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 appropriately continue operation according to the UE-side model when UE moves from a first node to a second node.SOLUTION: A terminal includes a control unit that executes operations corresponding to a model that can be used as a model for artificial intelligence or machine learning, and a receiving unit that, when moving from a first node to a second node, receives an instruction indicating whether to continue or deactivate the operations corresponding to the model that have been executed in the first node in the second node, and the control unit determines whether to continue or deactivate the operations corresponding to the model on the basis of the instruction, and the instruction is generated on the basis of information used to identify a combination of a first beam of the first node and a second beam of the second node that has an environment similar to the environment of the first beam.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 (AI / 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

[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, after careful consideration, have found that in cases where the UE-side model is adopted, when the UE moves from a first node to a second node, it is necessary to determine whether or not to continue operation using the UE-side model.

[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 appropriately continue to operate in accordance with the UE-side model when a UE moves from a first node to a second node.

[0008] The summary of the disclosure is a terminal comprising: a control unit that executes operations corresponding to a model that can be used as a model related to artificial intelligence or machine learning; and a receiving unit that, when moving from a first node to a second node, receives an instruction indicating whether to continue or deactivate the operations corresponding to the model that were being executed in the first node at the second node, wherein the control unit decides whether to continue or deactivate the operations corresponding to the model based on the instruction, and the instruction is generated based on information used to identify a combination of a first beam of the first node and a second beam of the second node that has an environment similar to the environment of the first beam.

[0009] The outline of the disclosure 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; and a transmission unit that transmits information about the model to another network device, the information being used to identify a combination of a first beam of a first node and a second beam of a second node that has an environment similar to that of the first beam.

[0010] The outline of the disclosure is a wireless communication system comprising a terminal and a network device provided in a network, wherein the terminal comprises a control unit that executes operations corresponding to a model that can be used as a model for artificial intelligence or machine learning, and the network device comprises a transmitting unit that transmits information regarding the model to another network device, the information being used to identify a combination of a first beam of a first node and a second beam of a second node having an environment similar to that of the first beam.

[0011] The outline of the disclosure is a wireless communication method comprising: step A: executing an operation corresponding to a model available as a model related to artificial intelligence or machine learning; step B: when moving from a first node to a second node, receiving an instruction indicating whether to continue or deactivate the operation corresponding to the model that was being executed in the first node in the second node; and step C: deciding whether to continue or deactivate the operation corresponding to the model based on the instruction, wherein the instruction is generated based on information used to identify a combination of a first beam of the first node and a second beam of the second node that has an environment similar to that of the first beam. [Brief explanation of the drawings]

[0012] [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 for explaining the first operation example. [Figure 12] FIG. 12 is a diagram illustrating the first operation example. [Figure 13] FIG. 13 is a diagram illustrating the second operation example. [Figure 14] FIG. 14 is a diagram illustrating the second modification. [Figure 15] FIG. 15 is a diagram illustrating the third modification. [Figure 16] FIG. 16 is a diagram showing an example of the hardware configuration of gNB100 and UE200. [Figure 17] FIG. 17 is a diagram showing an example of the configuration of a vehicle 2001. DETAILED DESCRIPTION OF THE INVENTION

[0013] 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.

[0014] [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.

[0015] 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.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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:

[0021] 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.

[0022] 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.

[0023] Second, the cellular network may correspond to the radio frames, subframes and slots shown in FIG.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] (2) Functional block configuration of wireless communication system The functional block configuration of the wireless communication system 10 will be described below.

[0028] First, the functional block configuration of the UE 200 will be described.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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).

[0033] 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 .

[0034] 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.

[0035] 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).

[0036] DM-RS is a terminal-specific 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.

[0037] 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.

[0038] 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).

[0039] 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.

[0040] 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.

[0041] 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.

[0042] The encoding / decoding unit 250 performs data division / concatenation and channel coding / decoding for each predetermined communication destination (gNB100 or another gNB).

[0043] 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.

[0044] 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).

[0045] 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).

[0046] In the embodiment, the radio signal transceiver unit 210 may be configured as a receiver that receives, when moving from a first node to a second node, an indication indicating whether to continue or deactivate an operation corresponding to a model (AI / ML model) executed in the first node in the second node. The first node may be read as a first RAN node, a first gNB, or a source node. The second node may be read as a second RAN node, a second gNB, or a target node. The first beam may be read as a first cell, and the second beam may be read as a second cell. The movement from the first node to the second node may include HO (Handover), CHO (Conditional Handover), or LTM (Lower layer Triggered Mobility). CHO may include CPC (Conditional PSCell Change) or CPC (Conditional PSCell Addition).

