Terminal, network device, wireless communication system, and wireless communication method

Abstract

To provide a terminal, a network device, a wireless communication system, and a wireless communication method that can improve the throughput of LTM target nodes by obtaining AI / ML L1 measurement reporting.SOLUTION: A terminal includes a control unit that performs operations corresponding to a model that can be used as a model for artificial intelligence or machine learning, a receiving unit that receives a command instructing a cell switch that is performed at a layer lower than a wireless resource control layer, and a transmission unit that transmits measurement results of the lower layer predicted by operations corresponding to the model for a target node of the cell switch.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates to a terminal, a network device, a wireless communication system, and a wireless communication method that support a UE-side model related to AI / ML technology. [Background technology]

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

[0003] Furthermore, 3GPP is studying a framework for a Radio Access Network (RAN) (AI / Artificial Intelligence Machine Learning (ML) technology) that is realized by artificial intelligence (AI) (for example, Non-Patent Document 1). [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] “New WID on Artificial Intelligence (AI) / Machine Learning (ML) for NR Air Interface”, RP-234039, 3GPP TSG RAN Meeting #102, 3GPP, December 2023 Summary of the Invention [Problem to be solved by the invention]

[0005] In the above-mentioned AI / ML technology, functions such as model training, inference, performance monitoring, and data collection are assumed to be implemented as a UE-side model on the UE side and a NW-side model on the NW side.

[0006] Against this background, the inventors have conducted extensive research and found that in cases where a UE-side model is adopted, it is effective for the target node to (early) obtain L1 measurement reporting predicted by AI / ML technology (hereinafter referred to as AI / ML L1 measurement reporting) in order to improve throughput at the target node of LTM (Lower-layer Triggered Mobility).

[0007] Therefore, the present disclosure has been made to solve the above-mentioned problems, and aims to provide a terminal, a network device, a wireless communication system, and a wireless communication method that can improve the throughput of an LTM target node by obtaining AI / ML L1 measurement reporting. [Means for solving the problem]

[0008] The disclosed aspect is a terminal comprising: a control unit that performs operations corresponding to a model that can be used as a model for artificial intelligence or machine learning; a receiving unit that receives a command instructing a cell switch that is performed at a layer lower than the radio resource control layer; and a transmitting unit that transmits measurement results of the lower layer predicted by operations corresponding to the model for a target node of the cell switch.

[0009] The disclosed aspect is a network device comprising: a control unit that assumes that a terminal will perform operations corresponding to a model that can be used as a model for artificial intelligence or machine learning; a transmitting unit that transmits a command instructing a cell switch that is performed at a layer lower than the radio resource control layer; and a receiving unit that receives measurement results of the lower layer predicted by operations corresponding to the model for a target node of the cell switch.

[0010] The disclosed aspect is a wireless communication system comprising a terminal and a network device provided in a network, wherein the terminal comprises a control unit that performs operations corresponding to a model that can be used as a model for artificial intelligence or machine learning, a receiving unit that receives a command instructing a cell switch that is performed at a layer lower than the radio resource control layer, and a transmitting unit that transmits measurement results of the lower layer predicted by operations corresponding to the model for a target node of the cell switch.

[0011] The disclosed aspect is a wireless communication method comprising step A of performing an operation corresponding to a model available as a model for artificial intelligence or machine learning, step B of receiving a command instructing a cell switch performed at a layer lower than the radio resource control layer, and step C of transmitting lower layer measurement results predicted by the operation corresponding to the model for a target node of the cell switch. [Effects of the Invention]

[0012] According to the present disclosure, it is possible to provide a terminal, a network device, a wireless communication system, and a wireless communication method that can improve the throughput of an LTM target node by obtaining AI / ML L1 measurement reporting. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a diagram showing the overall schematic configuration of a wireless communication system 10. As shown in FIG. [Figure 2]FIG. 2 shows a diagram illustrating frequency ranges used in cellular networks. [Figure 3] FIG. 3 is a diagram showing an example of the configuration of a radio frame, a subframe, and a slot used in a cellular network. [Figure 4] FIG. 4 is a functional block diagram of the UE 200. [Figure 5] FIG. 5 is a functional block diagram of the network device 50. As shown in FIG. [Figure 6] FIG. 6 is a diagram for explaining the AI / ML model. [Figure 7] FIG. 7 is a diagram illustrating the UE-side model. [Figure 8] FIG. 8 is a diagram for explaining the first operation example. [Figure 9] FIG. 9 is a diagram for explaining the first operation example. [Figure 10] FIG. 10 is a diagram for explaining the first operation example. [Figure 11] FIG. 11 is a diagram illustrating the second operation example. [Figure 12] FIG. 12 is a diagram illustrating the second operation example. [Figure 13] FIG. 13 is a diagram illustrating an example of the hardware configuration of the network device 50 and the UE 200. As shown in FIG. [Figure 14] FIG. 14 is a diagram showing an example of the configuration of a vehicle 2001. DETAILED DESCRIPTION OF THE INVENTION

[0014] 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.

[0015] [Embodiment] (1) Overall configuration of the wireless communication system 1 is a diagram showing an overall schematic configuration of a wireless communication system 10 according to an embodiment. The wireless communication system 10 includes a terminal 200 (hereinafter referred to as UE (User Equipment) 200), a first network 10A, and a second network 10B.

[0016] The first network 10A has a radio access network 20A and a core network 30A. The radio access network 20A includes a base station 100A that performs radio communication with the UE 200. Note that the first network 10A may not have the radio access network 20A but may have the base station 100A. The first network 10A may not have the core network 30A. The base station 100A may be configured by a DU (Distributed Unit) and a CU (Central Unit). The DU may perform processing of layers below the MAC layer. The CU may perform processing above the PDCP layer.

[0017] The first network 10A may be a network conforming to a new technology (6G). 6G may be referred to as Beyond 5G or 5G Evolution. The first network 10A may be a network conforming to an existing technology (5G). 5G may be referred to as 5G New Radio (NR).

[0018] The second network 10B has a radio access network 20B and a core network 30B. The radio access network 20B includes a base station 100B that performs radio communication with the UE 200. Note that the second network 10B may not have the radio access network 20B but may have the base station 100B. The second network 10B may not have the core network 30B. The base station 100B may be configured by a DU and a CU.

[0019] The second network 10B may be a network conforming to existing technology (5G). 5G may be referred to as 5G New Radio (NR). The second network 10B may be a network conforming to new technology (6G). 6G may be referred to as Beyond 5G or 5G Evolution.

[0020] Here, the first network 10A and the second network 10B may have the same or different radio access schemes, for example, a radio access scheme of a cellular network called 5G, Beyond 5G, 5G Evolution, 6G, or the like.

[0021] First, the cellular network may support multiple frequency ranges (FR) as shown in Figure 2. For example, as shown in Figure 2, the cellular network may support FR1 and FR2. The frequency bands of each FR are as follows:

[0022] FR1: 410 MHz to 7.125 GHz FR2-1: 24.25 GHz to 52.6 GHz ·FR2-2: More than 52.6GHz~71GHz FR1 may use a Sub-Carrier Spacing (SCS) of 15, 30, or 60 kHz, and may use a bandwidth (BW) of 5 to 100 MHz. FR2 is a higher frequency than FR1, and may use an SCS of 60 kHz or 120 kHz (including 240 kHz), and may use a bandwidth (BW) of 50 to 400 MHz.

