of measurement objects (e.g., each indicated by MeasObjectId) as cell measurement settings, a plurality of report settings (e.g., each indicated by ReportConfigId), and a plurality of linkage IDs (e.g., MeasId) linking them.

[0119] According to one embodiment of the present disclosure, in step 5-20, the base station may indicate that the reporting configuration is to be used for AI / ML prediction by including an AI / ML prediction indicator or related configuration information in the reporting configuration (e.g., ReportConfig). Upon receiving this, the terminal may perform AI / ML prediction during cell measurement / reporting associated with the above-indicated reporting configuration in steps 5-25 and 5-30, and report the AI / ML prediction result value (e.g., predicted RSRP, RSRQ, SINR value, etc.) to the base station.

[0120] According to one embodiment of the present disclosure, in step 5-20, the base station may indicate a report configuration ID (e.g., ReportConfigId) to use AI / ML prediction. Upon receiving this, the terminal may perform AI / ML prediction during cell measurement / reporting associated with the report configuration indicated above (e.g., the report configuration indicated by ReportConfigId) in steps 5-25 and 5-30, and report the AI / ML prediction result value (e.g., predicted RSRP, RSRQ, SINR values, etc.) to the base station.

[0121] According to one embodiment of the present disclosure, in step 5-20, the base station may indicate a link ID (e.g., MeasId) to use for AI / ML prediction. Upon receiving this, the terminal may perform AI / ML prediction during cell measurement / reporting indicated by the link ID (e.g., MeasId) indicated above in steps 5-25 and 5-30, and report the AI / ML prediction results (e.g., predicted RSRP, RSRQ, SINR values) to the base station.

[0122] According to one embodiment of the present disclosure, in step 5-20, the base station (5-10) may indicate that the measurement object (e.g., MeasObjectNR) is to be used for AI / ML prediction by including an AI / ML prediction indicator or related configuration information in the measurement object. The terminal (5-05) receiving this may perform AI / ML prediction when measuring / reporting cells associated with the measurement object indicated above in steps 5-25 and 5-30, and report the AI / ML prediction result value (e.g., predicted RSRP, RSRQ, SINR value, etc.) to the base station (5-10).

[0123] According to one embodiment of the present disclosure, in step 5-20, the base station (5-10) may indicate a measurement object ID (e.g., MeasObjectId) to use AI / ML prediction. Upon receiving this, the terminal may perform AI / ML prediction when measuring / reporting a cell associated with the measurement object indicated above (e.g., indicated by MeasObjectId) in steps 5-25 and 5-30, and report the AI / ML prediction result value (e.g., predicted RSRP, RSRQ, SINR value, etc.) to the base station.

[0124] According to one embodiment of the present disclosure, in step 5-20, a measurement object (e.g., MeasObjectAIML) to utilize AI / ML prediction may be defined separately from a cell measurement object (e.g., MeasObject), and the base station (5-10) may use the separate measurement object to indicate the measurement object to utilize AI / ML prediction.

[0125] According to one embodiment, the terminal (5-05) may perform AI / ML prediction in steps 5-25 and 5-30 when measuring / reporting cells associated with the measurement object indicated in step 5-20 and report the AI / ML prediction result values ​​(e.g., predicted RSRP, RSRQ, SINR values, etc.) to the base station (5-10).

[0126] According to one embodiment, in step 5-20, a reporting configuration (e.g., ReportConfigAIML) to utilize AI / ML prediction may be defined separately from a cell reporting configuration (e.g., ReportConfig), and the base station (5-10) may use the separate reporting configuration to instruct the terminal (5-05) on the reporting configuration to utilize AI / ML prediction.

[0127] According to one embodiment, the terminal (5-05) may perform AI / ML prediction in cell measurement / reporting linked to the reporting settings indicated in step 5-20 at steps 5-25 and 5-30 and report the AI / ML prediction results (e.g., predicted RSRP, RSRQ, SINR values, etc.) to the base station (5-10).

