Method and device for supporting ai / ml-based mobility in mobile communication system

Abstract

The present disclosure relates to a 5G or 6G communication system for supporting higher data transmission rates. A method performed by a UE in a wireless communication system may comprise the steps of: transmitting, to a base station, capability information for a report including first measurement information expected on the basis of an artificial intelligence (AI) model; receiving, from the base station, configuration information for the report including the first measurement information on the basis of the capability information; and transmitting, to the base station, the report including the first measurement information on the basis of the configuration information.

Description

Method and device for supporting AI / ML-based mobility in a mobile communication system

[0001] The present disclosure relates to a wireless communication system, and more particularly, to a method and device for supporting AI / ML-based mobility in a mobile communication system.

[0002] 5G mobile communication technology defines a wide frequency band to enable fast transmission speeds and new services, and can be implemented not only in 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 band (for example, the 3 terahertz (3THz) 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] According to one embodiment of the present disclosure, a device and method for effectively providing a service in a mobile communication system are provided.

[0009] According to one embodiment of the present disclosure, a method performed by a UE in a wireless communication system may include the steps of transmitting capability information for a report including first measurement information expected based on an artificial intelligence (AI) model to a base station, receiving, from the base station, configuration information for the report including the first measurement information based on the capability information, and transmitting, to the base station, the report including the first measurement information based on the configuration information.

[0010] According to one embodiment of the present disclosure, a device and method for effectively providing a service in a wireless communication system can be provided.

[0011] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, 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.

[0012] FIG. 1a is a diagram illustrating the structure of a mobile communication system according to one embodiment of the present disclosure.

[0013] FIG. 1b is a diagram for explaining a wireless connection state transition in a mobile communication system according to an embodiment of the present disclosure.

[0014] FIG. 1c is a diagram illustrating a method for generating AI / ML-based results and reporting them to a network in a mobile communication system according to an embodiment of the present disclosure.

[0015] FIG. 1d is a flowchart of a procedure for reporting AI / ML-based measurement results to a network in a mobile communication system according to an embodiment of the present disclosure.

[0016] FIG. 1e is a flowchart of a procedure for proposing an AI / ML-based optimal measurement event to a network in a mobile communication system according to an embodiment of the present disclosure.

[0017] FIG. 1f is a flowchart of a procedure for performing event-triggered measurement reporting by applying AI / ML-based measurement results in a mobile communication system according to an embodiment of the present disclosure.

[0018] FIG. 1g is a flowchart of a procedure for performing event-triggered measurement reporting by applying AI / ML-based optimal measurement events in a mobile communication system according to an embodiment of the present disclosure.

[0019] FIG. 1h is a flowchart of a procedure for reporting a handover failure or RLF (Radio Link Failure) derived based on AI / ML to a network in a mobile communication system according to an embodiment of the present disclosure.

[0020] FIG. 1i is a flowchart of a procedure for reporting AI / ML-based UE assistance information to a network in a mobile communication system according to an embodiment of the present disclosure.

[0021] FIG. 1J is a flowchart of a terminal operation for reporting AI / ML-based results to a network in a mobile communication system according to an embodiment of the present disclosure.

[0022] FIG. 1k is a flowchart of network operations for collecting AI / ML-based results in a mobile communication system according to an embodiment of the present disclosure.

[0023] FIG. 1l is a block diagram illustrating the internal structure of a terminal according to one embodiment of the present disclosure.

[0024] FIG. 1m is a block diagram showing the configuration of a base station according to one embodiment of the present disclosure.

[0025] In the following description of the present disclosure, detailed descriptions of related known functions or configurations may be omitted if they are deemed to unnecessarily obscure the gist of the present invention. Embodiments of the present invention are described below with reference to the attached drawings.

[0026] Figure 1a is a diagram illustrating the structure of a next-generation mobile communication system.

[0027] Referring to FIG. 1a, a wireless access network of a mobile communication system (or, next generation mobile communication system) (New Radio, NR) according to an embodiment may include a next generation base station (New Radio Node B, hereinafter referred to as gNB (next generation node B)) (1a-10) and / or an access and mobility management function (AMF) entity (1a-05, New Radio Core Network). A user terminal (New Radio User Equipment, hereinafter referred to as NR UE or terminal) (1a-15) may access an external network via the gNB (1a-10) and the AMF (1a-05).

[0028] In Fig. 1a, the gNB can correspond to the eNB (E-UTRAN (Evolved Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access) Node B) of the LTE system. For example, the gNB is connected to the NR UE via a wireless channel and can provide a service superior to the existing Node B (1a-20). In the next-generation mobile communication system, since all user traffic is serviced through a shared channel, a device that collects status information such as the buffer status of UEs, available transmission power status, and / or channel status and performs scheduling is required, and the function of collecting information and performing scheduling can be performed by the gNB (1a-10). One gNB typically controls multiple cells. In order to implement ultra-high-speed data transmission compared to LTE, it can have a bandwidth higher than the existing maximum, and orthogonal frequency division multiplexing (OFDM) can be used as a wireless access technology, and additional beamforming technology can be incorporated. Additionally, an adaptive modulation and coding (AMC) method that determines the modulation scheme and channel coding rate according to the channel status of the terminal may be applied.

[0029] According to one embodiment, the AMF (1a-05) can perform functions such as mobility support, bearer setup, and QoS (quality of service) setup. The AMF (1a-05) is a device that is responsible for various control functions as well as mobility management functions for terminals and is connected to multiple base stations. In addition, the next-generation mobile communication system can also be linked with the LTE system, and the AMF (1a-05) can be connected to the mobile management entity (MME) (1a-25) through a network interface. The MME can be connected to the existing base station, the eNB (1a-30). A terminal that supports LTE-NR Dual Connectivity can transmit and receive data while maintaining a connection to not only the gNB but also the eNB (1a-35).

[0030] Figure 1b is a diagram for explaining a wireless connection state transition in a mobile communication system.

[0031] Referring to Figure 1b, there are three radio connection states (RRC states) in a mobile communication system. For example, the connected mode (RRC_CONNECTED, 1b-05) is a radio connection state in which the terminal can transmit and receive data. For example, the idle mode (RRC_IDLE, 1b-30) is a radio connection state in which the terminal monitors whether a paging message is being sent to it. The two modes described above are radio connection states that are also applicable to the LTE system, and the detailed technology is substantially identical to that of the LTE system. In the next-generation mobile communication system, a new inactive (RRC_INACTIVE) radio connection state (1b-15) has been defined. In the radio connection state, the UE context is maintained between the base station and the terminal, and RAN-based paging can be supported. The characteristics of this new radio connection state are listed below.

[0032] - Cell re-selection mobility;

[0033] - CN (core network) - NR RAN (radio access network) connection (both C (control) / U (user)-planes) has been established for UE;

[0034] - The UE AS(access stratum) context is stored in at least one gNB and the UE;

[0035] - Paging is initiated by NR RAN;

[0036] - RAN-based notification area is managed by NR RAN;

[0037] - NR RAN knows the RAN-based notification area which the UE belongs to;

[0038] According to one embodiment, a terminal can transition from a new INACTIVE radio connection state to a connected mode or a standby mode using a specific procedure. For example, the terminal can transition from INACTIVE mode to a connected mode through a Resume procedure, and can transition from a connected mode to INACTIVE mode through a Release procedure including suspend configuration information (1b-10). In the above-described procedure, one or more RRC messages are transmitted and received between the terminal and the base station, and can consist of one or more steps. In addition, the terminal can transition from INACTIVE mode to standby mode through a Release procedure after Resume (1b-20). The transition between the connected mode and the standby mode follows LTE technology. That is, the transition between the modes can be accomplished through an establishment or release procedure (1b-25).

