Terminal and network device

Abstract

This terminal predicts the occurrence of a handover failure or a radio link failure using a learning model, and transmits, to a network, prediction information indicating the prediction result of the handover failure or the radio link failure.

Description

Terminals and network devices

[0001] The present disclosure relates to a terminal and a network device that utilizes an AI / ML model.

[0002] The 3rd Generation Partnership Project (3GPP: registered trademark) has developed specifications for Long Term Evolution (LTE) and 5th generation mobile communication systems (5G, also known as New Radio (NR) or Next Generation (NG)), and is also developing specifications for the next generation, known as Beyond 5G, 5G Evolution, or 6G.

[0003] In 3GPP Release 19, a working item (WI) on artificial intelligence / machine learning models (AI / ML Model) has been established (Non-Patent Document 1).

[0004] For example, the use of AI / ML models to predict handover failure (HOF) or radio link failure (RLF) will be considered.

[0005] "Revised SID on AIML for mobility in NR", RP-240082, 3GPP TSG RAN Meeting #103, 3GPP, March 2024

[0006] The AI / ML model can be installed in the terminal (User Equipment, UE) (called a UE-sided model). In the UE-sided model, the network (radio base station (gNB)) cannot know in advance the type and content of the HOF and RLF predicted values ​​reported by the UE. This poses a problem in that the network side cannot effectively utilize the predicted values.

[0007] Therefore, the following disclosure has been made in consideration of the above circumstances, and aims to provide a terminal and a network device that can effectively utilize predicted values ​​of handover failure (HOF) and radio link failure (RLF) on the network side even in the case of a UE-sided model.

[0008] One aspect of the present disclosure is a terminal (UE200) comprising a receiving unit (measurement processing unit 220) that receives a measurement configuration from a network that configures measurements using a learning model, a control unit (control unit 240) that applies the learning model to measurement targets included in the measurement configuration and generates measurement results, and a transmitting unit (measurement processing unit 220) that transmits a measurement report including the measurement results to the network based on the measurement configuration.

[0009] One aspect of the present disclosure is a radio base station (gNB100) that includes a transmitter (AI / ML model unit 130) that transmits a measurement configuration to a terminal that configures measurements using a learning model, and a receiver (AI / ML model unit 130) that receives from the terminal a measurement report that includes measurement results generated by applying the learning model to a measurement target included in the measurement configuration.

[0010] One aspect of the present disclosure is a wireless communication method in a terminal, including steps of receiving a measurement configuration from a network that configures measurements using a learning model, applying the learning model to measurement targets included in the measurement configuration to generate measurement results, and transmitting a measurement report including the measurement results to the network based on the measurement configuration.

[0011] One aspect of the present disclosure is a terminal (UE200) that includes a control unit (control unit 240) that predicts the occurrence of handover failure or radio link failure using a learning model, and a transmission unit (AI / ML model unit 215) that transmits prediction information indicating the predicted result of the handover failure or the radio link failure to a network.

[0012] One aspect of the present disclosure is a network device (OAM / RIC40) that includes a control unit that predicts the occurrence of handover failure or radio link failure using a learning model, and a transmission unit that transmits prediction information indicating the predicted result of the handover failure or the radio link failure to a network.

[0013] One aspect of the present disclosure is a terminal (UE200) that includes a control unit (control unit 240) that generates a predicted value of a specified target using a learning model, and a transmission unit (AI / ML model unit 215) that transmits the predicted value and a prediction result including the accuracy of the predicted value to a network.

[0014] One aspect of the present disclosure is a radio base station (gNB100) that includes a transmitter (AI / ML model unit 130) that transmits a request to a terminal to report the accuracy of a predicted value based on a learning model of a specified target, and a receiver (AI / ML model unit 130) that receives from the terminal a prediction result including the predicted value and the accuracy of the predicted value.

[0015] One aspect of the present disclosure is a wireless communication method in a terminal, comprising: a step of generating a predicted value of a specified target using a learning model; and a step of transmitting a prediction result including the predicted value and the accuracy of the predicted value to a network.

[0016] FIG. 1 is a diagram illustrating an overall schematic configuration of a wireless communication system 10. FIG. 2 is a functional block diagram of a gNB 100. FIG. 3 is a functional block diagram of a UE 200. FIG. 4 is a diagram illustrating an example of the functional architecture of an AI / ML model. FIG. 5 is a diagram illustrating an example of a sequence related to setting AI / ML reporting (Measurement Report) according to an operation example 1. FIG. 6 is a diagram illustrating an example of the configuration of an information element (IE) of AI / ML reporting. FIG. 7 is a diagram illustrating an example of a sequence related to AI / ML reporting according to an operation example 2. FIG. 8 is a diagram illustrating the relationship between a movement trajectory of a UE and neighboring cells according to an operation example 2. FIG. 9 is a diagram illustrating an example of AI / ML reporting according to an operation example 2. FIG. 10 is a diagram illustrating an example of the configuration of a RIC based on an O-RAN architecture. FIG. 11 is a diagram illustrating an example of a sequence related to setting AI / ML reporting (Measurement Report) according to an operation example 3. FIG. 12 is a diagram illustrating an example of the hardware configuration of the gNB 100 and the UE 200. FIG. 13 is a diagram illustrating an example of the configuration of a vehicle 2001.

[0017] Hereinafter, embodiments will be described with reference to the drawings. Note that the same or similar reference numerals are used to designate the same functions or configurations, and descriptions thereof will be omitted as appropriate.

[0018] (1) Overall Schematic Configuration of Wireless Communication System Fig. 1 is a diagram showing the overall schematic configuration of a wireless communication system 10 according to this embodiment. The wireless communication system 10 is a wireless communication system conforming to 5G New Radio (NR) and includes a Next Generation-Radio Access Network 20 (hereinafter, NG-RAN 20) and a terminal 200 (User Equipment 200, hereinafter, UE 200).

[0019] The wireless communication system 10 may be a wireless communication system conforming to a standard called Beyond 5G, 5G Evolution, or 6G, or may include a wireless communication system conforming to a standard called Long Term Evolution (LTE) or 4G. The wireless communication system 10 may support functions related to the Industrial Internet of Things (IIoT) and Ultra-Reliable and Low Latency Communications (URLLC). The wireless communication system 10 may also be configured using multiple radio access technologies (RATs), for example, 4G / LTE and 5G.

[0020] The NG-RAN 20 includes a radio base station 100 (hereinafter, gNB 100). Note that the specific configuration of the radio communication system 10, including the number of gNBs (or eNBs, etc.) and UEs, is not limited to the example shown in FIG. 1 .

[0021] The gNB 100 may also employ a fronthaul (FH) interface defined by the Open Radio Access Network Alliance (O-RAN). The gNB 100 may include an O-RAN Distributed Unit (O-DU) and an O-RAN Radio Unit (O-RU). The gNB 100 can function as a type of NG-RAN node.

[0022] The NG-RAN 20 actually includes multiple NG-RAN nodes, specifically, gNBs (or ng-eNBs), and is connected to a 5G-compliant core network (5GC, not shown). In the 5GC, the concept of CUPS (Control and User Plane Separation) may be introduced, in which the functions of the user plane and the control plane are clearly separated.

[0023] The NG-RAN 20 may be connected to the OAM / RIC 40 and the NF 50 via 5GC or directly from the NG-RAN 20. The OAM / RIC 40 (network device) can provide functions related to operation and maintenance (OAM) of the wireless communication system 10. The OAM / RIC 40 can also provide functions related to control of the NG-RAN 20 (RIC: RAN Intelligent Controller). The specific functions of the RIC are defined by the O-RAN specifications (e.g., O-RAN Architecture-Description 6.0). In this embodiment, the OAM / RIC 40 may constitute an entity that performs operation, maintenance, or control.

