WO2025210868A1 - Terminal, wireless base station, and wireless communication method - Google Patents

Abstract

A terminal according to the present invention uses a training model to generate a predicted value for a designated target, and transmits a prediction result, which includes said predicted value and the accuracy of said predicted value, to a network.

Description

Terminal, wireless base station, and wireless communication method

This disclosure relates to a terminal, a wireless base station, and a wireless communication method that utilizes an AI/ML model.

The 3rd Generation Partnership Project (3GPP: registered trademark) is developing 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.

3GPP Release 19 has established a work item (WI) on artificial intelligence/machine learning models (AI/ML Models) (Non-Patent Document 1).

For example, it is planned to consider using AI/ML models to predict cell quality measurement results, handover failures (HOF), radio link failures (RLF), etc.

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

Various predicted values using AI/ML models by terminals (User Equipment, UE) etc. can be highly accurate or less accurate. If there is a discrepancy between the predicted value by the AI/ML model and the actual measured value, the predicted value may not be very practical and may not necessarily be of great value to network operators. For this reason, simply using the predicted value may not always be appropriate.

The following disclosure has been made in light of this situation, and aims to provide a terminal, wireless base station, and wireless communication method that can contribute to utilization according to the accuracy of various predicted values using AI/ML models.

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

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 that configures measurements using a learning model to a terminal, 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.

One aspect of the present disclosure is a wireless communication method in a terminal that includes the steps of receiving, from a network, a measurement configuration 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, to the network, a measurement report that includes the measurement results based on the measurement configuration.

One aspect of the present disclosure is a terminal (UE200) that includes a control unit (control unit 240) that uses a learning model to predict the occurrence of a handover failure or a radio link failure, 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 the network.

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

One aspect of the present disclosure is a terminal (UE200) that includes a control unit (control unit 240) that uses a learning model to generate a predicted value for a specified target, 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.

One aspect of the present disclosure is a radio base station (gNB100) that includes a transmitter (AI/ML model unit 130) that transmits to a terminal a request to report the accuracy of a predicted value based on a specified target learning model, 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.

One aspect of the present disclosure is a wireless communication method in a terminal that includes the steps of generating a predicted value of a specified target using a learning model, and transmitting the predicted value and a prediction result including the accuracy of the predicted value to a network.

FIG. 1 is a diagram showing the overall configuration of a wireless communication system 10. Figure 2 is a functional block diagram of gNB100. FIG. 3 is a functional block diagram of the UE 200. Figure 4 shows an example of the functional architecture of an AI/ML model. Figure 5 is a diagram showing an example sequence for setting up AI/ML reporting (Measurement Report) for operation example 1. Figure 6 shows an example of the configuration of information elements (IEs) for AI/ML reporting. Figure 7 is a diagram showing an example sequence for AI/ML reporting related to operation example 2. FIG. 8 is a diagram illustrating the relationship between the movement trajectory of a UE and neighboring cells according to the second operation example. Figure 9 is a diagram showing an example of AI/ML reporting related to operation example 2. FIG. 10 is a diagram illustrating an example of the configuration of a RIC based on the O-RAN architecture. Figure 11 is a diagram showing an example sequence for setting up AI/ML reporting (Measurement Report) for operation example 3. Figure 12 is a diagram showing an example of the hardware configuration of gNB100 and UE200. FIG. 13 is a diagram showing an example of the configuration of a vehicle 2001.

The following describes embodiments based on the drawings. Note that identical or similar reference symbols are used to designate identical functions and configurations, and descriptions of these will be omitted where appropriate.

(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).

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

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 Figure 1.

The gNB100 may also employ a fronthaul (FH) interface specified by the Open Radio Access Network Alliance (O-RAN). The gNB100 may include an O-DU (O-RAN Distributed Unit) and an O-RU (O-RAN Radio Unit). The gNB100 can function as a type of NG-RAN node.

NG-RAN20 actually includes multiple NG-RAN nodes, specifically gNBs (or ng-eNBs), and is connected to a 5G-compliant core network (5GC, not shown). 5GC may introduce the concept of CUPS (Control and User Plane Separation), which clearly separates the functions of the user plane and the control plane.