[0039] FIG. 1c is a diagram illustrating a method for generating AI / ML-based results and reporting them to a network in a mobile communication system according to an embodiment of the present disclosure.

[0040] Referring to Figure 1c, AI (Artificial Intelligence) / ML (Machine Learning) techniques can be applied to various fields across industries and are helping to efficiently achieve improved performance. Research is also underway in the mobile communications field to utilize AI / ML techniques. For example, AI / ML techniques can reduce the frequency with which terminals or networks (e.g., base stations and / or core networks) measure beams by predicting beam states. For example, based on AI / ML-based information, the network can predict or identify the optimal handover timing for a specific terminal.

[0041] According to one embodiment, the base station (1c-05) may transmit a reference signal, such as a synchronization signal block (SSB) or a channel state information-reference signal (CSI-RS), to the terminal (1c-10). The terminal (1c-10) may use the received reference signal to identify a wireless channel state.

[0042] For example, the terminal (1c-10) may internally include an RRC (radio resource control) (1c-15) and an entity (1c-20) that performs AI / ML functions. The RRC (1c-15) may process a reference signal (e.g., Layer-3 filtering) and transmit it to the AI / ML entity. The AI / ML entity (1c-20) may use the received information according to a predetermined AI / ML technique to derive or identify predetermined prediction information.

[0043] For example, in addition to RRC (1c-15), other internal components of the terminal (e.g., physical layer, MAC (medium access control) layer, etc.) may also provide the AI / ML entity (15-20) with certain information necessary to derive AI / ML results.

[0044] According to one embodiment, the derived AI / ML results may be transmitted to the RRC (1c-15), and the terminal (1c-10) may report the AI / ML results to the base station (1c-05) according to a predetermined rule. The base station (1c-05) that receives the AI / ML results may utilize the AI / ML results to determine a predetermined operation, such as a handover. For example, the base station (1c-05) may be a gNB and / or an eNB.

[0045] In this disclosure, a method is proposed in which a terminal (1c-15) reports certain AI / ML-based information to a network in order to maximize or increase the mobility performance of a terminal (1c-10).

[0046] FIG. 1d is a flowchart of a procedure for reporting AI / ML-based measurement results to a network in a mobile communication system according to an embodiment of the present disclosure.

[0047] Referring to FIG. 1d, a terminal (1d-05) according to an embodiment may report its capability information (e.g., UE capabilities) to a base station (1d-10) in step 1d-15. For example, the capability information may include an indicator indicating that the terminal (1d-05) can derive its own predicted cell or beam-based signal strength / quality (e.g., Reference Signal Received Power (RSRP), and / or Reference Signal Received Quality (RSRQ), etc.) and report the cell or beam-based signal strength / quality to the network. For example, the capability information may include one indicator indicating that the terminal (1d-05) can identify cell or beam-based signal strength / quality based on AI / ML and report information about the identified strength / quality to the base station (1d-10). For example, the capability information may include a first indicator indicating that the terminal (1d-05) can derive cell or beam-based signal strength / quality based on AL / ML and / or a second indicator indicating that the terminal can report the derived signal strength / quality.

[0048] According to one embodiment, the base station (1d-10) may determine to request the terminal (1d-05) for the cell or beam-based signal strength / quality result predicted by the terminal (1d-05) in step 1d-20. For example, the base station (1d-10) may identify that the terminal (1d-05) is capable of transmitting information on the cell or beam-based signal strength / quality result based on AI / ML based on the received UE capability information, and may determine to request information on the signal strength / quality result.

[0049] According to one embodiment, the base station (1d-10) instructs the terminal (1d-05) to report the predicted cell or beam-based signal strength / quality result using a predetermined RRC message (e.g., RRCReconfiguration message) in step 1d-25. For example, when the base station (1d-10) provides cell measurement configuration information (e.g., measConfig IE (information element)) to the terminal (1d-05), the base station (1d-10) may include an indicator requesting reporting of the predetermined predicted result in the cell measurement configuration information. For example, the cell measurement configuration information may include an indicator requesting reporting of the signal strength / quality result measured based on AI / ML to the base station (1d-25). RRCreconfiguration(measConfig)

[0050] According to one embodiment, the base station (1d-10) may selectively select cells, frequencies and / or measurement event information for which the terminal (1d-05) can report predicted results and set them to the terminal (1d-05). For example, if the base station (1d-05) sets the terminal (1d-05) to report predicted results only for the first frequency, the terminal (1d-05) may report cell-based or beam-based measurement results predicted based on AI / ML only for certain cells belonging to the first frequency to the base station (1d-10). In one example, the predicted results may correspond to a near future (e.g., after a certain time (T1) or at the location of the terminal), wherein the preceding time (T1) or the location of the terminal may be determined by the terminal (1d-05) or set by the base station (1d-10). For example, the prediction result may include a cell measurement result at a time after a predetermined time (T1) has elapsed from the current time (e.g., the time when the AI / ML-based cell measurement request is received). For example, the prediction result may include a cell measurement result at a time after a predetermined time (T1) has elapsed from the location of the current terminal (1d-05) (e.g., the first location), and after the predetermined time (T1), the terminal (1d-05) may be located at a second location. For example, the prediction result may include a cell measurement result at the second location assuming that the current terminal (1d-5) has moved a specified distance from the location (e.g., the first location).

[0051] According to one embodiment, if the base station (1d-10) requests a prediction result at a predetermined predicted time (or, a time in advance, a future time after the predetermined time has elapsed) or a location (or a terminal movement path) of the terminal (1d-05) (or a future location after the predetermined time has elapsed, a predicted location), the terminal (1d-05) receiving the request may derive the prediction result based on AI / ML and then report the prediction result to the base station (1d-10) immediately or within a predetermined time after setting. For example, the predetermined time may be defined as a new timer (e.g., T32x). For example, the terminal (1d-05) may start the timer when it receives setting information (e.g., cell measurement information). If a prediction result cannot be derived before the timer expires, the terminal (1d-05) may report to the base station (1d-10) a MeasurementReport message including an indicator indicating that a prediction result cannot be derived before the timer expires and / or not including a prediction result.

[0052] According to one embodiment, the terminal (1d-05) can measure the preset intra- / inter-frequency based on the cell measurement setting information in step 1d-30.

[0053] In one embodiment, the measurement results may be used to derive predicted cell or beam-based results together with previously collected measurement results. For example, the terminal (1d-05) may derive AI / ML-based cell or beam measurement results based on the previously collected measurement results in step 1d-35 and the results measured in step 1d-30. For example, the previously collected measurement results and the results measured in step 1d-30 may be utilized as training data and / or input data for an AI / ML model (e.g., an artificial neural network).

[0054] According to one embodiment, the terminal (1d-05) may report a MeasurementReport message containing predicted results (e.g., RSRP, RSRQ, etc.) to the base station (1d-10) one-shot (e.g., reporting at a predetermined point in time after setup), periodically, or on an event-triggered basis in step 1d-40.

[0055] In one embodiment, a measurement report message (e.g., a MeasurementReport message) may additionally include an indicator indicating that the result is predicted based on AI / ML. In another example, a measurement report message (e.g., a MeasurementReport message) may define a new field or Information Element (IE) containing the predicted result.

[0056] In one embodiment, a measurement report message (e.g., MeasurementReport) may include both measurement results based on existing measurements and predicted results. For example, the two types of results (e.g., measurement results based on existing measurements and predicted results) should be distinguished from each other by designated methods (e.g., new indicators or separate fields / IEs). For example, the measurement report message may include a first indicator (or field, IE) indicating that the first measurement result is a result based on existing measurements and / or a second indicator (or field, IE) indicating that the second measurement result is a predicted result. As another example, the measurement report message may include an indicator (or field, IE) indicating that the second measurement result is a predicted result, and in the case of a first measurement result without an indicator (or field, IE), the base station (1d-05) may implicitly identify that the first measurement result is a measurement result based on existing measurements.