[0047] The indication may be generated based on information used to identify a combination of a first beam of the first node and a second beam of the second node having an environment similar to that of the first beam. The indication may be included in an HO command. The HO command may be included in a higher layer message (e.g., RRC reconfiguration).

[0048] In the embodiment, the wireless signal transmitting / receiving unit 210 may be configured as a transmitting unit that transmits information used to identify a combination of the first beam and the second beam as information related to the model (AI / ML model). The information used to identify a combination of the first beam and the second beam may be referred to as AI / ML related information.

[0049] 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, may be a CU constituting part of the base station 100A, or may be a DU constituting part of the base station 100A. The network device 50 may be the base station 100B, may be a CU constituting part of the base station 100B, or may be a DU constituting part of the base station 100B. The network device 50 may be a first node, a first RAN node, or a first gNB. The network device 50 may be a second node, a second RAN node, or a second gNB.

[0050] As shown in FIG. 5, the network device 50 includes a receiving unit 51, a transmitting unit 52, and a control unit 53.

[0051] 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.

[0052] 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.

[0053] 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).

[0054] In an embodiment, the transmitter 52 may be configured as a transmitter that transmits, as information related to the model, information (AI / ML related information) used to identify a combination of a first beam of a first node and a second beam of a second node having an environment similar to that of the first beam to another network device. When the network device 50 is a device related to the first node, the other network device may be a device related to the second node. When the network device 50 is a DU, the other network device may be a CU. When the network device 50 is a CU, the other network device may be a DU.

[0055] (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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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).

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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).

[0067] In Step 4, the UE reports the AI / ML functionality that the UE can execute to the NW (Applicable functionality reporting).

[0068] 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.

[0069] In Step 5, 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.

[0070] (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.

[0071] Against this background, the inventors, after careful consideration, have found that in cases where the UE-side model is adopted, when the UE moves from a first node to a second node, it is necessary to determine whether or not to continue operation using the UE-side model.

[0072] (5) Example of operation To solve the above-described problem, the following operation may be performed. Specifically, a concept of information used to identify a combination of a first beam of a first node and a second beam of a second node (AI / ML related information) is introduced as information for determining whether an AI / ML model executed in a first node can be continued in a second node. Here, the second beam is a beam having an environment similar to that of the first beam. The beam environment may include a radio wave environment and a channel environment. The beam environment may be referred to as a cell / beam additional condition.

[0073] For example, consider a case where Cell / beam#1 to Cell / beam#4 are used in a first gNB, and Cell / beam#5 to Cell / beam#8 are used in a second gNB. In such a case, if the environment of Cell / beam#1 and the environment of Cell / beam#8 are similar, the combination of Cell / beam#1 and Cell / beam#8 can be considered a Cell / beam combination that can maintain an AI / ML model. Similarly, if the environment of Cell / beam#2 and the environment of Cell / beam#5 are similar, the combination of Cell / beam#2 and Cell / beam#5 can be considered a Cell / beam combination that can maintain an AI / ML model. If the environment of Cell / beam#3 and the environment of Cell / beam#7 are similar, the combination of Cell / beam#3 and Cell / beam#7 can be considered a Cell / beam combination that can maintain an AI / ML model. If the environment of Cell / beam#4 and the environment of Cell / beam#6 are similar, the combination of Cell / beam#4 and Cell / beam#6 can be considered a Cell / beam combination that can sustain the AI / ML model. Note that there may be cases where the environment of the Cell / beam of the first gNB is not similar to the environment of any of the Cell / beams of the second gNB, so each of the Cell / beams of the first gNB does not need to be combined with any of the Cell / beams of the second gNB.

[0074] The environment of the cell / beam of the first gNB being similar to the environment of the cell / beam of the second gNB may mean that when the updated inference configuration of the second gNB set in Step 5 shown in Figure 7 is set in addition to the inference configuration of the first gNB set in Step 3 shown in Figure 7, functionality applicability reporting can be performed for the cell / beam of the second gNB in ​​the same way as for the cell / beam of the first gNB, or it may mean that inference can be performed for the cell / beam of the second gNB in ​​the same way as for the cell / beam of the first gNB.