NG-RAN20 may be connected to OAM/RIC40 and NF50 via 5GC or directly from NG-RAN20. OAM/RIC40 (network device) can provide functions related to operation and maintenance of the wireless communication system 10 (OAM). OAM/RIC40 can also provide functions related to control of NG-RAN20 (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, OAM/RIC40 may constitute an entity that performs operation, maintenance, or control.

NF50 may be interpreted as a logical node that provides network functions. NF50 may include the Access and Mobility Management Function (AMF), which is included in the 5G system architecture and provides access and mobility management functions for UE200, the Session Management Function (SMF), which provides session management functions, and the Location Management Function (LMF), which is responsible for communication control related to location information services specified in 5GC. Furthermore, UDM/UDR (Unified Data Management/User Data Repository) may be connected to the AMF and/or SMF. NG-RAN20 and 5GC may also be simply referred to as "networks."

In addition, NG-RAN20 may be connected to a server managed by a 3GPP service provider or a server managed by a party other than that provider (3GPP or non-3GPP server).

gNB100 is a radio base station that complies with NR and performs radio communication with UE200 that complies with NR. Note that gNB100 may be composed of 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 a CU. Furthermore, gNB100 (gNB-CU) may be connected to each other via an Xn interface, and CUs and DUs may be connected via an F1 interface.

The gNB100 and UE200 are capable of supporting Massive MIMO, which generates a more directional beam (BM) by controlling the radio signals transmitted from multiple antenna elements; Carrier Aggregation (CA), which aggregates multiple component carriers (CCs); and Dual Connectivity (DC), which enables simultaneous communication between the UE and multiple NG-RAN nodes. The UE200 may also perform handover (HO) to a different RAT. The UE200 may also perform handover between cells A to D (serving cells or neighboring cells).

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 interpreted as transition, cell transition, cell selection, etc.) of the UE 200.

AI/ML Model may also be expressed as other terms that refer to AI or ML, such as artificial intelligence (AI) model or machine learning (ML) model.

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

UE200 may periodically perform measurement reporting. UE200 may also 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. Existing events may include the events shown below (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, and the leaving condition may be interpreted as a condition for determining whether or not to exclude a measurement report from the measurement report.

(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.

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

(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.

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

(iii) Event A3 (Neighbor becomes offset better than SpCell)
Event A3 is an event in which the reception quality of the neighboring cell is better than the reception quality of the serving cell by an offset. 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.

Here, Mn is the reception quality of the neighboring cell, Ofn is an offset specific to the measurement target, and Ocn is an offset specific to the cell. Mp is the reception quality of the serving cell, Ofp is an offset specific to the measurement target, and Ocp is an offset specific to the cell. Hys is the hysteresis parameter, and Off is the parameter used in Event A3.

(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.

Here, Mn is the reception quality of the neighboring cell, Ofn is the offset specific to the measurement target, and Ocn is the offset specific to the cell. Hys is the hysteresis parameter, and Thresh is the threshold value.

(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.

Here, Ms is the reception quality of the serving cell, Hys is the hysteresis parameter, and Thresh1 is the threshold value. Mn is the reception quality of the neighboring cell, Ofn is an offset specific to the measurement target, and Ocn is an offset specific to the cell. Hys is the hysteresis parameter, and Thresh2 is the threshold value.

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

In addition to the events mentioned above, events related to inter-RAT (Radio Access Technology) (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 also be included.

Here, Mn is the reception quality of the neighboring cell, and Ocn is an offset specific to the cell. Ms is the reception quality of the SCell, and Ocs is an offset specific to the cell. Hys is the hysteresis parameter, and Off is the parameter used in Event A6.

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 in the gNB 100. The AI/ML model may also be provided in the UE 200.

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

(2.1) gNB100
As shown in FIG. 2, the gNB 100 includes a wireless communication unit 110, a handover processing unit 120, an AI/ML model unit 130, and a control unit 140.

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 transmit/receive points (TRPs). In this embodiment, a TRP may be interpreted as meaning multiple DL transmit antennas.