[0023] Furthermore, cellular networks may also support higher frequency bands than the FR2 frequency band, specifically, frequency bands above 52.6 GHz up to 71 GHz or 114.25 GHz.

[0024] Second, the cellular network may correspond to the radio frames, subframes and slots shown in FIG.

[0025] As shown in Figure 3, one slot consists of 14 symbols, and the larger (wider) the SCS, the shorter the symbol period (and slot period). In addition to 15 kHz, 30 kHz, 60 kHz, 120 kHz, and 240 kHz, the SCS may also use 480 kHz, 960 kHz, etc.

[0026] Furthermore, the number of symbols constituting one slot does not necessarily have to be 14 (for example, 28 symbols or 56 symbols). Furthermore, the number of slots per subframe may differ depending on the SCS.

[0027] The time direction (t) shown in Fig. 3 may be called a time domain, a symbol period, or a symbol time, etc. The frequency direction may be called a frequency domain, a resource block, a subcarrier, a bandwidth part (BWP), etc.

[0028] (2) Functional block configuration of wireless communication system The functional block configuration of the wireless communication system 10 will be described below.

[0029] First, the functional block configuration of the UE 200 will be described.

[0030] Fig. 4 is a functional block diagram of UE 200. As shown in Fig. 4, UE 200 includes radio signal transmitting / receiving unit 210, amplifier unit 220, modem unit 230, control signal / reference signal processing unit 240, encoding / decoding unit 250, data transmitting / receiving unit 260, and control unit 270.

[0031] The radio signal transmitting / receiving unit 210 transmits and receives radio signals conforming to 5G or 6G. The radio signal transmitting / receiving unit 210 supports Massive MIMO, CA that uses a bundle of multiple CCs, and DC that simultaneously communicates between a UE and two NG-RAN nodes.

[0032] The amplifier unit 220 is configured by a PA (Power Amplifier) / LNA (Low Noise Amplifier), etc. The amplifier unit 220 amplifies the signal output from the modulation / demodulation unit 230 to a predetermined power level. The amplifier unit 220 also amplifies the RF signal output from the radio signal transmission / reception unit 210.

[0033] The modem unit 230 performs data modulation / demodulation, transmission power setting, resource block allocation, etc. for each predetermined communication destination (gNB 100 or another gNB). The modem unit 230 may apply Cyclic Prefix-Orthogonal Frequency Division Multiplexing (CP-OFDM) / Discrete Fourier Transform - Spread (DFT-S-OFDM). Furthermore, DFT-S-OFDM may be used not only for the uplink (UL) but also for the downlink (DL).

[0034] The control signal / reference signal processor 240 performs processing related to various control signals transmitted and received by the UE 200 and processing related to various reference signals transmitted and received by the UE 200 .

[0035] Specifically, the control signal / reference signal processor 240 receives various control signals, for example, control signals of a radio resource control layer (RRC), transmitted via a predetermined control channel from the gNB 100. The control signal / reference signal processor 240 also transmits various control signals to the gNB 100 via a predetermined control channel.

[0036] The control signal / reference signal processor 240 performs processing using reference signals (RS) such as a Demodulation Reference Signal (DM-RS) and a Phase Tracking Reference Signal (PT-RS).

[0037] DM-RS is a reference signal (pilot signal) known between the base station and the terminal for estimating the fading channel used for data demodulation. PT-RS is a terminal-specific reference signal for estimating phase noise, which is an issue in high frequency bands.

[0038] In addition to DM-RS and PT-RS, the reference signals may also include a Channel State Information-Reference Signal (CSI-RS), a Sounding Reference Signal (SRS), and a Positioning Reference Signal (PRS) for position information.

[0039] The channels include control channels and data channels, such as a PDCCH (Physical Downlink Control Channel), a PUCCH (Physical Uplink Control Channel), a RACH (Random Access Channel), Downlink Control Information (DCI) including a Random Access Radio Network Temporary Identifier (RA-RNTI), and a Physical Broadcast Channel (PBCH).

[0040] Furthermore, the data channel includes a PDSCH (Physical Downlink Shared Channel) and a PUSCH (Physical Uplink Shared Channel). Data refers to data transmitted via the data channel. The data channel may be interpreted as a shared channel.

[0041] Here, the control signal and reference signal processor 240 may receive downlink control information (DCI). The DCI includes existing fields for storing DCI Formats, Carrier indicator (CI), BWP indicator, Frequency Domain Resource Assignment (FDRA), Time Domain Resource Assignment (TDRA), Modulation and Coding Scheme (MCS), HARQ Process Number (HPN), New Data Indicator (NDI), Redundancy Version (RV), etc.

[0042] The value stored in the DCI Format field is an information element that specifies the format of the DCI. The value stored in the CI field is an information element that specifies the CC to which the DCI applies. The value stored in the BWP indicator field is an information element that specifies the BWP to which the DCI applies. The BWP that can be specified by the BWP indicator is set by an information element (BandwidthPart-Config) included in the RRC message. The value stored in the FDRA field is an information element that specifies the frequency domain resource to which the DCI applies. The frequency domain resource is identified by the value stored in the FDRA field and an information element (RA Type) included in the RRC message. The value stored in the TDRA field is an information element that specifies the time domain resource to which the DCI applies. The time domain resource is identified by the value stored in the TDRA field and information elements (pdsch-TimeDomainAllocationList, pusch-TimeDomainAllocationList) included in the RRC message. The time domain resource may be identified by the value stored in the TDRA field and a default table. The value stored in the MCS field is an information element that specifies the MCS to which the DCI applies. The MCS is identified by the value stored in the MCS and an MCS table. The MCS table may be specified by an RRC message or may be determined by RNTI scrambling. The value stored in the HPN field is an information element that specifies the HARQ process to which the DCI is applied. The value stored in the NDI field is an information element for specifying whether the data to which the DCI is applied is initial transmission data. The value stored in the RV field is an information element that specifies the redundancy of the data to which the DCI is applied.

[0043] The encoding / decoding unit 250 performs data division / concatenation and channel coding / decoding for each predetermined communication destination (gNB100 or another gNB).

[0044] Specifically, the encoding / decoding unit 250 divides the data output from the data transmitting / receiving unit 260 into pieces of a predetermined size, performs channel coding on the divided data, decodes the data output from the modem unit 230, and concatenates the decoded data.

[0045] The data transmitter / receiver 260 transmits and receives Protocol Data Units (PDUs) and Service Data Units (SDUs). Specifically, the data transmitter / receiver 260 assembles and disassembles PDUs / SDUs in multiple layers (such as a Medium Access Control layer (MAC), a Radio Link Control layer (RLC), and a Packet Data Convergence Protocol layer (PDCP)). The data transmitter / receiver 260 also performs data error correction and retransmission control based on HARQ (Hybrid Automatic Repeat Request).