[0024] The NF 50 may be interpreted as a logical node that provides a network function. The NF 50 may include an Access and Mobility Management Function (AMF) that is included in the 5G system architecture and provides access and mobility management functions for the UE 200, a Session Management Function (SMF) that provides session management functions, and a Location Management Function (LMF) that controls communications related to location-based services defined in 5GC. Furthermore, a UDM / UDR (Unified Data Management / User Data Repository) may be connected to the AMF and / or SMF. The NG-RAN 20 and 5GC may simply be referred to as a "network."

[0025] In addition, the NG-RAN 20 may be connected to a server managed by a 3GPP service provider or a server (3GPP or non-3GPP server) managed by a party other than the provider.

[0026] The gNB100 is a radio base station conforming to NR and performs radio communication with the UE200 conforming to NR. The gNB100 may be configured with a CU (Central Unit) and a DU (Distributed Unit), and the DU may be separated from the CU and installed in a different geographical location. One or more DUs may be connected to the CU. The gNB100 (gNB-CU) may be connected to each other via an Xn interface, and the CU and DU may be connected to each other via an F1 interface.

[0027] The gNB 100 and the UE 200 can support Massive MIMO, which generates a more directional beam (BM) by controlling radio signals transmitted from multiple antenna elements, Carrier Aggregation (CA), which aggregates multiple component carriers (CCs), and Dual Connectivity (DC), which simultaneously communicates between the UE and multiple NG-RAN nodes. The UE 200 may also perform handover (HO) to a different RAT. The UE 200 may also perform handover between cells A to D (serving cells or neighboring cells).

[0028] In the wireless communication system 10, artificial intelligence (AI) / machine learning (ML) may be applied in the NG-RAN 20. Specifically, a learning model (herein referred to as an AI / ML model) may be used to optimize the mobility or handover (which may also be read as transition, cell transition, cell selection, etc.) of the UE 200.

[0029] The AI / ML model may also be expressed as another term meaning AI or ML, such as an artificial intelligence (AI) model or a machine learning (ML) model.

[0030] In a broad sense, the mobility of UE200 may mean the ease of movement and maneuverability of UE200, but in this embodiment, it may also mean minimizing call drops, radio link (including beam) failures, unnecessary handovers, ping-pong states, etc.

[0031] The UE 200 may periodically perform measurement reporting. The UE 200 may perform measurement reporting for each event. An entering condition for starting measurement reporting and a leaving condition for ending measurement reporting may be defined for each event. The existing events may include the following events (see 3GPP TS38.331). Note that the entering condition may be interpreted as a condition for determining whether or not to include a measurement report target, and the leaving condition may be interpreted as a condition for determining whether or not to exclude a measurement report target.

[0032] (i) Event A1 (Serving becomes better than threshold) Event A1 is an event in which the reception quality of the serving cell becomes better than a threshold. For example, the entering condition is Ms - Hys > Thresh, and the leaving condition is Ms + Hys < Thresh.

[0033] Here, Ms is the reception quality of the serving cell, Hys is a hysteresis parameter, and Thresh is a threshold value.

[0034] (ii) Event A2 (Serving Becomes Worse Than Threshold) Event A2 is an event in which the reception quality of the serving cell becomes worse than a threshold. For example, the entering condition is Ms + Hys < Thresh, and the leaving condition is Ms - Hys > Thresh.

[0035] Here, Ms is the reception quality of the serving cell, Hys is a hysteresis parameter, and Thresh is a threshold value.

[0036] (iii) Event A3 (Neighbor becomes offset better than SpCell) Event A3 is an event in which the reception quality of a neighboring cell becomes offset better than the reception quality of the serving cell. For example, the entering condition is Mn + Ofn + Ocn - Hys > Mp + Ofp + Ocp + Off, and the leaving condition is Mn + Ofn + Ocn + Hys < Mp + Ofp + Ocp + Off.

[0037] where Mn is the reception quality of the neighboring cell, Ofn is the offset specific to the measurement object, and Ocn is the offset specific to the cell. Mp is the reception quality of the serving cell, Ofp is the offset specific to the measurement object, and Ocp is the offset specific to the cell. Hys is the hysteresis parameter, and Off is the parameter used in Event A3.

[0038] (iv) Event A4 (Neighbor becomes better than threshold) Event A4 is an event in which the reception quality of a neighboring cell becomes better than a threshold. For example, the entering condition is Mn + Ofn + Ocn - Hys > Thresh, and the leaving condition is Mn + Ofn + Ocn + Hys < Thresh.

[0039] where Mn is the reception quality of the neighboring cell, Ofn is an offset specific to the measurement object, Ocn is an offset specific to the cell, Hys is a hysteresis parameter, and Thresh is a threshold value.

[0040] (v) Event A5 (SpCell becomes worse than threshold1 and neighbor becomes better than threshold2) Event A5 is an event in which the reception quality of the serving cell becomes worse than a threshold and the reception quality of the neighboring cell becomes better than a threshold. For example, the entering condition is Mp + Hys < Thresh1 and Mn + Ofn + Ocn - Hys > Thresh2, and the leaving condition is Mp - Hys > Thresh1 and Mn + Ofn + Ocn + Hys < Thresh2.

[0041] where Ms is the receiving quality of the serving cell, Hys is a hysteresis parameter, Thresh1 is a threshold, Mn is the receiving quality of the neighboring cell, Ofn is a measurement object-specific offset, and Ocn is a cell-specific offset, Hys is a hysteresis parameter, and Thresh2 is a threshold.

[0042] (vi) Event A6 (Neighbor becomes offset better than SCell) Event A6 is an event in which the reception quality of a neighboring cell becomes offset better than the reception quality of an SCell (Secondary Cell). For example, the entering condition is Mn + Ocn - Hys > Ms + Ocs + Off, and the leaving condition is Mn + Ocn + Hys < Ms + Ocs + Off.

[0043] In addition to the events described above, events related to RATs (Radio Access technologies) (e.g., B1 (Inter RAT neighbor becomes better than threshold), B2 (Serving becomes worse than threshold1 and inter RAT neighbor becomes better than threshold2)) may be included.

[0044] Here, Mn is the reception quality of the neighboring cell, Ocn is a cell-specific offset, Ms is the reception quality of the SCell, Ocs is a cell-specific offset, Hys is a hysteresis parameter, and Off is a parameter used in Event A6.

[0045] In the wireless communication system 10, such an AI / ML model can be used to optimize the mobility or handover of the UE 200. The AI / ML model may be provided in the OAM / RIC 40 or the gNB 100. Furthermore, the AI / ML model may be provided in the UE 200.

[0046] Next, the functional block configuration of the wireless communication system 10 will be described. Specifically, the functional block configurations of the gNB 100 and the UE 200 will be described. Fig. 2 is a functional block configuration diagram of the gNB 100. Fig. 3 is a functional block configuration diagram of the UE 200.

[0047] (2.1) gNB100 As shown in FIG. 2, the gNB100 includes a wireless communication unit 110, a handover processing unit 120, an AI / ML model unit 130, and a control unit 140.

[0048] The wireless communication unit 110 transmits downlink signals (DL signals) conforming to NR. The wireless communication unit 110 also receives uplink signals (UL signals) conforming to NR. The wireless communication unit 110 may transmit DL signals and receive UL signals using one or more transmission / reception points (TRPs). In this embodiment, a TRP may be interpreted as meaning multiple DL transmission antennas.

[0049] The handover processing unit 120 executes handover of the UE 200. Specifically, the handover processing unit 120 executes handover of the UE 200 from a serving cell to another nearby cell.

[0050] The serving cell may be simply interpreted as a cell to which the UE 200 is connected, but more precisely, in the case of an RRC_CONNECTED UE (connected state in the radio resource control layer) in which carrier aggregation (CA) is not configured, there is only one serving cell that constitutes the primary cell. In the case of an RRC_CONNECTED UE configured using CA, the serving cell may be interpreted as indicating a set of one or more cells including the primary cell and all secondary cells.