[0057] According to one embodiment, the terminal (1d-05) may report a series of cell or beam-based prediction results based on its own movement trajectory path (e.g., UE trajectory). The terminal (1d-05) may also report information on the accuracy of the prediction results to the base station (1d-10).

[0058] According to one embodiment, the accuracy of the prediction result may be indicated in various forms. For example, the accuracy of the prediction result may be expressed as an accuracy probability (%), an average and an error / variability rate, a confidence level, and / or a standard deviation. For example, accuracy information may be provided to the base station (1d-10) by cell, frequency, and / or prediction result. For example, the prediction result may correspond to the near future, i.e., a predetermined advance time (or prediction time) (T1) or a terminal location (or a future terminal location).

[0059] According to one embodiment, the terminal (1d-05) may include information about a previous time (or, information about a time at which derivation of a prediction result is requested, information about a future time after a specified amount of time has elapsed from the current point in time) and / or a terminal location (e.g., information about a location at which derivation of a prediction result is requested, information about a future location of the terminal that has moved a specified distance from the current location) in the measurement result message. For another example, when the base station (1d-10) provides time or location information to the terminal (1d-05), only the information about a previous time or the prediction result corresponding to the terminal location may be reported to the base station (1d-10).

[0060] If the prediction result cannot be derived until the new timer (e.g., T32x) expires, the terminal (1d-05) may report to the base station (1d-10) an indicator indicating that the prediction result cannot be derived until a specified time (e.g., until the timer expires) and / or a measurement report message (e.g., MeasurementReport message) that does not include the requested prediction result.

[0061] In one embodiment, since many factors affecting the prediction result change over time, the prediction result may be defined as valid only for a certain period of time. Accordingly, information regarding the validity time of the prediction result may also be included in the measurement report message, and the base station (1d-10) receiving the prediction result may consider or identify the received prediction result as valid only during the validity time. However, the present disclosure is not limited thereto. For example, the validity time may not be included in the measurement report message, and the base station (1d-10) may independently determine or identify the validity time based on the times corresponding to the measurement results included in the measurement report message.

[0062] According to one embodiment, the valid time information may be indicated as an absolute time, a timer-based time, and / or an elapsed time based on a specific time. For example, the prediction result may be valid until the indicated absolute time. For example, the specific time may mean a reference time provided by the terminal (1d-05), or a time at which a specific operation is performed (e.g., a time at which the base station (1d-10) receives the MeasurementReport, etc.). The elapsed time may be determined by the terminal (1d-05) or the base station (1d-10). That is, the valid time information may be indicated as an absolute time (e.g., date, hour, minute, second), or may be indicated as an elapsed time from a first specific time (e.g., a time at which the measurement result information is received).

[0063] In one embodiment, when the terminal (1d-05) determines the elapsed time, the elapsed time information may be reported to the base station (1d-10) together with the prediction result in a measurement information message (e.g., in a MeasurementReport message). In another example, when the base station (1d-10) determines the elapsed time, it may be provided to the terminal (1d-05) in a configuration step (e.g., transmitting the configuration information in step 1d-25). The terminal (1d-05) may include information about a cell suitable as a target cell for the handover (e.g., cell global ID(identity) (CGI), physical cell ID(identity) (PCI), and / or cell frequency, etc.) in the measurement report message (e.g., MeasurementReport message).

[0064] According to one embodiment, a base station (1d-10) that has received a measurement report message (MeasurementReport message) including a prediction result can determine whether to set up additional cell measurement or handover to an adjacent cell for a terminal (1d-05) based on the prediction result in step 1d-45.

[0065] FIG. 1e is a flowchart of a procedure for proposing an AI / ML-based optimal measurement event to a network in a mobile communication system according to an embodiment of the present disclosure.

[0066] Referring to FIG. 1e, a terminal (1e-05) according to one embodiment may report its capability information to a base station (1e-10) (1e-15). For example, the capability information (e.g., UE capabilities) may include an indicator indicating that the terminal (1e-05) can derive or identify its own predicted optimal measurement event and report the optimal measurement event to the network.

[0067] For example, a measurement event may be a condition that can trigger the transmission of a measurement report message (e.g., MeasurementReport message) by a terminal (1e-05) in event-triggered measurement reporting. For example, the following measurement events are defined in NR (new radio). If at least one of the conditions described below is satisfied, the terminal (1e-05) can transmit a measurement report message (e.g., MeasurementReport message) containing a cell measurement result to the base station (1e-10). At this time, the RSRP (dBm), which is a signal strength value of the serving and neighboring cells, or the RSRQ (dB), which is a signal quality value, are applied to the following formula.

[0068] - Event A1: Serving becomes better than absolute threshold

[0069] - Event A2: Serving becomes worse than absolute threshold

[0070] - Event A3: Neighbor becomes amount of offset better than PCell(primary cell) / PSCell(primary SCG cell)

[0071] - Event A4: Neighbor becomes better than absolute threshold

[0072] - Event A5: PCell / PSCell becomes worse than absolute threshold1 AND Neighbor / SCell becomes better than another absolute threshold2

[0073] - Event A6: Neighbor becomes amount of offset better than SCell(secondary cell)

[0074] According to one embodiment, the base station (1e-10) may decide to request the terminal (1e-05) for the optimal measurement event predicted by the terminal (1e-05) (1e-20).

[0075] According to one embodiment, the base station (1e-10) may instruct the terminal (1e-05) to report the predicted optimal measurement event (1e-25) using a predetermined RRC message (e.g., RRCReconfiguration message). For example, when the base station (1e-10) provides the terminal (1e-05) with the MeasConfig and / or OtherConfig IE, the base station (1e-10) may include an indicator requesting reporting of the predetermined predicted measurement event in the MeasConfig and / or OtherConfig IE. RRCReconfiguration(measConfig or otherconfig)

[0076] In one embodiment, the base station (1e-10) may optionally configure the terminal (1e-05) to select the cell, frequency, and / or measurement event type to be considered in the predicted measurement event. For example, the base station (1e-10) may allow only predicted information for Events A2 and A3. In this case, if the terminal determines that Event A2 is optimal, it may report Event A2 to the base station (1e-10) along with recommended setting values ​​of parameters related to Event A2 (e.g., threshold, hysteresis, and / or time-to-trigger, etc.). In one example, the terminal (1d-05) may suggest to the base station (1d-10) the type of quantity to be applied to the predicted measurement event (e.g., RSRP or RSRQ, RSSI (Received Signal Strength Indicator), etc.).

[0077] As another example, the predicted measurement event may be optimal at a predetermined advance time (or, predicted time) (T2) and / or terminal location, where the advance time (T2) or terminal location may be determined by the terminal (1e-05) or set by the base station (1e-10). If the base station (1e-10) requests the predicted measurement event at a predetermined advance time (T2) or terminal location (or terminal movement path), the terminal (1e-05) that receives the request may report the predicted measurement event to the base station (1e-10) immediately or within a predetermined time after deriving the predicted measurement event.

[0078] For example, a predetermined time can be defined as a new timer (e.g., T32x). The timer can be started by the terminal (1e-05) when the terminal (1e-05) receives the configuration information. If the predicted measurement event cannot be derived until the timer expires, the terminal (1e-05) can report to the base station (1e-10) an indicator indicating that the predicted measurement event cannot be derived until the timer expires and / or a predetermined message that does not include the predicted measurement event.

[0079] According to one embodiment, the terminal (1e-05) can measure the preset intra- / inter-frequency based on cell measurement configuration information (1e-30).

[0080] In one embodiment, the measurement results can be used to derive a predicted measurement event together with previously collected measurement results (1e-35).