[0128] According to one embodiment of the present disclosure, in step 5-30, AI / ML prediction result values ​​(e.g., predicted RSRP, RSRQ, SINR values, etc.) that the terminal (5-05) can transmit may be transmitted together with actual cell measurement values ​​(e.g., actual measured RSRP, RSRQ, SINR values, etc.). In order to distinguish between AI / ML prediction result values ​​that the terminal (5-05) can include in the measurement report and actual cell measurement values, an IE or parameter dedicated to AI / ML prediction result values ​​may be defined / used in the measurement report. For example, a new IE (e.g., MeasResultsAIML, etc.) may be defined, and actual cell measurement values ​​in the measurement report message (e.g., MeasurementReport) that the terminal (5-05) transmits to the base station (5-10) may be included in MeasResults and AI / ML prediction result values ​​may be included in MeasResultsAIML and transmitted.

[0129] In one embodiment, separate parameters (e.g., measResultServingMOListAIML, measResultNeighCellsAIML, etc.) containing AI / ML prediction results in MeasResults may be newly defined and reported to the base station (5-10).

[0130] According to one embodiment of the present disclosure, in step 5-30, the terminal (5-05) may report to the base station (5-10) a measurement value (e.g., RSRP, RSRQ, SINR, etc.) for one measurement point in time per cell (e.g., which may be indicated by PhyscellId) or per beam for conventional cell measurement values ​​(e.g., MeasResultNR), but may report for multiple future points in time for AI / ML prediction values, including measurement values. For example, the terminal may transmit to the base station (5-10) a predicted RSRP and RSRQ and SINR value for 2 seconds in the future, a predicted RSRP and RSRQ and SINR value for 4 seconds in the future, and a predicted RSRP and RSRQ and SINR value for 6 seconds in the future for a specific cell in step 5-30.

[0131] According to one embodiment, the base station (5-10) can set a time interval (e.g., 2 seconds) between prediction values ​​for the terminal (5-05) in step 5-20. For example, if the channel condition of the terminal (5-05) changes rapidly (e.g., the terminal moves), the base station (5-10) can set a short prediction interval in the future time to receive a result that predicts information about the rapidly changing channel condition quickly and accurately.

[0132] In one embodiment, a base station (5-10) may want to obtain intermittent prediction results by setting a long prediction interval for a terminal (5-05) with a relatively static channel.

[0133] According to one embodiment, the base station (5-10) may, in step 5-20, set a maximum number of prediction values ​​that the terminal (5-05) will report at one time (e.g., in MeasResultNR). For example, if the base station (5-10) determines that signaling overhead is large, it may instruct the terminal (5-05) to report a relatively small number of prediction values. Furthermore, if the base station (5-10) wants to obtain a large number of prediction values ​​from the terminal (5-05), it may instruct the terminal (5-05) to report a relatively large number of prediction values.

[0134] In one embodiment of the present disclosure, in step 5-30, the terminal (5-05) may transmit to the base station (5-10) not the absolute value (e.g., -70 dBm) of the predicted value, but rather the difference (e.g., -10 dB) between the current measured value and the predicted value immediately before (e.g., -60 dBm). For example, the terminal (5-05) may transmit to the base station (5-10) the difference (e.g., 3 dB) between the current measured value and the predicted RSRP value 2 seconds later (e.g., -40 dBm) for a specific cell, the difference (e.g., 5 dB) between the current measured value and the predicted RSRP value 4 seconds later (e.g., -38 dBm), or the difference (e.g., -2 dB) between the current measured value and the predicted RSRP value 6 seconds later (e.g., -45 dBm). For example, the terminal (5-05) can transmit to the base station (5-10) the difference (e.g., 3 dB) between the current measured RSRP (e.g., -43 dBm) for a specific cell and the predicted RSRP value 2 seconds later (e.g., -40 dBm) compared to the previous predicted value, the difference (e.g., 2 dB) between the predicted RSRP value 4 seconds later (e.g., -38 dBm) compared to the previous measured value (e.g., -40 dBm) and the predicted RSRP value 6 seconds later (e.g., -45 dBm) compared to the previous measured value (e.g., -38 dBm) to the base station (5-10).