[0051] The handover may also include a conditional handover (CHO) and / or a dual active protocol stack (DAPS) handover. CHO can execute a handover initiated by the UE 200 when a specific execution condition is met. If CHO is not applicable, a normal handover may be executed (which may be called CHO recovery). In CHO recovery, the UE 200 executes cell selection after a CHO failure. If a CHO candidate cell is selected, the UE 200 can directly apply conditional RRC Reconfiguration of the selected cell to reconnect without transmitting an RRC Reestablishment Request to the candidate target cell.

[0052] The execution condition may consist of one or two trigger conditions (CHO event A3 / A5 specified in 3GPP TS38.331). A single reference signal (RS) type may be triggered, and up to two different trigger quantities (e.g., Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ), RSRP and Signal-to-Interference plus Noise power Ratio (SINR)) may be simultaneously set for the evaluation of the CHO execution condition for a single candidate cell.

[0053] The AI / ML model unit 130 executes a process using a learning model (AI / ML model). Specifically, the AI / ML model unit 130 executes a process using an AI / ML model that is applied to optimization of mobility and / or handover of the UE 200.

[0054] In particular, in this embodiment, the AI / ML model unit 130 may transmit a measurement configuration (MeasConfig) that configures measurements using the AI / ML model to the UE 200. In this embodiment, the AI / ML model unit 130 may configure a transmission unit that transmits the measurement configuration to the terminal. The measurement configuration may be transmitted to the UE 200 by, for example, a message of a radio resource control layer (RRC).

[0055] The AI / ML model unit 130 may receive a measurement report including a measurement result generated by applying an AI / ML model to a measurement object (MeasObject) included in the measurement configuration from the UE 200. In this embodiment, the AI / ML model unit 130 may constitute a receiving unit that receives the measurement report from the terminal.

[0056] Furthermore, the AI / ML model unit 130 may transmit to the UE 200 a request for reporting the accuracy of a predicted value by the AI / ML model of a designated target. In this embodiment, the AI / ML model unit 130 may configure a transmitter that transmits the report request to the terminal. The designated target may refer to a target of prediction by the AI / ML model, and may include, for example, a cell quality measurement value (e.g., RSRP), a probability of handover failure (HOF), a probability of radio link failure (RLF), etc.

[0057] The AI / ML model unit 130 may receive a prediction result including a predicted value of the target predicted by the AI / ML model and the accuracy of the predicted value from the UE 200. In this embodiment, the AI / ML model unit 130 may configure a receiving unit that receives the prediction result from the terminal. Specifically, the AI / ML model unit 130 may receive a prediction result including a predicted value and the prediction accuracy of the predicted value.

[0058] The accuracy of the predicted value may be indicated by a percentage or by multiple stages. Furthermore, an evaluation value for the predicted value (which may also be called an estimated value) may be used. The evaluation value may be a parameter indicating the degree of agreement between a predicted value based on past prediction results and an actual measurement value, or a parameter indicating the degree of deviation between the predicted value and the actual measurement value. More specific details of the prediction results will be described later.

[0059] The control unit 140 controls each functional block constituting the gNB 100. In particular, in this embodiment, the control unit 140 may use the AI / ML model unit 130 to obtain predicted values ​​such as measured values ​​of cell quality, probability of HOF, and probability of RLF, and may perform control related to self-organizing networks (SON) according to the predicted values ​​and the accuracy of the predicted values.

[0060] Furthermore, the control unit 140 may perform mobility control of the UE 200, including handover, based on the cell quality measurement results and HOF / RLF reports acquired from the UE 200. The measurement results and reports may be predicted using an AI / ML model.

[0061] In this embodiment, the channels include a control channel and a data channel, such as a physical downlink control channel (PDCCH), a physical uplink control channel (PUCCH), a physical random access channel (PRACH), and a physical broadcast channel (PBCH).

[0062] The data channels include a physical downlink shared channel (PDSCH) and a physical uplink shared channel (PUSCH).

[0063] The reference signal includes a Demodulation Reference Signal (DMRS), a Sounding Reference Signal (SRS), a Phase Tracking Reference Signal (PTRS), and a Channel State Information-Reference Signal (CSI-RS), and the signal includes a channel and a reference signal. Furthermore, the data may refer to data transmitted via a data channel.

[0064] (2.2) UE 200 As shown in FIG. 3 , the UE 200 includes a radio communication unit 210, an AI / ML model unit 215, a measurement processing unit 220, a handover execution unit 230, and a control unit 240.

[0065] The wireless communication unit 210 transmits an uplink signal (UL signal) conforming to NR. The wireless communication unit 210 also receives an uplink signal (DL signal) conforming to NR.

[0066] The AI / ML model unit 215 executes processing using a learning model (AI / ML model). The AI / ML model unit 215 may have the same functions as the AI / ML model unit 130 of the gNB 100. The AI / ML model unit may be provided in either the gNB 100 or the UE 200, or may be provided in both.

[0067] The AI / ML model unit 215 may transmit prediction information indicating a predicted result of handover failure (HOF) or radio link failure (RLF) to the network. In this embodiment, the AI / ML model unit 215 may constitute a transmitting unit that transmits the prediction information to the network.

[0068] The prediction results of HOF and RLF may be targeted at the serving cell or at neighboring cells (which may also be called adjacent cells, peripheral cells, etc.).

[0069] The AI / ML model unit 215 may transmit prediction information including at least one of the probability of handover failure and the probability of radio link failure. The probability of occurrence may be indicated by a percentage or by multiple stages. The prediction information may be transmitted to the network by an AI / ML report or a Measurement Report.

[0070] The AI / ML model unit 215 may transmit prediction information including the probability of HOF / RLF occurring in the serving cell or neighboring cells, and may also include the probability of beam-level failure (BF).

[0071] Furthermore, the AI / ML model unit 215 may transmit prediction information including a degree of match between the positions of neighboring cells and the movement trajectory of the UE 200. The movement trajectory of the UE 200 may be interpreted as time-series information indicating the position of the UE 200. The movement trajectory may be past information or predicted future information. The movement trajectory may be information that allows determination of the positional relationship with a neighboring cell (which may be a serving cell) (such as the distance from a predetermined position of the cell).

[0072] The AI / ML model unit 215 may transmit prediction information including a future quality prediction value in a neighboring cell. Specifically, the AI / ML model unit 215 may predict a future value of cell quality (RSRP, RSRQ, etc.) in a neighboring cell (which may be a serving cell) and transmit prediction information including the predicted value. The future is not particularly limited, but considering the accuracy of the predicted value, it is preferable that the target time be a point in time several tens of milliseconds in the future.

[0073] Furthermore, the AI / ML model unit 215 may transmit prediction results including various predicted values ​​by the AI / ML model and the accuracy of the predicted values ​​to a network. In this embodiment, the AI / ML model unit 215 may constitute a transmitting unit that transmits the prediction results to a network.

[0074] As described above, the accuracy of the predicted value may be indicated by a percentage, or may be indicated by multiple stages, etc. The prediction result may include the predicted value and the accuracy of the predicted value, but the predicted value and the accuracy of the predicted value do not necessarily have to be transmitted together, and the accuracy of the predicted value may be transmitted less frequently than the predicted value.

[0075] The AI / ML model unit 215 may transmit a prediction result including the degree of agreement between a past predicted value of a specified target (measurement value, HOF / RLF, etc.) and an actual measured value of the target (which may include the actual occurrence of HOF / RLF). The degree of agreement may be calculated based on the past predicted value and the actual measured value. The degree of agreement may be expressed as a percentage or in multiple stages. Note that the accuracy of the predicted value does not necessarily take into account past performance, and may be uniquely determined depending on the length of time from the present to the time of prediction, the type of quality, etc.

[0076] The AI / ML model unit 215 may receive a request to report the accuracy of the predicted value from the network. In this embodiment, the AI / ML model unit 215 may constitute a receiving unit that receives the report request from the network.

[0077] Based on the received report request, the AI / ML model unit 215 may obtain a predicted value and the accuracy of the predicted value within a specified time period and transmit the prediction result including the predicted value and the accuracy to the network. The AI / ML model unit 215 may also transmit the prediction result including the actual measured value of the specified prediction target. In other words, the AI / ML model unit 215 can report the predicted value, the accuracy of the predicted value, and the actual measured value to the network.