[0046] The control unit 270 controls each functional block constituting the UE 200. In an embodiment, the control unit 270 may be configured as a control unit that executes an operation corresponding to a model that can be used as a model related to artificial intelligence or machine learning (hereinafter, referred to as an AI / ML model).

[0047] In the embodiment, the radio signal transmitting and receiving unit 210 may be configured as a receiving unit that receives a command (LTM cell switch command) instructing a cell switch (LTM) that is executed in a layer lower than the radio resource control layer.

[0048] In an embodiment, the radio signal transceiver unit 210 may constitute a transmitter that transmits lower layer measurement results (e.g., AI / ML L1 measurement results described below) predicted by operations corresponding to an AI / ML model for a target node of a cell switch (LTM).

[0049] Here, measurements of layers lower than the radio resource control layer may be referred to as L1 measurements or CSI (Channel State Indicator) measurements.

[0050] A lower layer measurement performed by an operation that does not correspond to an AI / ML model may be read as an existing lower layer measurement. The existing lower layer measurement may simply be referred to as an L1 measurement or as a CSI measurement. A lower layer measurement result performed by an operation that does not correspond to an AI / ML model may be read as an existing lower layer measurement result. The existing lower layer measurement result may simply be referred to as an L1 measurement, a CSI measurement, an L1 measurement result, or a CSI measurement result.

[0051] A lower layer measurement performed by an operation corresponding to an AI / ML model may be referred to as an AI / ML L1 measurement or an AI / ML CSI measurement, in terms of distinguishing it from existing lower layer measurements. A lower layer measurement result performed by an operation corresponding to an AI / ML model may be referred to as an AI / ML L1 measurement, an AI / ML CSI measurement, an AI / ML L1 measurement result, an AI / ML CSI measurement result, an AI / ML L1 measurement prediction, or an AI / ML CSI measurement prediction, in terms of distinguishing it from existing lower layer measurements.

[0052] The AI / ML L1 measurement prediction may include a prediction of the quality of a reference signal (e.g., CSI-RS) (e.g., Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Signal to Interference-plus-Noise Ratio (SINR), etc.). The AI / ML L1 measurement prediction may include a prediction of the quality of a reference signal for a Lower-layer Triggered Mobility (LTM) target node, or may include a prediction of the quality of a reference signal for an LTM source node. The LTM target node may be read as a candidate target cell, a candidate target beam, or a potential candidate target cell. It may also be read as a potential candidate target beam.

[0053] The AI / ML L1 measurement prediction may include predictions of the CSI-RS Resource Index (CRI), Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), and Rank Indicator (RI) of the candidate target cell. RI may be the rank of precoding using a Type I codebook. The AI / ML L1 measurement prediction may include predictions of the CRI, CQI, PMI, and RI of the candidate target beam.

[0054] In addition, the information for lower layer measurements may include configuration information regarding resources used in lower layer measurements (e.g., CSI-RS resource configuration), and may also include configuration information regarding reporting of lower layer measurement results (e.g., CSI report config).

[0055] The information for measurements in lower layers that is for operations that do not correspond to an AI / ML model may be replaced with information for existing L1 measurement reporting. The information for existing L1 measurement reporting may include a CSI-RS resource configuration or a CSI report configuration.

[0056] Information for measurements in lower layers and for operations corresponding to AI / ML models is configured separately from information for existing L1 measurement reporting. Information for measurements in lower layers and for operations corresponding to AI / ML models may be referred to as information for AI / ML L1 measurement reporting in order to distinguish it from information for existing L1 measurement reporting. Information for AI / ML L1 measurement reporting may include configuration information related to resources used in lower layer measurements (e.g., AI / ML CSI-RS resource configuration) and may include configuration information related to reporting of lower layer measurement results (e.g., AI / ML CSI report config). Furthermore, information for measurements in AI / ML lower layers may include a reference signal (A / ML performance monitoring RS) monitored by performance monitoring, which is a function of the AI / ML model, and may include configuration information for performance monitoring, which is a function of the AI / ML model (AI / ML performance monitoring config).

[0057] Second, the functional block configuration of the network device 50 will be described. For example, the network device 50 is provided in the first network 10A or the second network 10B. That is, the network device 50 may be the base station 100A, a CU constituting part of the base station 100A, or a DU constituting part of the base station 100A. The network device 50 may be the base station 100B, a CU constituting part of the base station 100B, or a DU constituting part of the base station 100B. The network device 50 may be a source node of the LTM or a target node of the LTM. The source node of the LTM may include a source DU of the intra-CU LTM or a source gNB of the inter-CU LTM. The target node of the LTM may include a target DU of the intra-CU LTM or a target gNB of the inter-CU LTM.

[0058] As shown in FIG. 5, the network device 50 includes a receiving unit 51, a transmitting unit 52, and a control unit 53.

[0059] The receiver 51 receives various signals from the UE 200. The receiver 51 may receive a control signal (PUCCH) or a data signal (PUSCH). The receiver 51 may also receive information from other network devices.

[0060] The transmitter 52 transmits various signals to the UE 200. The transmitter 52 may transmit a control signal (PDCCH) or a data signal (PDSCH). The transmitter 52 may receive information from other network devices.

[0061] The control unit 53 controls each block constituting the network device 50. The control unit 53 may be configured as a control unit that assumes that the UE 200 performs an operation corresponding to a model that can be used as a model related to artificial intelligence or machine learning (AI / ML model).

[0062] In the embodiment, the transmitter 52 may be configured as a transmitter that transmits a command (LTM cell switch command) instructing a cell switch (LTM) that is executed in a layer lower than the radio resource control layer.

[0063] In an embodiment, the receiver 51 may be configured to receive a lower layer measurement result (e.g., an AI / ML L1 measurement result) predicted by an operation corresponding to an AI / ML model for a target node of a cell switch (LTM).

[0064] (3) AI / ML model First, the following describes AI / ML models. The AI / ML models may be used in various functions (AI / ML functionality). The AI / ML functionality may include one or more functionalities selected from AIML for beam management, AIML for CSI prediction, AIML for CSI compression, AIML for positioning, and AIML for mobility.

[0065] As shown in Figure 6, an AI / ML model may include functions such as data collection, model training, model interface, model management / performance monitoring, and model storage.

[0066] Data collection collects model input data used to measure (predict) prediction information. Data collection outputs input data (Training Data) to Model training. Data collection outputs input data (Monitoring Data) to Model Management / Performance monitoring. Data collection outputs input data (Interface Data) to Model Interface.

[0067] Model training performs training, validation, testing, etc. of a model used to measure (predict) predictive information based on training data. Model training may also perform pre-processing such as cleaning, formatting, and conversion of training data. Model training outputs the trained or updated model to model storage.

[0068] The Model Interface uses a model retrieved from the Model storage to output prediction information (Output) corresponding to input data (Interface Data). The Model Interface may also output the prediction information (Output) as feedback to Model Management / Performance monitoring.

