Terminal and base station

Abstract

To allow a learning model on a terminal side to be linked to a learning model on a network side and AI / ML functions to be executed appropriately.SOLUTION: A terminal includes a memory unit that stores a learning model on the terminal side, and a transmitting unit that transmits to a base station at least one of information related to the consistency between the learning model on the terminal side and a learning model on the base station side, or information indicating whether settings related to AI / ML (Artificial Intelligence / Machine Learning) functions can be applied to the terminal.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to a terminal and a base station in a wireless communication system. [Background technology]

[0002] The 3GPP (registered trademark) (3rd Generation Partnership Project) is considering the application of artificial intelligence / machine learning (AI / ML) technology to wireless communication technology in the sixth generation mobile communication system (6G).

[0003] 3GPP is studying a two-side model, in which an AI / ML model is implemented in both the UE (User Equipment) and the network (NW) (e.g., gNB) as one of the AI / ML technologies to be applied to wireless communication systems, and which operates in cooperation with each other. To ensure proper operation of the two-side model, the UE and gNB need to notify each other whether the UE side model and the gNB side model match and whether the UE and NW (gNB) can apply settings related to AI / ML functions. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] 3GPP TS 38.300 V18.5.0(2025-03) [Non-patent document 2] 3GPP TS 38.401 V18.5.0(2025-03) Summary of the Invention [Problem to be solved by the invention]

[0005] However, there has been no established method between the UE and gNB to mutually notify whether the UE side model matches the gNB side model and whether settings related to AI / ML functions are applicable. Furthermore, it is unclear how such a method should be applied to the CU-DU split architecture. As a result, there is a risk that the wireless communication system will not be able to properly operate under the two-side model. [Means for solving the problem]

[0006] The terminal in this embodiment includes a memory unit that stores a learning model on the terminal side, and a transmission unit that transmits to the base station at least one of information related to the consistency between the learning model on the terminal side and the learning model on the base station side, or information indicating whether settings related to AI / ML functions can be applied to the terminal. [Effects of the Invention]

[0007] According to this embodiment, the learning model on the terminal side and the learning model on the network side are linked to each other, thereby enabling appropriate execution of AI / ML functions. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a diagram illustrating an example of a wireless communication system according to an embodiment of the present invention. [Figure 2] FIG. 10 is a sequence diagram showing an example of an operation procedure for reporting applicability in the present embodiment. [Figure 3] 1 is a diagram showing an example of an information element structure of an RRC message used in the wireless communication system of this embodiment. FIG. [Figure 4] FIG. 10 is a diagram illustrating an explanation of each field of an ApplicabilityReportList in this embodiment. [Figure 5] FIG. 4 is a sequence diagram illustrating an example of an operation procedure of the wireless communication system according to the first embodiment. [Figure 6] FIG. 10 is a sequence diagram illustrating an example of an operation procedure of the wireless communication system according to another example of the first embodiment. [Figure 7] FIG. 10 is a sequence diagram illustrating an example of an operation procedure of the wireless communication system according to the second embodiment. [Figure 8] FIG. 10 is a sequence diagram illustrating an example of an operation procedure of a wireless communication system according to another example of the second embodiment. [Figure 9] FIG. 2 is a diagram illustrating an example of a functional configuration of a base station according to the present embodiment. [Figure 10] FIG. 2 is a diagram illustrating an example of a functional configuration of a terminal according to the present embodiment. [Figure 11] FIG. 2 is a diagram illustrating an example of a hardware configuration of a base station or a terminal according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] The present embodiment will be described below with reference to the drawings. Note that the embodiment described below is an example, and the embodiment to which the present invention is applied is not limited to the following embodiment.

[0010] In operation of the wireless communication system of this embodiment, existing technologies (e.g., LTE and NR (5G)) or future technologies (e.g., 6G) may be used as appropriate. The technologies used in the wireless communication system of this embodiment may not be limited to the above-mentioned LTE, NR, and 6G.

[0011] In the present embodiment described below, terms used in existing technologies, such as SS (Synchronization signal), PSS (Primary SS), SSS (Secondary SS), PBCH (Physical broadcast channel), PRACH (Physical random access channel), PDCCH (Physical Downlink Control Channel), PDSCH (Physical Downlink Shared Channel), PUCCH (Physical Uplink Control Channel), and PUSCH (Physical Uplink Shared Channel), are used. This is for convenience of description, and similar signals, functions, etc. may be called by other names. The above-mentioned terms in NR correspond to NR-SS, NR-PSS, NR-SSS, NR-PBCH, NR-PRACH, etc. However, even signals used in NR are not necessarily designated as "NR-".

[0012] In this embodiment, the duplex method may be a time division duplex (TDD) method, a frequency division duplex (FDD) method, or other methods (for example, flexible duplex, etc.).

[0013] In this embodiment, "configuring" radio parameters etc. may mean that predetermined values ​​are pre-configured, or that radio parameters notified from a base station or a terminal are set.

[0014] (System Configuration) Fig. 1 is a diagram for explaining a wireless communication system in this embodiment. As shown in Fig. 1, the wireless communication system in this embodiment includes a base station (gNB) 10 and a terminal (UE (User Equipment)) 20. Fig. 1 shows one base station 10 and one terminal 20, but this is an example, and there may be a plurality of each.

[0015] The base station 10 is a communication device that provides one or more cells and performs wireless communication with the terminal 20. The physical resources of a wireless signal are defined in the time domain and the frequency domain, and the time domain may be defined by the number of Orthogonal Frequency Division Multiplexing (OFDM) symbols, and the frequency domain may be defined by the number of subcarriers or the number of resource blocks. A TTI (Transmission Time Interval) in the time domain may be a slot, or a TTI may be a subframe.

[0016] The base station 10 transmits a synchronization signal and system information to the terminal 20. The synchronization signal is, for example, NR-PSS and NR-SSS. The system information is transmitted, for example, via the NR-PBCH and is also referred to as broadcast information. The synchronization signal and system information may be referred to as an SSB (SS / PBCH block). As shown in FIG. 1 , the base station 10 transmits control signals or data to the terminal 20 via DL (Downlink) and receives control signals or data from the terminal 20 via UL (Uplink). Both the base station 10 and the terminal 20 are capable of transmitting and receiving signals by performing beamforming. Both the base station 10 and the terminal 20 are capable of applying MIMO (Multiple Input Multiple Output) communication to DL or UL. Both the base station 10 and the terminal 20 may communicate via a secondary cell (SCell) and a primary cell (PCell) using Carrier Aggregation (CA). Furthermore, the terminal 20 may perform communication via a primary cell of the base station 10 and a primary secondary cell group cell (PSCell: Primary SCG Cell) of another base station 10 using DC (Dual Connectivity).