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

Note that the serving cell may be interpreted simply as the cell to which 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.

The handover may also include a conditional handover (CHO) and/or a dual active protocol stack (DAPS) handover. CHO can perform a UE200-initiated handover when certain execution conditions are met. If CHO is not applicable, a normal handover may be performed (which may be called CHO recovery). In CHO recovery, UE200 performs cell selection after a CHO failure, but if a CHO candidate cell is selected, it can directly apply conditional RRC Reconfiguration of that cell to reconnect without sending an RRC Reestablishment Request to the candidate target cell.

The execution conditions may consist of one or two trigger conditions (CHO event A3/A5 as 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), etc.) may be set simultaneously for the evaluation of the CHO execution conditions for a single candidate cell.

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

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

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

Furthermore, the AI/ML model unit 130 may transmit to the UE 200 a request to report the accuracy of the predicted value by the AI/ML model for the specified target. In this embodiment, the AI/ML model unit 130 may constitute a transmission unit that transmits the report request to the terminal. The specified target may refer to the target of prediction by the AI/ML model, and may include, for example, a measured value of cell quality (such as RSRP), the probability of handover failure (HOF), the probability of radio link failure (RLF), etc.

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

The accuracy of the predicted value may be expressed as a percentage, or may be expressed in multiple stages. An evaluation value for the predicted value (which may also be called an estimated value) may also 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 discussed further below.

The control unit 140 controls each functional block that makes up 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 cell quality, HOF probability, and RLF probability, and may perform control related to SON (Self-Organizing Networks) according to the predicted values and their accuracy.

In addition, 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 obtained from the UE 200. The measurement results and reports may be predicted using an AI/ML model.

In addition, in this embodiment, channels include control channels and data channels. Control channels include PDCCH (Physical Downlink Control Channel), PUCCH (Physical Uplink Control Channel), PRACH (Physical Random Access Channel), and PBCH (Physical Broadcast Channel), etc.

Data channels also include PDSCH (Physical Downlink Shared Channel) and PUSCH (Physical Uplink Shared Channel).

Note that reference signals include Demodulation reference signals (DMRS), Sounding Reference Signals (SRS), Phase Tracking Reference Signals (PTRS), and Channel State Information-Reference Signals (CSI-RS), and signals include channels and reference signals. Note that data may refer to data transmitted via a data channel.

(2.2) UE200
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 .

The wireless communication unit 210 transmits uplink signals (UL signals) that comply with NR. The wireless communication unit 210 also receives uplink signals (DL signals) that comply with NR.

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 gNB100. The AI/ML model unit may be provided in either the gNB100 or the UE200, or in both.

The AI/ML model unit 215 may transmit prediction information indicating the 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 transmission unit that transmits the prediction information to the network.

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

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 may be indicated by multiple stages, etc. The prediction information may be transmitted to the network via an AI/ML report (AI/ML reporting), or may be transmitted to the network via a Measurement Report.

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

The AI/ML model unit 215 may also transmit prediction information including the degree of match between the positions of neighboring cells and the movement trajectory of UE200. The movement trajectory of UE200 may be interpreted as time-series information indicating the position of UE200. The movement trajectory may be past information or predicted future information. The movement trajectory may be information that allows the positional relationship with a neighboring cell (which may be the serving cell) to be determined (such as the distance from a specified position of the cell).

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

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

As mentioned above, the accuracy of the predicted value may be expressed as a percentage, or may be expressed in multiple stages. 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.

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

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.

Based on the received report request, the AI/ML model unit 215 may obtain the predicted value and the accuracy of the predicted value within a specified time period, and transmit the prediction result including the predicted value and 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.

The measurement processing unit 220 can measure the quality of the serving cell of the UE 200 and the cells neighboring the serving cell, and report the measurement results to the network (Measurement Report). The measurement processing unit 220 may perform measurement reports of the source cell and target cell during handover.

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

The measurement processing unit 220 receives measurement settings (MeasConfig) from the network, which configure measurements using an AI/ML model. In this embodiment, the measurement processing unit 220 may constitute a receiving unit that receives the measurement settings from the network.

