Terminal and communication method

Abstract

This terminal comprises: a reception unit that receives, from a master node performing dual connectivity, a configuration for predicting occurrence of a failure in a secondary cell group (SCG); a control unit that, on the basis of the configuration for predicting occurrence of a failure in the SCG, predicts occurrence of a failure in the SCG using an artificial intelligence and machine learning model; and a transmission unit that transmits, to the master node, occurrence of a failure in the SCG predicted by the model.

Description

Terminal and Communication Method

[0001] The present invention relates to a terminal and a communication method in a communication system.

[0002] In 3GPP (registered trademark) (3rd Generation Partnership Project), studies on a radio communication scheme called 5G or NR (New Radio) (hereinafter, the radio communication scheme is referred to as "5G" or "NR") are progressing in order to achieve further increase in system capacity, further increase in data transmission speed, further reduction of delay in a radio section, and the like. In 5G, various radio technologies are being studied to satisfy the requirement that the delay in the radio section is 1 ms or less while realizing a throughput of 10 Gbps or more.

[0003] In NR, a network architecture including 5GC (5G Core Network) corresponding to EPC (Evolved Packet Core) which is a core network in the network architecture of LTE (Long Term Evolution) and NG-RAN (Next Generation - Radio Access Network) corresponding to E-UTRAN (Evolved Universal Terrestrial Radio Access Network) which is RAN (Radio Access Network) in the network architecture of LTE is being studied (for example, Non-Patent Document 1).

[0004] Furthermore, various requirements are being studied for next-generation 6G. For example, the requirements include ultra broadband communication, mission critical communication, ultra massive connection, universal coverage, intelligent connection, ubiquitous sensing, and the like.

[0005] To meet these requirements, the new concept aims for extensibility (e.g., making it more effective for future use), ease of operation, customizability (e.g., making it easier to operate), and sustainability (e.g., cost reduction, a more robust configuration, and resilience). Furthermore, guaranteed communication, ensuring a minimum level of performance at all times, is being considered.

[0006] Furthermore, to enhance mobility, functional enhancements are expected to be made to support cases where inter-Central Unit (CU) LTM (Lower Layer Triggered Mobility) and DC (Dual Connectivity) are performed while maintaining LTM (for example, Non-Patent Document 2).

[0007] Furthermore, the use of artificial intelligence / machine learning models (AI (Artificial Intelligence) / ML (Machine Learning) models) to perform various controls such as network management or automation is being considered (for example, Non-Patent Document 3).

[0008] 3GPP TS 23.501 V18.7.0 (2024-09)3GPP TSG-RAN Meeting #101 RP-232618, Bengaluru, India, 11-15 September 20233GPP TSG-RAN Meeting #105 RP-242393, Melbourne, Australia, 9-12 September 2024

[0009] With the introduction of 6G, AIML may be supported for mobility. For example, dual connectivity may be supported, where a device connects to both a 6G-RAN node and a 5G-RAN node. Similarly, dual connectivity may be supported, where a device connects to both a 5G-RAN node and a 6G-RAN node. However, methods for applying AIML to inter-RAT measurements have not been defined.

[0010] This invention has been made in view of the above points, and aims to realize dual connectivity by applying artificial intelligence and machine learning models to the measurement of cell groups.

[0011] According to the disclosed technology, a terminal is provided having: a receiving unit that receives fault prediction settings in a Secondary Cell Group (SCG) from a master node performing dual connectivity; a control unit that performs fault prediction in the SCG based on the fault prediction settings in the SCG using artificial intelligence and machine learning models; and a transmitting unit that transmits the fault in the SCG predicted by the model to the master node.

[0012] According to the disclosed technology, dual connectivity can be achieved by applying artificial intelligence and machine learning models to the measurement of cell groups.