[0078] The measurement processing unit 220 can measure the quality of the serving cell of the UE 200 and neighboring cells of the serving cell and report the measurement result to the network (Measurement Report). The measurement processing unit 220 can perform measurement reporting of the source cell and the target cell during handover.

[0079] The quality to be measured may be, for example, the quality (for example, RSRP, RSRQ) included in the Measurement Report specified in 3GPP TS38.331.

[0080] The measurement processing unit 220 receives a measurement configuration (MeasConfig) that configures a measurement using an AI / ML model from a network. In this embodiment, the measurement processing unit 220 may constitute a receiving unit that receives the measurement configuration from the network.

[0081] The measurement processing unit 220 may perform measurement using the AI / ML model unit 215 in accordance with the received measurement configuration and under the control of the control unit 240. Measurement using an AI / ML model may mean predicting cell quality (which may include beam quality) for future or different radio resources (e.g., frequencies) based on actual measurement values ​​of cell quality.

[0082] The measurement processing unit 220 may receive a measurement configuration including identification information for identifying an AI / ML model or a function of the AI / ML model. Specifically, an AI model ID for identifying the AI / ML model itself may be used, or an AI functionality ID for identifying a function of the AI / ML model may be used.

[0083] The measurement processing unit 220 may receive a measurement configuration including a measurement report condition. Note that the measurement report here may be a report related to AI / ML (AI / ML reporting) or a Measurement Report. The condition may be, for example, the reporting period of AI / ML reporting, the volume (amount) of AI / ML reporting, the number of reports, or whether a predetermined event is satisfied. The predetermined event may be, for example, an event related to an AI / ML model or an event related to cell quality.

[0084] The measurement processing unit 220 may transmit a measurement report including the measurement result using the AI / ML model to the network based on the received measurement configuration. In this embodiment, the measurement processing unit 220 may constitute a transmitting unit that transmits the measurement report to the network.

[0085] Furthermore, the measurement processing unit 220 may transmit the measurement report based on the above-mentioned conditions. Specifically, the measurement processing unit 220 may transmit the measurement report when the conditions are satisfied (or not satisfied).

[0086] The measurement processing unit 220 may receive a stop instruction from the network to stop the measurement report. Based on the received stop instruction, the measurement processing unit 220 may stop transmitting the measurement report including the measurement result using the AI / ML model to the network. The stop instruction may be temporary or permanent. The stop may be interpreted as a cancellation or a stop.

[0087] The handover execution unit 230 executes handover of the UE 200. Specifically, the handover execution unit 230 may execute handover to a transfer destination cell (NG-RAN node) based on control by the gNB 100.

[0088] Furthermore, the handover execution unit 230 can execute processes related to normal handover (legacy handover) and conditional handover (CHO).

[0089] In the case of CHO, the handover execution unit 230 may transition to the candidate cell when an execution condition is satisfied. As described above, the execution condition may be determined based on the quality of the reference signal (RS), specifically, the value of RSRP, RSRQ, or SINR.

[0090] In addition, the destination of the CHO may or may not be accompanied by an SCG. In other words, the destination cell of the CHO may be a single cell or may be composed of multiple cells (which may be read as a cell group) according to the DC.

[0091] Furthermore, the handover execution unit 230 can receive a handover request (handover command) of the UE 200 from the network. In this embodiment, the handover execution unit 230 may configure a receiving unit. The handover command may include an indication to instruct deletion of an AI / ML model for the source cell of the handover source.

[0092] The control unit 240 controls each functional block constituting the UE 200. In particular, in this embodiment, the control unit 240 may use the AI / ML model unit 215 to execute control related to measurement configuration and measurement reporting by the measurement processing unit 220.

[0093] Specifically, the control unit 240 may apply an AI / ML model to a measurement object (MeasObject) included in a measurement configuration received from the network to generate a measurement result. The control unit 240 may also predict the occurrence of a handover failure (HOF) or a radio link failure (RLF) using the AI / ML model.

[0094] The control unit 240 may generate a predicted value of a designated target using the AI / ML model. As described above, the designated target may refer to a target to be predicted by the AI / ML model, and may include, for example, a cell quality measurement value (e.g., RSRP), a probability of handover failure (HOF), a probability of radio link failure (RLF), etc.

[0095] The control unit 240 may select an AI / ML model or a function of the AI / ML model based on identification information that identifies the AI / ML model or a function of the AI / ML model. Specifically, the control unit 240 can select the AI / ML model itself or a function that operates in the AI / ML model unit 215 based on an AI model ID that identifies the AI / ML model itself or an AI functionality ID that identifies a function of the AI / ML model.

[0096] (3) Operation of the Wireless Communication System Next, the operation of the wireless communication system 10 will be described. Specifically, the operation of predicting the measurement results of cell quality using an AI / ML model will be described. Note that, although the following operation example will be mainly described with respect to a cell, a similar operation may also be performed with respect to a beam BM (see FIG. 1).

[0097] (3.1) Example of AI / ML Model Configuration Figure 4 shows an example of the functional architecture of an AI / ML model. As shown in Figure 4, the architecture may include the following functions:

[0098] Data collection: Providing input data for model training and model inference functions.

[0099] Model training: Train, validate, and test ML models. As part of the model testing procedure, model performance metrics may be generated.

[0100] The model training function may also be responsible for data preparation (data pre-processing and cleaning, formatting, transformation, etc.).

[0101] Model inference: Provides inference output (such as a prediction or decision). The model inference function may provide control of the model inference to the model management / performance monitoring function.

[0102] Model management / performance monitoring: Manage ML models and monitor model performance.

[0103] (3.2) Operation Example 1 In this operation example, settings related to AI / ML reporting (or Measurement Report) are performed on the UE.

[0104] Fig. 5 shows an example of a sequence relating to setting of AI / ML reporting (Measurement Report) according to Operation Example 1. Fig. 6 shows an example of the configuration of an information element (IE) of AI / ML reporting.

[0105] The gNB may instruct the UE of the AI / ML measurement configuration in the following manner:

[0106] - AI / ML measurement objects may be specified (see Figure 6).

[0107] The AI / ML measurement object may refer to an object to be predicted using an AI / ML model (e.g., a specific frequency (band)).

[0108] - An AI model ID may be specified. Also, an AI functionality ID may be specified (see Figure 6).

[0109] As described above, the AI ​​model ID may identify the AI / ML model itself, and the AI ​​functionality ID may identify a function of the AI / ML model.

[0110] An AI / ML measurement ID and an AI / ML reporting config ID may be specified (see Figure 6).

[0111] The AI / ML measurement ID may have a role in managing the number of AI / ML measurements. The AI / ML measurement ID may have a role in associating an AI / ML measurement object with an AI / ML reporting config, an AI model ID, or an AI functionality ID.

[0112] ・AI / ML reporting config may include conditions for AI / ML reporting.

[0113] For example, a predetermined period for the UE to periodically transmit the AI / ML reporting may be set. The predetermined period may be set in advance by the gNB.

[0114] In addition, the UE may transmit reports to the network according to a predetermined report amount, which may be preset by the gNB.

[0115] The UE may transmit the AI / ML reporting to the gNB up to a predetermined maximum number of times, or may transmit the AI / ML reporting when a predetermined event is satisfied.

[0116] In this way, the UE can perform AI / ML reporting (Measurement Report), but may want to discontinue (stop) AI / ML reporting if the accuracy of the AI / ML model is poor or if the network is congested.

[0117] Therefore, when the UE receives an instruction to cancel / stop AI / ML reporting from the network, the UE may stop AI / ML reporting.

[0118] The cancellation / stop instruction may be realized by an RRC message, a Medium Access Control (MAC) control element (MAC CE), or a PDCCH. The cancellation / stop instruction may be per AI / ML measurement object or per frequency, or per AI model ID or AI functionality ID. Alternatively, the cancellation / stop instruction may be per AI / ML measurement ID or per AI / ML reporting ID.