[0081] According to one embodiment, the terminal (1e-05) can report a predicted measurement event to the base station (1e-10) using a predetermined RRC message (1e-40). For example, the RRC message can be MeasurementReport, UEAssistanceInformation, and / or a new RRC message. For example, when information about the measurement event is reported using a UEAssistanceInformation (UAI) message, a predetermined prohibit timer can be applied to prevent frequent transmission of UAI messages. The terminal (1e-05) can start the prohibit timer when transmitting the UAI, and the terminal (1e-05) may not transmit a UAI message for the same purpose until the timer (e.g., prohibit timer) expires. RRC message, e.g., MeasurementReport, UEAssistanceInformation, or a new RRC message

[0082] According to one embodiment, the terminal (1e-05) can report the optimal measurement event to the base station (1e-10) according to its own movement path.

[0083] According to one embodiment, a base station (1e-10) that receives a message including a predicted measurement event can determine whether to set an additional cell measurement operation for a terminal (1e-05) based on the predicted event (1e-45).

[0084] In one embodiment, the base station (1e-10) can set a measurement event (1e-50) to the terminal (1e-05). RRCReconfiguration (measConfig with the predicted measurement event)

[0085] FIG. 1f is a flowchart of a procedure for performing event-triggered measurement reporting by applying AI / ML-based measurement results in a mobile communication system according to an embodiment of the present disclosure.

[0086] Referring to FIG. 1f, a terminal (1f-05) according to one embodiment may report its capability information to a base station (1f-10) (1f-15). For example, the capability information may include an indicator indicating that the terminal (1f-05) can perform event-triggered measurement reporting by applying cell- or beam-based measurement results (e.g., RSRP, RSRQ, etc.) predicted by the terminal itself.

[0087] In one embodiment, the base station (1f-10) can determine to evaluate a measurement event using the predicted measurement results of the terminal (1f-05) (1f-20). Using predicted results based on AI / ML can reduce the burden on the terminal for actual measurement. Furthermore, utilizing predicted values ​​for the near future can allow the base station (1f-10) to receive a measurement report message (e.g., a MeasurementReport message) early on, thereby triggering a handover of the terminal (1f-05) to occur promptly.

[0088] According to one embodiment, the base station (1f-10) can configure a measurement event that can be evaluated using the measurement result predicted by the terminal (1f-05) (1f-25). For example, configuration information related to each event-triggered measurement reporting can be typically included in the EventTriggerConfig IE, and an indicator indicating whether the terminal (1f-05) can apply the cell signal strength / quality value predicted by the terminal (1f-05) when evaluating the measurement event corresponding to the EventTriggerConfig IE can be included. As another example, a measurement event evaluated by applying the cell signal strength / quality value predicted by the terminal (1f-05) can be defined and configured as a separate new measurement event. RRCReconfiguration(measConfig)

[0089] According to one embodiment, the terminal (1f-05) can predict cell signal strength and quality values ​​that can be measured at present or in the near future through AI / ML-based analysis of measurement results collected in the past (1f-30).

[0090] According to one embodiment, the terminal (1f-05) can evaluate a measurement event to be satisfied at the present or near future point in time by applying the predicted value (1f-35).

[0091] According to one embodiment, if at least one measurement event is satisfied by applying the predicted value, the terminal (1f-05) can trigger transmission of a MeasurementReport message (1f-40).

[0092] In one embodiment, a terminal (1f-05) may transmit a MeasurementReport message to a base station (1f-10) (1f-45). For example, a measurement report message (e.g., a MeasurementReport message) may include an indicator indicating that the measurement report message was transmitted when a reporting condition applied to a cell signal strength / quality value predicted by the terminal is satisfied. For example, a measurement result message may include a cell signal strength / quality value predicted by the terminal (1f-05) that was applied to trigger transmission of the measurement result message and / or a cell signal strength / quality value that was most recently measured before transmission of the measurement report message.

[0093] In one embodiment, the two types (e.g., predicted value and measured value) may be reported to the base station (1f-10) as separate IEs. For example, the terminal (1f-05) may report a series of cell-based or beam-based predicted results based on the movement path of the terminal (1f-05), etc. For example, the measurement report message may include information indicating which future point in time or terminal position the reported predicted value corresponds to. For example, to indicate how far in advance the evaluation point in time the predicted value is expected to be, the terminal (1f-05) may include time information between two points in time (e.g., the evaluation point in time and the predicted point in time) in the measurement result message. For example, information on the accuracy and validity time of the reported predicted value may also be reported to the base station (1f-10), and substantially the same may be applied as long as the descriptions of accuracy and validity time described above are not contradictory.

[0094] According to one embodiment, a base station (1f-10) that receives a MeasurementReport message including a prediction result can use the prediction result to determine whether to set up additional cell measurements or handover to an adjacent cell for a terminal (1f-05) (1f-50).

[0095] FIG. 1g is a flowchart of a procedure for performing event-triggered measurement reporting by applying AI / ML-based optimal measurement events in a mobile communication system according to an embodiment of the present disclosure.

[0096] Referring to FIG. 1g, a terminal (1g-05) according to one embodiment may report its capability information to a base station (1g-10) (1g-15). For example, the capability information may include an instruction indicating that the terminal (1g-05) can derive its own predicted optimal measurement event, evaluate the optimal measurement, and report a MeasurementReport message to the network when the conditions for the measurement event are satisfied.

[0097] In one embodiment, the base station (1g-10) may allow the terminal (1g-05) to transmit a MeasurementReport message based on a predicted and selected measurement event (1g-20). For example, the base station (1g-10) may configure the terminal (1g-05) to predict and evaluate a measurement event on its own and transmit a MeasurementReport message to the terminal (1g-05) using a predetermined RRC message (1g-25). In conclusion, the above-described method relates to a method for the terminal (1g-05) to transmit a MeasurementReport message to the base station (1g-10) when it determines that it is necessary. RRCReconfiguration(measConfig)

[0098] In one embodiment, the base station (1g-10) may limit the measurement events that the terminal (1g-05) may consider. For example, the base station (1g-10) may configure Event A1, Event A2, and Event A3 so that the terminal (1g-05) can select them on its own, and may configure the remaining events so that the terminal (1g-05) cannot select them on its own.

[0099] According to one embodiment, the base station (1g-10) can be configured so that the terminal (1g-05) can determine whether to transmit the MeasurementReport only when a certain condition is satisfied. For example, the base station (1g-10) may wish to receive a MeasurementReport message from the terminal (1g-05) when the terminal (1g-05) is located in a cell edge area in order to determine whether to set up a handover. Accordingly, the operation of the terminal (1g-05) to determine and transmit the message may be permitted by the base station (1g-05) only when the terminal (1g-05) is located in a cell edge area. To this end, the base station (1g-05) may provide the terminal (1g-05) with relevant configuration information so that the terminal can determine whether it is in a cell edge area.

[0100] According to one embodiment, the terminal (1g-05) can predict a measurement event to be evaluated now or in the near future based on the received configuration information (1g-30). For example, predicting a measurement event can be referred to as the terminal (1g-05) deriving and selecting, based on AI / ML, the optimal values ​​of parameters applied to the conditional formula corresponding to the type of measurement event to be evaluated (e.g., Event A3, etc.) and the event.

[0101] According to one embodiment, the terminal (1g-05) can evaluate whether the condition is satisfied by substituting the measured or predicted cell signal strength or quality value into the conditional formula of the predicted measurement event (1g-35).

[0102] According to one embodiment, if at least one measurement event is satisfied, the terminal (1g-05) can trigger transmission of a MeasurementReport message (1g-40).