[0013] This is a diagram illustrating an example of a communication system. This is a diagram illustrating an example of a communication system in a roaming environment. This is a sequence diagram illustrating an example of prediction by AIML in an embodiment of the present invention (1). This is a sequence diagram illustrating an example of prediction by AIML in an embodiment of the present invention (2). This is a sequence diagram illustrating an example of prediction by AIML in an embodiment of the present invention (3). This is a sequence diagram illustrating an example of prediction by AIML in an embodiment of the present invention (4). This is a sequence diagram illustrating an example of prediction by AIML in an embodiment of the present invention (5). This is a sequence diagram illustrating an example of prediction by AIML in an embodiment of the present invention (6). This is a sequence diagram illustrating an example of prediction by AIML in an embodiment of the present invention (7). This is a sequence diagram illustrating an example of prediction by AIML in an embodiment of the present invention (8). This is a sequence diagram illustrating an example of prediction by AIML in an embodiment of the present invention (9). This is a sequence diagram illustrating an example of prediction by AIML in an embodiment of the present invention (10). This is a sequence diagram illustrating an example of prediction by AIML in an embodiment of the present invention (11). This is a sequence diagram illustrating an example of prediction by AIML in an embodiment of the present invention (12). This figure shows an example of the functional configuration of the base station 10 and network node 30 in an embodiment of the present invention. This figure shows an example of the functional configuration of the terminal 20 in an embodiment of the present invention. This figure shows an example of the hardware configuration of the base station 10 and terminal 20 in an embodiment of the present invention.

[0014] Embodiments of the present invention will be described below with reference to the drawings. Note that the embodiments described below are examples, and the embodiments to which the present invention is applied are not limited to those described below.

[0015] In the operation of the wireless communication system according to the embodiments of the present invention, existing technologies may be used as appropriate. However, such existing technologies include, for example, existing LTE, but are not limited to existing LTE. Furthermore, the term "LTE" as used herein has a broad meaning that includes LTE-Advanced, LTE-Advanced and later technologies (e.g., NR), or wireless LAN (Local Area Network), unless otherwise specified.

[0016] Furthermore, in the embodiments of the present invention, "configuring" wireless parameters means that predetermined values ​​are pre-configured, or that wireless parameters notified from the network node 30 or terminal 20 are configured.

[0017] Figure 1 is a diagram illustrating an example of a communication system. As shown in Figure 1, the communication system consists of a terminal 20 (UE) and multiple network nodes 30. Hereafter, one network node 30 will be assumed to correspond to each function, but one network node 30 may implement multiple functions, or multiple network nodes 30 may implement one function. Also, the "connection" described below may be a logical connection or a physical connection.

[0018] The RAN (Radio Access Network) is a network node 30 having wireless access functionality, which may include a base station 10, and is connected to a UE, AMF (Access and Mobility Management Function), and UPF (User plane function). The AMF is a network node 30 having functions such as terminating the RAN interface, terminating the NAS (Non-Access Stratum), registration management, connection management, reachability management, and mobility management. The UPF is a network node 30 interconnected with the DN (Data Network) and having functions such as a PDU (Protocol Data Unit) session point to the outside, packet routing and forwarding, and user plane QoS (Quality of Service) handling. The UPF and DN constitute a network slice. In the wireless communication network in the embodiment of the present invention, multiple network slices are constructed.

[0019] AMF is connected to UE, RAN, SMF (Session Management function), NSSF (Network Slice Selection Function), NEF (Network Exposure Function), NRF (Network Repository Function), UDM (Unified Data Management), AUSF (Authentication Server Function), PCF (Policy Control Function), and AF (Application Function). AMF, SMF, NSSF, NEF, NRF, UDM, AUSF, PCF, and AF are network nodes 30 that are interconnected via interfaces based on their respective services: Namf, Nsmf, Nnssf, Nnef, Nnrf, Nudm, Nausf, Npcf, and Naf.

[0020] The SMF is a network node 30 that has functions such as session management, IP (Internet Protocol) address allocation and management for UEs, DHCP (Dynamic Host Configuration Protocol) functionality, ARP (Address Resolution Protocol) proxy, and roaming functionality. The NEF is a network node 30 that has the function of notifying other NFs (Network Functions) of capabilities and events. The NSSF is a network node 30 that has functions such as selecting the network slice to which the UE connects, determining the allowed NSSAI (Network Slice Selection Assistance Information), determining the NSSAI to be set, and determining the AMF set to which the UE connects. The PCF is a network node 30 that has the function of controlling network policies. The AF is a network node 30 that has the function of controlling application servers. The NRF is a network node 30 that has the function of discovering NF instances that provide services. The UDM is a network node 30 that manages subscriber data and authentication data. The UDM is connected to the UDR (User Data Repository) that holds the said data.