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

The measurement processing unit 220 may receive a measurement configuration that includes identification information that identifies the AI/ML model or a function of the AI/ML model. Specifically, an AI model ID that identifies the AI/ML model itself may be used, or an AI functionality ID that identifies a function of the AI/ML model may be used.

The measurement processing unit 220 may receive measurement settings that include conditions for measurement reporting. Note that the measurement report here may be a report related to AI/ML (AI/ML reporting) or a Measurement Report. The conditions 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 specified event is satisfied. The specified event may be, for example, an event related to an AI/ML model or an event related to cell quality.

The measurement processing unit 220 may transmit a measurement report to the network that includes measurement results using the AI/ML model based on the received measurement settings. In this embodiment, the measurement processing unit 220 may constitute a transmitting unit that transmits the measurement report to the network.

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

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 sending the measurement report, including the measurement results 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.

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

In addition, the handover execution unit 230 can perform processing related to normal handover (legacy handover) and conditional handover (CHO).

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

Furthermore, the destination of a CHO may or may not involve an SCG. In other words, the destination cell of a CHO may be a single cell, or may be composed of multiple cells (which may be interpreted as a cell group) according to a DC.

Furthermore, the handover execution unit 230 can receive a handover request (handover command) for the UE 200 from the network. In this embodiment, the handover execution unit 230 may constitute a receiving unit. The handover command may include an indication to delete an AI/ML model, etc. for the source cell from which the handover originates.

The control unit 240 controls each functional block that constitutes the UE 200. In particular, in this embodiment, the control unit 240 may use the AI/ML model unit 215 to perform control related to measurement settings and measurement reports by the measurement processing unit 220.

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

The control unit 240 may use an AI/ML model to generate a predicted value for a specified target. As described above, the specified target may refer to the target of prediction by the AI/ML model, and may include, for example, a cell quality measurement value (such as RSRP), the probability of handover failure (HOF), the probability of radio link failure (RLF), etc.

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.

(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).

(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:

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

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

The model training function may also be responsible for data preparation (data preprocessing and cleaning, formatting, conversion, etc.).

- Model inference: Provides inference output (such as predictions or decisions). The model inference function may also provide control of model inference to the model management/performance monitoring function.

・Model management/performance monitoring: Manage ML models and monitor model performance.

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

Figure 5 shows an example sequence for setting up AI/ML reporting (Measurement Report) for Operation Example 1. Figure 6 shows an example configuration of an information element (IE) for AI/ML reporting.

The gNB may instruct the UE on the AI/ML measurement configuration in the following ways:

- An AI/ML measurement object may be specified (see Figure 6).

An AI/ML measurement object may refer to the object to be predicted using an AI/ML model (e.g., a specific frequency (band)).

- An AI model ID may be specified. An AI functionality ID may also be specified (see Figure 6).

As mentioned above, the AI model ID may identify the AI/ML model itself, and the AI functionality ID may identify the functionality of the AI/ML model.

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

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

- The AI/ML reporting config may include AI/ML reporting conditions.

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

The UE may also send reports to the network according to a predetermined report amount (volume). The report amount may be pre-configured by the gNB.

The UE may send an AI/ML reporting to the gNB up to a predetermined maximum number of times. The UE may also be configured to send an AI/ML reporting when a predetermined event is satisfied.

In this way, the UE can perform AI/ML reporting (Measurement Report), but there may be times when you want to cancel (pause) AI/ML reporting if the accuracy of the AI/ML model is poor or if there is network congestion.

Therefore, if the UE receives an instruction to cancel/stop AI/ML reporting from the network, it may cancel AI/ML reporting.

The cancellation/stop instruction may be realized by an RRC message, a Medium Access Control Layer (MAC) control element (MAC CE), or 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.

The above-mentioned operational example clarifies the configuration of the AI/ML reporting information element (IE), enabling appropriate and efficient configuration and reporting using the AI/ML model between the UE and gNB.