[0135] According to one embodiment, the terminal (5-05) may transmit the predicted value to the base station (5-10) using a smaller number of bits than that indicating the absolute value of the RSRP, RSRQ, SINR, etc. illustrated.

[0136] According to one embodiment of the present disclosure, in step 5-20, the base station (5-10) may set restrictions on the cell or neighboring cell on which the terminal performs and / or reports AI / ML prediction-related operations.

[0137] According to one embodiment, the base station (5-10) may set the above restrictions to reduce the computer resource and energy consumption of the terminal (5-05) due to excessive AI / ML model operation of the terminal (5-05) and, relatedly, to reduce excessive use of wireless resources due to excessive AI / ML prediction-related reporting.

[0138] According to one embodiment, the base station (5-10) may indicate multiple cells or neighboring cells within the measurement object in step 5-20. The terminal (5-05) may perform AI / ML prediction and / or reporting for one or more cells that may be indicated in steps 5-25 and 5-30, and may not perform AI / ML prediction and / or reporting for cells that are not indicated.

[0139] According to one embodiment, the base station (5-10) may limit the maximum number of cells or neighboring cells for which the terminal (5-05) performs and / or reports AI / ML predictions in step 5-20. The terminal (5-05) may perform AI / ML predictions and / or reports for cells within the maximum number that may be indicated in steps 5-25 and 5-30.

[0140] According to one embodiment of the present disclosure, if a number of cells or neighboring cells is detected greater than the indicated maximum number, the terminal (5-05) may select cells within the indicated maximum number based on criteria such as a high order of actual cell measurement values ​​(RSRP, RSRQ, SINR, etc.). The terminal (5-05) may then perform AI / ML prediction and / or reporting on the selected terminals.

[0141] According to one embodiment of the present disclosure, if a number of cells or neighboring cells is detected greater than the maximum number indicated above, the terminal (5-05) may first perform AI / ML prediction on all cells detected / measured. Thereafter, the terminal (5-05) may select cells within the maximum number indicated above based on criteria such as a cell measurement value that can be predicted (e.g., RSRP, RSRQ, SINR) or a high order of prediction accuracy / probability. The terminal (5-05) may then perform AI / ML reporting on the selected terminals.

[0142] According to one embodiment of the present disclosure, the terminal (5-05) can predict not only the predicted cell measurement value but also a specific event (e.g., handover failure, RLF, Event A2) and report it to the base station (5-10) as an AI / ML model operation result (output) in step 5-30. For example, the terminal (5-05) can include in the report, as an AI / ML model operation result (output), the time point or section at which the occurrence of the event is predicted (e.g., the event is satisfied at least once within the section or the event is satisfied throughout the section). For example, the terminal (5-05) can include in the report, as an AI / ML model operation result (output), an actual cell measurement value and / or a predicted cell measurement value. For example, the terminal (5-05) can include in the report, as an AI / ML model operation result (output), the probability of the occurrence of the event or the prediction accuracy. According to one embodiment, the AI / ML model driven by the terminal (5-05) may generate different outputs depending on input information (e.g., terminal mobility, cell measurement values, location information), so that what the terminal previously reported may change. For example, the terminal (5-05) predicts the detection of a specific event (e.g., predicts the event of time t1 at time T1, T1 < t1) and reports this to the base station (5-10) (report 1), but the terminal (5-05) may later predict that the event will no longer occur (e.g., predicts that the event at time t1 will not occur at time T2, T1 < T2 < t1). At this time, the terminal may indicate to the base station (5-10) through a new report (report 2) that the previous report (report 1) is no longer valid or that at least one of the conditions exemplified below is satisfied.According to one embodiment, the terminal (5-05) may indicate / report that the previous prediction is invalid or that the condition is satisfied if at least one of the conditions exemplified below is satisfied.