[0021] Figure 2 is a diagram illustrating an example of a communication system in a roaming environment. As shown in Figure 2, the network consists of a terminal 20 (UE) and multiple network nodes 30. Hereafter, one network node 30 will be assigned to each function, but one network node 30 may implement multiple functions, or multiple network nodes 30 may implement one function. Also, the "connection" described below may be a logical connection or a physical connection.

[0022] The RAN is a network node 30 having wireless access functionality and is connected to the UE, AMF, and UPF. The AMF is a network node 30 having functions such as RAN interface termination, NAS termination, registration management, connection management, reachability management, and mobility management. The UPF is a network node 30 interconnected with the DN, having functions such as external PDU session point, packet routing and forwarding, and user plane QoS handling. The UPF and DN constitute a network slice. In the wireless communication network according to the embodiment of the present invention, multiple network slices are constructed.

[0023] AMF is connected to UE, RAN, SMF, NSSF, NEF, NRF, UDM, AUSF, PCF, AF, and SEPP (Security Edge Protection Proxy). AMF, SMF, NSSF, NEF, NRF, UDM, AUSF, PCF, and AF are network nodes 30 that are interconnected via interfaces based on their respective services: Namf, Nsmf, Nnssf, Nnef, Nnrf, Nudm, Nausf, Npcf, and Naf.

[0024] SMF is a network node 30 that has functions such as session management, IP address allocation and management for UEs, DHCP functionality, ARP proxy, and roaming functionality. NEF is a network node 30 that has the function of notifying other NFs of capabilities and events. NSSF is a network node 30 that has functions such as selecting the network slice to which the UE connects, determining which NSSAIs are allowed, determining which NSSAIs are configured, and determining which AMF set the UE connects to. PCF is a network node 30 that has the function of controlling network policies. AF is a network node 30 that has the function of controlling application servers. NRF is a network node 30 that has the function of discovering NF instances that provide services. SEPP is an opaque proxy that filters control plane messages between PLMNs (Public Land Mobile Networks). In Figure 2, vSEPP is the SEPP in the visited network, and hSEPP is the SEPP in the home network.

[0025] As shown in Figure 2, the UE is in a roaming environment connected to the RAN and AMF in the Visited PLMN. The Visited PLMN and Home PLMN are connected via vSEPP and hSEPP. The UE can communicate with the UDM of the Home PLMN, for example, via the AMF of the Visited PLMN.

[0026] Furthermore, in an NG-RAN (Next Generation Radio Access Network), a gNB, which is a RAN node, may have an architecture separated into gNB-CU (Central Unit) and gNB-DU (Distributed Unit). One gNB-CU may accommodate multiple gNB-DUs, and one gNB-DU may accommodate multiple cells.

[0027] The application of AIML to mobility is being considered. It is also anticipated that AIML will be applied to mobility in 6G.

[0028] The application of AIML to enhance mobility in RRC Connected Mode is being considered (see Non-Patent Document 3). For example, the following may be performed on an AIML basis: • RRM (Radio Resource Management) measurement and event prediction • Cell-level measurement prediction including intra and inter-frequency • Cell beam measurement prediction for L3 mobility • Handover failure prediction, RLF (Radio Link Failure) prediction • Measurement event prediction

[0029] Other UE assistance information may be used on the network side. The evaluation of AIML-assisted mobility may take into account the trade-offs between handover performance and complexity. The testability, interoperability, impact on RRM requirements, and performance of AIML-assisted mobility may also be considered.

[0030] The 6G first release may support AIML for mobility. In 6G, the UE may perform inter-RAT handover from a 6GRAN node to a 5GRAN node or from a 5GRAN node to a 6GRAN node. The application of AIML to inter-RAT handover may be specified.