[0069] Model Management / Performance monitoring outputs information (Model Interface Control) to the Model Interface that is used to identify the model used in the Model Interface. Identification may also be referred to as Activate, Deactivate, Select, Switch, Fallback, etc. Model Management / Performance monitoring outputs information (Model training control) to Model training that is used to retrain or update the model based on input data (Monitoring Data) and prediction information (Output).

[0070] The Model storage stores the model output from Model training. The Model storage outputs the stored model to the Model Interface. The output of the model may also be referred to as Model deliver / transfer.

[0071] Secondly, we will explain the UE-side model, which has functions such as model training, inference, performance monitoring, and data collection on the UE side.

[0072] As shown in Figure 7, in Step 1, the NW sends information (UECapabilityEnquiry) inquiring about AI / ML capabilities to the UE. The UECapabilityEnquiry may be considered as a message that initiates reporting of the AI / ML functionality supported by the UE.

[0073] In Step 2, the UE transmits information indicating its AI / ML capabilities (UECapabilityInformation) to the NW. The UECapabilityInformation may include AI / ML models that the UE can support and AI / ML functionality that the UE can support. In Step 2, the UE may perform UAI (User Assistance Information) based on other settings.

[0074] In Step 2', the NW distributes AI / ML models that the UE can support based on the UECapabilityInformation so that the UE can execute the AI / ML functionality that it can support.

[0075] In Step 3, the NW transmits a message (RRCReconfiguration) including a setting (inference configuration) to be used in inference using the AI / ML model to the UE. The inference configuration may be configured by the first node. The inference configuration may include information for configuring the AI / ML model in the UE, may include information for configuring AI / ML functionality in the UE, or may include information for configuring the content to be reported from the UE to the NW as information inferred using the AI / ML model. The inference configuration may include a condition (additional condition) to be added on the NW (e.g., first node) side. The inference configuration may be associated with information identifying the inference configuration (e.g., associated ID=1).

[0076] In Step 4, the UE reports the AI / ML functionality that the UE can execute to the NW (Applicable functionality reporting).

[0077] In Step 5, the NW transmits a message (RRCReconfiguration) including an updated configuration (updated inference configuration) to the UE as necessary to be used in inference using the AI / ML model. The updated inference configuration may be associated with information identifying the updated inference configuration (e.g., associated ID=2). The updated inference configuration may be configured by the second node. The updated inference configuration may include information for configuring the AI / ML model in the UE, information for configuring AI / ML functionality in the UE, or information for configuring the content to be reported from the UE to the NW as information inferred using the AI / ML model. The updated inference configuration may include a condition (additional condition) to be added on the NW (e.g., second node) side. The associated ID identifying the updated inference configuration in Step 5 may be different from the associated ID identifying the inference configuration in Step 3.

[0078] In Step 6, the UE activates or deactivates the AI / ML model. The activation or deactivation of the AI / ML model may be instructed to the UE by the NW, or may be performed autonomously by the UE without an instruction from the NW. The activation or deactivation of the AI / ML model may include activation or deactivation of inference using the AI / ML model, and may also include activation or deactivation of monitoring using the AI / ML model.

[0079] (4) Issues In the AI / ML technology described above, functions such as model training, inference, performance monitoring, and data collection are expected to be implemented as a UE-side model on the UE side and a NW-side model on the NW side.

[0080] Against this background, the inventors, as a result of intensive study, have focused on cases in which, in cases where a UE-side model is adopted, LTM using L1 measurement reporting predicted by AI / ML technology (AI / ML L1 measurement reporting) is assumed, and information for AI / ML L1 measurement reporting is set separately from information for existing L1 measurement reporting, and have found the need to clarify a mechanism for setting information for AI / ML L1 measurement reporting.

[0081] (5) Example of operation To solve the above-described problem, the following operation may be performed: Specifically, the UE 200 transmits a lower layer measurement result (e.g., an AI / ML L1 measurement result) predicted by an operation corresponding to an AI / ML model for a target node of the LTM (a target gNB-DU of an Intra-CU LTM or a target gNB of an Inter-CU LTM).

[0082] UE 200 may receive information for AI / ML L1 measurement reporting and predict an AI / ML L1 measurement result based on the information for AI / ML L1 measurement reporting. Predicting an AI / ML L1 measurement result may be interpreted as performing AI / ML L1 measurement. The information for AI / ML L1 measurement reporting may be set separately from existing information for L1 measurement reporting.

[0083] As an example of the operation, the following operation example is conceivable.

[0084] (5.1) Example 1 In operation example 1, the Intra-CU LTM will be described.

[0085] As shown in FIG. 8, in step S10, the UE sends an L3 measurement reporting to the CU.

[0086] In step S11, the CU sends a UE context setup request to the Target gNB-DU.

[0087] In step S12, the target gNB-DU sends a UE context setup response to the CU. The UE context setup response may include information for AI / ML L1 measurement reporting (e.g., AI / ML CSI-RS resource configuration, AI / ML CSI report config, AI / ML performance monitoring RS, and AI / ML performance monitoring config). The UE context setup response may include information for AI / ML L1 measurement reporting for the target gNB-DU.

[0088] In step S13, the CU sends a UE context modification request to the Source gNB-DU. The UE context modification request may include information for AI / ML L1 measurement reporting (e.g., AI / ML CSI-RS resource configuration, AI / ML CSI report config, AI / ML performance monitoring RS, and AI / ML performance monitoring config). The UE context modification request may include information for AI / ML L1 measurement reporting for the Target gNB-DU. The UE context modification request may include information for AI / ML L1 measurement reporting included in the UE context setup response.

[0089] In step S14, the Source gNB-DU sends a UE context modification response to the CU. The UE context modification response may include information for AI / ML L1 measurement reporting (e.g., AI / ML CSI-RS resource configuration, AI / ML CSI report configuration, AI / ML performance monitoring RS, and AI / ML performance monitoring configuration). The UE context modification response may include information for AI / ML L1 measurement reporting for the Source gNB-DU.

[0090] In step S15, the CU sends a DL RRC MESSAGE TRANSFER to the Source gNB-DU. The DL RRC MESSAGE TRANSFER may include information for AI / ML L1 measurement reporting (e.g., AI / ML CSI-RS resource configuration, AI / ML CSI report config, AI / ML performance monitoring RS, and AI / ML performance monitoring config). The DL RRC MESSAGE TRANSFER may include the information for AI / ML L1 measurement reporting included in the UE context modification response. The DL RRC MESSAGE TRANSFER may include information for AI / ML L1 measurement reporting determined or selected by the CU. The CU may determine or select the information for AI / ML L1 measurement reporting based on the information for AI / ML L1 measurement reporting for the Source gNB-DU and the Target gNB-DU.

[0091] In step S16, the Source gNB-DU transmits RRC Reconfiguration to the UE. The RRC Reconfiguration may include information for AI / ML L1 measurement reporting (e.g., AI / ML CSI-RS resource configuration, AI / ML CSI report configuration, AI / ML performance monitoring RS, and AI / ML performance monitoring configuration). The RRC Reconfiguration may include the information for AI / ML L1 measurement reporting included in the DL RRC MESSAGE TRANSFER. The RRC Reconfiguration may include at least information for AI / ML L1 measurement reporting regarding the Target gNB.