[0075] Whether the environment of the cell / beam of the first gNB is similar to the environment of the cell / beam of the second gNB may be determined based on characteristics of SSB observed on the UE side. The characteristics of the SSB may include delay characteristics, strength characteristics, Doppler characteristics, etc. For example, if the difference between the delay of the SSB of the cell / beam of the first gNB and the delay of the SSB of the cell / beam of the second gNB is within a threshold, it may be determined that the environment of the cell / beam of the first gNB and the environment of the cell / beam of the second gNB are similar. If the difference between the strength of the SSB of the cell / beam of the first gNB and the strength of the SSB of the cell / beam of the second gNB is within a threshold, it may be determined that the environment of the cell / beam of the first gNB and the environment of the cell / beam of the second gNB are similar. If the difference between the Doppler of the SSB of the Cell / beam of the first gNB and the Doppler of the SSB of the Cell / beam of the second gNB is within a threshold, it may be determined that the environment of the Cell / beam of the first gNB and the environment of the Cell / beam of the second gNB are similar. Whether the environment of the Cell / beam of the first gNB and the environment of the Cell / beam of the second gNB are similar may be determined based on two or more characteristics selected from delay characteristics, intensity characteristics, and Doppler characteristics.

[0076] The following options are possible as information (AI / ML related information) used to identify the combination of the first beam of the first node and the second beam of the second node.

[0077] In option 1, the AI / ML related information may include information indicating a mapping between an additional condition of the first beam and an additional condition of the second beam. For example, when the environment of Cell / beam#1 and the environment of Cell / beam#8 are similar, the information may indicate a mapping between an additional condition set for Cell / beam#1 and an additional condition set for Cell / beam#8.

[0078] In option 2, the AI / ML related information may include identification information (mapping ID) indicating mapping between the additional condition of the first beam and the additional condition of the second beam. For example, when the environment of Cell / beam#1 and the environment of Cell / beam#8 are similar, the information may indicate mapping between the additional condition set for Cell / beam#1 and the additional condition set for Cell / beam#8.

[0079] In option 3, the AI / ML related information may include information indicating a group including the first beam and the second beam. For example, if the environment of Cell / beam#1 and the environment of Cell / beam#8 are similar, the information may indicate a group including Cell / beam#1 and Cell / beam#8.

[0080] Two or more options selected from Option 1 to Option 3 may be combined.

[0081] Under the above-mentioned premise, the following operation example is conceivable.

[0082] (5.1) Example 1 In the first operational example, a case will be described in which the network device 50 transmits AI / ML related information to another network device. As the first operational example, the following options are possible.

[0083] Option 1-1 describes a case in which AI / ML related information is exchanged between RAN nodes. As shown in FIG. 8, in step S10, RAN Node 1 may transmit AI / ML related information to RAN Node 2. In step S11, RAN Node 2 may transmit AI / ML related information to RAN Node 1. In FIG. 8, either step S10 or step S11 may be executed. In option 1-1, RAN Node 1 may acquire characteristics of SSBs (SSBs of RAN Node 1 and RAN Node 2) observed by the UE from the UE, and generate AI / ML related information based on the acquired SSB characteristics. Similarly, RAN Node 2 may acquire characteristics of SSBs (SSBs of RAN Node 1 and RAN Node 2) observed by the UE from the UE, and generate AI / ML related information based on the acquired SSB characteristics.

[0084] Option 1-2 describes a case where AI / ML related information is transmitted from the UE to the NW. As shown in Fig. 9, in step S20, the UE may transmit the AI / ML related information to the NW. In option 1-2, the UE may observe the characteristics of the SSBs of the first node and the second node, and generate the AI / ML related information based on the characteristics of the observed SSBs.

[0085] Options 1-3 will explain Split gNB. As shown in FIG. 10, in step S30, DU1 transmits AI / ML related information to CU1. In step S31, CU1 transmits the AI / ML related information to CU2. In step S32, CU2 transmits the AI / ML related information to DU1. In options 1-3, DU1 may acquire characteristics of SSBs (SSBs of DU1 and DU2) observed by the UE from the UE, and generate AI / ML related information based on the acquired SSB characteristics.