[0031] When a UE connects to a 6GRAN node, it may receive measurement prediction configurations from the 6GRAN node, including predictions for the L1 / L3 quality (RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality), SINR (Signal-to-Interference-plus-Noise Ratio)) of 5G cells and / or beams (SSB (SS / PBCH block), CSI-RS (Channel State Information - Reference Signal)). The prediction targets for the measurement prediction configuration may also be 5G frequencies / cells / beams (SSB, CSI-RS).

[0032] After the UE receives the above measurement prediction settings from the 6GRAN node, AIML may perform predictions of the L1 / L3 quality (RSRP, RSRQ, SINR) of the 5G cell and / or beam (SSB, CSI-RS). AIML predicted measurement reporting may be performed when the following event conditions are satisfied within TimeToTrigger. AIML predicted measurement reporting may be performed periodically at a predetermined number of times N after the event conditions are satisfied. The UE may report the predicted L1 / L3 quality (RSRP, RSRQ, SINR) of the 5G cell and / or beam (SSB, CSI-RS) to the 6GRAN node.

[0033] Examples: Predicted Event B1: The predicted inter-RAT neighbor cell becomes better than the threshold. Predicted Event B2: The predicted PCell becomes worse than threshold 1 and the predicted inter-RAT neighbor cell becomes better than threshold 2. Predicted Event LTM B1: The predicted inter-RAT neighbor cell's beam becomes better than the threshold. Predicted Event LTM B2: The predicted beam of the serving cell becomes worse than threshold 1 and the predicted inter-RAT neighbor cell's beam becomes better than threshold 2.

[0034] When the UE reports predictive measurements to the 6GRAN node, it may include and report the L1 / L3 quality (RSRP, RSRQ, SINR) of the measured 5G cells and / or beams (SSB, CSI-RS) together or simultaneously.

[0035] When the UE reports predictive measurement data to the 6GRAN node, the time information (e.g., timestamp) of the predicted L1 / L3 quality (RSRP, RSRQ, SINR) value of each predicted 5G cell and / or beam (SSB, CSI-RS) may be associated with that predicted value. Alternatively, an indicator of accuracy (e.g., accuracy rate, confidence score, or prediction success rate) may be associated with the L1 / L3 quality (RSRP, RSRQ, SINR) of each predicted 5G cell and / or beam (SSB, CSI-RS).

[0036] The 6GRAN node may trigger an L3 handover or LTM based on the predicted L1 / L3 quality (RSRP, RSRQ, SINR) of the 5G cell and / or beam (SSB, CSI-RS) reported by the UE. For example, it may send a handover command or cell switchover notification to the UE.

[0037] The 6GRAN node may also send the L1 / L3 quality (RSRP, RSRQ, SINR) of the predicted 5G cells and / or beams (SSB, CSI-RS) to the target 5GRAN node.

[0038] When a UE connects to a 5GRAN node, it may receive measurement prediction configurations from the 5GRAN node, and may also receive prediction configurations for the L1 / L3 quality (RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality), SINR (Signal-to-Interference-plus-Noise Ratio)) of 6G cells and / or beams (SSB (SS / PBCH block), CSI-RS (Channel State Information - Reference Signal)). The prediction target for the measurement prediction configuration may be the 6G frequency / cell / beam (SSB, CSI-RS).

[0039] After the UE receives the above measurement prediction settings from the 5GRAN node, AIML may perform predictions of the L1 / L3 quality (RSRP, RSRQ, SINR) of the 6G cell and / or beam (SSB, CSI-RS). AIML predicted measurement reporting may be performed when the following event conditions are satisfied within TimeToTrigger. AIML predicted measurement reporting may be performed periodically at a predetermined number of times N after the event conditions are satisfied. The UE may report the predicted L1 / L3 quality (RSRP, RSRQ, SINR) of the 6G cell and / or beam (SSB, CSI-RS) to the 5GRAN node.