[0143] - Condition 1. If the terminal predicts that the event predicted in the previous report will no longer occur (e.g., if the leaving condition of the event is satisfied).

[0144] - Condition 2. If the predicted probability for an event predicted by the terminal in the previous report (e.g., at the present or at a specific point in time t) is less than a certain threshold.

[0145] - Condition 3. If the predicted probability for an event predicted by the terminal in the previous report (e.g., at the present or at a specific point in time t) is less than the predicted probability in the previous report.

[0146] - Condition 4. If the predicted probability for an event predicted by the terminal in the previous report (e.g., at the present or at a specific point in time) is less than the predicted probability in the previous report minus a specific hysteresis value.

[0147] - Condition 5. If the terminal's prediction execution time (e.g., T2) (for example, at the present or at a specific point in time) is earlier than the time (e.g., t1) or interval at which the event was indicated to occur in the last report.

[0148] - Condition 5. If the prediction time window at which the terminal makes a prediction includes a time or interval within the past prediction time window where the event was indicated to occur in the last report (for example, the base station may set the terminal to predict an event for an interval between 3 seconds and 10 seconds in step 5-20, and the terminal may predict an event occurring within the corresponding 7-second prediction time window. The window may move together on the time axis as time passes).

[0149] According to one embodiment, the threshold, hysteresis, or t value related to the above-described condition may be a fixed value defined in the standard, or may be a value set / indicated by the base station (5-10) in step 5-20.

[0150] In one embodiment, in step 5-30, the terminal (5-05) may indicate that the previous measurement report for the corresponding linked ID is invalid or satisfies one of the above conditions by including a linked ID (e.g., MeasId) and / or a specific new indicator. Alternatively, the terminal (5-05) may indicate that the previous measurement report is invalid or satisfies one of the above conditions by reporting a linked ID (e.g., MeasId) but not including information about the time or time interval when the corresponding event occurred. In this case, if the terminal (5-05) reports a linked ID (e.g., MeasId) and includes information about the time or time interval when the event is expected to occur, this may mean that the terminal (5-05) changes / updates the time or time interval when the corresponding event occurs.

[0151] FIG. 6 is a diagram illustrating the internal structure of a terminal according to an embodiment of the present disclosure.

[0152] Referring to FIG. 6, the terminal may include an RF (Radio Frequency) processing unit (6-10), a baseband processing unit (6-20), a storage unit (6-30), and a control unit (6-40).

[0153] The RF processing unit (6-10) may perform functions for transmitting and receiving signals through a wireless channel, such as signal band conversion and amplification. For example, the RF processing unit (6-10) may up-convert a baseband signal provided from the baseband processing unit (6-20) into an RF band signal and transmit the same through an antenna, and may down-convert an RF band signal received through the antenna into a baseband signal. For example, the RF processing unit (6-10) may include a transmission filter, a reception filter, an amplifier, a mixer, an oscillator, a digital to analog convertor (DAC), an analog to digital convertor (ADC), etc. In Fig. 6, only one antenna is illustrated, but the terminal may be equipped with multiple antennas. In addition, the RF processing unit (6-10) may include multiple RF chains. In addition, the RF processing unit (6-10) may perform beamforming. For the above beamforming, the RF processing unit (6-10) can adjust the phase and size of each signal 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.