[0103] According to one embodiment, a terminal (1g-05) may transmit a measurement report message (e.g., a MeasurementReport message) to a base station (1g-10) (1g-45). For example, the measurement report message (e.g., a MeasurementReport message) may include an indicator indicating that the message was transmitted when a reporting condition of a measurement event determined by the terminal is satisfied. For example, the measurement report message may include an indicator indicating whether the terminal (1g-05) applied a predicted cell signal strength / quality value or an actually measured cell signal strength / quality value that was applied to trigger transmission of the measurement report message. For example, the measurement report message may include a cell signal strength / quality value predicted by the terminal and / or a cell signal strength / quality value that was most recently measured before transmission of the measurement report message.

[0104] According to one embodiment, the terminal (1g-05) may report a series of cell or beam-based prediction results based on the terminal movement path. For example, the measurement report message may include information indicating at which near future point in time or at which terminal location the reported prediction value corresponds. For example, the measurement report message may include information about the time associated with the prediction value and the location of the terminal. For example, to indicate at what point in time (or, at the prediction point) before the evaluation point in time, the terminal (1g-05) may include information about the time between two points in time (e.g., the evaluation point in time and the prediction point in time) in the measurement report message. For example, information about the accuracy and the validity time of the reported prediction value may also be reported, and the descriptions of the accuracy and the validity time may be substantially the same as those described above, as long as they do not contradict each other.

[0105] According to one embodiment, a base station (1g-10) that receives a measurement report message (MeasurementReport message) can use the prediction result to determine whether to set up additional cell measurements or handover to an adjacent cell for a terminal (1g-05) (1g-50).

[0106] FIG. 1h is a flowchart of a procedure for reporting a handover failure or RLF derived based on AI / ML to a network in a mobile communication system according to an embodiment of the present disclosure.

[0107] Referring to FIG. 1h, in one embodiment, another terminal (1h-05) may report its capability information to a base station (1h-10) (1h-15). For example, the capability information may include an indicator indicating that the terminal (1h-05) may report information related to a predicted handover failure (HOF) or radio link failure (RLF) to the network via a predetermined RRC message.

[0108] In one embodiment, the base station (1h-10) may decide to allow the terminal (1h-05) to report predicted HOF-related information and / or RLF-related information (1h-20).

[0109] According to one embodiment, the base station (1h-10) can be configured to report predicted HOF-related information and / or RLF-related information to the terminal (1h-05) via a predetermined RRC message (e.g., RRCReconfiguration message) (1h-25). RRCReconfiguration(measConfig or otherConfig)

[0110] According to one embodiment, a terminal (1h-05) that has received configuration information can predict information related to HOF and / or information related to RLF. (1h-35)

[0111] According to one embodiment, if a predetermined condition is satisfied, the terminal (1h-05) may include HOF-related information and / or RLF-related information in a predetermined RRC message (UEInformationResponse, MeasurementReport, UEAssistanceInformation, and / or a new RRC message) and report it to the base station (1h-10) (1h-40). For example, the base station (1h-10) may provide general or condition-based handover configuration information (e.g., RRCReconfiguration (ReconfigurationWithSync)) to the terminal (1h-05) (1h-30).

[0112] If a handover is performed to a target cell indicated in the handover configuration information, the terminal (1h-05) can report the predicted handover-related information to the base station (1h-10) when a predetermined condition is satisfied (e.g., if the probability of handover failure is greater than a certain threshold value) through prediction based on AI / ML. For example, the base station (1h-10) can transmit the threshold value information to the terminal (1h-05) through a predetermined RRC message. The general or condition-based handover-related information that can be reported by the terminal (1h-05) can include at least some of the following information.

[0113] - Information on the target cell(s) for optimal condition-based or general handover suggested by the terminal (e.g. PCI, CGI, cell frequency information, etc.)

[0114] - Signal strength or quality information of the proposed target cell (e.g. RSRP, RSRQ, etc.)

[0115] - When the base station performs a handover to a target cell that has been previously set, the expected handover failure probability (%), the probability of a ping-pong phenomenon occurring (%), the probability of an early or late handover (%), the probability of an RLF occurring soon after a successful handover (i.e., RLF shortly after HO) (%), the expected time of stay in the target cell, and / or the expected interruption time (ms) during the handover.

[0116] - When performing a handover to a target cell based on the optimal conditions suggested by the terminal or a general handover, the predicted handover failure probability (%), the probability of a ping-pong phenomenon occurring (%), the probability of an early or late handover (%), the probability of an RLF occurring soon after a successful handover (i.e., an RLF shortly after HO) (%), the expected time of stay in the target cell, and / or the expected interruption time (ms) during the handover.

[0117] According to one embodiment, the terminal (1h-05) may also report accuracy information of the mentioned prediction result to the base station (1h-10). For example, accuracy may be indicated in various forms. For example, the accuracy of the prediction result may be expressed as an accuracy probability (%), an average and error / variability rate, a confidence level, and / or a standard deviation.

[0118] According to one embodiment, the base station (1h-05) that receives the prediction result may consider or identify the prediction result as valid only for the validity time. For example, the validity time information may be indicated as an absolute time, a timer-based time, or an elapsed time based on a specific time. For example, the prediction result may be valid until the indicated absolute time (e.g., date, hour, minute, second). For example, the specific time may mean a reference time provided by the terminal (1h-05) or a time point at which a specific operation is performed (e.g., a time point at which the base station (1h-10) receives the prediction result, etc.). The elapsed time may be determined by the terminal (1h-05) or the base station (1h-10).

[0119] According to one embodiment, when the terminal (1h-05) determines the elapsed time, the elapsed time information should be reported to the base station (1h-10) together with the prediction result, and when the base station (1h-10) determines the elapsed time, it may be provided to the terminal (1h-05) in a setup step (e.g., an RRC setup step of step 1g-25).

[0120] If the base station (1h-10) has configured a conditional handover (CHO), multiple candidate target cells may be indicated in the configuration information. If CHO is performed with the target cells indicated in the handover configuration information, and if the probability of handover failure for all target cells is greater than a certain threshold value through prediction based on AI / ML, the terminal (1h-05) may report handover-related information predicted for each candidate target cell to the base station (1h-10). If the CHO condition is satisfied for multiple candidate target cells, the terminal (1h-05) may select one target cell to perform a handover. In this case, the terminal (1h-05) may compare the handover failure probabilities predicted based on AI / ML for each target cell that satisfies the CHO condition, and perform a handover to the target cell with the lowest probability.

[0121] According to one embodiment, the terminal (1h-05) can predict information related to a change in the SCG (Secondary Cell Group). When a predetermined condition is satisfied, the terminal (1h-05) can include information related to the SCG change in a predetermined RRC message and report it to the base station (1h-10). The base station (1h-10) can set Dual Connectivity (DC) for the terminal (1h-05). For example, the DC technology can be referred to as a technology in which the terminal (1h-05) is simultaneously connected to two base stations for the purpose of maximizing or increasing the data transmission rate and receiving wireless data transmission and reception services. In this case, a group of cells provided by an additionally connected base station is called an SCG, and addition or change may be possible depending on the base station's settings (i.e., SCG change). The base station (1h-10) can set the addition or change of the PSCell (primary SCG cell), which is a representative cell of the SCG (secondary cell group), through a predetermined RRC message. At this time, if the probability of failure of SCG addition or change is greater than a certain threshold value based on AI / ML prediction, the terminal (1h-05) may report information related to the predicted SCG addition or change to the base station (1h-10). Information related to SCG addition or change that may be reported by the terminal (1h-05) may include at least some of the following information.

[0122] - Information on target PSCell cell(s) for optimal SCG addition or change suggested by the terminal (e.g. PCI, CGI, cell frequency information, etc.)

[0123] - Signal strength or quality information of the proposed target PSCell cell (e.g. RSRP, RSRQ, etc.)