[0086] Option 1-4 describes Dual Connectivity. As shown in Fig. 11, in step S40, a Master Node (MN) transmits AI / ML related information to a Secondary Node (SN). In option 1-4, the MN may acquire characteristics of the SSB (SSBs of the MN and SN) observed by the UE from the UE, and generate AI / ML related information based on the acquired SSB characteristics.

[0087] Option 1-5 describes Dual Connectivity. As shown in Fig. 12, in step S50, the SN transmits AI / ML related information to the MN. In option 1-5, the SN may acquire characteristics of the SSB (SSB of the MN and SN) observed by the UE from the UE, and generate AI / ML related information based on the acquired SSB characteristics.

[0088] (5.2) Example 2 In Operation Example 2, a handover from a Source Node to a Target Node will be described. For Operation Example 2, the following options are possible:

[0089] Option 2-1 describes a case in which the Target Node decides whether to continue or deactivate the operation corresponding to the AI / ML model that was running on the Source Node.

[0090] As shown in FIG. 13, in step S60, the UE transmits a measurement report to the source node.

[0091] In step S61, the Source Node transmits a Handover request to the Target Node. The Handover request may include the AI / ML related information described above.

[0092] In step S62, the target node determines, based on the AI / ML related information, whether to continue or deactivate the operation corresponding to the AI / ML model that was being executed in the source node.

[0093] In step S63, the Target Node transmits a Handover request ACK to the Source Node. The Handover request ACK includes an indication of whether the operation corresponding to the AI / ML model executed in the Source Node should be continued or deactivated in the Target Node.

[0094] In step S64, the source node transmits an HO command. The HO command includes an indication of whether to continue or deactivate the operation corresponding to the AI / ML model executed in the source node in the target node. The HO command may be included in RRC Reconfiguration. The RRC Reconfiguration may include the updated inference configuration shown in Step 5 of FIG. 7.

[0095] Here, if the beam environment of the target node is similar to the beam environment of the source node, the UE receives an indication to continue the operation corresponding to the AI / ML model and continues the operation corresponding to the AI / ML model at the target node.On the other hand, if the beam environment of the target node is not similar to the beam environment of the source node, the UE receives an indication to deactivate the operation corresponding to the AI / ML model and deactivates the operation corresponding to the AI / ML model at the target node.

[0096] In step S65, the UE sends an RRCReconfigurationComplete.

[0097] In step S66, the UE transmits an Applicable functionality reporting. If the operation corresponding to the AI / ML model is to be continued, the Applicable functionality reporting may include the same AI / ML functionality as that reported to the Source Node. If the operation corresponding to the AI / ML model is to be deactivated, the Applicable functionality reporting may include information for identifying the deactivated AI / ML functionality. The deactivated AI / ML functionality may be identified by the AI / ML functionality that the UE can execute at the Target Node.

[0098] Option 2-2 describes the case where the Source Node decides whether to continue or deactivate the operation corresponding to the AI / ML model that was running on the Source Node on the Target Node.

[0099] As shown in FIG. 14, in step S70, the UE transmits a measurement report to the source node.

[0100] In step S71, the Source Node transmits a Handover request to the Target Node.

[0101] In step S72, the Target Node transmits a Handover request ACK to the Source Node. The Handover request ACK may include the AI / ML related information described above.

[0102] In step S73, the Source Node determines, based on the AI / ML related information, whether to continue or deactivate the operation corresponding to the AI / ML model that was being executed in the Source Node in the Target Node.

[0103] In step S74, the source node transmits an HO command. The HO command includes an indication of whether to continue or deactivate the operation corresponding to the AI / ML model executed in the source node in the target node. The HO command may be included in RRC Reconfiguration. The RRC Reconfiguration may include the updated inference configuration shown in Step 5 of FIG. 7.

[0104] Here, if the beam environment of the target node is similar to the beam environment of the source node, the UE receives an indication to continue the operation corresponding to the AI / ML model and continues the operation corresponding to the AI / ML model at the target node.On the other hand, if the beam environment of the target node is not similar to the beam environment of the source node, the UE receives an indication to deactivate the operation corresponding to the AI / ML model and deactivates the operation corresponding to the AI / ML model at the target node.

[0105] In step S75, the UE sends an RRCReconfigurationComplete.