[0040] Examples: Predicted Event B1: The predicted inter-RAT neighbor cell becomes better than the threshold. Predicted Event B2: The predicted PCell becomes worse than threshold 1 and the predicted inter-RAT neighbor cell becomes better than threshold 2. Predicted Event LTM B1: The predicted inter-RAT neighbor cell's beam becomes better than the threshold. Predicted Event LTM B2: The predicted beam of the serving cell becomes worse than threshold 1 and the predicted inter-RAT neighbor cell's beam becomes better than threshold 2.

[0041] When the UE reports predictive measurements to the 5GRAN node, it may include and report the L1 / L3 quality (RSRP, RSRQ, SINR) of the measured 6G cells and / or beams (SSB, CSI-RS) together or simultaneously.

[0042] When a UE sends a predictive measurement report to a 5G RAN node, the time information (e.g., timestamp) of the predicted value may be associated with the L1 / L3 quality (RSRP, RSRQ, SINR) value of each predicted 6G cell and / or beam (SSB, CSI-RS). In addition, an indicator representing accuracy (e.g., accuracy rate, confidence score, or prediction success rate) may be associated with the L1 / L3 quality (RSRP, RSRQ, SINR) of each predicted 6G cell and / or beam (SSB, CSI-RS).

[0043] The 5G RAN node may trigger L3 handover or LTM based on the predicted L1 / L3 quality (RSRP, RSRQ, SINR) of 6G cells and / or beams (SSB, CSI-RS) reported by the UE. For example, a handover command or a cell switch notification may be sent to the UE.

[0044] The 5G RAN node may send the predicted L1 / L3 quality (RSRP, RSRQ, SINR) of 6G cells and / or beams (SSB, CSI-RS) to the target 6G RAN node.

[0045] When connecting to a 6G RAN node, the UE may also connect to a 5G RAN node (performing 6G-5G DC (Dual Connectivity)). The UE may predict inter-RAT measurement via AIML, which can be applied to 6G-5G DC setup or SCG (Secondary Cell Group) modification.

[0046] When connecting to a 5G RAN node, the UE may also connect to a 6G RAN node (performing 5G-6G DC (Dual Connectivity)). The UE may predict inter-RAT measurement via AIML, which can be applied to 5G-6G DC setup or SCG modification.

[0047] Furthermore, in the case of DC, the UE may be allowed to predict SCG failure, SCG RLF, or SCG modification failure via AIML and report it to the network.

[0048] When a UE connects to a 6G RAN node, it may report the predicted L1 / L3 quality (RSRP, RSRQ, SINR) of 5G cells and / or beams (SSB, CSI-RS) to the 6G RAN node through the method described above.

[0049] The 6G RAN node may select the target 5G PSCell from the predicted L1 / L3 quality (RSRP, RSRQ, SINR) of 5G cells and / or beams (SSB, CSI-RS).

[0050] The 6G RAN node may include the predicted L1 / L3 quality (RSRP, RSRQ, SINR) of 5G cells and / or beams (SSB, CSI-RS) in the CGConfigInfo in the S-NODE ADDITION REQUEST message and send it to the 5G RAN node. The 5G RAN node may return an S-NODE ADDITION REQUEST ACKNOWLEDGE message to the 6G RAN node.

[0051] The 6G RAN node may include the predicted L1 / L3 quality (RSRP, RSRQ, SINR) of 5G cells and / or beams (SSB, CSI-RS) in the CGConfigInfo in the S-NODE MODIFICATION REQUEST message and send it to the 5G RAN node. The 5G RAN node may return an S-NODE MODIFICATION REQUEST ACKNOWLEDGE message to the 6G RAN node.

[0052] When a UE connects to a 5G RAN node, it may report the predicted L1 / L3 quality (RSRP, RSRQ, SINR) of 6G cells and / or beams (SSB, CSI-RS) to the 5G RAN node through the method described above.

[0053] The 5G RAN node may select the target 6G PSCell from the predicted L1 / L3 quality (RSRP, RSRQ, SINR) of 6G cells and / or beams (SSB, CSI-RS).