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

Figure 7 shows an example sequence related to AI/ML reporting for Operation Example 2. Figure 8 shows the relationship between the UE movement trajectory and neighboring cells for Operation Example 2. Figure 9 shows an example of AI/ML reporting for Operation Example 2.

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

- 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.

- Future quality (RSRP, RSRQ, SINR) of neighboring cells (e.g., 20ms, 30ms, 40ms later)
The predetermined timing may be the timing when the UE generates an AI/ML reporting other than the timing when the AI/ML reporting is received. Alternatively, it may be the timing when the UE transmits an AI/ML reporting, or may be a timing designated by the network, etc. The above-mentioned information may be included in the Measurement Report.

In addition, HOF/RLF predictions using an AI/ML model may be generated by an O-RAN Non-Real Time RIC or Near-Real Time RIC.

Figure 10 shows an example configuration of a RIC based on the O-RAN architecture. As shown in Figure 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).

The Near-Real Time RIC may be connected to the O-eNB (radio base station) via an interface (E2). The RIC included in such an O-RAN architecture may constitute an OAM/RIC40.

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.

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 the UE ID).

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 to transmit the predicted values (i.e., they may be associated with the UE ID).

・Probability of RLF occurring in the serving cell Y ms after a specified timing (e.g., the timing when the gNB receives the AI/ML reporting (time point X)) ・Probability of HOF occurring when handing over Y ms after a specified timing (e.g., the timing when the gNB receives the AI/ML reporting (time point X)) ・Probability of HOF occurring when handing over to a neighboring cell Y ms after a specified timing (e.g., the timing when the gNB receives the AI/ML reporting (time point X)) ・Degree of agreement between the neighboring cell and the future UE trajectory (UE movement trajectory) Y ms after a specified timing (e.g., the timing when the gNB receives the AI/ML reporting (time point X)) ・Quality (RSRP, RSRQ, SINR) of each neighboring cell in the future (e.g., 20 ms, 30 ms, 40 ms later)
The predetermined timing may be the timing when the UE generates an AI/ML reporting other than the timing when the AI/ML reporting is received, or may be the timing when the UE transmits an AI/ML reporting, or may be a timing designated by the network or the like.

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

As a result, the HOF and RLF predicted values can be effectively utilized on the network side, contributing to the construction of SON (Self-Organizing Networks).

(3.4) Operation example 3
In this operation example, the UE reports the predicted value by the AI/ML model and information indicating the accuracy of the predicted value 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.

Figure 11 shows an example sequence for setting up AI/ML reporting (Measurement Report) for Operation Example 3.

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

The parameter may be an evaluation value for the AI/ML estimated value within the UE. For example, the evaluation value may be the degree of match calculated from the relationship between the AI/ML estimated value and the measured value within a specified period of time in the past. The degree of match may be expressed as a percentage, or in multiple levels (5 levels, 3 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.

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 a report of the parameter for a specific prediction target (e.g., the probability of RLF occurrence in the serving cell).

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

The UE may report actual information in AI/ML reporting. For example, the UE may report the quality information (RSRP, RSRQ, SINR) of the UE's own cell (beam) and/or neighboring cells (beams) that it has measured.

In AI/ML reporting, the UE may report AI/ML predicted values separately from the information that actually occurred (measured values), or may report them together. When reporting them together, a display may be added to distinguish between the AI/ML predicted values and the actual measured values. Alternatively, the AI/ML predicted values and the actual measured values may be included in separate containers (specified locations within the report).

In the example operation described above, the UE reports the predicted value by the AI/ML model and information indicating the accuracy of that predicted value to the network. This allows the network to appropriately determine how to use the predicted value based on that 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.

(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.

For example, in the above-described embodiment, operation examples 1 to 3 were described, but only operations related to some of these operation examples may be applied.

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

Furthermore, specific, dedicated, UE-specific, and UE-individual may be read as interchangeable. Similarly, common, shared, group-common, UE-common, and UE-shared may be read as interchangeable.

In this 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," and "panel" may be used interchangeably.