[0119] According to the above-described operational example, the configuration of the information element (IE) for AI / ML reporting becomes clear, and configuration and reporting using the AI / ML model can be performed appropriately and efficiently between the UE and the gNB.

[0120] (3.3) Operational Example 2 In this operational example, HOF / RLF (or occurrence probability) is predicted using an AI / ML model. In addition, in this operational example, the predicted HOF / RLF may be reported to the network by the UE via the OAM / RIC 40 (see FIG. 1).

[0121] Fig. 7 shows an example sequence related to AI / ML reporting according to operation example 2. Fig. 8 shows the relationship between a movement trajectory of a UE and neighboring cells according to operation example 2. Fig. 9 shows an example of AI / ML reporting according to operation example 2.

[0122] The UE may send an AI / ML reporting to the gNB containing the following information:

[0123] - Probability of RLF occurrence in the serving cell Y ms after a predetermined timing (e.g., the timing when the gNB receives the AI / ML reporting (time point X)) - Probability of beam failure occurrence in the serving cell Y ms after a predetermined timing (e.g., the timing when the gNB receives the AI / ML reporting (time point X)) - Probability of HOF occurring during handover Y ms after a predetermined timing (e.g., the timing when the gNB receives the AI / ML reporting (time point X)) - Probability of HOF occurring during handover to a neighboring cell Y ms after a predetermined timing (e.g., the timing when the gNB receives the AI / ML reporting (time point X)) - Degree of match between neighboring cells and future UE trajectory (UE movement trajectory) Y ms after a predetermined timing (e.g., the timing when the gNB receives the AI / ML reporting (time point X)) The UE movement trajectory is, for example, a predicted future trajectory along the dotted line shown in Figure 8, and it is sufficient if the positional relationship (distance, etc.) with a specific neighboring cell (e.g., cell C) can be determined.

[0124] The quality (RSRP, RSRQ, SINR) of each neighboring cell in the future (for example, 20 ms, 30 ms, 40 ms later) The predetermined timing may be the timing when the AI / ML reporting is received or the timing when the UE generates the AI / ML reporting. Alternatively, it may be the timing when the UE transmits the AI / ML reporting, or may be the timing specified by the network, etc. The above information may be included in the Measurement Report.

[0125] In addition, the HOF / RLF prediction value by the AI / ML model may be generated by the O-RAN Non-Real Time RIC or Near-Real Time RIC.

[0126] Fig. 10 shows an example of the configuration of a RIC based on the O-RAN architecture. As shown in Fig. 10, the RIC may include a Near-Real Time RIC and / or a Non-Real Time RIC. The Near-Real Time RIC may be connected to the O-DU and the Non-Real Time RIC via interfaces (A1, E2).

[0127] The Near-Real Time RIC may be connected to an O-eNB (radio base station) via an interface (E2). Such a RIC included in the O-RAN architecture may constitute an OAM / RIC 40.

[0128] In such a RIC architecture, feedback on the performance of the AI / ML model may be provided to the Near-Real Time RIC or the Non-Real Time RIC.

[0129] The O-RAN Non-Real Time RIC may notify the O-CU-CP or O-DU of the following AI / ML predicted values ​​via the O1 interface. The predicted values ​​may be per UE, and UE associated signaling may be used to transmit the predicted values ​​(i.e., they may be associated with UE IDs).

[0130] Alternatively, the O-RAN Near-Real Time RIC may notify the O-CU-CP or O-DU of the following AI / ML predicted values ​​via the E2 interface: The predicted values ​​may be per UE, and UE associated signaling may be used for transmitting the predicted values ​​(i.e., the predicted values ​​may be associated with the UE ID).

[0131] the probability of RLF occurring in the serving cell Y ms after a predetermined timing (e.g., the timing when the gNB receives the AI / ML reporting (time point X)); the probability of HOF occurring during handover Y ms after a predetermined timing (e.g., the timing when the gNB receives the AI / ML reporting (time point X)); the probability of HOF occurring during handover to a neighboring cell Y ms after a predetermined timing (e.g., the timing when the gNB receives the AI / ML reporting (time point X)); the degree of match between a neighboring cell and a future UE trajectory (UE movement trajectory) Y ms after a predetermined timing (e.g., the timing when the gNB receives the AI / ML reporting (time point X)); and the quality (RSRP, RSRQ, SINR) of neighboring cells in the future (e.g., 20 ms, 30 ms, 40 ms later). The predetermined timing may be the timing when the AI / ML reporting is received or the timing when the UE generates the AI / ML reporting. Alternatively, it may be the timing when the UE transmits the AI / ML reporting, or a timing specified by the network, etc.

[0132] According to the above-described operation example, the UE (and the OAM / RIC) can transmit predicted values ​​of handover failure (HOF) and radio link failure (RLF) to the network. Specifically, the UE can report the future occurrence probability of HOF and RLF (which may include beams) to the network.

[0133] Therefore, the predicted values ​​of HOF and RLF can be effectively utilized on the network side, which can contribute to the construction of SON (Self-Organizing Networks).

[0134] (3.4) Operation Example 3 In this operation example, a predicted value by an AI / ML model and information indicating the accuracy of the predicted value are reported from the UE to the network. The predicted value (estimated value) by the AI / ML model may have good or bad accuracy (prediction accuracy), and if the predicted value deviates from the actual measured value, the predicted value may be difficult for the network operator to use.

[0135] FIG. 11 shows an example of a sequence relating to setting of AI / ML reporting (Measurement Report) according to the third operation example.

[0136] As shown in FIG. 11, when sending an AI / ML reporting (or Measurement Report), the UE may add a parameter (prediction accuracy) indicating the accuracy (precision) of the predicted value (AI / ML estimated value) by the AI / ML model.

[0137] The parameter may be an evaluation value for the AI / ML estimated value in the UE. For example, the evaluation value may be a degree of match calculated from the relationship between the AI / ML estimated value and the measured value within a predetermined time period in the past. The degree of match may be expressed as a percentage or in multiple levels (five levels, three levels, etc.). Alternatively, the evaluation value may be a parameter indicating the degree of deviation between the AI / ML estimated value and the actual measured value.

[0138] The gNB may request the UE to report the parameter (prediction accuracy) for the immediate future (e.g., 30 minutes). The gNB may also request the UE to report the parameter for a specific prediction target (e.g., the RLF occurrence probability in the serving cell).

[0139] The UE may report the parameter periodically based on the report request, or may report the parameter when a predetermined event is met (such as when the parameter falls below or exceeds a predetermined threshold).

[0140] The UE may report actual information in the AI / ML reporting, such as the measured quality information (RSRP, RSRQ, SINR) of its own cell (beam) and / or neighboring cells (beams).

[0141] In the AI / ML reporting, the UE may report the AI / ML estimated values ​​and the actual information (measured values) separately, or may report them together. When reporting them together, an indication may be added to distinguish between the AI / ML estimated values ​​and the actual measured values. Alternatively, the AI / ML estimated values ​​and the actual measured values ​​may be included in separate containers (predetermined locations in the report).

[0142] According to the above-described operational example, the UE reports the predicted value by the AI / ML model and information indicating the accuracy of the predicted value to the network. Therefore, the network can appropriately determine how to use the predicted value based on the accuracy. In other words, reporting information indicating the accuracy of the measured value can contribute to utilizing various predicted values ​​using the AI / ML model according to their accuracy.

[0143] (4) Other Embodiments Although the embodiments have been described above, it will be obvious to those skilled in the art that the present invention is not limited to the description of the embodiments, and that various modifications and improvements are possible.

[0144] For example, in the above-described embodiment, operation examples 1 to 3 have been described, but only operations according to some of the operation examples may be applied.

[0145] In the above description, configure, activate, update, indicate, enable, specify, and select may be interchangeable. Similarly, link, associate, correspond, and map may be interchangeable, and allocate, assign, monitor, and map may be interchangeable.