[0017] The base station 10 may be configured with a CU (Central Unit)-DU (Distributed Unit) split architecture. In this architecture, the functions of the base station (gNB) 10 are divided between a CU (gNB-CU) 10B and a DU (gNB-DU) 10A. The CU 10B typically processes higher layer protocol functions such as RRC and PDCP, controls one or more DUs 10A, and manages a wide coverage area and a large number of terminals 20. On the other hand, the DU 10A processes lower layer protocol functions such as RLC, MAC, and the physical layer, and is responsible for direct communication with the radio interface under the control of the CU 10B, transmitting and receiving radio signals to and from the terminals 20. The base stations (CUs 10B) may be connected to each other via an Xn interface. The CU 10B and the DU 10A may be connected to each other via an F1 interface. The DU 10A is an example of a first unit included in the base station 10 and having a radio interface with the terminals 20. The CU 10B is an example of a second unit that is included in the base station 10 and controls the DU 10A.

[0018] The terminal 20 is a communication device equipped with a wireless communication function, such as a smartphone, a mobile phone, a tablet, a wearable terminal, or an M2M (Machine-to-Machine) communication module. As shown in Fig. 1, the terminal 20 receives control signals or data from the base station 10 via DL and transmits control signals or data to the base station 10 via UL, thereby utilizing various communication services provided by the wireless communication system. The terminal 20 receives various reference signals transmitted from the base station 10 and measures the propagation path quality based on the reception results of the reference signals.

[0019] AI / ML may be applied to the wireless communication system in this embodiment. AI / ML is a technology that learns based on data, recognizes patterns, and makes predictions. In wireless communication systems, AI / ML is used, for example, to improve the efficiency of data collection, optimize wireless resource management, detect / recover network faults, optimize mobility (cell switching, etc.), and improve beam management.

[0020] An AI / ML model refers to a mathematical or algorithmic structure that realizes such AI / ML functionality. An AI / ML model may be trained using training data and configured to perform a specific task (e.g., logging L1 measurements, determining applicable functions, predicting radio measurements).

[0021] The functions of an AI / ML model architecture may include, for example, data collection, AI / ML model training (hereinafter referred to as "model training"), and model inference. Data collection is the function that provides input data to the model training and model inference functions. Model training performs training, validation, and testing of the AI / ML model. As part of the model testing procedure, performance metrics of the AI / ML model may be generated. Model training may also be responsible for data preparation (e.g., data preprocessing and cleaning, formatting, and transformation). Model inference is the function that provides inference output (e.g., predictions or decisions). Model inference may provide control of model inference to model management / performance monitoring functions.

[0022] In this embodiment, at least one of a network-side AI / ML model and a UE-side AI / ML model may be used. The network-side AI / ML model and the UE-side AI / ML model may work together with data collection and applicability reporting mechanisms for different purposes. For example, data collection may be performed for network-side model training, and applicability may be reported for the UE-side model.

[0023] Data collection for model training in this embodiment will now be described.

[0024] Data collection for network-side model training may be initiated by OAM (Operation, Administration, and Maintenance) or the gNB.

[0025] For data collection for UE-side model training, the UE 20 may be configured by the gNB 10 to log L1 measurements in RRC messages. The UE 20 may be configured by the gNB 10 to report the logged data or L1 measurements in an Access Stratum (AS) buffer.

[0026] Periodic and radio condition-based event-triggered data logging may be supported. The UE 20 may be configured to perform data logging based on an L3 measurement event trigger. The UE 20 stores the logged data in an AS layer buffer. When the memory reserved for storing logged data becomes full, the UE 20 stops measuring and logging for data collection purposes and notifies the network that data is available. This data availability notification may also be sent when an absolute UE buffer threshold is reached or when a low power state of the UE 20 is detected. Upon receiving the availability notification, the network may request the UE 20 to transmit the available data.

[0027] To reduce power consumption of the UE 20, if a low power condition is detected, the UE 20 may notify the network of the low power condition. Upon receiving the low power condition notification, the network may deconfigure the UE 20 to release the data collection setting. How the buffer threshold is reached and the low power condition are determined may be based on the implementation of the UE 20. If the low power condition is cleared or the UE buffer is emptied, no additional signaling from the UE 20 may be required.

[0028] The network may configure the UE 20 whether to retain the logged data during handover. If configured to retain the data, the UE 20 may retain the logged data during handover and notify the network of the availability of the logged data after handover. If the UE 20 transitions to RRC_IDLE / INACTIVE or if the UE 20 detects a Radio Link Failure (RLF), the UE 20 may discard all stored data.

[0029] When the UE 20 logs data, the UE 20 may include:

[0030] - Data logging setting ID - Indication of a logging gap time interval longer than the set logging cycle - The serving cell's available NCGI (New Cell Global Identity) If NCGI is not available, the UE 20 may include an available PCI (Physical Cell Identity) and ARFCN (Absolute Radio Frequency Channel Number). Regarding data collection for model training on the UE side, the network may configure whether to allow the UE 20 to initiate a request for data collection configuration (e.g., start / stop, preferred configuration from a list of candidate configurations provided by the network). The network may provide data collection configuration to the UE 20 or release data collection configuration with or without a request from the UE 20.

[0031] Data collection settings in beam management may include:

[0032] - CSI-ResourceConfigId of set A - CSI-ResourceConfigId of set B - One or two association IDs (depending on whether set B is equal to or a subset of set A)

[0033] Applicability reporting supported by the wireless communication system in this embodiment will be described.

[0034] For the UE-side model, the network (e.g., gNB 10) provides inferred configuration based on the supported capabilities of the UE 20. The UE 20 may report its applicable capabilities, inapplicable capabilities, and their subsequent changes to the network. When the UE 20 reports that a capability has become inapplicable, the UE 20 may also indicate its preference for releasing the configuration (e.g., due to unavailability of the model at the local device). The applicability reporting procedure is shown in Figure 2.

[0035] Step S1 in FIG. 2: The network inquires about UE capability information.

[0036] Step S2: The UE 20 indicates its supported capabilities (capabilities that the UE 20 can indicate to the network) via UE capability information (eg, RRC / LPP signaling).

[0037] Step S3: The network provides the UE with an inference configuration (i.e., a complete inference configuration and / or a set of inference-related parameters) along with network-side additional conditions (if provided) via the CSI reporting configuration or OtherConfig.