[0092] The UE predicts the AI / ML L1 measurement result by operating using an AI / ML model based on the information for AI / ML L1 measurement reporting. Predicting the AI / ML L1 measurement result may also be read as executing the AI / ML L1 measurement.

[0093] In step S17, the UE sends RRCReconfigurationComplete to the Source gNB-DU.

[0094] In step S18, the Source gNB-DU sends an UL RRC MESSAGE TRANSFER to the CU.

[0095] In the first operational example, under these conditions, the following options may be executed.

[0096] In Option 1-1, as shown in Figure 9, in step S20, the Source gNB-DU transmits an instruction regarding LTM (e.g., PDCCH order, PDCCH, MAC CE, etc.) to the UE. The instruction regarding LTM may include information specifying a candidate target cell.

[0097] In step S21, the UE transmits a RACH preamble to the target gNB-DU.

[0098] In step S22, the Target gNB-DU transmits a TA (Timing Advance) value to the CU.

[0099] In step S23, the CU transmits the TA (Timing Advance) value to the Source gNB-DU.

[0100] In step S24, the UE transmits an existing L1 measurement reporting to the Source gNB-DU.

[0101] In step S31A, the UE transmits an AI / ML L1 measurement reporting (AI / ML L1 measurement result or AI / ML L1 measurement prediction) to the Source gNB-DU.

[0102] In step S32A, the Source gNB-DU transmits AI / ML L1 measurements (AI / ML L1 measurement results or AI / ML L1 measurement predictions) to the CU. The AI / ML L1 measurements may include AI / ML L1 measurements (AI / ML L1 measurement results or AI / ML L1 measurement predictions) received from the UE.

[0103] In step S33A, the CU transmits AI / ML L1 measurements (AI / ML L1 measurement results or AI / ML L1 measurement predictions) to the Target gNB-DU. The AI / ML L1 measurements may include AI / ML L1 measurements (AI / ML L1 measurement results or AI / ML L1 measurement predictions) received from the Source gNB.

[0104] In step S25, the Source gNB-DU may determine a cell switch based on existing L1 measurement reporting. The Source gNB-DU may determine a cell switch based on AI / ML L1 measurement reporting. The Source gNB-DU may determine a cell switch based on existing L1 measurement reporting and AI / ML L1 measurement reporting.

[0105] In step S26, the Source gNB-DU sends a command (LTM cell switch command) to the UE instructing a cell switch.

[0106] In step S27, the UE sends RRCReconfigurationComplete to the Target gNB-DU. The RRCReconfigurationComplete may be an example of a reconfiguration completion notification related to a cell switch.

[0107] In step S28, the Target gNB-DU sends the RRCReconfigurationComplete received from the UE to the CU.

[0108] As described above, in Option 1-1, the UE may send an AI / ML L1 measurement reporting to the Source gNB-DU before receiving a command instructing a cell switch (LTM cell switch command). The AI / ML L1 measurement reporting may include at least an AI / ML L1 measurement (AI / ML L1 measurement result or AI / ML L1 measurement prediction) for the Target gNB-DU.

[0109] In Option 1-2, as shown in Fig. 10, in step S20, the Source gNB-DU transmits an instruction regarding LTM (e.g., PDCCH order, PDCCH, MAC CE, etc.) to the UE. The instruction regarding LTM may include information specifying a candidate target cell.

[0110] In step S21, the UE transmits a RACH preamble to the target gNB-DU.

[0111] In step S22, the Target gNB-DU transmits a TA (Timing Advance) value to the CU.

[0112] In step S23, the CU transmits the TA (Timing Advance) value to the Source gNB-DU.

[0113] In step S24, the UE transmits an existing L1 measurement reporting to the Source gNB-DU.

[0114] In step S25, the Source gNB-DU may determine a cell switch based on existing L1 measurement reporting. Unlike Option 1-1, the AI / ML L1 measurement reporting may not be used in determining a cell switch.

[0115] In step S26, the Source gNB-DU sends a command (LTM cell switch command) to the UE instructing a cell switch.

[0116] In step S27, the UE sends RRCReconfigurationComplete to the Target gNB-DU. The RRCReconfigurationComplete may be an example of a reconfiguration completion notification related to a cell switch.

[0117] In step S28, the Target gNB-DU sends the RRCReconfigurationComplete received from the UE to the CU.

[0118] In step S31B, the UE transmits an AI / ML L1 measurement reporting (AI / ML L1 measurement result or AI / ML L1 measurement prediction) to the target gNB-DU.

[0119] In step S32B, the target gNB-DU transmits AI / ML L1 measurements (AI / ML L1 measurement results or AI / ML L1 measurement predictions) to the CU. The AI / ML L1 measurements may include AI / ML L1 measurements (AI / ML L1 measurement results or AI / ML L1 measurement predictions) received from the UE.

[0120] Here, step S31B may be executed simultaneously with step S27. That is, RRCReconfigurationComplete may include AI / ML L1 measurement reporting. Similarly, step S32B may be executed simultaneously with step S28. That is, RRCReconfigurationComplete may include AI / ML L1 measurement (AI / ML L1 measurement result or AI / ML L1 measurement prediction) received from the UE.

[0121] As described above, in Option 1-2, the UE may send an AI / ML L1 measurement reporting to the Target gNB-DU together with a reconfiguration completion notification (RRCReconfigurationComplete) regarding the cell switch. The AI / ML L1 measurement reporting may include at least the AI / ML L1 measurement (AI / ML L1 measurement result or AI / ML L1 measurement prediction) regarding the Target gNB-DU.

[0122] (5.2) Example 2 In the second operational example, Inter-CU LTM will be described.

[0123] As shown in Figures 11 and 12, in step S40, the UE sends an L3 measurement reporting to the TargetN gNB.

[0124] In step S41, the Source gNB sends a Handover request to the Target gNB. The Handover request may include information for AI / ML L1 measurement reporting (e.g., AI / ML CSI-RS resource configuration, AI / ML CSI report configuration, AI / ML performance monitoring RS, and AI / ML performance monitoring configuration). The Handover request may include information for AI / ML L1 measurement reporting regarding the Source gNB.

[0125] In step S42, the target gNB sends a Handover request ACK to the source gNB. The Handover request ACK may include information for AI / ML L1 measurement reporting (e.g., AI / ML CSI-RS resource configuration, AI / ML CSI report config, AI / ML performance monitoring RS, and AI / ML performance monitoring config). The Handover request ACK may include information for AI / ML L1 measurement reporting for the target gNB. The Handover request ACK may include information for AI / ML L1 measurement reporting determined or selected by the target gNB. The target gNB may determine or select information for AI / ML L1 measurement reporting based on information for AI / ML L1 measurement reporting for the source gNB and the target gNB.