[0146] Furthermore, specific, dedicated, UE-specific, and UE-dedicated may be interchangeable. Similarly, common, shared, group-common, UE-common, and UE-shared may be interchangeable.

[0147] In the present disclosure, terms such as "precoding," "precoder," "weight (precoding weight)," "Quasi-Co-Location (QCL)," "Transmission Configuration Indication state (TCI state)," "spatial relation," "spatial domain filter," "transmit power," "phase rotation," "antenna port," "antenna port group," "layer," "number of layers," "rank," "resource," "resource set," "resource group," "beam," "beam width," "beam angle," "antenna," "antenna element," "panel," etc. may be used interchangeably.

[0148] Furthermore, the block diagrams (FIGS. 2 and 3) used in the description of the above-described embodiments show functional blocks. These functional blocks (components) are realized by any combination of hardware and / or software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (e.g., wired, wireless, etc.) and these multiple devices. The functional block may also be realized by combining software with the single device or multiple devices.

[0149] Functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how each is implemented.

[0150] Furthermore, the above-described gNB 100 and UE 200 (the devices) may function as a computer that performs processing of the wireless communication method of the present disclosure. Figure 12 is a diagram showing an example of the hardware configuration of the devices. As shown in Figure 12, the devices may be configured as a computer including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

[0151] In the following description, the term "apparatus" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the apparatus may be configured to include one or more of the apparatuses shown in the drawings, or may be configured to exclude some of the apparatuses.

[0152] Each functional block of the device (see FIGS. 2 and 3) is realized by any hardware element of the computer device or a combination of the hardware elements.

[0153] In addition, each function of the device is realized by loading specified software (programs) onto hardware such as processor 1001 and memory 1002, causing processor 1001 to perform calculations, control communication via communication device 1004, and control at least one of reading and writing data in memory 1002 and storage 1003.

[0154] The processor 1001 controls the entire computer by running, for example, an operating system, and may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control unit, an arithmetic unit, and registers.

[0155] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. Furthermore, the various processes described above may be executed by a single processor 1001, or may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may be transmitted from a network via a telecommunications line.

[0156] The memory 1002 is a computer-readable recording medium and may be configured by at least one of, for example, a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 1002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 may store a program (program code), a software module, etc., capable of executing a method according to an embodiment of the present disclosure.

[0157] Storage 1003 is a computer-readable recording medium, and may be, for example, at least one of an optical disk such as a Compact Disc ROM (CD-ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray disc), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy disk, a magnetic strip, etc. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned recording medium may be, for example, a database, a server, or other suitable medium including at least one of memory 1002 and storage 1003.

[0158] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, or a communication module.

[0159] The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. to realize, for example, at least one of Frequency Division Duplex (FDD) and Time Division Duplex (TDD).

[0160] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that outputs to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0161] Furthermore, each device such as the processor 1001 and the memory 1002 is connected to a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0162] Furthermore, the device may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0163] Furthermore, the notification of information is not limited to the aspects / embodiments described in the present disclosure, and may be performed using other methods. For example, the notification of information may be performed by physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), higher layer signaling (e.g., RRC signaling, Medium Access Control (MAC) signaling, broadcast information (Master Information Block (MIB), System Information Block (SIB))), other signals, or a combination thereof. Furthermore, the RRC signaling may be referred to as an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.

[0164] Each aspect / embodiment described in the present disclosure may be applied to at least one of a system using Long Term Evolution (LTE), LTE-Advanced (LTE-A), SUPER 3G, IMT-Advanced, a 4th generation mobile communication system (4G), a 5th generation mobile communication system (5G), a 6th generation mobile communication system (6G), an xth generation mobile communication system (xG) (where x is, for example, an integer or a decimal), Future Radio Access (FRA), New Radio (NR), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), or other suitable system, and a next-generation system extended based on these. Furthermore, a combination of multiple systems (e.g., a combination of at least one of LTE and LTE-A with 5G) may also be applied.

[0165] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0166] In the present disclosure, a specific operation described as being performed by a base station may also be performed by its upper node in some cases. In a network consisting of one or more network nodes having a base station, it is clear that various operations performed for communication with a terminal may be performed by at least one of the base station and another network node other than the base station (e.g., MME or S-GW, etc., but are not limited to these). Although the above example illustrates a case where there is one other network node other than the base station, a combination of multiple other network nodes (e.g., MME and S-GW) may also be used.

[0167] Information, signals (information, etc.) may be output from a higher layer (or a lower layer) to a lower layer (or a higher layer), or may be input and output via multiple network nodes.

[0168] The input and output information may be stored in a specific location (for example, a memory) or may be managed using a management table. The input and output information may be overwritten, updated, or added to. The output information may be deleted. The input information may be transmitted to another device.

[0169] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0170] The aspects / embodiments described in this disclosure may be used alone, in combination, or switched depending on the implementation. Notification of predetermined information (e.g., notification that "X is true") is not limited to explicit notification, but may be implicit (e.g., not notifying the predetermined information).

[0171] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0172] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then these wired and / or wireless technologies are included within the definition of transmission media.

[0173] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0174] Note that terms described in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of a channel and a symbol may be a signal (signaling). Furthermore, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, a cell, a frequency carrier, etc.

[0175] As used in this disclosure, the terms "system" and "network" are used interchangeably.

[0176] Furthermore, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information. For example, a radio resource may be indicated by an index.

[0177] The names used for the above-described parameters are not intended to be limiting in any way. Furthermore, the mathematical expressions using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (e.g., PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.

[0178] In this disclosure, terms such as "base station (BS)," "radio base station," "fixed station," "NodeB," "eNodeB (eNB)," "gNodeB (gNB)," "access point," "transmission point," "reception point," "transmission / reception point," "cell," "sector," "cell group," "carrier," and "component carrier" may be used interchangeably. Base stations may also be referred to by terms such as macrocell, small cell, femtocell, and picocell.

[0179] A base station can accommodate one or more (e.g., three) cells (also called sectors). When a base station accommodates multiple cells, the overall coverage area of ​​the base station can be divided into multiple smaller areas, and each smaller area can be provided with communication services by a base station subsystem (e.g., a small indoor base station (Remote Radio Head: RRH)).

[0180] The terms "cell" or "sector" refer to part or all of the coverage area of ​​a base station and / or base station subsystem that provides communication services within that coverage area.

[0181] In the present disclosure, the base station transmitting information to a terminal may be interpreted as the base station instructing the terminal to control or operate based on the information.

[0182] In this disclosure, the terms "Mobile Station (MS)," "user terminal," "User Equipment (UE)," "terminal," etc. may be used interchangeably.

[0183] A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.

[0184] At least one of the base station and the mobile station may be referred to as a transmitting device, a receiving device, a communication device, etc. At least one of the base station and the mobile station may be a device mounted on a mobile object, the mobile object itself, etc. The mobile object refers to a movable object, and may move at any speed. Naturally, this also includes cases where the mobile object is stationary. Examples of the mobile object include, but are not limited to, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, handcars, rickshaws, ships and other watercraft, airplanes, rockets, satellites, drones (registered trademark), multicopters, quadcopters, balloons, and objects mounted thereon. The mobile object may also be a mobile object that moves autonomously based on an operational command. It may be a vehicle (e.g., a car, an airplane, etc.), an unmanned mobile object (e.g., a drone, an autonomous vehicle, etc.), or a robot (manned or unmanned). At least one of the base station and the mobile station may be a device that does not necessarily move during communication operations. For example, at least one of the base station and the mobile station may be an IoT (Internet of Things) device such as a sensor.

[0185] Furthermore, a base station in the present disclosure may be read as a mobile station (user terminal, the same applies hereinafter). For example, the aspects / embodiments of the present disclosure may be applied to a configuration in which communication between a base station and a mobile station is replaced with communication between multiple mobile stations (which may be called, for example, Device-to-Device (D2D) or Vehicle-to-Everything (V2X)). In this case, the mobile station may be configured to have the functions of a base station. Furthermore, terms such as "uplink" and "downlink" may be read as terms corresponding to terminal-to-terminal communication (for example, "side"). For example, terms such as an uplink channel and a downlink channel may be read as a side channel (or sidelink).