[0038] Step S4: The UE 20 determines the applicable AI / ML functions based on the network side additional conditions (if provided), the UE side additional conditions (known internally by the UE) and the availability of models in the UE.

[0039] Step S5: The UE 20 reports its initial feature applicability in an RRCReconfigurationComplete message.

[0040] Step S6: If the CSI reporting configuration provides a periodic CSI configuration that matches the reported UE capabilities, the UE 20 autonomously activates the applicable AI / ML functions upon reporting the applicable AI / ML functions. If a semi-persistent CSI and / or aperiodic CSI configuration is provided, after reporting the applicable AI / ML functions, activation of the applicable AI / ML functions follows CSI measurement and reporting. That is, semi-persistent reporting is activated by MAC CE / DCI, and aperiodic CSI reporting is activated by DCI.

[0041] If no inference configurations are provided in step 3, the network may send an RRCReconfiguration message containing the inference configurations to the UE. Upon receiving one or more inference configurations, the UE maintains all inference configurations, regardless of whether they are applicable or not, until explicitly released by the network.

[0042] Step S7: If the network has configured applicability reporting for the UE 20 and applicability reporting is enabled via OtherConfig, and if the applicability of a feature has changed, the UE 20 may report the updated applicability and non-applicability of the feature in the UEAssistanceInformation message. If the Periodic CSI-ReportConfig becomes inapplicable, the UE 20 may notify the network of this fact without autonomously releasing the configuration, and the network may release the configuration. The UE 20 may continue inferring and reporting until the configuration is released. If an activated AI / ML feature becomes inapplicable, the UE 20 may not autonomously deactivate it, but may notify the network of the change in the applicability of the feature. Upon receiving notification from the UE that the feature has become inapplicable, the network may deactivate or release the activated feature.

[0043] During the handover, the UE 20 may receive additional network-side conditions and / or inference settings related to the target gNB via a handover command, and may then report applicable / inapplicable AI / ML capabilities to the target gNB after the handover is complete.

[0044] For the network-side model, CSI measurements and CSI reports may be used to obtain input data for inference. For the network-side model, additional network-side requirements may be required based on the network implementation.

[0045] An example of an RRC IE used in this embodiment will be described below. Fig. 3 is a diagram illustrating an example of information element structures of RRCReconfigurationComplete, UEAssistanceInformation, and ApplicabilityReportList used in the wireless communication system according to this embodiment. Fig. 4 is a diagram illustrating an explanation of each field of ApplicabilityReportList. applicabilityCellId indicates an index of a serving cell to which the applicability report refers. applicabilityReportConfigIdList indicates a list of applicability reports for each configuration ID associated with a configuration that is the target of the applicability determination procedure. applicabilityReportConfigId indicates a CSI-ReportConfigId associated with a CSI report configured for radio measurement prediction. applicabilityStatus indicates whether a CSI report configured for radio measurement prediction and associated with the applicabilityReportConfigId is applicable or inapplicable. inapplicabilityCause indicates a cause why a CSI report configured for radio measurement prediction and associated with the applicabilityReportConfigId is inapplicable.

[0046] The wireless communication system in this embodiment supports a two-side model. The two-side model is an AI / ML model that is implemented in each of the UE 20 and the network (NW) (e.g., gNB 10) in the wireless communication system and operates in cooperation with each other. In the two-side model, the AI / ML model on the UE side and the AI / ML model on the NW (gNB) side may be referred to as the UE side model and the NW side model (gNB side model), respectively.

[0047] An example of applying the two-side model to AI / ML functions is shown below.

[0048] As an example of the application of the two-side model in data collection and model updating, the UE-side model continuously collects data according to a specific communication environment (e.g., radio wave conditions, surrounding UE density, traffic patterns, etc.) and transmits that data to the network side. The network-side model updates its own model using data collected from the UE or data aggregated from multiple UEs or other network nodes. The updated network-side model feeds back the results to the UE side, and the UE-side model is updated accordingly. This cycle continuously improves the accuracy of the model through cooperation between the UE and the network.

[0049] As an example of the application of a two-side model in cooperative inference or control, the UE side model and the NW side model each perform inference independently, while exchanging and integrating their inference results to make more accurate overall decisions. For example, the UE side performs primary inference based on local environmental information, while the NW side performs secondary inference based on wide-area network information and information from other UEs. Combining these inference results enables, for example, more optimal resource allocation, interference suppression, or traffic prediction.

[0050] As an example of the application of a two-side model to distribute some of the model functions, the functions of a large machine learning model may be divided and implemented on the UE side and the network side. For example, the parts of the model that require low latency are placed on the UE side, while the parts that require large-scale computing power and wide-area data are placed on the network side. This allows for the realization of high-performance machine learning functions overall while taking into account the constraints of the UE's computing power and power consumption.

[0051] To perform the operation of the two-sided model, the UE side model and the gNB side model need to be paired. The pairing of the UE side model and the gNB side model may refer to the UE side model (or the ID of the UE side model) and the gNB side model (or the ID of the gNB side model) being consistent, corresponding, matching, or associated with each other.

[0052] On the other hand, if the UE side model and the gNB side model are not paired properly in the two-side model, the operation of the two-side model may not be performed properly.Furthermore, if the AI / ML function or setting is not applicable to at least one of the UE or the gNB, the operation based on the two-side model may not be performed.

[0053] As described above, to ensure proper operation of the two-side model, the UE and the gNB need to notify each other whether the UE side model and the gNB side model are compatible and / or whether the two-side model can be applied to the UE and the NW (gNB), respectively. However, conventionally, there has been no established method for the UE and the gNB to notify each other whether the UE side model and the gNB side model are compatible and whether AI / ML functions or settings can be applied. Furthermore, it has not been clear how such a method should be applied to the CU-DU split architecture. As a result, there is a risk that the wireless communication system will not be able to properly perform the two-side model operation.

[0054] In this embodiment, a method for reporting information (AI / ML related information) for performing operations based on a two-side model is specified between the UE 20 and the gNB 10 and in a CU-DU separated architecture.

[0055] In this embodiment, the information for executing an operation based on the two side model may be, for example, at least one of information related to the consistency between the UE side model and the gNB side model in the two side model, or information indicating the applicability of AI / ML functions or settings related to the AI / ML functions in the UE 20 and the gNB 10. The applicability of settings related to the AI / ML functions may indicate whether or not an AI / ML function such as radio measurement prediction is applicable to the UE 20 or the gNB 10, or may indicate whether or not an operation based on the two side model is applicable.