[0106] In step S76, the UE transmits an Applicable functionality reporting. If the operation corresponding to the AI / ML model is to be continued, the Applicable functionality reporting may include the same AI / ML functionality as that reported to the Source Node. If the operation corresponding to the AI / ML model is to be deactivated, the Applicable functionality reporting may include information for identifying the deactivated AI / ML functionality. The deactivated AI / ML functionality may be identified by the AI / ML functionality that the UE can execute at the Target Node.

[0107] (5.3) Example 3 In the third operational example, the associated ID in the handover from the source node to the target node will be described.

[0108] As shown in FIG. 14, in step S70, the UE transmits a measurement report to the source node.

[0109] In step S81, the Source Node transmits a Handover request to the Target Node. The Handover request includes an inference configuration to be applied to the Source cell / beam of the Source Node.

[0110] In step S82, the target node generates an updated inference configuration to be applied to the target cell / beam of the target node based on the inference configuration received from the source node and the inference configuration to be applied to the target cell / beam of the target node (hereinafter referred to as the target inference config), and generates an associated ID to identify the generated updated inference config. The target node transmits a Handover request ACK to the source node. The Handover request ACK may include the target inference config and the associated ID. The Handover request ACK may also include the updated inference config and the associated ID.

[0111] In step S83, the source node transmits an HO command. The HO command may include a target inference config and an associated ID. The HO command may also include an updated inference config and an associated ID.

[0112] In step S84, the UE sends an RRCReconfigurationComplete.

[0113] In step S85, the UE sends an applicable functionality reporting using the associated ID (at Target Cell). In other words, the applicable functionality reporting may include an associated ID (at Target Cell) that identifies the target inference config or the updated inference config.

[0114] Note that the Applicable functionality reporting may be included in the RRCReconfigurationComplete in step S84.

[0115] (5.4) Other In the above-described operation example, AI / ML related information (i.e., information used to identify a combination of beams having a similar environment) may be generated for each UE, may be generated for each UE Type, or may be generated for each RAN Node regardless of the UE or UE Type. UE Types may include types such as RedCAP (Reduced Capability) UE / Non-RedCAP UE, and may also include types such as Normal UE, IoT UE, and XR UE.

[0116] In the above-described operation example, whether the beam environments are similar or not is determined based on the characteristics of the SSB observed by the UE. However, whether the beam environments are similar or not may be determined based on information held by the network (e.g., antenna ports, cell coverage areas, etc.).

[0117] (6) Action and effect In the embodiment, the network device 50 may transmit information (AI / ML related information) used to identify a combination of a first beam of the first node and a second beam having an environment similar to that of the first beam to another network device (Operation Example 1). With this configuration, it is possible to appropriately determine whether to continue or deactivate the operation corresponding to the AI / ML model that was being executed in the first node in the second node.

[0118] In an embodiment, UE200 may transmit information (AI / ML related information) to the network used to identify a combination of a first beam of the first node and a second beam having an environment similar to that of the first beam (operation example 1).

[0119] In the embodiment, the UE 200 may receive an indication indicating whether to continue or deactivate, at the second node, the operation corresponding to the AI / ML model that has been executed at the first node, and may continue or deactivate, at the second node, the operation corresponding to the AI / ML model based on the received indication (Operation Example 2). With this configuration, it is possible to appropriately continue the operation corresponding to the UE-side model.

[0120] In the embodiment, when the network device 50 is a target node, the network device 50 may generate an updated inference config to be applied to a target cell / beam of the target node based on the inference config and the target inference config to be applied to a source cell / beam of the source node, and may generate an associated ID that identifies the generated updated inference config. According to this configuration, the associated ID is notified to the UE 200, so that the UE 200 can appropriately execute applicable functionality reporting.

[0121] (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.

[0122] In the above disclosure, L3 handover has been mainly described, but the above disclosure may also be applied to LTM (Lower layer Triggered Mobility), CHO (Conditional Handover), and the like.

[0123] 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.

[0124] 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.

[0125] 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. 16 is a diagram showing an example of the hardware configuration of the device. As shown in Fig. 16, 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] As used in this disclosure, the terms "system" and "network" are used interchangeably.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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)).

[0155] 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.