[0124] - When the base station adds or changes a target PSCell cell that has been set in advance, the predicted probability of SCG addition or failure (%)

[0125] - When performing the optimal SCG addition or change suggested by the terminal, the predicted SCG addition or failure probability (%)

[0126] According to one embodiment, the terminal (1h-05) can evaluate whether the PCell (primary cell) or PSCell provides a sufficient radio link to receive data transmission and reception services through an RLM (Radio Link Monitoring) operation for the PCell or PSCell. The physical layer of the terminal (1h-05) can measure the downlink signal quality from the cell specific reference signal (CRS) of the serving cell. The terminal (1h-05) can determine whether the signal quality is lower than a specific threshold value (Qout). For example, the threshold value can be a signal quality value corresponding to a specific BLER (bit error rate, block error rate) measured on a physical downlink control channel (PDCCH). If the signal quality is lower than the specific threshold value (Qout), the physical layer of the terminal (1h-05) can transmit an 'out-of-sync' indicator to a higher layer. In LTE technology, the operation described above is called RLM. If a directive (e.g., out-of-sync) is passed to the upper layer more than a certain number of times, the upper layer can start a specific timer, and when the timer expires, declare an RLF. For example, an RLF can be declared based on the result from the RLM.

[0127] According to one embodiment, the terminal physical layer determines whether the downlink signal quality from the CRS of the serving cell is lower than a specific threshold value (Qout) at every specific period, threshold evaluation period (e.g., Qout evaluation period). If the signal quality is lower than the specific threshold value (Qout), the physical layer may transmit an 'out-of-sync' indicator to the upper layer. After the minimum indicator (e.g., out-of-sync) is transmitted to the upper layer, a specific timer (e.g., T310) may be started when the indicator (e.g., out-of-sync) is transmitted to the upper layer a specific number of times (e.g., N310). For example, the timer (e.g., T310) may be started when the indicator (e.g., out-of-sync) is transmitted to the upper layer a specified number of times within a specific period.

[0128] In one embodiment, the physical layer also determines whether the downlink signal quality from the CRS of the serving cell is equal to or higher than a specific threshold (Qin). If the signal quality is equal to or higher than the specific threshold (Qin), the physical layer may transmit an indicator (e.g., an 'in-sync' indicator) to the upper layer. If the indicator (e.g., the in-sync indicator) is transmitted to the upper layer a specific number of times, the terminal (or the upper layer) may stop the running T310 timer. If the T310 timer is not stopped and expires, the upper layer may declare an RLF. After declaring an RLF, the terminal (1h-05) may start another timer (e.g., T311).

[0129] The terminal (1h-05) searches for a new suitable cell, and if it fails to find a new suitable cell before the timer (e.g., T311) expires, it may switch to standby mode. If it finds a new suitable cell before the timer (e.g., T311) expires, the terminal (1h-05) may start the T301 timer and perform the process of re-establishing with the cell. If the re-establishment is not successfully completed before the T301 timer expires, the terminal (1h-05) may switch to standby mode. If the re-establishment is successful, the terminal (1h-05) may continue to be connected to the cell.

[0130] According to one embodiment, an RLF may be declared by an RLM operation, and may also be declared according to other conditions. For example, an RLF may be declared even if a random access fails. For example, an RLF may be declared even if a packet is not successfully delivered even if the maximum number of retransmissions is reached in the RLC (radio link control) layer. For example, the description of the operations of Timer T301 and / or Timer T311 is as shown in [Table 1]. However, [Table 1] is only an example, and the present disclosure is not limited thereto.

[0131]

[0132] According to one embodiment, if the terminal (1h-05) predicts the occurrence of RLF in advance and reports it to the base station (1h-10), the base station (1h-10) may perform certain settings to avoid RLF, for example, handover to another cell that provides better signal strength. The terminal (1h-10) may report the predicted RLF-related information to the base station (1h-10) through a certain RRC message (e.g., UEInformationResponse, MeasurementReport, UEAssistanceInformation, or a new RRC message) when a certain condition is satisfied (e.g., if the probability of RLF occurrence is greater than a certain threshold value) through AI / ML-based prediction. The base station (1h-10) may transmit the threshold value information to the terminal through a certain RRC message (e.g., RRCReconfiguration message). For example, the terminal (1h-05) may report at least some of the predicted RLF-related information to the base station (1h-10) as follows.

[0133] - An indicator indicating whether an RLF (or SCG failure) occurs at a given point in time (present or near future) or at the terminal location in the current PCell (or PSCell).

[0134] - Information on the specific time or terminal location at which the predicted RLF (or SCG failure) occurs

[0135] - The cause of the predicted RLF (or SCG failure), for example, T310 expiration, RACH (random access channel) problem, RLC problem, etc.

[0136] - Cell or beam-based signal measurement results of the current serving cell and neighboring cells (RSRP, RSRQ, etc.)

[0137] - To avoid RLF (or SCG failure), preferred target cell information (e.g. CGI, PCI, and / or cell frequency) in handover or SCG change.

[0138] - Accuracy information of the predicted RLF (or SCG failure). For example, accuracy probability (%), mean and error / variability, confidence, and standard deviation.

[0139] - Information on the validity period of the predicted RLF (or SCG failure). The description of the validity period information may be applied as long as it does not contradict the above-described contents (e.g., the description of FIGS. 1d and 1h), and for more details, reference may be made to the above-described contents (e.g., the description of FIGS. 1d and 1h).

[0140] According to one embodiment, the terminal (1h-05) may report the optimal handover time point and / or corresponding target cell information to the base station (1h-10). For example, when a predetermined condition is satisfied (e.g., when the probability of RLF occurrence due to non-performance of handover exceeds a specific threshold value), the terminal (1h-05) may report a predetermined RRC message including the optimal target cell information predicted based on AI / ML (e.g., CGI, PCI, cell frequency information, etc.), the most recent cell measurement information for the serving cell and the proposed target cell (e.g., cell, beam-based RSRP, and / or RSRQ information), the predicted cell information for the serving cell and the proposed target cell (e.g., cell, beam-based RSRP, RSRQ information), and / or handover time point information to the base station (1h-10).

[0141] According to one embodiment, the base station (1h-10) that received the message can decide whether to (re)configure additional cell measurements or handover to an adjacent cell for the terminal (1h-10) based on the prediction result (1h-45).

[0142] According to one embodiment, the base station (1h-10) may provide handover configuration information to the terminal (1h-05) including an indicator indicating that the handover was triggered using AI / ML-based prediction information when setting up the handover (1h-50). RRCReconfiguration(ReconfigurationWithSync)

[0143] According to one embodiment, the terminal (1h-05) can perform random access to the target cell indicated in the configuration information (1h-55). If the handover is not successfully completed within a predetermined period of time, the terminal (1h-05) can consider or identify the handover as a failure (1h-60). At this time, the terminal (1h-05) can store predetermined information related to the handover failure as RLF Report content, and can additionally store an indicator indicating that the handover was triggered using AI / ML-based prediction information as RLF Report content (1h-65).

[0144] FIG. 1i is a flowchart of a procedure for reporting AI / ML-based UE assistance information to a network in a mobile communication system according to an embodiment of the present disclosure.

[0145] Referring to FIG. 1i, a terminal (1i-05) according to one embodiment may report its capability information to a base station (1i-10) (1i-15). For example, the capability information may include an indicator indicating that the terminal (1i-05) can report certain information predicted by the terminal to the network via a certain RRC message.

[0146] In one embodiment, the base station (1i-10) may decide to allow the terminal (1i-05) to report certain information it has predicted (1i-20).