[0056] The information related to the compatibility between the UE side model and the gNB side model in the two side model may be a result indicating whether the UE side model and the gNB side model are compatible, or may be information used to determine the compatibility between the UE side model and the gNB side model (for example, at least one of the ID of the UE side model or the ID of the gNB side model).

[0057] The UE side model may be referred to as a UE side learning model or a UE side AI / ML model, or may be expressed by any name. The gNB side model may be referred to as a gNB side learning model or a gNB side AI / ML model, or may be expressed by any name.

[0058] Operation based on the two-side model may mean operation based on cooperation or coordination between the UE side model and the gNB side model.

[0059] Examples of this embodiment will be described below. Each example may be implemented independently, or a plurality of examples may be implemented in combination.

[0060] Example 1 According to the first embodiment, the UE 20 may report information for performing an operation based on the two-side model to the gNB 10. For example, as shown in Fig. 5, the UE 20 may report the applicabilityReportList or the matchabilityReportList to the gNB 10 using an RRC message (step S101). The applicabilityReportList or the matchabilityReportList may be transmitted not only by an RRC message but also by a MAC CE (Control Element).

[0061] The applicabilityReportList is an example of information indicating whether a predetermined AI / ML function or setting is applicable to each entity. The predetermined AI / ML function or setting may be, for example, a CSI report set for radio measurement prediction.

[0062] The matchabilityReportList is an example of information related to the compatibility between the UE side model and the gNB side model.

[0063] The report from the UE 20 to the gNB 10 may include at least one of the following information:

[0064] - applicabilityCellId matchabilityCellID: Indicates the index of the serving cell to which the applicability or consistency report pertains.

[0065] - applicabilityReportConfigId (or csi-ReportConfigId) or MatchabilityReportConfigId: An index that identifies a report on applicability (applicability) or consistency.

[0066] - applicabilityStatus (applicable or inapplicable): indicates whether a given AI / ML feature or configuration is applicable or inapplicable to the entity (UE or gNB). For example, this information may indicate whether a CSI report configured for radio measurement prediction and associated with applicabilityReportConfigId is applicable or inapplicable.

[0067] - MatchabilityStatus (Matchable or Not Matchable): Indicates whether the UE side model and the gNB side model are matched or not.

[0068] - inapplicabilityCause: Indicates the cause (Cause value) of the inapplicability of a given AI / ML feature or configuration for the entity (UE or gNB). For example, this information may indicate the cause of the inapplicability of a CSI report configured for radio measurement prediction and associated with applicabilityReportConfigId.

[0069] - UnmatchCause: Indicates the cause (Cause value) of the mismatch between the UE side model and the gNB side model.

[0070] - Pairing ID: Indicates the pairing ID between the UE side model and the gNB side model.

[0071] - UE side model ID: Indicates an ID that uniquely identifies the UE side model.

[0072] - gNB side model ID: Indicates an ID that uniquely identifies the gNB side model.

[0073] - Time information (e.g., timestamp): Since the UE side model and the gNB side model may or may not be consistent depending on the time of day, the time information may be the timestamp when the report is generated or transmitted, the timestamp when the consistency is determined, or may indicate the time period when the UE side model and the gNB side model are compatible.

[0074] Location Info (3D physical location information): 3D physical location information (latitude, longitude, altitude) may be included since the UE side model and the gNB side model may or may not match depending on the location.

[0075] - Area ID (area or site ID consisting of the number of cells or tracking area): The area ID may be included because the UE side model and the gNB side model may or may not be consistent depending on the location.

[0076] For example, when a report from the UE 20 includes a pairing ID, the gNB 10 may identify a gNB side model corresponding to the pairing ID and determine that the identified gNB side model and the UE side model are consistent. When a gNB side model corresponding to the pairing ID does not exist in the gNB 10, the gNB 10 may determine that the UE side model and the gNB side model are inconsistent (pairing is not possible). These operations by the gNB 10 may be applied as operations by the UE 20 in other examples of the first embodiment described later.

[0077] For example, when a report from the UE 20 includes a UE side model ID, the gNB 10 may identify a gNB side model having a gNB side model ID corresponding to the UE side model ID and determine that the identified gNB side model and the UE side model are consistent. When a gNB side model having a gNB side model ID corresponding to the pairing ID does not exist in the gNB 10, the gNB 10 may determine that the UE side model and the gNB side model are inconsistent (pairing is not possible). These operations by the gNB 10 may be applied as operations by the UE 20 in other examples of the first embodiment described later.

[0078] For example, the gNB 10 may determine whether the UE side model and the gNB side model are consistent using at least one of time information, location information, and area ID included in the report. These operations by the gNB 10 may be applied as operations by the UE 20 in other examples of the first embodiment described later.

[0079] According to another example of the first embodiment, the gNB 10 may report information for performing an operation based on the two-side model (information regarding the applicability and / or compatibility of a predetermined AI / ML function or setting). For example, as shown in Fig. 6, the gNB 100 may transmit an applicabilityReportList or a matchabilityReportList to the UE 20 using an RRC message (step S201). The applicabilityReportList or the matchabilityReportList may be transmitted using a MAC CE, not limited to an RRC message.

[0080] The information notified from gNB 10 to UE 20 in step S201 may include at least one of the above-mentioned information reported from UE 20 to gNB 10.

[0081] According to the above-mentioned first embodiment, the learning model on the terminal side and the learning model on the network side are linked to each other, and the AI / ML function can be executed appropriately.

[0082] Example 2 In Example 2, a method is specified for notifying information (information regarding the applicability and / or consistency of a specified AI / ML function or setting) from a CU to a DU in a CU-DU separated architecture (CU-DU separated gNB).

[0083] The CU 10B may transmit the applicabilityReportList or matchabilityReportList to the DU 10A using UE associated signaling after receiving the applicabilityReportList or matchabilityReportList from the UE 20. The UE associated signaling transmitted from the CU 10B to the DU 10A may include, for example, at least one of the following information:

[0084] - applicabilityCellId matchabilityCellID: Indicates the index of the serving cell to which the applicability or consistency report pertains.

[0085] - applicabilityReportConfigId (or csi-ReportConfigId) or MatchabilityReportConfigId: An index that identifies a report on applicability (applicability) or consistency.