[0126] In step S43, the Source gNB transmits RRC Reconfiguration to the UE. The RRC Reconfiguration may include information for AI / ML L1 measurement reporting (e.g., AI / ML CSI-RS resource configuration, AI / ML CSI report configuration, AI / ML performance monitoring RS, and AI / ML performance monitoring configuration). The RRC Reconfiguration may include the information for AI / ML L1 measurement reporting included in the Handover request ACK. The RRC Reconfiguration may include at least information for AI / ML L1 measurement reporting regarding the Target gNB.

[0127] The UE predicts the AI / ML L1 measurement result by operating using an AI / ML model based on the information for AI / ML L1 measurement reporting. Predicting the AI / ML L1 measurement result may also be read as executing the AI / ML L1 measurement.

[0128] In step S44, the UE sends an RRCReconfigurationComplete to the Source gNB.

[0129] In the second operational example, under these assumptions, the following options may be implemented.

[0130] In Option 2-1, as shown in Fig. 11, in step S50, the source gNB transmits an instruction regarding LTM (e.g., PDCCH order, PDCCH, MAC CE, etc.) to the UE. The instruction regarding LTM may include information specifying a candidate target cell.

[0131] In step S51, the UE transmits a RACH preamble to the target gNB.

[0132] In step S52, the Target gNB transmits a TA (Timing Advance) value to the Source gNB.

[0133] In step S53, the UE transmits an existing L1 measurement reporting to the Source gNB.

[0134] In step S61A, the UE transmits an AI / ML L1 measurement reporting (AI / ML L1 measurement result or AI / ML L1 measurement prediction) to the Source gNB.

[0135] In step S62A, the Source gNB transmits AI / ML L1 measurements (AI / ML L1 measurement results or AI / ML L1 measurement predictions) to the Target gNB. The AI / ML L1 measurements may include AI / ML L1 measurements (AI / ML L1 measurement results or AI / ML L1 measurement predictions) received from the UE.

[0136] In step S54, the Source gNB may determine a cell switch based on existing L1 measurement reporting. The Source gNB may determine a cell switch based on AI / ML L1 measurement reporting. The Source gNB may determine a cell switch based on existing L1 measurement reporting and AI / ML L1 measurement reporting.

[0137] In step S55, the Source gNB sends a command (LTM cell switch command) to the UE instructing a cell switch.

[0138] In step S56, the UE sends an RRCReconfigurationComplete to the Target gNB. The RRCReconfigurationComplete may be an example of a reconfiguration completion notification related to a cell switch.

[0139] As described above, in Option 2-1, the UE may transmit an AI / ML L1 measurement reporting to the Source gNB before receiving a command instructing a cell switch (LTM cell switch command). The AI / ML L1 measurement reporting may include at least an AI / ML L1 measurement (AI / ML L1 measurement result or AI / ML L1 measurement prediction) regarding the Target gNB.

[0140] In Option 2-2, as shown in Fig. 12, in step S50, the source gNB transmits an instruction regarding LTM (e.g., PDCCH order, PDCCH, MAC CE, etc.) to the UE. The instruction regarding LTM may include information specifying a candidate target cell.

[0141] In step S51, the UE transmits a RACH preamble to the target gNB.

[0142] In step S52, the Target gNB transmits a TA (Timing Advance) value to the Source gNB.

[0143] In step S53, the UE transmits an existing L1 measurement reporting to the Source gNB.

[0144] In step S54, the Source gNB may determine a cell switch based on existing L1 measurement reporting. Unlike Option 1-1, the AI / ML L1 measurement reporting may not be used in determining a cell switch.

[0145] In step S55, the Source gNB sends a command (LTM cell switch command) to the UE instructing a cell switch.

[0146] In step S56, the UE sends an RRCReconfigurationComplete to the Target gNB. The RRCReconfigurationComplete may be an example of a reconfiguration completion notification related to a cell switch.

[0147] In step S61B, the UE transmits an AI / ML L1 measurement reporting (AI / ML L1 measurement result or AI / ML L1 measurement prediction) to the target gNB.

[0148] Here, step S61B may be executed simultaneously with step S56. That is, RRCReconfigurationComplete may include AI / ML L1 measurement reporting.

[0149] As described above, in Option 2-2, the UE may send an AI / ML L1 measurement reporting to the target gNB together with a reconfiguration completion notification (RRCReconfigurationComplete) regarding the cell switch. The AI / ML L1 measurement reporting may include at least the AI / ML L1 measurement (AI / ML L1 measurement result or AI / ML L1 measurement prediction) regarding the target gNB.

[0150] (5.3) Example 3 A variation of the above-mentioned operation example will be described in Operation Example 3. As Operation Example 3, the following options are possible.

[0151] In option 3-1, when there are multiple candidate target cells, UE200 may predict the AI / ML L1 measurement result for each of the multiple candidate target cells. Similarly, when there are multiple candidate target beams, UE200 may predict the AI / ML L1 measurement result for each of the multiple candidate target beams.

[0152] In option 3-2, when there are multiple candidate target cells, UE200 may predict the AI / ML L1 measurement result for one or more candidate target cells explicitly specified by the NW. Similarly, when there are multiple candidate target beams, UE200 may predict the AI / ML L1 measurement result for one or more candidate target beams explicitly specified by the NW.

[0153] In option 3-3, when there are multiple candidate target cells, UE 200 may predict the AI / ML L1 measurement result for one or more candidate target cells determined by UE 200. Similarly, when there are multiple candidate target beams, UE 200 may predict the AI / ML L1 measurement result for one or more candidate target beams determined by UE 200. For example, UE 200 may predict the AI / ML L1 measurement result for N candidate target cells / beams in descending order of quality (L1 RSRP, L1 RSRQ, L1 SINR, L3 RSRP, L3 RSRQ, L3 SINR).

[0154] In option 3-4, the UE 200 may predict the AI / ML L1 measurement result for potential candidate target cells or beams notified from the NW in the Conditional LTM.

[0155] In options 3-5, UE 200 may preferentially predict AI / ML L1 measurement results for candidate target cells or beams having valid TA values ​​in Conditional LTM.

[0156] In options 3-6, UE 200 may transmit the AI / ML L1 measurement result based on a configured grant, or may transmit the AI / ML L1 measurement result based on a dynamic grant.

[0157] In options 3-7, the UE 200 may periodically transmit the AI / ML L1 measurement result, or may transmit the AI / ML L1 measurement result triggered by an event. The event may include an event in which the AI / ML prediction accuracy score is equal to or greater than a threshold, or an event in which the AI / ML L1 measurement result is equal to or greater than a threshold. The threshold may be set or specified by the network, or may be predefined in the wireless communication system 10.

[0158] In option 3-8, the UE 200 may transmit an indication to the NW that the AI / ML L1 measurement result report is ready (e.g., AI / ML L1 measurement result is ready). The UE 200 may receive a request for the AI / ML L1 measurement result from the NW and transmit the AI / ML L1 measurement result in response to the request.

[0159] In option 3-9, in Intra-CU LTM, the Source gNB-DU may transmit the AI / ML L1 measurement result to the CU via the F1 interface, and the CU may transmit the AI / ML L1 measurement result to the Target gNB-DU via the F1 interface (e.g., Operation Example 1).