[0147] According to one embodiment, the base station (1i-10) can be configured to report predetermined predicted information to the terminal (1i-05) via a predetermined RRC message (e.g., RRCReconfiguration message) (1i-25). RRCReconfiguration(otherConfig) For example, the predetermined information may include UE movement trajectory information (e.g., trajectory) and / or AI / ML prediction model indication information. For example, the UE movement trajectory information may be composed of way points (location points) that the terminal (1i-05) must pass through and expected time information when passing each way point.

[0148] According to one embodiment, the terminal (1i-05) may trigger transmission of UE movement trajectory information when receiving configuration information or when there is a predetermined change in previously reported UE movement trajectory information (1i-30). The terminal (1i-05) may report, to the base station (1i-05), along with the movement trajectory information, accuracy information of the movement trajectory (e.g., accuracy probability (%), mean and error / variability, reliability and standard deviation, etc.) and / or validity time information of the predicted information.

[0149] According to one embodiment, the terminal (1i-05) may indicate predetermined information corresponding to a predetermined point at each predetermined point of the movement trajectory and report it to the base station (1i-10). For example, the information reported to the base station (1i-10) and corresponding to a predetermined point may include indicator information indicating that a specific point of the movement trajectory is a point for performing a handover to a specific target cell, information on a list of optimal candidate target cells at the specific point, the type and setting value of a measurement event expected to be satisfied at the specific point, and / or information mentioned in the embodiments above (e.g., cell or beam-based predicted signal strength / quality value (e.g., RSRP, RSRQ), handover and RLF related information, etc.). That is, the above-described information (e.g., indicator information, target cell list information, measurement event type and setting value information, information mentioned in the embodiments) may be information corresponding to a specific point of the movement trajectory of the terminal (1i-05). For example, when the terminal (1i-05) is at the first point, the first candidate target cell list information reported to the base station (1i-10) may be based on the location of the terminal (1i-05) (e.g., the first point). When the terminal (1i-05) is at the second point, the second candidate target cell list information reported to the base station (1i-10) may be based on the location of the terminal (1i-05) (e.g., the first point). For example, the first candidate target cell list information and the second candidate target cell list information may be different. However, the present disclosure is not limited thereto, and the candidate target cell list information may be substantially the same even if the locations are different.

[0150] According to one embodiment, the terminal (1i-05) can report a predetermined RRC message including UE movement trajectory information to the base station (1i-10) (1i-35).

[0151] In one embodiment, a base station (1i-10) that receives a message (e.g., a report message) can use the prediction result to determine whether to set up additional cell measurements or handover to an adjacent cell for a terminal (1i-05) (1i-40).

[0152] FIG. 1J is a flowchart of a terminal operation for reporting AI / ML-based results to a network in a mobile communication system according to an embodiment of the present disclosure.

[0153] According to one embodiment, in step 1j-05, a terminal (e.g., terminal (1c-10) of FIG. 1c) may report its capability information to a base station (e.g., base station (1c-05) of FIG. 1c). For example, the capability information may include an indicator indicating that the terminal may report certain information predicted based on AI / ML to the base station. For example, the capability information may include information on an AI / ML prediction model supported by the terminal.

[0154] According to one embodiment, in step 1j-10, the terminal may receive a predetermined RRC message including configuration information instructing the base station to report predetermined information predicted based on AI / ML. Various configuration information related to the aforementioned embodiments may be included in the RRC message. For example, the RRC message may include information about the AI / ML prediction model applied when deriving the predicted value.

[0155] In one embodiment, in step 1j-15, the terminal measures intra- / inter-frequency, which can be used to derive predetermined prediction information.

[0156] According to one embodiment, in step 1j-20, the terminal can derive an AI / ML-based prediction result based on the received configuration information.

[0157] In one embodiment, at step 1j-25, the terminal may decide to report the derived AI / ML-based prediction result if a predetermined condition is satisfied.

[0158] According to one embodiment, in step 1j-30, the terminal may report a predetermined RRC message including the derived AI / ML-based prediction result to the base station.

[0159] FIG. 1k is a flowchart of network operations for collecting AI / ML-based results in a mobile communication system according to an embodiment of the present disclosure.

[0160] In one embodiment, at step 1k-05, the base station may receive capability information from the terminal. For example, the capability information may include an indicator indicating that the terminal can report certain information predicted based on AI / ML to the base station. For example, the capability information may include information about the AI / ML prediction model supported by the terminal.

[0161] In one embodiment, at step 1k-10, the base station may transmit to the terminal a predetermined RRC message including configuration information instructing the terminal to report predetermined information predicted based on AI / ML.

[0162] According to one embodiment, at step 1k-15, the base station may receive a predetermined RRC message including an AI / ML-based prediction result from the terminal.

[0163] In one embodiment, at step 1k-20, the base station applies prediction results (e.g., AI / ML-based prediction results) to support terminal mobility. For example, the base station may determine whether to handover or change cells for the terminal based on the received prediction results.

[0164] In one embodiment, at step 1k-25, the base station can set cell measurement or handover to the terminal based on the prediction result.

[0165] Figure 1l is a block diagram showing the internal structure of a terminal to which the present invention is applied.

[0166] Referring to FIG. 1l, the terminal may include an RF (Radio Frequency) processing unit (1l-10), a baseband processing unit (1l-20), a storage unit (1l-30), and / or a control unit (1l-40).

[0167] According to one embodiment, the RF processing unit (11-10) may perform functions for transmitting and receiving signals through a wireless channel, such as signal band conversion and amplification. That is, the RF processing unit (11-10) up-converts a baseband signal provided from the baseband processing unit (11-20) into an RF band signal and transmits it through an antenna, and down-converts an RF band signal received through the antenna into a baseband signal. For example, the RF processing unit (11-10) 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 the drawing, only one antenna is illustrated, but the terminal may be equipped with multiple antennas. In addition, the RF processing unit (11-10) may include a plurality of RF chains. Furthermore, the RF processing unit (11-10) may perform beamforming. For the above beamforming, the RF processing unit (11-10) 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, and can receive multiple layers when performing the MIMO operation.

[0168] According to one embodiment, the baseband processing unit (11-20) may 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 (11-20) may generate complex symbols by encoding and modulating a transmission bit stream. In addition, when receiving data, the baseband processing unit (11-20) may restore a reception bit stream by demodulating and decoding a baseband signal provided from the RF processing unit (11-10). For example, in the case of following the OFDM (orthogonal frequency division multiplexing) method, when transmitting data, the baseband processing unit (11-20) may generate complex symbols by encoding and modulating a transmission bit stream, map the complex symbols to subcarriers, and then 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 (11-20) divides the baseband signal provided from the RF processing unit (11-10) into OFDM symbol units, restores signals mapped to subcarriers through FFT (fast Fourier transform) operation, and then restores the received bit string through demodulation and decoding.

[0169] According to one embodiment, the baseband processing unit (11-20) and the RF processing unit (11-10) transmit and receive signals as described above. Accordingly, the baseband processing unit (11-20) and the RF processing unit (11-10) may be referred to as a transmitter, a receiver, a transceiver, or a communication unit. Furthermore, at least one of the baseband processing unit (11-20) and the RF processing unit (11-10) 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 (11-20) and the RF processing unit (11-10) 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.

[0170] According to one embodiment, the storage unit (1l-30) can store data such as basic programs, application programs, and setting information for the operation of the terminal. In particular, the storage unit (1l-30) can store information related to a second access node that performs wireless communication using a second wireless access technology. In addition, the storage unit (1l-30) provides the stored data upon request from the control unit (1l-40).

[0171] According to one embodiment, the controller (11-40) can control the overall operations of the terminal. For example, the controller (11-40) can transmit and / or receive signals through the baseband processing unit (11-20) and the RF processing unit (11-10). In addition, according to one embodiment, the controller (11-40) records and reads data in the storage unit (11-40). For this purpose, the controller (11-40) can include at least one processor. For example, the controller (11-40) can include a communication processor (CP) that performs control for communication and / or an application processor (AP) that controls upper layers such as application programs.