[0086] - applicabilityStatus (applicable or inapplicable): indicates whether a given AI / ML feature or configuration is applicable or inapplicable to the entity (UE or gNB). For example, this information may indicate whether a CSI report configured for radio measurement prediction and associated with applicabilityReportConfigId is applicable or inapplicable.

[0087] - MatchabilityStatus (Matchable or Not Matchable): Indicates whether the UE side model and the gNB side model are matched or not.

[0088] - inapplicabilityCause: Indicates the cause (Cause value) of the inapplicability of a given AI / ML feature or configuration for the entity (UE or gNB). For example, this information may indicate the cause of the inapplicability of a CSI report configured for radio measurement prediction and associated with applicabilityReportConfigId.

[0089] - UnmatchCause: Indicates the cause (Cause value) of the mismatch between the UE side model and the gNB side model.

[0090] - Pairing ID: Indicates the pairing ID between the UE side model and the gNB side model.

[0091] - UE side model ID: Indicates an ID that uniquely identifies the UE side model.

[0092] - gNB side model ID: Indicates an ID that uniquely identifies the gNB side model.

[0093] - Time information (e.g., timestamp) - Area ID (area or site ID consisting of the number of cells or tracking area)

[0094] Fig. 7 is a sequence diagram illustrating an example of a UE context setup operation in the CU-DU split architecture according to embodiment 2. In the example of Fig. 7, ApplicabilityReportList or matchabilityReportList is included in a UE context setup request.

[0095] The CU 10B transmits a UE context setup request to the DU 10A (step S301). The UE context setup request includes CU to DU RRC information, and the CU to DU RRC information further includes an ApplicabilityReportList or a matchabilityReportList.

[0096] In step S302, the DU 10A sends a UE context setup response to the CU 10B.

[0097] Fig. 8 is a sequence diagram illustrating an example of a UE context modification operation in a CU-DU split architecture in another example of embodiment 2. In the example of Fig. 8, ApplicabilityReportList or matchabilityReportList is included in a UE context modification request.

[0098] In step S401, the UE 20 sends an RRCReconfigurationComplete message or a UEAssistanceInfo message to the CU 10B. The RRCReconfigurationComplete message or the UEAssistanceInfo message includes an ApplicabilityReportList or a matchabilityReportList.

[0099] In step S402, the CU 10B sends a UE context modification request to the DU 10A. The UE context modification request includes CU to DU RRC information, and the CU to DU RRC information further includes ApplicabilityReportList or matchabilityReportList.

[0100] In step S403, the DU 10A sends a UE context modification response to the CU 10B.

[0101] According to the above-mentioned second embodiment, even in a CU-DU separated architecture, the learning model on the terminal side and the learning model on the network side can be linked to appropriately execute AI / ML functions.

[0102] (Device configuration) Next, a description will be given of an example of the functional configuration of the base station (gNB) 10 and the terminal (UE) 20 that execute the processes and operations described above. The base station 10 and the terminal 20 include functions for executing the above-described embodiments. However, the base station 10 and the terminal 20 may each include only a part of the functions in the embodiments.

[0103] <Base station (gNB)> Fig. 9 is a diagram showing an example of the functional configuration of the base station 10 in this embodiment. As shown in Fig. 9, the base station 10 has a transmitting unit 110, a receiving unit 120, a setting unit 130, and a control unit 140. The functional configuration shown in Fig. 9 is merely an example. As long as the operations in this embodiment can be performed, the names of the functional divisions and functional units may be any. The transmitting unit 110 and the receiving unit 120 may be collectively referred to as a communication unit.

[0104] The transmitter 110 has a function of generating a signal to be transmitted to the terminal 20 and transmitting the signal wirelessly. The transmitter 110 transmits setting information, instructions, notifications, etc. related to a low-power wake-up signal to the terminal 20. The transmitter 110 transmits notifications related to switching of monitoring operations to the terminal. The receiver 120 has a function of receiving various signals transmitted from the terminal 20 and acquiring, for example, information of higher layers from the received signals. The transmitter 110 has a function of transmitting PSS, SSS, PBCH, DL / UL control signals, etc. to the terminal 20. The receiver 120 receives inter-network node messages from other network nodes.

[0105] The setting unit 130 stores preset setting information and various setting information to be transmitted to the terminal 20. The content of the setting information is, for example, information on the operations explained in the embodiments.

[0106] The control unit 140 controls the settings, instructions, and notifications related to the operations described in the embodiments. The function unit related to signal transmission in the control unit 140 may be included in the transmitting unit 110, and the function unit related to signal reception in the control unit 140 may be included in the receiving unit 120.

[0107] <Device (UE)> Fig. 10 is a diagram showing an example of the functional configuration of the terminal 20 in this embodiment. As shown in Fig. 10, the terminal 20 has a transmitting unit 210, a receiving unit 220, a setting unit 230, and a control unit 240. The functional configuration shown in Fig. 10 is merely an example. The names of the functional divisions and functional units may be any as long as they can execute the operations in this embodiment. The transmitting unit 210 and the receiving unit 220 may be collectively referred to as a communication unit.

[0108] The transmitter 210 creates a transmission signal from the transmission data and transmits the transmission signal wirelessly. The transmitter 210 transmits capability information in a low-power wake-up signal to the base station 10. The receiver 220 receives various signals wirelessly and acquires higher layer signals from the received physical layer signals. The receiver 220 has a function of receiving PSS, SSS, PBCH, DL / UL / SL control signals, etc. transmitted from the base station 10. The receiver 220 receives paging notification information and configuration information, instructions, and notifications related to the low-power wake-up signal from the base station 10. For example, the receiver 220 receives a low-power wake-up signal from the base station 10. The configuration unit 230 stores various configuration information received from the base station 10 by the receiver 220. The configuration unit 230 also stores pre-configured configuration information. The configuration information includes, for example, information on the operations described in the embodiments.

[0109] As described in the embodiments, the control unit 240 controls settings, instructions, and notifications related to the operations described in the embodiments. A functional unit related to signal transmission in the control unit 240 may be included in the transmitting unit 210, and a functional unit related to signal reception in the control unit 240 may be included in the receiving unit 220.

[0110] (Hardware configuration) The block diagrams (FIGS. 9 and 10) used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by hardware, software, or a combination of these. The method for realizing each functional block is not particularly limited. That is, each functional block may be realized by using a single device that is physically or logically coupled, or may be realized by using two or more physically or logically separated devices that are connected directly or indirectly (for example, using wires, wirelessly, etc.) and these multiple devices. The functional block may be realized by combining the single device or the multiple devices with software.