[0160] In option 3-10, in Inter-CU LTM, the Source gNB may transmit the AI / ML L1 measurement result to the Target gNB via the Xn interface (e.g., Operation Example 2).

[0161] (6) Action and effect In an embodiment, the introduction of AI / ML L1 measurement can reduce the burden on existing L1 measurement.

[0162] In the embodiment, the UE 200 transmits a lower layer measurement result (e.g., an AI / ML L1 measurement result) predicted by an operation corresponding to an AI / ML model for an LTM target node (a target gNB-DU of an Intra-CU LTM or a target gNB of an Inter-CU LTM) (Operation Examples 1 and 2). Such a configuration clarifies the mechanism by which the LTM target node acquires the AI / ML L1 measurement result, and can expect an improvement in the throughput of the LTM target node.

[0163] In an embodiment, UE 200 may transmit an AI / ML L1 measurement reporting (AI / ML L1 measurement result or AI / ML L1 measurement prediction) before receiving an LTM cell switch command (option 1-1 or option 2-1). With this configuration, the LTM target node can acquire the AI / ML L1 measurement reporting early, which can be expected to improve the throughput of the LTM target node.

[0164] In an embodiment, UE 200 may transmit AI / ML L1 measurement reporting together with RRCReconfigurationComplete (option 1-2 or option 2-2). With this configuration, the target node of the LTM can acquire the AI / ML L1 measurement reporting early, which can be expected to improve the throughput of the target node of the LTM.

[0165] (7) Other embodiments The present invention has been described above in accordance with the embodiments, but it will be obvious to those skilled in the art that the present invention is not limited to these descriptions and that various modifications and improvements are possible.

[0166] In the above disclosure, the AI / ML L1 measurement reporting has been mainly described, but the AI / ML L1 measurement result (AI / ML L1 measurement prediction) may be used in L3 measurement reporting. Such L3 measurement reporting may be referred to as AI / ML L3 measurement reporting in order to distinguish it from existing L3 measurement reporting.

[0167] Although not specifically mentioned in the above disclosure, in operation example 1, the information for AI / ML L1 measurement reporting to be applied to the UE may be determined or selected by the Source gNB-DU, may be determined or selected by the Target gNB-DU, or may be selected by the CU.

[0168] Although not specifically mentioned in the above disclosure, in operation example 2, the information for AI / ML L1 measurement reporting to be applied to the UE may be determined or selected by the Source gNB or by the Target gNB.

[0169] Although not particularly mentioned in the above disclosure, which of Operational Examples 1 to 3 to use (which aspect to use) may be set by a higher layer parameter. Which of each option of Operational Examples 1 to 3 to use may be set by a higher layer parameter. Which aspect to support may be reported from UE 200 as UE capability(ies). Which aspect to use may be defined in advance in wireless communication system 10. Which aspect to use may be set by a higher layer parameter and reported from UE 200 as UE capability(ies).

[0170] Although not specifically mentioned in the above disclosure, the following UE capability(ies) may be defined. UE capability(ies) may be defined for each A-IoT device, for each FR (e.g., FR1, FR2, FR2-1, FR2-2, FR3), for each SCS, for each band, for each Bandwidth Combination (BC), or for each Frequency Combination (FC). UE capability(ies) may be included in a signal reported from the UE 200 to the gNB 100, or may be included in a signal configured by the gNB 100 to the UE 200.

[0171] The block diagrams (FIGS. 4 and 5) used in the description of the above-described embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of 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 connected directly or indirectly (for example, by wire, wirelessly, etc.) and these multiple devices. The functional block may be realized by combining the single device or the multiple devices with software.

[0172] Functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, establishment, comparison, assumption, expectation, regard, 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.

[0173] Furthermore, the above-described network device 50 and UE 200 (the device) may function as a computer that performs processing of the wireless communication method of the present disclosure. Fig. 13 is a diagram showing an example of the hardware configuration of the device. As shown in Fig. 13, the device 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.

[0174] 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.

[0175] Each functional block of the device (see FIGS. 4 and 5) is realized by any hardware element of the computer device or a combination of the hardware elements.

[0176] 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.

[0177] 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, a register, and the like.

[0178] 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-mentioned embodiments. Furthermore, the various processes described above may be executed by one 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.

[0179] The memory 1002 is a computer-readable recording medium and may be configured by, for example, at least one of 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 can store a program (program code), a software module, etc., that can execute a method according to an embodiment of the present disclosure.

[0180] 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 appropriate medium including at least one of memory 1002 and storage 1003.

[0181] 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.

[0182] 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).

[0183] The input device 1005 is an input device (for example, 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 (for example, a display, a speaker, an LED lamp, etc.) that performs output to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one device (for example, a touch panel).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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), 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 and 5G) may also be applied.

[0188] 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.

[0189] In the present disclosure, a specific operation described as being performed by a base station may 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 (for example, but not limited to, an MME or an S-GW). 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 (for example, an MME and an S-GW) may also be used.

[0190] 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 / output via multiple network nodes.

[0191] The input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. The input and output information may be overwritten, updated, or added. The output information may be deleted. The input information may be sent to another device.

[0192] 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).

[0193] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched depending on the implementation. Furthermore, notification of predetermined information (e.g., notification that "X is true") is not limited to being done explicitly, but may be done implicitly (e.g., by not notifying the predetermined information).

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] As used in this disclosure, the terms "system" and "network" are used interchangeably.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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 service by a base station subsystem (e.g., a small indoor base station (Remote Radio Head: RRH)).

[0203] 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.

[0204] In this disclosure, the terms "Mobile Station (MS)," "user terminal," "User Equipment (UE)," "terminal," etc. may be used interchangeably.

[0205] 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.

[0206] At least one of the base station and the mobile station may be called 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 body, the mobile body itself, etc. The mobile body may be a vehicle (e.g., a car, an airplane, etc.), an unmanned mobile body (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 also include devices that do not necessarily move during communication operations. For example, at least one of the base station and the mobile station may be an Internet of Things (IoT) device such as a sensor.

[0207] 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 communication between terminals (for example, "side"). For example, terms such as uplink channel and downlink channel may be read as side channel.

[0208] 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.

[0209] A radio frame may be composed of one or more frames in the time domain, each of which may be called a subframe.

[0210] A subframe may further be composed of one or more slots in the time domain, and may have a fixed time length (e.g., 1 ms) that is independent of numerology.

[0211] Numerology may be a communication parameter applied to at least one of transmission and 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 a transceiver in the frequency domain, and specific windowing operations performed by a transceiver in the time domain.

[0212] A slot may consist of one or more symbols in the time domain (such as an Orthogonal Frequency Division Multiplexing (OFDM) symbol or a Single Carrier Frequency Division Multiple Access (SC-FDMA) symbol). A slot may be a time unit based on numerology.

[0213] A slot may include multiple minislots. Each minislot may consist of one or multiple 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.

[0214] 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.

[0215] 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. 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.

[0216] Here, TTI refers to, for example, the smallest time unit for scheduling in wireless communication. For example, in an LTE system, a base station performs scheduling to allocate radio resources (such as frequency bandwidth and transmission power that can be used by each user terminal) to each user terminal in TTI units. However, the definition of TTI is not limited to this.