[0054] The 5GRAN node may include the L1 / L3 quality (RSRP, RSRQ, SINR) of the predicted 6G cells and / or beams (SSB, CSI-RS) in the CGConfigInfo within the S-NODE ADDITION REQUEST message and send it to the 6GRAN node. The 6GRAN node may return an S-NODE ADDITION REQUEST ACKNOWLEDGE message to the 5GRAN node.

[0055] The 5GRAN node may include the L1 / L3 quality (RSRP, RSRQ, SINR) of the predicted 6G cells and / or beams (SSB, CSI-RS) in the CGConfigInfo within the S-NODE MODIFICATION REQUEST message and send it to the 6GRAN node. The 6GRAN node may return an S-NODE MODIFICATION REQUEST ACKNOWLEDGE message to the 5GRAN node.

[0056] Figure 3 is a sequence diagram illustrating an example (1) of AIML prediction in an embodiment of the present invention. In step S101, the 6GRAN node transmits a measurement prediction configuration to the UE. In step S102, the UE uses AIML to perform prediction of the L1 / L3 quality (RSRP, RSRQ, SINR) of the 5G cell and / or beam (SSB, CSI-RS). In step S103, the UE transmits the AIML predicted measurement reporting (predicted L1 / L3 quality (RSRP, RSRQ, SINR) of the 5G cell and / or beam (SSB, CSI-RS)) to the 6GRAN node. In step S104, the 6GRAN node sends a secondary node addition request (S-NODE ADDTION REQUEST) (CGConfigInfo (L1 / L3 quality (RSRP, RSRQ, SINR) of predicted 5G cells and / or beams (SSB, CSI-RS)) to the 5GRAN node. In step S105, the 5GRAN node sends a secondary node addition request response (S-NODE ADDTION REQUEST ACK) to the 6GRAN node.

[0057] Figure 4 is a sequence diagram illustrating an example (2) of prediction by AIML in an embodiment of the present invention. In step S201, the 6GRAN node transmits a measurement prediction configuration to the UE. In step S202, the UE uses AIML to perform prediction of the L1 / L3 quality (RSRP, RSRQ, SINR) of the 5G cell and / or beam (SSB, CSI-RS). In step S203, the UE transmits the AIML predicted measurement reporting (predicted L1 / L3 quality (RSRP, RSRQ, SINR) of the 5G cell and / or beam (SSB, CSI-RS)) to the 6GRAN node. In step S204, the 6GRAN node sends a Secondary Node Modification Request (S-NODE MODIFICATION REQUEST) (CGConfigInfo (Predicted L1 / L3 Quality (RSRP, RSRQ, SINR) of 5G Cells and / or Beams (SSB, CSI-RS)) to the 5GRAN node. In step S105, the 5GRAN node sends a Secondary Node Modification Request Response (S-NODE MODIFICATION REQUEST ACK) to the 6GRAN node.

[0058] Figure 5 is a sequence diagram illustrating an example (3) of AIML prediction in an embodiment of the present invention. In step S301, the 6GRAN node transmits a measurement prediction configuration to the UE. In step S302, the UE uses AIML to perform prediction of the L1 / L3 quality (RSRP, RSRQ, SINR) of the 5G cell and / or beam (SSB, CSI-RS). In step S303, the UE transmits the AIML predicted measurement reporting (predicted L1 / L3 quality (RSRP, RSRQ, SINR) of the 5G cell and / or beam (SSB, CSI-RS)) to the 6GRAN node. In step S304, the 6GRAN node sends a Secondary Node Addition Request (S-NODE ADDTION REQUEST) (CGConfigInfo (L1 / L3 quality (RSRP, RSRQ, SINR) of predicted 5G cells and / or beams (SSB, CSI-RS))) to the target 5GRAN node. In step S305, the target 5GRAN node sends a Secondary Node Addition Request Response (S-NODE ADDTION REQUEST ACK) to the 6GRAN node. In step S306, the 6GRAN node sends a Secondary Node Release Request (S-NODE RELEASE REQUEST) to the source 5GRAN node. In step S307, the source 5GRAN node sends a Secondary Node Release Request Response (S-NODE RELEASE REQUEST ACK) to the 6GRAN node.