[0111] For example, the base station, terminal, network node, etc. in this embodiment may function as a computer that performs processing of the wireless communication method of the present disclosure. Fig. 11 is a diagram showing an example of the hardware configuration of a base station and a terminal in one embodiment of the present disclosure. The above-mentioned base station 10 and terminal 20 may be physically configured as a computer device 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.

[0112] In the present disclosure, the term "apparatus" may be interchangeable with any two terms selected from a set of terms such as circuit, device, unit, module, chip, means, etc. The hardware configurations of the base station 10 and the terminal 20 may be configured to include one or more of the devices shown in the drawings, or may be configured to exclude some of the devices.

[0113] Each function in the base station 10 and the terminal 20 is realized by loading predetermined software (programs) onto hardware such as the processor 1001, memory 1002, etc., so that the processor 1001 performs calculations, controls communication by the communication device 1004, and controls the reading, writing, or both reading and writing of data in the memory 1002 and storage 1003.

[0114] The processor 1001, for example, runs an operating system to control the entire computer. The processor 1001 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, a baseband signal processing unit, a call processing unit, etc. may be realized by the processor 1001. Although only one processor 1001 is shown in the figure, there may be multiple processors.

[0115] The processor 1001 reads programs (program codes), software modules, data, etc. from the storage 1003, the communication device 1004, or both the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes in accordance with the programs. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. For example, the control unit 401 of the terminal 20 may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and similar implementations may be made for other functional blocks. While the above-described various processes have been described as being executed by a single processor 1001, they may also 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, or may be provided to the computer via the communication device 1004, for example.

[0116] The present disclosure also provides a computer program product including a computer program, which may implement the steps of the methods described in the above embodiments when the computer program is executed by a computer (e.g., the processor 1001).

[0117] The memory 1002 is a computer-readable recording medium and may be configured, for example, as a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), or a combination of at least two of these. The memory 1002 may also be called a register, a cache, a main memory (primary storage device), or the like. The memory 1002 can store executable programs (program codes), software modules, and the like for executing the wireless communication method according to one embodiment of the present disclosure.

[0118] Storage 1003 is a computer-readable recording medium, and may be, for example, an optical disk such as a CD-ROM (Compact Disc 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, or a combination of at least two of these. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, memory 1002, storage 1003, or a database, server, or other appropriate medium including both memory 1002 and storage 1003.

[0119] The communication device 1004 is hardware (transmitting / receiving device) for communicating between computers via a wired network, a wireless network, or both wired and wireless networks, and is also referred to as, for example, a network device, a network controller, a network card, or a communication module. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, or a combination of at least two of these. For example, a transmitting / receiving antenna, an amplifier unit, a transmitting / receiving unit, or a transmission path interface may be realized by the communication device 1004. The transmitting / receiving unit may be implemented as a transmitting unit and a receiving unit that are physically or logically separated.

[0120] The input device 1005 is an input device that receives input from the outside (for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, or a combination of at least two of these). The output device 1006 is an output device that performs output to the outside (for example, a display, a speaker, an LED lamp, or a combination of at least two of these). The input device 1005 and the output device 1006 may be integrated into one device (for example, a touch panel).

[0121] The processor 1001, memory 1002, and other devices are connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between the devices.

[0122] The base station 10 and the terminal 20 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), a field programmable gate array (FPGA), a graphics processing unit (GPU), a neural processing unit (NPU), or a combination of at least two of these, 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.

[0123] <Additional notes> (Additional note 1) a storage unit that stores a learning model on the terminal side; A terminal comprising: a transmitter that transmits to a base station at least one of information related to the consistency between a learning model on the terminal side and a learning model on the base station side or information indicating whether settings related to AI / ML (Artificial Intelligence / Machine Learning) functions can be applied to the terminal. (Additional note 2) The terminal described in Appendix 1, wherein the information related to the consistency includes at least one of a result indicating whether or not the learning model on the terminal side is consistent with the learning model on the base station side, or information used to determine the consistency between the learning model on the terminal side and the learning model on the base station side. (Additional note 3) The terminal according to claim 1, further comprising a receiving unit that receives from the base station at least one of information related to consistency between a learning model on the terminal side and a learning model on the base station side or information indicating whether settings related to the AI / ML function can be applied to the base station. (Additional note 4) a storage unit that stores a learning model on the base station side; A base station comprising: a transmitter that transmits to a terminal at least one of information related to the consistency between a learning model on the terminal side and a learning model on the base station side, or information indicating whether settings related to AI / ML (Artificial Intelligence / Machine Learning) functions can be applied to the base station. (Additional note 5) The base station according to claim 4, further comprising a receiving unit that receives from the terminal at least one of information related to consistency between a learning model on the terminal side and a learning model on the base station side, or information indicating whether settings related to the AI / ML function can be applied to the terminal. (Additional note 6) the base station includes a first unit having a radio interface with the terminal and a second unit controlling the first unit; The base station described in Supplementary Claim 5, wherein, based on receiving at least one of information related to the compatibility or information indicating the applicability from the terminal, the second unit transmits at least one of information related to the compatibility or information indicating the applicability to the first unit.

[0124] According to the configuration described in the supplementary paragraph, the learning model on the terminal side and the learning model on the network side can be linked to appropriately execute AI / ML functions.

[0125] (Supplementary explanation of the embodiment) Although the present embodiment has been described above, the disclosed invention is not limited to such an embodiment, and those skilled in the art will understand various modifications, alterations, alternatives, and substitutions. While specific numerical examples have been used to facilitate understanding of the invention, unless otherwise specified, these numerical values ​​are merely examples, and any appropriate values ​​may be used. The division of items in the above description is not essential to the present invention; matters described in two or more items may be used in combination as needed, and matters described in one item may apply to matters described in another item (unless inconsistent). The boundaries between functional units or processing units in the functional block diagram do not necessarily correspond to the boundaries between physical components. The operations of multiple functional units may be performed by a single physical component, or the operations of a single functional unit may be performed by multiple physical components. The order of the processing steps described in the embodiments may be reversed as long as there is no contradiction. For convenience of processing description, the base station 10 and terminal 20 have been described using functional block diagrams. However, such devices may be implemented using hardware, software, or a combination thereof. The software operated by the processor of the base station 10 according to this embodiment and the software operated by the processor of the terminal 20 according to this embodiment may each be stored in random access memory (RAM), flash memory, read-only memory (ROM), EPROM, EEPROM, registers, hard disk (HDD), removable disk, CD-ROM, database, server or any other suitable storage medium.