[0156] In this disclosure, the terms "Mobile Station (MS)," "user terminal," "User Equipment (UE)," "terminal," etc. may be used interchangeably.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] A radio frame may be composed of one or more frames in the time domain, each of which may be called a subframe.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] The reference signal may also be abbreviated as Reference Signal (RS), and may also be called a pilot depending on the applicable standard.

[0183] 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."

[0184] The "means" in the configuration of each of the above devices may be replaced with "part," "circuit," "device," etc.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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."

[0190] Fig. 17 shows an example of the configuration of a vehicle 2001. As shown in Fig. 17, 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.

[0191] The drive unit 2002 is composed of, for example, an engine, a motor, or a hybrid of an engine and a motor.

[0192] 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.

[0193] 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).

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (Addendum) The above disclosure may be expressed as follows:

[0203] A first feature is a terminal comprising: 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; and a receiving unit that, when moving from a first node to a second node, receives an instruction indicating whether to continue or deactivate the operation corresponding to the model that was being executed in the first node in the second node, wherein the control unit decides whether to continue or deactivate the operation corresponding to the model based on the instruction, and the instruction is generated based on information used to identify a combination of a first beam of the first node and a second beam of the second node that has an environment similar to the environment of the first beam.

[0204] A second feature is the terminal of the first feature, further comprising a transmitting unit that transmits information used to identify a combination of the first beam and the second beam as information related to the model.

[0205] A third feature is a network device comprising: a control unit that assumes that a terminal performs an operation corresponding to a model that can be used as a model for artificial intelligence or machine learning; and a transmitting unit that transmits information about the model to another network device, the information being used to identify a combination of a first beam of a first node and a second beam of a second node that has an environment similar to that of the first beam.

[0206] A fourth feature is a network device in the third feature, further comprising a transmitting unit that transmits an instruction indicating whether to continue or deactivate the operation corresponding to the model that was being executed in the first node in the second node based on information used to identify a combination of the first beam and the second beam.

[0207] 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 executes operations corresponding to a model that can be used as a model for artificial intelligence or machine learning, and the network device comprises a transmitting unit that transmits information regarding the model to another network device, the information being used to identify a combination of a first beam of a first node and a second beam of a second node having an environment similar to that of the first beam.

[0208] A sixth feature is a wireless communication method comprising: a step A of executing an operation corresponding to a model available as a model related to artificial intelligence or machine learning; a step B of, when moving from a first node to a second node, receiving an instruction indicating whether to continue or deactivate the operation corresponding to the model that was being executed in the first node in the second node; and a step C of deciding whether to continue or deactivate the operation corresponding to the model based on the instruction, wherein the instruction is generated based on information used to identify a combination of a first beam of the first node and a second beam of the second node having an environment similar to that of the first beam. [Explanation of symbols]

[0209] 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 that, when moving from a first node to a second node, receives an instruction indicating whether to continue or deactivate an operation corresponding to the model that has been executed in the first node in the second node; The control unit determines whether to continue or deactivate the operation corresponding to the model based on the instruction; A terminal, wherein the instruction is generated based on information used to identify a combination of a first beam of the first node and a second beam of the second node having an environment similar to the environment of the first beam.

2. The terminal according to claim 1 , further comprising a transmitting unit that transmits information used to identify a combination of the first beam and the second beam as information about the model.

3. 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 network device comprising: a transmitting unit that transmits information about the model to other network devices, the information being used to identify a combination of a first beam of a first node and a second beam of a second node having an environment similar to the environment of the first beam.

4. The network device of claim 3, further comprising a transmitting unit that transmits an instruction indicating whether to continue or deactivate the operation corresponding to the model that was being executed in the first node in the second node based on information used to identify the combination of the first beam and the second beam.

5. A terminal and a network device provided in the network, The terminal includes: 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 wireless communication system, wherein the network device comprises a transmitter that transmits information about the model to other network devices, the information being used to identify a combination of a first beam of a first node and a second beam of a second node having an environment similar to that of the first beam.

6. Step A: performing operations corresponding to an available model related to artificial intelligence or machine learning; B. when moving from a first node to a second node, receiving an instruction indicating whether to continue or deactivate at the second node the operation corresponding to the model that was being executed at the first node; and C. determining whether to continue or deactivate the operation corresponding to the model based on the instruction; A wireless communication method, wherein the instruction is generated based on information used to identify a combination of a first beam of the first node and a second beam of the second node having an environment similar to that of the first beam.

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