[0059] Figure 6 is a sequence diagram illustrating an example (4) of prediction by AIML in an embodiment of the present invention. In step S401, the 5GRAN node transmits a measurement prediction configuration to the UE. In step S402, the UE uses AIML to perform prediction of the L1 / L3 quality (RSRP, RSRQ, SINR) of the 6G cell and / or beam (SSB, CSI-RS). In step S403, the UE transmits the AIML predicted measurement reporting (predicted L1 / L3 quality (RSRP, RSRQ, SINR) of the 6G cell and / or beam (SSB, CSI-RS)) to the 5GRAN node. In step S404, the 5GRAN node sends a secondary node addition request (S-NODE ADDTION REQUEST) (CGConfigInfo (L1 / L3 quality (RSRP, RSRQ, SINR) of the predicted 6G cells and / or beams (SSB, CSI-RS)) to the 6GRAN node. In step S405, the 6GRAN node sends a secondary node addition request response (S-NODE ADDTION REQUEST ACK) to the 5GRAN node.

[0060] Figure 7 is a sequence diagram illustrating an example (5) of prediction by AIML in an embodiment of the present invention. In step S501, the 5GRAN node transmits a measurement prediction configuration to the UE. In step S502, the UE uses AIML to perform prediction of the L1 / L3 quality (RSRP, RSRQ, SINR) of the 6G cell and / or beam (SSB, CSI-RS). In step S503, the UE transmits the AIML predicted measurement reporting (predicted L1 / L3 quality (RSRP, RSRQ, SINR) of the 6G cell and / or beam (SSB, CSI-RS)) to the 5GRAN node. In step S504, the 5GRAN node sends a Secondary Node Modification Request (S-NODE MODIFICATION REQUEST) (CGConfigInfo (L1 / L3 quality (RSRP, RSRQ, SINR) of the predicted 6G cells and / or beams (SSB, CSI-RS)) to the 6GRAN node. In step S505, the 6GRAN node sends a Secondary Node Modification Request Response (S-NODE MODIFICATION REQUEST ACK) to the 5GRAN node.

[0061] Figure 8 is a sequence diagram illustrating an example (6) of prediction by AIML in an embodiment of the present invention. In step S601, the 5GRAN node transmits a measurement prediction configuration to the UE. In step S602, the UE uses AIML to perform prediction of the L1 / L3 quality (RSRP, RSRQ, SINR) of the 6G cell and / or beam (SSB, CSI-RS). In step S603, the UE transmits the AIML predicted measurement reporting (predicted L1 / L3 quality (RSRP, RSRQ, SINR) of the 6G cell and / or beam (SSB, CSI-RS)) to the 5GRAN node. In step S604, the 5GRAN node sends a Secondary Node Addition Request (S-NODE ADDTION REQUEST) (CGConfigInfo (L1 / L3 quality (RSRP, RSRQ, SINR) of the predicted 6G cells and / or beams (SSB, CSI-RS)) to the target 6GRAN node. In step S605, the target 6GRAN node sends a Secondary Node Addition Request Response (S-NODE ADDTION REQUEST ACK) to the 5GRAN node. In step S606, the 5GRAN node sends a Secondary Node Release Request (S-NODE RELEASE REQUEST) to the source 6GRAN node. In step S307, the source 6GRAN node sends a Secondary Node Release Request Response (S-NODE RELEASE REQUEST ACK) to the 5GRAN node.

[0062] The UE may report the predicted SCGFailureInfo to the MN. The predicted SCGFailureInfo may include the predicted SCG RLF (e.g., T310 Expiry) and the time and location information of the SCG RLF (e.g., T310 Expiry) that will occur. It may also be associated with something that represents the accuracy of the above information (e.g., accuracy rate, confidence score, or prediction success rate). The MN may be a 6-GRAN node and the SN may be a 6-GRAN node. The MN may be a 6-GRAN node and the SN may be a 5-GRAN node and the SN may be a 6-GRAN node. The MN may be a 5-GRAN node and the SN may be a 5-GRAN node.