[0186] Similarly, a mobile station in the present disclosure may be interpreted as a base station, in which case the base station may have the functions of a mobile station.

[0187] A radio frame may be composed of one or more frames in the time domain. Each of the one or more frames in the time domain may be called a subframe. A subframe may further be composed of one or more slots in the time domain. A subframe may have a fixed time length (e.g., 1 ms) that is independent of numerology.

[0188] Numerology may be communication parameters that apply to the transmission and / or reception of a signal or channel, such as subcarrier spacing (SCS), bandwidth, symbol length, cyclic prefix length, transmission time interval (TTI), number of symbols per TTI, radio frame structure, specific filtering operations performed by the transceiver in the frequency domain, and specific windowing operations performed by the transceiver in the time domain.

[0189] A slot may consist of one or more symbols in the time domain (such as an Orthogonal Frequency Division Multiplexing (OFDM) symbol, a Single Carrier Frequency Division Multiple Access (SC-FDMA) symbol, etc.) A slot may be a numerology-based time unit.

[0190] A slot may include multiple minislots. Each minislot may consist of one or more symbols in the time domain. A minislot may also be called a subslot. A minislot may consist of fewer symbols than a slot. A PDSCH (or PUSCH) transmitted in a time unit larger than a minislot may be called PDSCH (or PUSCH) mapping type A. A PDSCH (or PUSCH) transmitted using a minislot may be called PDSCH (or PUSCH) mapping type B.

[0191] The radio frame, subframe, slot, minislot, and symbol all represent time units for transmitting signals, and may be referred to by other names corresponding to the radio frame, subframe, slot, minislot, and symbol.

[0192] For example, one subframe may be referred to as a transmission time interval (TTI), multiple consecutive subframes may be referred to as a TTI, or one slot or one minislot may be referred to as a TTI. That is, at least one of the subframe and the TTI may be a subframe (1 ms) in existing LTE, a period shorter than 1 ms (e.g., 1-13 symbols), or a period longer than 1 ms. Note that the unit representing the TTI may be called a slot, minislot, etc., instead of a subframe.

[0193] Here, TTI refers to, for example, the smallest time unit for scheduling in wireless communication. For example, in an LTE system, a base station schedules each user terminal to allocate radio resources (such as frequency bandwidth and transmission power that can be used by each user terminal) in TTI units. Note that the definition of TTI is not limited to this.

[0194] The TTI may be a transmission time unit for a channel-encoded data packet (transport block), a code block, a code word, etc., or may be a processing unit for scheduling, link adaptation, etc. When a TTI is given, the time interval (e.g., the number of symbols) to which a transport block, a code block, a code word, etc. is actually mapped may be shorter than the TTI.

[0195] In addition, when one slot or one minislot is called a TTI, one or more TTIs (i.e., one or more slots or one or more minislots) may be the minimum time unit for scheduling, and the number of slots (minislots) constituting the minimum time unit for scheduling may be controlled.

[0196] A TTI having a time length of 1 ms may be referred to as a regular TTI (TTI in LTE Rel. 8-12), normal TTI, long TTI, regular subframe, normal subframe, long subframe, slot, etc. A TTI shorter than a regular TTI may be referred to as a shortened TTI, short TTI, partial or fractional TTI, shortened subframe, short subframe, minislot, subslot, slot, etc.

[0197] In addition, a long TTI (e.g., a normal TTI, a subframe, etc.) may be interpreted as a TTI having a time length of more than 1 ms, and a short TTI (e.g., a shortened TTI, etc.) may be interpreted as a TTI having a TTI length shorter than the TTI length of a long TTI and equal to or greater than 1 ms.

[0198] A resource block (RB) is a resource allocation unit in the time domain and the frequency domain, and may include one or more consecutive subcarriers in the frequency domain. The number of subcarriers included in an RB may be the same regardless of numerology, for example, 12. The number of subcarriers included in an RB may be determined based on numerology.

[0199] The time domain of an RB may include one or more symbols and may have a length of one slot, one minislot, one subframe, or one TTI, each of which may consist of one or more resource blocks.

[0200] Note that one or more RBs may also be called a physical resource block (PRB), a sub-carrier group (SCG), a resource element group (REG), a PRB pair, an RB pair, etc.

[0201] Furthermore, a resource block may be composed of one or more resource elements (REs). For example, one RE may be a radio resource region of one subcarrier and one symbol.

[0202] A Bandwidth Part (BWP) (which may also be referred to as a fractional bandwidth) may represent a subset of contiguous common resource blocks (RBs) for a given numerology on a given carrier, where the common RBs may be identified by their index relative to a common reference point of the carrier. PRBs may be defined in a given BWP and numbered within that BWP.

[0203] The BWP may include a BWP for UL (UL BWP) and a BWP for DL ​​(DL BWP). One or more BWPs may be configured for a UE within one carrier.

[0204] At least one of the configured BWPs may be active, and the UE may not expect to transmit or receive a given signal / channel outside the active BWP. Note that the terms "cell," "carrier," etc. in this disclosure may be read as "BWP."

[0205] The above-described structures of the radio frame, subframe, slot, minislot, and symbol are merely examples. For example, the number of subframes included in a radio frame, the number of slots per subframe or radio frame, the number of minislots included in a slot, the number of symbols and RBs included in a slot or minislot, the number of subcarriers included in an RB, the number of symbols in a TTI, the symbol length, the cyclic prefix (CP) length, and other configurations may be changed in various ways.

[0206] The terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using one or more wires, cables, and / or printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0207] The reference signal may also be abbreviated as Reference Signal (RS) and may be called a pilot depending on the applicable standard.

[0208] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0209] The "means" in the configuration of each of the above devices may be replaced with "part," "circuit," "device," etc.

[0210] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed therein or that the first element must precede the second element in some way.

[0211] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.

[0212] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0213] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Furthermore, "judgment" and "decision" can include regarding resolving, selecting, choosing, establishing, comparing, etc. as having been "judged" or "decided." In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

[0214] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different."

[0215] 13 shows an example of the configuration of a vehicle 2001. As shown in Fig. 13, the vehicle 2001 includes a drive unit 2002, a steering unit 2003, an accelerator pedal 2004, a brake pedal 2005, a shift lever 2006, left and right front wheels 2007, left and right rear wheels 2008, an axle 2009, an electronic control unit 2010, various sensors 2021 to 2029, an information service unit 2012, and a communication module 2013.

[0216] The drive unit 2002 is composed of, for example, an engine, a motor, or a hybrid of an engine and a motor. The steering unit 2003 includes at least a steering wheel (also called a handle) and is configured to steer at least one of the front wheels and the rear wheels based on the operation of the steering wheel operated by the user. The electronic control unit 2010 is composed of a microprocessor 2031, memory (ROM, RAM) 2032, and a communication port (IO port) 2033. Signals from various sensors 2021 to 2027 provided in the vehicle are input to the electronic control unit 2010. The electronic control unit 2010 may also be called an ECU (Electronic Control Unit).

[0217] The signals from the various sensors 2021 to 2028 include a current signal from a current sensor 2021 that senses the current of the motor, a rotation speed signal of the front and rear wheels obtained by a rotation speed sensor 2022, an air pressure signal of the front and rear wheels obtained by an air pressure sensor 2023, a vehicle speed signal obtained by a vehicle speed sensor 2024, an acceleration signal obtained by an acceleration sensor 2025, an accelerator pedal depression amount signal obtained by an accelerator pedal sensor 2029, a brake pedal depression amount signal obtained by a brake pedal sensor 2026, a shift lever operation signal obtained by a shift lever sensor 2027, and a detection signal for detecting obstacles, vehicles, pedestrians, etc. obtained by an object detection sensor 2028.