[0154] The baseband processing unit (6-20) above can perform a conversion function between a baseband signal and a bit stream according to the physical layer specifications of the system. For example, when transmitting data, the baseband processing unit (6-20) can generate complex symbols by encoding and modulating a transmission bit stream. In addition, when receiving data, the baseband processing unit (6-20) can restore a reception bit stream by demodulating and decoding a baseband signal provided from the RF processing unit (6-10). For example, in the case of following the OFDM (orthogonal frequency division multiplexing) method, when transmitting data, the baseband processing unit (6-20) can generate complex symbols by encoding and modulating a transmission bit stream, and after mapping the complex symbols to subcarriers, can configure OFDM symbols through an inverse fast Fourier transform (IFFT) operation and a cyclic prefix (CP) insertion. In addition, when receiving data, the baseband processing unit (6-20) can divide the baseband signal provided from the RF processing unit (6-10) into OFDM symbol units, restore signals mapped to subcarriers through FFT (fast Fourier transform) operation, and then restore the received bit string through demodulation and decoding.

[0155] The baseband processing unit (6-20) and the RF processing unit (6-10) can transmit and receive signals as described above. Accordingly, the baseband processing unit (6-20) and the RF processing unit (6-10) may be referred to as a transmitter, a receiver, a transceiver, or a communication unit. Furthermore, at least one of the baseband processing unit (6-20) and the RF processing unit (6-10) may include a plurality of communication modules to support a plurality of different wireless access technologies. In addition, at least one of the baseband processing unit (6-20) and the RF processing unit (6-10) may include different communication modules to process signals of different frequency bands. For example, the different wireless access technologies may include a wireless LAN (e.g., IEEE 802.11), a cellular network (e.g., LTE), etc. Additionally, the different frequency bands may include a super high frequency (SHF) (e.g., 2.NRHz, NRhz) band and a millimeter wave (mm wave) (e.g., 60GHz) band.

[0156] The storage unit (6-30) can store data such as basic programs, application programs, and setting information for the operation of the terminal. In particular, the storage unit (6-30) can store information related to a second access node that performs wireless communication using wireless access technology. In addition, the storage unit (6-30) can provide the stored data at the request of the control unit (6-40).

[0157] The above control unit (6-40) can control the overall operations of the terminal. For example, the control unit (6-40) can transmit and receive signals through the baseband processing unit (6-20) and the RF processing unit (6-10). In addition, the control unit (6-40) can record and read data in the storage unit (6-30). For this purpose, the control unit (6-40) can include at least one processor. For example, the control unit (6-40) can include a communication processor (CP) that performs control for communication and an application processor (AP) that controls upper layers such as application programs, and can include a multi-connection processing unit (6-42) as illustrated in the drawing.

[0158] FIG. 7 is a diagram illustrating the structure of a base station according to one embodiment of the present disclosure.

[0159] Referring to FIG. 7, according to an example of the present disclosure, a base station may be configured to include an RF processing unit (7-10), a baseband processing unit (7-20), a backhaul communication unit (7-30), a storage unit (7-40), and a control unit (7-50).

[0160] The RF processing unit (7-10) above 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 (7-10) can up-convert a baseband signal provided from the baseband processing unit (7-20) into an RF band signal and then transmit it through an antenna, and can down-convert an RF band signal received through the antenna into a baseband signal. For example, the RF processing unit (7-10) can include a transmission filter, a reception filter, an amplifier, a mixer, an oscillator, a DAC, an ADC, etc. In the drawing, only one antenna is shown, but the base station can be equipped with multiple antennas. In addition, the RF processing unit (7-10) can include multiple RF chains. In addition, the RF processing unit (7-10) can perform beamforming. For the above beamforming, the RF processing unit (7-10) can adjust the phase and size of each signal transmitted and received through multiple antennas or antenna elements. The RF processing unit can perform a downlink MIMO operation by transmitting one or more layers.