Furthermore, the block diagrams (Figures 2 and 3) used to explain the above-mentioned embodiments show functional blocks. These functional blocks (components) are realized by any combination of hardware and/or software. Furthermore, there are no particular limitations on how each functional block is realized. 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 connected directly or indirectly (for example, using wires, wirelessly, etc.) and these multiple devices. A functional block may also be realized by combining software with the single device or multiple devices.

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

Furthermore, the above-mentioned gNB100 and UE200 (the device) 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 device. As shown in Figure 12, the device may be configured as a computer device including a processor 1001, memory 1002, storage 1003, a communication device 1004, an input device 1005, an output device 1006, and a bus 1007.

In the following explanation, the term "apparatus" can be interpreted as a circuit, device, unit, etc. The hardware configuration of the apparatus may be configured to include one or more of the devices shown in the diagram, or may be configured to exclude some of the devices.

Each functional block of the device (see Figures 2 and 3) is realized by one of the hardware elements of the computer device, or a combination of those hardware elements.

Furthermore, each function of the device is realized by loading specific software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communications via the communications device 1004, and control at least one of reading and writing data from and to the memory 1002 and storage 1003.

The processor 1001, for example, runs an operating system to control the entire computer. The processor 1001 may be configured as a central processing unit (CPU) that includes an interface with peripheral devices, a control unit, an arithmetic unit, registers, etc.

Furthermore, the processor 1001 reads programs (program code), 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-mentioned 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 also be transmitted from a network via telecommunications lines.

Memory 1002 is a computer-readable recording medium and may be composed of, for example, at least one of Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), Random Access Memory (RAM), etc. Memory 1002 may also be called a register, cache, main memory (primary storage device), etc. Memory 1002 can store a program (program code), software module, etc. that can execute a method according to one embodiment of the present disclosure.

Storage 1003 is a computer-readable recording medium, and may be composed of, 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 (registered trademark) disc), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) 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.

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 referred to as a network device, network controller, network card, communication module, etc.

The communication device 1004 may be configured to include high-frequency switches, duplexers, filters, frequency synthesizers, etc. to realize, for example, at least one of Frequency Division Duplex (FDD) and Time Division Duplex (TDD).

The input device 1005 is an input device (e.g., a keyboard, mouse, microphone, switch, button, sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, speaker, 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).

Furthermore, each device, such as the processor 1001 and memory 1002, is connected by 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.

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.

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 of these. Furthermore, 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, etc.

Each aspect/embodiment described in this disclosure may be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 6th generation mobile communication system (6G), xth generation mobile communication system ( xG) (x is, for example, an integer or decimal point), 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 appropriate systems, and may be applied to at least one of next-generation systems that are extended based on these. Also, multiple systems may be combined (for example, a combination of at least one of LTE and LTE-A with 5G).

The order of the processing steps, sequences, flowcharts, etc. of each aspect/embodiment described in this disclosure may be changed unless inconsistent. 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.

In this disclosure, certain operations that are described as being performed by a base station may in some cases be performed by its upper node. In a network consisting of one or more network nodes that have base stations, it is clear that various operations performed for communication with terminals may be performed by at least one of the base station and other network nodes other than the base station (such as, but not limited to, an MME or S-GW). While the above example shows a case where there is one other network node other than the base station, it may also be a combination of multiple other network nodes (for example, an MME and an S-GW).

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

Input and output information may be stored in a specific location (e.g., memory) or may be managed using a management table. Input and output information may be overwritten, updated, or appended. Output information may be deleted. Input information may be sent to another device.

The determination may be made based on a value represented by a single bit (0 or 1), a Boolean value (true or false), or a numerical comparison (for example, comparison with a predetermined value).

Each aspect/embodiment described in this disclosure may be used alone, in combination, or switched between depending on the implementation. Furthermore, notification of specified information (e.g., notification that "X is true") is not limited to being done explicitly, but may also be done implicitly (e.g., not notifying the specified information).

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.

Software, instructions, information, etc. may also be transmitted and received via 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, or Digital Subscriber Line (DSL)) and/or wireless technologies (such as infrared or microwave), then these wired and/or wireless technologies are included within the definition of a transmission medium.

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.

Note that terms explained 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.