[0172] Figure 1m is a block diagram showing the configuration of a base station according to the present invention.

[0173] Referring to FIG. 1m, a base station according to one embodiment may include an RF processing unit (1m-10), a baseband processing unit (1m-20), a backhaul communication unit (1m-30), a storage unit (1m-40), and / or a control unit (1m-50).

[0174] According to one embodiment, the RF processing unit (1m-10) may perform functions for transmitting and / or receiving signals through a wireless channel, such as signal band conversion and amplification. For example, the RF processing unit (1m-10) may up-convert a baseband signal provided from the baseband processing unit (1m-20) into an RF band signal and transmit the up-converted signal 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 (1m-10) may include a transmission filter, a reception filter, an amplifier, a mixer, an oscillator, a DAC, an ADC, etc. In FIG. 1m, only one antenna is illustrated, but the first access node may have multiple antennas. In addition, the RF processing unit (1m-10) may include multiple RF chains. Furthermore, the RF processing unit (1m-10) may perform beamforming. For the above beamforming, the RF processing unit (1m-10) can adjust the phase and magnitude of each 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.

[0175] According to one embodiment, the baseband processing unit (1m-20) may perform a conversion function between a baseband signal and a bit stream according to the physical layer specification of the first wireless access technology. For example, when transmitting data, the baseband processing unit (1m-20) may generate complex symbols by encoding and modulating a transmission bit stream. In addition, when receiving data, the baseband processing unit (1m-20) may restore a reception bit stream by demodulating and decoding a baseband signal provided from the RF processing unit (1m-10). For example, in the case of OFDM, when transmitting data, the baseband processing unit (1m-20) may generate complex symbols by encoding and modulating a transmission bit stream, map the complex symbols to subcarriers, and then configure OFDM symbols through an IFFT operation and CP insertion. In addition, when receiving data, the baseband processing unit (1m-20) can divide the baseband signal provided from the RF processing unit (1m-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 (1m-20) and the RF processing unit (1m-10) can transmit and / or receive signals as described above. Accordingly, the baseband processing unit (1m-20) and the RF processing unit (1m-10) may be referred to as a transmitter, a receiver, a transceiver, a communication unit, or a wireless communication unit.

[0176] According to one embodiment, the backhaul communication unit (1m-30) may provide an interface for performing communication with other nodes within the network. That is, the backhaul communication unit (1m-30) may convert a bit string transmitted from the main base station to another node, such as an auxiliary base station or a core network, into a physical signal, and may convert a physical signal received from the other node into a bit string.

[0177] According to one embodiment, the storage unit (1m-40) can store data such as basic programs, application programs, and configuration information for the operation of the main base station. In particular, the storage unit (1m-40) can store information on bearers assigned to connected terminals, measurement results reported from connected terminals, and the like. In addition, the storage unit (1m-40) can store information that serves as a basis for determining whether to provide or terminate multiple connections to a terminal. The storage unit (1m-40) can provide stored data upon request from the control unit (1m-50).

[0178] According to one embodiment, the control unit (1m-50) controls the overall operations of the base station. For example, the control unit (1m-50) can transmit and / or receive signals through the baseband processing unit (1m-20) and the RF processing unit (1m-10) or through the backhaul communication unit (1m-30). The control unit (1m-50) records and reads data in the storage unit (1m-40). For this purpose, the control unit (1m-50) can include at least one processor.

Claims

1. A method performed by a UE (user equipment) in a wireless communication system, A step of transmitting capability information for a report including expected first measurement information based on an AI (artificial intelligence) model to a base station; A step of receiving, from the base station, setting information for the report including the first measurement information based on the capability information; and A method comprising the step of transmitting, to the base station, the report including the first measurement information based on the setting information.

2. In claim 1, The above configuration information includes at least one of information about a cell for the report, information about a frequency band for the report, or information about a measurement event that triggers the report. The step of transmitting the first measurement information based on the above setting information is: A step of identifying second measurement information for the frequency band based on the above setting information, and A method comprising a step of identifying the first measurement information output from the AI model into which the second measurement information is input.

3. In claim 1, The report transmitted to the base station based on the above setting information includes at least one of accuracy information of the first measurement information, validity time information of the first measurement information, information about a time associated with the first measurement information, or information about a location of the UE associated with the first measurement information. A method wherein transmission of the above report is triggered based on a measurement event included in the above setup information or the first measurement information.

4. In claim 1, A step of identifying prediction information associated with at least one of HOF (handover failure) or RLF (radio link failure) based on the first measurement information; and A method further comprising the step of transmitting the prediction information to the base station.

5. A method performed by a base station in a wireless communication system, A step of receiving capability information for a report including expected first measurement information based on an artificial intelligence (AI) model from a UE (user equipment); A step of transmitting, to the UE, setting information for the report including the first measurement information based on the capability information; and A method comprising the step of receiving, from the UE, the report including the first measurement information based on the setting information.

6. In claim 5, The above configuration information includes at least one of information about a cell for the report, information about a frequency band for the report, or information about a measurement event that triggers the report. A method wherein the first measurement information is output from the AI model into which second measurement information for the frequency band is input.

7. In claim 5, The report received from the UE based on the above configuration information includes at least one of accuracy information of the first measurement information, validity time information of the first measurement information, information about a time associated with the first measurement information, or information about a location of the UE associated with the first measurement information. A method wherein receipt of said report is triggered based on a measurement event included in said setup information or said first measurement information.

8. In claim 5, Further comprising a step of receiving prediction information associated with at least one of a handover failure (HOF) or a radio link failure (RLF) from the UE, A method wherein the above prediction information is based on the first measurement information.

9. In a wireless communication system, in the UE (user equipment), transceiver; and A controller coupled with the above transceiver, The above controller: Transmit capability information to the base station for a report including expected first measurement information based on an AI (artificial intelligence) model, Receive setting information for the report including the first measurement information based on the capability information from the base station, A UE configured to transmit, to the base station, the report including the first measurement information based on the configuration information.

10. In claim 9, The above configuration information includes at least one of information about a cell for the report, information about a frequency band for the report, or information about a measurement event that triggers the report. The above controller: Identifying second measurement information for the frequency band based on the above setting information, A UE configured to identify the first measurement information output from the AI model into which the second measurement information has been input.

11. In claim 9, The report transmitted to the base station based on the above setting information includes at least one of accuracy information of the first measurement information, validity time information of the first measurement information, information about a time associated with the first measurement information, or information about a location of the UE associated with the first measurement information. Transmission of the above report is triggered by a UE based on a measurement event included in the above configuration information or the first measurement information.

12. In claim 9, The above controller: Identifying prediction information associated with at least one of HOF (handover failure) or RLF (radio link failure) based on the first measurement information, A UE configured to transmit the prediction information to the base station.

13. In a base station in a wireless communication system, transceiver; and A controller coupled with the above transceiver, The above controller: Receive capability information for a report including expected first measurement information based on an AI (artificial intelligence) model from a UE (user equipment), Transmitting to the UE, the configuration information for the report including the first measurement information based on the capability information, A base station configured to receive, from the UE, the report including the first measurement information based on the configuration information.

14. In claim 13, The above configuration information includes at least one of information about a cell for the report, information about a frequency band for the report, or information about a measurement event that triggers the report. A base station, wherein the first measurement information is output from the AI model into which second measurement information for the frequency band is input.

15. In claim 13, The report received from the UE based on the above configuration information includes at least one of accuracy information of the first measurement information, validity time information of the first measurement information, information about a time associated with the first measurement information, or information about a location of the UE associated with the first measurement information. A base station, wherein receipt of the above report is triggered based on a measurement event included in the above setup information or the first measurement information.

Images