[0126] The aspects / embodiments described in the present disclosure may be categorized as Long Term Evolution (LTE), LTE-Advanced (LTE-A), International Mobile Telecommunications-Advanced (IMT-Advanced), 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G-Advanced (5G-A), 6th generation mobile communication system (6G), xth generation mobile communication system (x is, for example, an integer or a decimal number)), Future Radio Access (FRA), New Radio (NR), New radio access (NX), Future generation radio access (FX), Open Radio Access Network (O-RAN), Wideband Code Division Multiple Access (W-CDMA) (registered trademark), Global System for Mobile communications (GSM) (registered trademark), CDMA2000, Ultra Mobile Broadband (UMB), Institute of Electrical and Electronics Engineers (IEEE) Engineers) 802.11, IEEE802.11x (where x is any character string such as b, a, g, n, ac, ax, be, or bn, and when x=n it is called Wi-Fi4, when x=ac it is called Wi-Fi5, when x=ax it is called Wi-Fi6 or Wi-Fi6E, when x=be it is Wi-Fi7, and when x=bn it is called Wi-Fi8, etc. Wi-Fi is a registered trademark.), IEEE802.16 (WiMAX (registered trademark), IEEE802.20, UWB (Ultra-Wide Band), Bluetooth (registered trademark), network virtualization technology (e.g., NFV (Network Function Virtualization), SFC (Service Function Chaining), SDN (Software Defined Networking)), or LPWA (Low Power Wide Area). Each aspect / embodiment described in the present disclosure may be applied to a system based on a combination of at least two of these technologies. Of course, "based on" may refer not only to a system that uses the technology, but also to a system that uses an extension or modification of the technology.

[0127] In the present disclosure, any two terms selected from a set of terms such as "base station (BS)", "radio base station", "fixed station (fixed station)", "NodeB", "eNodeB (eNB)", "gNodeB (gNB)", "access point (AP)", "transmission point (TP)", "reception point (RP)", "transmission / reception point (TRP)", "radio unit (RU)", "remote unit (RU)", "control unit (CU)", "distributed unit (DU)", "remote radio head (RRH)", "node", "gateway", "terrestrial base station", "stratospheric base station", "unmanned aerial vehicle", "high altitude platform station (HAPS)", "airborne platform", "panel", "cell", "radio access network (RAN)", and "network" may be used interchangeably.

[0128] Each cell accommodated by a base station may be referred to by terms such as a macro cell, a small cell, a femto cell, a pico cell, a serving cell, or a super cell. In the present disclosure, any two terms selected from a set of terms such as "cell," "sector," "cell group," "carrier," "component carrier," "cluster," "bandwidth part (BWP)," and "carrier bandwidth" may be used interchangeably.

[0129] In the present disclosure, any two terms selected from the set of terms such as "Mobile Station (MS)", "user terminal", "User Equipment (UE 20)", "Device", "Module" and "Terminal" may be used interchangeably.

[0130] A terminal may be referred to 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, router (e.g., home router, mobile router, etc.), TCU (Telematics Control Unit), or some other suitable terminology.

[0131] The base station and the terminal may each be composed of one or more devices. The devices constituting at least a portion of the base station and the terminal may be called a transmitting device, a receiving device, a communication device, etc. Note that the devices constituting at least a portion of the base station and the terminal may be, for example, an object itself, such as a vehicle, a transport vehicle, an automobile, a motorcycle, a bicycle, a connected car, an excavator, a bulldozer, a wheel loader, a dump truck, a forklift, a train, a bus, a handcar, a rickshaw, a ship and other watercraft, an airplane, a rocket, an unmanned aerial vehicle, a stratospheric base station (e.g., a High Altitude Platform Station (HAPS)), an artificial satellite (e.g., a Low Earth Orbit (LEO) satellite, a Medium Earth Orbit (MEO) satellite, a Geostationary Earth Orbit (GEO) satellite), a drone (registered trademark), a multicopter, a quadcopter, a balloon, or an Internet of Things (IoT) device (e.g., a smart meter, a sensor), or may include, but are not limited to, an object or device mounted on the object. The object may be a moving object (hereinafter referred to as a "moving object"; this does not exclude the case where the moving object is in a stationary state where it is not moving), or may be a fixedly positioned object (hereinafter referred to as a "non-moving object").

[0132] A base station in the present disclosure may be read as a terminal. For example, the aspects / embodiments of the present disclosure may be applied to a configuration in which communication between a base station and a terminal is replaced with communication between multiple terminals (which may be called, for example, D2D (Device-to-Device) or V2X (Vehicle-to-Everything)) or communication of a non-terrestrial network (NTN). In this case, the terminal 20 may be configured to have at least some of the functions of the base station 10 described above. Terms such as "uplink" and "downlink" may be read as terms corresponding to communication between terminals (for example, "sidelink") or terms corresponding to NTN (for example, feeder link or service link). For example, an uplink channel or a downlink channel may be read as a sidelink channel.

[0133] The present disclosure is also applicable to cases where at least some of the devices constituting the base station and the terminal operate outside the ground (for example, in the atmosphere or outer space).

[0134] In this disclosure, the term "terminal" may be interpreted as a base station. In this case, the base station 10 may be configured to have the functions of the terminal 20 described above.

[0135] 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) and Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information (Master Information Block (MIB) and System Information Block (SIB))), other signals, or a combination of at least two of these. Note that the physical layer signaling may be referred to as Layer 1 (L1) control information. The MAC signaling may be referred to as a MAC Control Element (CE) or a MAC Protocol Data Unit (PDU), for example. The RRC signaling may be referred to as an RRC message or an information element (IE) in the RRC message. The RRC message may be, for example, a message used for controlling an RRC connection (for example, setup, reconfiguration, establishment, reestablishment, release, or resume), mobility, a measurement report, or notification of a terminal's capabilities, or may be an information element within the message. Notification of information may be explicit or implicit. Note that explicit notification of certain information means notification of the certain information itself, and implicit notification of certain information may mean notification of information other than the certain information, or may mean that the certain information is considered to have been notified when a certain condition is satisfied.Notification of information may include not only notification between the same layers of different devices (e.g., between a lower layer or an upper layer of the base station 10 and the terminal 20) but also notification between different layers in the same or different devices (e.g., between a lower layer and an upper layer in the base station 10 or the terminal 20). Notification of information from one device to another device may be performed via one or more devices. With regard to any information (e.g., a variable, a constant, a parameter, a setting) described in the present disclosure, even if not specifically specified in the above embodiments, information indicating / specifying (or related to) the any information (value) may be notified from any first device (e.g., a terminal / base station) to any second device (e.g., a base station / terminal).