[0161] The baseband processing unit (7-20) above can perform a conversion function between a baseband signal and a bit stream according to the physical layer specifications of the wireless access technology. For example, when transmitting data, the baseband processing unit (7-20) can generate complex symbols by encoding and modulating a transmission bit stream. In addition, when receiving data, the baseband processing unit (7-20) can restore a reception bit stream by demodulating and decoding the baseband signal provided from the RF processing unit (7-10). For example, in the case of OFDM, when transmitting data, the baseband processing unit (7-20) can generate complex symbols by encoding and modulating a transmission bit stream, and after mapping the complex symbols to subcarriers, configure OFDM symbols through IFFT operation and CP insertion. In addition, when receiving data, the baseband processing unit (7-20) can divide the baseband signal provided from the RF processing unit (7-10) into OFDM symbol units, restore the signals mapped to subcarriers through FFT operation, and then restore the received bit string through demodulation and decoding. The baseband processing unit (7-20) and the RF processing unit (7-10) can transmit and receive signals as described above. Accordingly, the baseband processing unit (7-20) and the RF processing unit (7-10) may be referred to as a transmitter, a receiver, a transceiver, a communication unit, or a wireless communication unit.

[0162] The above backhaul communication unit (7-30) can provide an interface for performing communication with other nodes within the network. That is, the backhaul communication unit (7-30) can convert a bit string transmitted from the main base station to another node, such as an auxiliary base station or core network, into a physical signal, and can convert a physical signal received from the other node into a bit string.

[0163] The storage unit (7-40) can store data such as basic programs, application programs, and setting information for the operation of the main base station. In particular, the storage unit (7-40) can store information on bearers assigned to connected terminals, measurement results reported from connected terminals, and the like. In addition, the storage unit (7-40) can store information that serves as a judgment criterion for whether to provide or terminate multiple connections to a terminal. In addition, the storage unit (7-40) can provide stored data at the request of the control unit (7-50).

[0164] The control unit (7-50) can control the overall operations of the base station. For example, the control unit (7-50) can transmit and receive signals through the baseband processing unit (7-20) and the RF processing unit (7-10) or through the backhaul communication unit (7-30). In addition, the control unit (7-50) can record and read data in the storage unit (7-40). For this purpose, the control unit (7-50) can include at least one processor and, as illustrated in the drawing, a multi-connection processing unit (7-52).

[0165] Meanwhile, the embodiments of the present disclosure disclosed in this specification and drawings are merely specific examples to easily explain the technical contents of the present disclosure and to help understand the present disclosure, and are not intended to limit the scope of the present disclosure. In other words, it will be apparent to those skilled in the art that other modifications based on the technical idea of ​​the present disclosure are possible. In addition, the above-described embodiments can be combined and operated as needed. For example, parts of one embodiment of the present disclosure and another embodiment can be combined to operate a base station and a terminal. Furthermore, the embodiments of the present disclosure can be applied to other communication systems, and other modifications based on the technical idea of ​​the embodiments can also be implemented. For example, the embodiments can be applied to LTE systems, 5G, NR systems, or 6G systems. Therefore, the scope of the present disclosure should not be limited to the described embodiments, but should be determined not only by the scope of the following claims but also by equivalents of the claims.

Claims

1. In a method performed by a terminal of a wireless communication system, A step of receiving setting information for measurement and prediction related to artificial intelligence (AI) / machine learning (ML) from a base station; A step of performing the measurement and the prediction related to the AI / ML based on the above setting information; and comprising a step of transmitting information about the result of at least one of the measurement or the prediction to the base station; A method characterized in that the above setting information includes at least one of information necessary to perform at least one of the measurement, the prediction, or the transmission related to the AI / ML, information on the maximum number of prediction values, information on the interval of the prediction values, or information on a cell related to the AI / ML.

2. In paragraph 1, A method comprising the step of transmitting, to the base station, capability information regarding the measurement and the prediction related to the AI / ML.

3. In paragraph 1, A method characterized in that the information about the result of at least one of the above measurement or the above prediction includes at least one of cell-level information, beam-level information, prediction information for an event, or information indicating a change related to the prediction.