[0218] The information service unit 2012 is composed of various devices, such as a car navigation system, an audio system, speakers, a television, and a radio, for providing (outputting) various types of information, such as driving information, traffic information, and entertainment information, and one or more ECUs for controlling these devices. The information service unit 2012 uses information acquired from external devices via the communication module 2013, etc., to provide various types of multimedia information and multimedia services to the occupants of the vehicle 1.

[0219] The information service unit 2012 may include input devices (e.g., keyboards, mice, microphones, switches, buttons, sensors, touch panels, etc.) that accept input from the outside, and may also include output devices (e.g., displays, speakers, LED lamps, touch panels, etc.) that output to the outside.

[0220] The driving assistance system unit 2030 is composed of various devices that provide functions for preventing accidents and reducing the driver's driving burden, such as millimeter-wave radar, LiDAR (Light Detection and Ranging), cameras, positioning locators (e.g., GNSS, etc.), map information (e.g., high-definition (HD) maps, autonomous vehicle (AV) maps, etc.), gyro systems (e.g., IMU (Inertial Measurement Unit), INS (Inertial Navigation System), etc.), AI (Artificial Intelligence) chips, and AI processors, as well as one or more ECUs that control these devices. The driving assistance system unit 2030 also transmits and receives various information via the communication module 2013 to realize driving assistance functions or autonomous driving functions.

[0221] The communication module 2013 can communicate with the microprocessor 2031 and components of the vehicle 1 via the communication port. For example, the communication module 2013 transmits and receives data via the communication port 2033 to and from a driving unit 2002, a steering unit 2003, an accelerator pedal 2004, a brake pedal 2005, a shift lever 2006, left and right front wheels 2007, left and right rear wheels 2008, an axle 2009, a microprocessor 2031 and memory (ROM, RAM) 2032 in the electronic control unit 2010, and sensors 2021 to 2028, which are provided in the vehicle 2001.

[0222] The communication module 2013 is a communication device that can be controlled by the microprocessor 2031 of the electronic control unit 2010 and can communicate with an external device. For example, it transmits and receives various information to and from the external device via wireless communication. The communication module 2013 may be located either inside or outside the electronic control unit 2010. The external device may be, for example, a base station, a mobile station, or the like.

[0223] The communication module 2013 may transmit at least one of signals from the above-mentioned various sensors 2021 to 2028 input to the electronic control unit 2010, information obtained based on the signals, and information based on input from the outside (user) obtained via the information service unit 2012 to an external device via wireless communication. The electronic control unit 2010, the various sensors 2021 to 2028, the information service unit 2012, etc. may be referred to as input units that accept input. For example, the PUSCH transmitted by the communication module 2013 may include information based on the above-mentioned input.

[0224] The communication module 2013 receives various information (traffic information, traffic signal information, vehicle-to-vehicle information, etc.) transmitted from external devices and displays it on an information service unit 2012 provided in the vehicle. The information service unit 2012 may also be called an output unit that outputs information (for example, outputs information to a device such as a display or speaker based on the PDSCH (or data / information decoded from the PDSCH) received by the communication module 2013). The communication module 2013 also stores the various information received from external devices in a memory 2032 that can be used by the microprocessor 2031. Based on the information stored in the memory 2032, the microprocessor 2031 may control the drive unit 2002, steering unit 2003, accelerator pedal 2004, brake pedal 2005, shift lever 2006, left and right front wheels 2007, left and right rear wheels 2008, axles 2009, sensors 2021 to 2028, and the like provided in the vehicle 2001.

[0225] (Additional Note) The above disclosure may be expressed as follows: A first feature is a terminal including: a receiving unit that receives, from a network, a measurement configuration that configures a measurement using a learning model; a control unit that applies the learning model to a measurement target included in the measurement configuration and generates a measurement result; and a transmitting unit that transmits, to the network, a measurement report that includes the measurement result based on the measurement configuration.

[0226] A second feature is that, in the first feature, the receiving unit receives the measurement setting including identification information that identifies the learning model or a function of the learning model, and the control unit selects the learning model or a function of the learning model based on the identification information.

[0227] A third feature based on the first or second feature is that the receiving unit receives the measurement configuration including a condition for the measurement report, and the transmitting unit transmits the measurement report based on the condition.

[0228] A fourth feature is that, in the first to third features, the receiving unit receives a stop instruction to stop the measurement report, and the transmitting unit stops transmitting the measurement report including the measurement result using the learning model based on the stop instruction.

[0229] A fifth feature is a terminal including a control unit that predicts the occurrence of handover failure or radio link failure using a learning model, and a transmission unit that transmits prediction information indicating the predicted result of the handover failure or the radio link failure to a network.

[0230] A sixth feature based on the fifth feature is that the transmitter transmits the prediction information including at least one of a probability of occurrence of the handover failure and a probability of occurrence of the radio link failure.

[0231] According to a seventh feature in the fifth or sixth feature, the transmitter transmits the prediction information including the occurrence probability in a serving cell or a neighboring cell.

[0232] As an eighth feature, in any one of the fifth to seventh features, the transmitter transmits the prediction information including a degree of match between a position of a neighboring cell and a movement trajectory of the terminal.

[0233] According to a ninth feature, in any one of the fifth to eighth features, the transmitter transmits the prediction information including a future quality prediction value in a neighboring cell.

[0234] A tenth feature is a terminal including a control unit that generates a predicted value of a specified target using a learning model, and a transmission unit that transmits the predicted value and a prediction result including the accuracy of the predicted value to a network.

[0235] According to an eleventh feature, in the tenth feature, the transmission unit transmits the prediction result including a degree of agreement between the predicted value in the past and an actual measured value of the target.

[0236] A twelfth feature, in the tenth or eleventh feature, includes a receiving unit that receives a request for reporting accuracy of the predicted value from the network, and the transmitting unit transmits the prediction result including the predicted value within a specified time period and the accuracy of the predicted value based on the report request.

[0237] According to a thirteenth feature, in any one of the tenth to twelfth features, the transmission unit transmits the prediction result including an actual measurement value of the target.

[0238] 10 Wireless communication system 20 NG-RAN 40 OAM / RIC 50 NF 100 gNB 110 Wireless communication unit 120 Handover processing unit 130 AI / ML model unit 140 Control unit 200 UE 210 Wireless communication unit 215 AI / ML model unit 220 Measurement processing unit 230 Handover execution unit 240 Control unit 1001 Processor 1002 Memory 1003 Storage 1004 Communication device 1005 Input device 1006 Output device 1007 Bus 2001 Vehicle 2002 Drive unit 2003 Steering unit 2004 Accelerator pedal 2005 Brake pedal 2006 Shift lever 2007 Left and right front wheels 2008 Left and right rear wheels 2009 Axle 2010 Electronic control unit 2012 Information service section 2013 Communication module 2021 Current sensor 2022 RPM sensor 2023 Air pressure sensor 2024 Vehicle speed sensor 2025 Acceleration sensor 2026 Brake pedal sensor 2027 Shift lever sensor 2028 Object detection sensor 2029 Accelerator pedal sensor 2030 Driving assistance system section 2031 Microprocessor 2032 Memory (ROM, RAM) 2033 Communication port

Claims

1. A terminal comprising: a control unit that predicts the occurrence of handover failure or radio link failure using a learning model; and a transmission unit that transmits prediction information indicating the predicted result of the handover failure or the radio link failure to a network.

2. The terminal according to claim 1, wherein the transmitting unit transmits the prediction information including at least one of the probability of handover failure and the probability of radio link failure.

3. The terminal according to claim 2, wherein the transmitting unit transmits the prediction information including the occurrence probability in a serving cell or a neighboring cell.

4. The terminal according to claim 1, wherein the transmitter transmits the prediction information including a degree of match between the positions of neighboring cells and the movement trajectory of the terminal.

5. The terminal according to claim 1, wherein the transmitter transmits the prediction information including a future quality prediction value in a neighboring cell.

6. A network device comprising: a control unit that predicts the occurrence of a handover failure or a radio link failure using a learning model; and a transmission unit that transmits prediction information indicating the predicted result of the handover failure or the radio link failure to a network.

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