[0136] 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.

[0137] In the present disclosure, a specific operation described as being performed by a base station may be performed by its upper node or by some of its upper nodes (e.g., CU, RU, or DU, etc.) in some cases. It is clear that various operations performed for communication with a terminal in a RAN or core network may be performed by at least some of the base station and other network nodes other than the base station. The other network node may be one node or a combination of multiple nodes. The network node is, for example, a node provided in various core networks such as EPC (Evolved Packet Core) and 5GC (5G Core Network), and provides one or more network functions (NF: Network Functions), but is not limited to this.

[0138] In the present disclosure, the action of "a terminal receives information from a base station" accompanies the action of "the base station transmits the information to the terminal", "the base station generates the information", or both. Similarly, the action of "a terminal transmits information to a base station" accompanies the action of "the base station receives the information from the terminal". The actions of "the terminal is configured to..." or "configure UE 20 to..." may include the action of "the base station transmits configuration information regarding the configuration of the terminal" and the action of "the terminal configures a predetermined operation based on the configuration information".

[0139] Each aspect / embodiment described in the present disclosure may be used alone, in combination, or switched depending on the implementation.

[0140] The present disclosure has been described above, but it is for illustrative purposes only, and the present invention is not limited to the aspects / embodiments described in the present disclosure. The present disclosure can be implemented in modified and altered forms without departing from the spirit of the invention. The present disclosure and its modifications and alterations are included in the scope of the present invention and its equivalents.

[0141] 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 or that the first element must in some way precede the second element.

[0142] The radio resource may be defined by a combination of resource units in one or more domains, such as the time domain, the frequency domain, the spatial domain, the code domain, and the power domain.

[0143] For example, resources in the time domain may be defined by one or more time units. The one or more time units may include, but are not limited to, a radio frame, a subframe, a slot, a symbol, a transmission time interval (TTI), or a combination of at least two of these. The time unit may be a fixed-length time unit independent of numerology, a variable-length time unit dependent on numerology, or both. Examples of fixed-length time units include, but are not limited to, a subframe consisting of one or more slots and a radio frame including multiple subframes. Examples of variable-length time units include, but are not limited to, a symbol and a slot including a fixed number of symbols. Note that a certain time unit may be divided into time units shorter than the certain time unit. Examples of shorter time units include, but are not limited to, a minislot consisting of fewer symbols than the number of symbols constituting a slot. The above-described time units may include, for example, time units used as units for scheduling, link adaptation, etc. Any time unit in the present disclosure may be read as another time unit.

[0144] Numerology is a parameter that defines the physical layer structure, and may be a parameter based on at least one of subcarrier spacing (SCS), symbol length, cyclic prefix length, and sampling time, for example.

[0145] Resources in the frequency domain may be defined, for example, by one or more frequency units. The one or more frequency units may include, for example, subcarriers, resource blocks (RBs), bandwidth parts (BWPs), carrier bandwidths, or a combination of at least two of these, but the terminology of the frequency units is not limited to these. The number of subcarriers included in a frequency unit may be a fixed number regardless of numerology, or may be a variable number that changes depending on numerology. For example, an RB is composed of a predetermined number of consecutive subcarriers in the frequency domain, and the number of subcarriers included in the RB may be the same regardless of numerology, for example, 12, but is not limited to this. A BWP may be composed, for example, of one or more consecutive RBs within a certain carrier bandwidth, but is not limited to this. One or more BWPs may be configured within one carrier for terminal 20, and at least one of the BWPs may be activated. Any frequency unit in the present disclosure may be interchangeable with another frequency unit.

[0146] Resources in both the time domain and the frequency domain may be defined by one or more time / frequency units, each of which is composed of a time unit and a frequency unit, such as, but not limited to, a resource element (RE) composed of one symbol and one subcarrier, a resource element group (REG) composed of a predetermined number of REs, or a control resource set (CORESET) composed of a predetermined number of symbols and a predetermined number of RBs.

[0147] The resources in the spatial domain may be defined, for example, by one or more spatial units, including, but not limited to, a beam, a layer of a multi-input multi-output (MIMO), an antenna port, or a combination of at least two of these.

[0148] The resources in the code domain may be defined by one or more code units, such as, but not limited to, a cyclic shift (CS), an orthogonal cover code (OCC), or a combination thereof.

[0149] 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.

[0150] The "means" in the configuration of each of the above devices may be replaced with "part," "circuit," "device," etc. [Explanation of symbols]

[0151] 10 base station 110 Transmitter 120 Receiver 130 Setting section 140 Control Unit 20 terminals 210 Transmitter 220 Receiving unit 230 Setting Section 240 Control Unit 1001 processor 1002 Storage device 1003 Auxiliary storage device 1004 Communication equipment 1005 Input Device 1006 Output Device

Claims

1. a storage unit that stores a learning model on the terminal side; A terminal comprising: a transmitter that transmits to a base station at least one of information related to the consistency between a learning model on the terminal side and a learning model on the base station side or information indicating whether settings related to AI / ML (Artificial Intelligence / Machine Learning) functions can be applied to the terminal.

2. The terminal of claim 1, wherein the information related to the consistency includes at least one of a result indicating whether or not the learning model on the terminal side is consistent with the learning model on the base station side, or information used to determine the consistency between the learning model on the terminal side and the learning model on the base station side.

3. 2. The terminal according to claim 1, further comprising: a receiving unit that receives from the base station at least one of information related to consistency between a learning model on the terminal side and a learning model on the base station side or information indicating whether settings related to the AI ​​ / ML function can be applied to the base station.

4. a storage unit that stores a learning model on the base station side; A base station comprising: a transmitter that transmits to a terminal at least one of information related to the consistency between a learning model on the terminal side and a learning model on the base station side, or information indicating whether settings related to AI / ML (Artificial Intelligence / Machine Learning) functions can be applied to the base station.

5. 5. The base station according to claim 4, further comprising a receiving unit that receives from the terminal at least one of information related to consistency between a learning model on the terminal side and a learning model on the base station side or information indicating whether settings related to the AI ​​ / ML function can be applied to the terminal.

6. the base station includes a first unit having a radio interface with the terminal and a second unit controlling the first unit; The base station according to claim 5, wherein, based on receiving at least one of information related to the compatibility or information indicating applicability from the terminal, the second unit transmits at least one of information related to the compatibility or information indicating applicability to the first unit.

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