4. In paragraph 1, The information about the cell related to the AI / ML includes at least one of information about the number of cells that will perform the measurement and the prediction, information about the number of cells on which reporting will be performed, or information about one or more specific cells, and A method characterized in that the information necessary to perform at least one of the measurement, the prediction, or the transmission related to the AI / ML includes information about at least one measurement object.

5. In a method performed by a base station of a wireless communication system, A step of transmitting setting information for measurement and prediction related to artificial intelligence (AI) / machine learning (ML) to the terminal; and A step of receiving information about the result of at least one of the measurement or the prediction from the terminal, A method characterized in that the above setting information includes at least one of information necessary to perform at least one of the measurement related to the AI / ML, the prediction, or the transmission of information on the result of the terminal, information on the maximum number of prediction values, information on the interval of the prediction values, or information on a cell related to the AI / ML.

6. In paragraph 5, A method comprising a step of receiving capability information for the measurement and the prediction related to the AI / ML from the terminal.

7. In paragraph 5, Information about the result of at least one of the above measurement or the above prediction includes at least one of cell-level information, beam-level information, prediction information for an event, or information indicating a change related to the prediction, The information about the cell related to the AI / ML includes at least one of information about the number of cells that will perform the measurement and the prediction, information about the number of cells on which reporting will be performed, or information about one or more specific cells, and A method characterized in that the information required to perform at least one of the measurement related to the AI / ML, the prediction, or the transmission of information on the result of the terminal includes information on at least one measurement object.

8. In the terminal of a wireless communication system, Transmitter and receiver; and Connected to the above transceiver, and receives setting information for measurement and prediction related to artificial intelligence (AI) / machine learning (ML) from the base station, Based on the above setting information, the above measurement and the above prediction related to the AI / ML are performed, and A control unit for transmitting information about the result of at least one of the measurement or the prediction to the base station, A terminal characterized in that the above setting information includes at least one of information necessary to perform at least one of the measurement, the prediction, or the transmission related to the AI / ML, information on the maximum number of prediction values, information on the interval of the prediction values, or information on a cell related to the AI / ML.

9. In paragraph 8, The above control unit is a terminal that transmits capability information for the measurement and prediction related to the AI / ML to the base station.

10. In paragraph 8, A terminal characterized in that information about at least one result of the measurement or the prediction includes at least one of cell unit information, beam unit information, prediction information for an event, or information indicating a change related to the prediction.

11. In paragraph 8, The information about the cell related to the AI / ML includes at least one of information about the number of cells that will perform the measurement and the prediction, information about the number of cells on which reporting will be performed, or information about one or more specific cells, and A terminal characterized in that the information necessary to perform at least one of the measurement, the prediction, or the transmission related to the AI / ML includes information about at least one measurement object.

12. In a base station of a wireless communication system, Transmitter and receiver; and Connected to the above-mentioned transceiver, transmits setting information for measurement and prediction related to artificial intelligence (AI) / machine learning (ML) to the terminal, and A control unit for receiving information on at least one result of the measurement or the prediction from the terminal, A base station characterized in that the above setting information includes at least one of information necessary to perform at least one of the measurement related to the AI / ML, the prediction, or information transmission on the result of the terminal, information on the maximum number of prediction values, information on the interval of the prediction values, or information on a cell related to the AI / ML.

13. In paragraph 12, The above control unit is a base station that receives capability information for the measurement and prediction related to the AI / ML from the terminal.

14. In paragraph 12, A base station, characterized in that information about at least one result of the measurement or the prediction includes at least one of cell unit information, beam unit information, prediction information for an event, or information indicating a change related to the prediction.

15. In paragraph 12, The information about the cell related to the AI / ML includes at least one of information about the number of cells that will perform the measurement and the prediction, information about the number of cells on which reporting will be performed, or information about one or more specific cells, and A base station, characterized in that the information required to perform at least one of the measurement related to the AI / ML, the prediction, or the transmission of information on the result of the terminal includes information on at least one measurement object.

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