[0217] 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.

[0218] 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. Also, the number of slots (minislots) constituting the minimum time unit for scheduling may be controlled.

[0219] A TTI having a time length of 1 ms may be called 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 called a shortened TTI, short TTI, partial or fractional TTI, shortened subframe, short subframe, minislot, subslot, slot, etc.

[0220] 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.

[0221] 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, for example, 12. The number of subcarriers included in an RB may also be determined based on numerology.

[0222] 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. One TTI, one subframe, etc. may each be composed of one or more resource blocks.

[0223] 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, or the like.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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."

[0228] The above-described structures of radio frames, subframes, slots, minislots, symbols, etc. 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, etc. may be changed in various ways.

[0229] 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.

[0230] The reference signal may also be abbreviated as Reference Signal (RS), and may also be called a pilot depending on the applicable standard.

[0231] 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."

[0232] The "means" in the configuration of each of the above devices may be replaced with "part," "circuit," "device," etc.

[0233] 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.

[0234] When used in this disclosure, the terms "include," "including," and variations thereof are intended to be inclusive, similar to the term "comprising." Furthermore, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0235] 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.

[0236] 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.

[0237] 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."

[0238] Fig. 14 shows an example of the configuration of a vehicle 2001. As shown in Fig. 14, 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.

[0239] The drive unit 2002 is composed of, for example, an engine, a motor, or a hybrid of an engine and a motor.

[0240] 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.

[0241] 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).

[0242] 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.

[0243] 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 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 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.

[0244] 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.

[0245] 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 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, 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.

[0246] 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.

[0247] The communication module 2013 transmits, via wireless communication to an external device, a current signal from the current sensor that is input to the electronic control unit 2010. The communication module 2013 also transmits, via wireless communication to an external device, the rotation speed signals of the front and rear wheels acquired by a rotation speed sensor 2022, the air pressure signals of the front and rear wheels acquired by an air pressure sensor 2023, the vehicle speed signal acquired by a vehicle speed sensor 2024, the acceleration signal acquired by an acceleration sensor 2025, the accelerator pedal depression amount signal acquired by an accelerator pedal sensor 2029, the brake pedal depression amount signal acquired by a brake pedal sensor 2026, the shift lever operation signal acquired by a shift lever sensor 2027, and the detection signals for detecting obstacles, vehicles, pedestrians, etc. acquired by an object detection sensor 2028, all of which are input to the electronic control unit 2010.

[0248] The communication module 2013 receives various information (traffic information, traffic signal information, vehicle distance information, etc.) transmitted from external devices and displays it on an information service unit 2012 provided in the vehicle. The communication module 2013 also stores the various information received from the 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 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, sensors 2021 to 2028, and the like provided in the vehicle 2001.

[0249] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0250] (Addendum) The above disclosure may be expressed as follows:

[0251] The first feature is a terminal comprising: a control unit that performs operations corresponding to a model that can be used as a model for artificial intelligence or machine learning; a receiving unit that receives a command instructing a cell switch that is performed at a layer lower than the radio resource control layer; and a transmitting unit that transmits measurement results of the lower layer predicted by operations corresponding to the model for a target node of the cell switch.

[0252] A second feature is the terminal based on the first feature, wherein the transmitter transmits the measurement result of the lower layer before receiving the command.

[0253] A third feature is the terminal based on the first feature, wherein the transmitter transmits the measurement result of the lower layer together with a reconfiguration completion notification regarding the cell switch.

[0254] A fourth feature is a network device comprising: a control unit that assumes that a terminal will perform operations corresponding to a model that can be used as a model for artificial intelligence or machine learning; a transmitting unit that transmits a command instructing a cell switch that is performed in a layer lower than the radio resource control layer; and a receiving unit that receives measurement results in the lower layer predicted by operations corresponding to the model for a target node of the cell switch.

[0255] A fifth feature is a wireless communication system comprising a terminal and a network device provided in a network, wherein the terminal comprises a control unit that performs operations corresponding to a model that can be used as a model for artificial intelligence or machine learning, a receiving unit that receives a command instructing a cell switch that is performed in a layer lower than the radio resource control layer, and a transmitting unit that transmits measurement results of the lower layer predicted by operations corresponding to the model for a target node of the cell switch.

[0256] A sixth feature is a wireless communication method comprising: step A of performing an operation corresponding to a model that can be used as a model for artificial intelligence or machine learning; step B of receiving a command instructing a cell switch that is performed in a layer lower than a radio resource control layer; and step C of transmitting measurement results of the lower layer predicted by the operation corresponding to the model for a target node of the cell switch. [Explanation of symbols]

[0257] 10. Wireless communication systems 10A Network 1 10B Second Network 20A, 20B Wireless Access Network 30A, 30B Core Network 50 Network Equipment 51 Receiving unit 52 Transmitter 53 Control Unit 100A,100B base station 200 UE 210 Radio signal transmitter / receiver 220 Amplifier section 230 Modulation and Demodulation Unit 240 Control signal / reference signal processing section 250 Encoding / Decoding Unit 260 Data transmission and reception unit 270 Control Unit 1001 processor 1002 memory 1003 Storage 1004 Communication equipment 1005 Input Device 1006 Output Device 1007 Bus 2001 Vehicle 2002 Drive unit 2003 Steering section 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 Services Department 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 Systems Department 2031 microprocessor 2032 memory (ROM, RAM) 2033 communication port

Claims

1. a control unit that executes an operation corresponding to a model that can be used as a model related to artificial intelligence or machine learning; a receiving unit for receiving a command instructing a cell switch to be executed in a layer lower than the radio resource control layer; A terminal comprising: a transmitter that transmits lower layer measurement results predicted by operations corresponding to the model for a target node of the cell switch.

2. The terminal according to claim 1 , wherein the transmitter transmits the measurement result of the lower layer before receiving the command.

3. The terminal according to claim 1 , wherein the transmitter transmits the measurement result of the lower layer together with a reconfiguration completion notification regarding the cell switch.

4. A control unit that assumes that the terminal executes an operation corresponding to a model that can be used as a model related to artificial intelligence or machine learning; a transmitter for transmitting a command instructing a cell switch to be executed in a layer lower than the radio resource control layer; A network device comprising: a receiver that receives lower layer measurement results predicted by operations corresponding to the model for a target node of the cell switch.

5. A terminal and a network device provided in the network, The terminal a control unit that executes an operation corresponding to a model that can be used as a model related to artificial intelligence or machine learning; a receiving unit for receiving a command instructing a cell switch to be executed in a layer lower than the radio resource control layer; A wireless communication system comprising: a transmitter that transmits lower layer measurement results predicted by operations corresponding to the model for a target node of the cell switch.

6. Step A: performing operations corresponding to an available model related to artificial intelligence or machine learning; Step B: receiving a command instructing a cell switch to be performed at a layer lower than the radio resource control layer; C. transmitting lower layer measurement results predicted by operations corresponding to the model for a target node of the cell switch.

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