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

Furthermore, the information, parameters, etc. described in this disclosure may be expressed using absolute values, relative values from a predetermined value, or other corresponding information. For example, radio resources may be indicated by an index.

The names used for the parameters described above are not intended to be limiting in any way. Furthermore, the mathematical formulas 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.

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.

A base station can accommodate one or more (e.g., three) cells (also called sectors). When a base station accommodates multiple cells, the base station's overall coverage area 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)).

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

In this disclosure, a 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.

In this disclosure, terms such as "Mobile Station (MS)," "user terminal," "User Equipment (UE)," and "terminal" may be used interchangeably.

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.

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 an object that can 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 operation 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). Note that 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.

Furthermore, the base station in the present disclosure may be interpreted 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 interpreted as terms corresponding to communication between terminals (for example, "side"). For example, terms such as uplink channel and downlink channel may be interpreted as side channel (or side link).

Similarly, the mobile station in this disclosure may be interpreted as a base station. In this case, the base station may be configured to have the functions of a mobile station.

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.

Numerology may be a communication parameter that applies to at least one of the transmission and reception of a signal or channel. Numerology may indicate, for example, at least one of the following: 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 transmitter and receiver in the frequency domain, and specific windowing operations performed by the transmitter and receiver in the time domain.

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

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.

Radio frame, subframe, slot, minislot, and symbol all represent time units for transmitting signals. Other names corresponding to radio frame, subframe, slot, minislot, and symbol may also be used.

For example, one subframe may be called a transmission time interval (TTI), multiple consecutive subframes may be called a TTI, or one slot or one minislot may be called a TTI. In other words, at least one of the subframe and 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.

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 by allocating radio resources (such as the frequency bandwidth and transmission power available for use by each user terminal) in TTI units. However, the definition of TTI is not limited to this.

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

Note that when one slot or one minislot is referred to as a TTI, one or more TTIs (i.e., one or more slots or one or more minislots) may be the smallest time unit for scheduling. Furthermore, the number of slots (minislots) that make up the smallest time unit for scheduling may be controlled.

A TTI with 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.

Note that a long TTI (e.g., a normal TTI, subframe, etc.) may be interpreted as a TTI with a time length exceeding 1 ms, and a short TTI (e.g., a shortened TTI, etc.) may be interpreted as a TTI with a TTI length shorter than the TTI length of a long TTI but equal to or greater than 1 ms.

A resource block (RB) is a resource allocation unit in the time domain and 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, and may be, for example, 12. The number of subcarriers included in an RB may also be determined based on numerology.

Furthermore, the time domain of an RB may include one or more symbols and may be one slot, one minislot, one subframe, or one TTI in length. One TTI, one subframe, etc. may each be composed of one or more resource blocks.

Note that one or more RBs may also be referred to as a physical resource block (PRB), sub-carrier group (SCG), resource element group (REG), PRB pair, RB pair, etc.

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

A Bandwidth Part (BWP) (which may also be referred to as a partial 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 the RB's index relative to the carrier's common reference point. PRBs may be defined in a given BWP and numbered within that BWP.

BWPs 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.

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

The structures of the radio frames, subframes, slots, minislots, and symbols described above 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, as well as the number of symbols within a TTI, the symbol length, and the cyclic prefix (CP) length can be changed in various ways.

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 at least one of one or more wires, cables, and 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.

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

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."

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

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 in some way precede the second element.

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.

In this disclosure, where articles are added by translation, such as a, an, and the in English, this disclosure may include the noun following these articles being plural.

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 a table, database, or other data structure), and ascertaining something as a "judging" or "determining." "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 ascertaining something as a "judging" or "determining." 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.

In this 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 "combined" may also be interpreted in the same way as "different."

FIG. 13 shows an example 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 2021-2029, an information service unit 2012, and a communication module 2013.

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, a memory (ROM, RAM) 2032, and a communication port (IO port) 2033. Signals are input to the electronic control unit 2010 from various sensors 2021 to 2027 provided in the vehicle. The electronic control unit 2010 may also be called an ECU (Electronic Control Unit).

Signals from the various sensors 2021-2028 include a current signal from a current sensor 2021 that senses the motor current, a front and rear wheel rotation speed signal obtained by a rotation speed sensor 2022, a front and rear wheel air pressure signal 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.

The information service unit 2012 is composed of various devices, such as a car navigation system, audio system, speakers, television, and radio, that provide (output) various types of information, including driving information, traffic information, and entertainment information, as well as one or more ECUs that control these devices. The information service unit 2012 uses information obtained 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.

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.

The driving assistance system unit 2030 is composed of various devices that provide functions to prevent accidents and reduce 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.

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 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, microprocessor 2031 and memory (ROM, RAM) 2032 in the electronic control unit 2010, and sensors 2021-2028, all of which are provided on the vehicle 2001.

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 external devices. For example, it transmits and receives various information to and from external devices 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, etc.

The communications module 2013 may transmit, via wireless communication, to an external device at least one of the signals from the various sensors 2021-2028 described above that are 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. The electronic control unit 2010, the various sensors 2021-2028, the information service unit 2012, etc. may also be referred to as input units that accept input. For example, the PUSCH transmitted by the communications module 2013 may include information based on the above input.

The communication module 2013 receives various information (traffic information, traffic signal information, vehicle-to-vehicle distance information, etc.) transmitted from external devices and displays it on the 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 memory 2032 that can be used by the microprocessor 2031. Based on the information stored in 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 2021-2028, and the like provided in the vehicle 2001.

(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 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.

A second feature is that in the first feature, the receiving unit receives the measurement configuration 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.

A third feature is the first or second feature, wherein the receiving unit receives the measurement configuration including conditions for the measurement report, and the transmitting unit transmits the measurement report based on the conditions.

A fourth feature is any one of the first to third features, wherein the receiving unit receives a stop instruction to stop the measurement report, and the transmitting unit stops transmitting the measurement report including the measurement results obtained using the learning model based on the stop instruction.

The fifth feature is a terminal equipped with a control unit that uses a learning model to predict the occurrence of a handover failure or a radio link failure, and a transmission unit that transmits prediction information indicating the predicted result of the handover failure or the radio link failure to the network.

The sixth feature is the fifth feature, wherein the transmitter transmits the prediction information including at least one of the probability of handover failure and the probability of radio link failure.

A seventh feature is the fifth or sixth feature, wherein the transmitter transmits the prediction information including the occurrence probability in a serving cell or a neighboring cell.

An eighth feature is any one of the fifth to seventh features, 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.

A ninth feature is any one of the fifth to eighth features, wherein the transmitter transmits the prediction information including a future quality prediction value in a neighboring cell.

A tenth feature is a terminal that includes a control unit that uses a learning model to generate a predicted value for a specified target, and a transmission unit that transmits the predicted value and a prediction result including the accuracy of the predicted value to a network.

An eleventh feature is the tenth feature, wherein the transmission unit transmits the prediction result including a degree of match between the past predicted value and the actual measured value of the target.

A twelfth feature is the tenth or eleventh feature, further comprising a receiving unit that receives a request for reporting the 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 reporting request.

A thirteenth feature is any one of the tenth to twelfth features, wherein the transmission unit transmits the prediction result including an actual measurement value of the target.

10 Wireless Communication Systems 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 unit 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 (6)

a control unit that generates a predicted value of a specified target using a learning model;
a transmitting unit configured to transmit the predicted value and a prediction result including accuracy of the predicted value to a network. The terminal according to claim 1, wherein the transmission unit transmits the prediction result including the degree of match between the past predicted value and the actual measured value of the target. a receiving unit that receives a request to report the accuracy of the predicted value from the network;
The terminal according to claim 1 , wherein the transmission 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. The terminal according to claim 1, wherein the transmission unit transmits the prediction result including the actual measured value of the target. a transmission unit that transmits a request to the terminal to report the accuracy of a predicted value by a learning model of a specified target;
a receiving unit that receives the predicted value and a prediction result including accuracy of the predicted value from the terminal. generating a prediction for a specified target using the learned model;
and transmitting a prediction result including the predicted value and accuracy of the predicted value to a network.