[0063] In the embodiments of the present invention, SCGFailureInfo, information relating to the occurrence of failures in a secondary cell group, was described as an example of a prediction target. However, the prediction target is not limited to SCGFailureInfo, and MCGFailureInfo, information relating to the occurrence of failures in a master cell group, may also be used as a prediction target. SCGFailureInfo in the embodiments of the present invention may be replaced with MCGFailureInfo.

[0064] The UE may report the predicted SCGFailureInfo to the MN. The predicted SCGFailureInfo may include the predicted SCG change failure (e.g., synchReconfigFailureSCG) and the time and location information in which the SCG change failure (e.g., synchReconfigFailureSCG) will occur. It may also be associated with something that represents the accuracy of the above information (e.g., accuracy rate, confidence score, or prediction success rate). The MN may be a 6-GRAN node and the SN may be a 6-GRAN node. The MN may be a 6-GRAN node and the SN may be a 5-GRAN node and the SN may be a 6-GRAN node. The MN may be a 5-GRAN node and the SN may be a 5-GRAN node.

[0065] Based on the predicted SCGFailureInfo received from the UE, MN may know that an SCG failure is likely to occur on the SCG side in the future and may trigger an SCG change in advance. This can prevent an SCG failure from occurring.

[0066] Alternatively, MN may send the predicted SCGFailureInfo received from UE to SN. Based on the predicted SCGFailureInfo, SN may know that an SCG failure will occur on the SCG side in the future and trigger an SCG change in advance. This can prevent an SCG failure from occurring.

[0067] AIML may predict a time instance of N310 consecutive Out-of-sync in the SCG, or it may predict the probability of N310 consecutive Out-of-sync occurring. AIML may also predict the interval from N310 consecutive Out-of-sync to RLF occurrence in the SCG. It may also predict the probability of RLF occurring in that interval. The interval from N310 consecutive Out-of-sync to RLF occurrence may be managed by T310 or by a new timer.

[0068] AIML may predict the time instance in which T310 / T312 or a new timer will start. The interval in which the probability of this may be predicted may be predicted as [t1, t2] or as [t1, t1 + timer length]. The timer may be T310 or a new timer.

[0069] AIML may predict the time instance in SCG where RLF occurs, or it may predict the probability of that occurring.

[0070] AIML may predict the time instance or time window in which a random access problem (RACH failure) occurs in the SCG. It may also predict the probability of such occurrence.

[0071] AIML may predict the time instance or time window in which rlc-MaxNumberRetransmission (RLC retransmission overload) occurs in the SCG. It may also predict the probability of such occurrence.

[0072] AIML may predict the time instance or time window in which a beam failure recovery failure will occur in the SCG. It may also predict the probability of such an occurrence.

[0073] AIML may predict the time instance or time window in which an LBT failure (LBTFailure) will occur in the SCG. It may also predict the probability of such an occurrence.

[0074] AIML may predict the time instance or time window in which a backhaul RLF recovery failure (BH-RLFRecoveryFailure) occurs in SCG. It may also predict the probability of such an occurrence.

[0075] AIML may predict the time instance in SCG when T310 / T312 or the new timer expires. It may also predict the probability of that expiration.

[0076] If AIML performs an SCG change failure prediction, it may predict the time instance in which T304 will expire. It may also predict the probability of that expiration.

[0077] AIML may predict the time instance at which T310 / T312 or the new timer will stop after it has started. It may also predict the probability of that stopping.

[0078] AIML may predict the time instance in which N311 consecutive in-sync occurs after T310 / T312 or the new timer is activated. It may also predict the probability of such occurrence.

[0079] The AIML model may reside on the UE side. The UE may report the above AIML predictions to the BS. Reporting may be done periodically or event-based. Event-based reporting may be done when the probability of RLF occurring is above a predetermined threshold (e.g., 60%, 70%, 80%, or 90%).

[0080] The UE may report the predicted SCGFailureInfo to the MN. The predicted SCGFailureInfo may include the predicted SCG beam failure and the time and location information of the predicted SCG beam failure. It may also be associated with an indicator of the accuracy of the above information (e.g., accuracy rate, confidence score, or prediction success rate). The MN may be a 6-GRAN node and the SN may be a 6-GRAN node. The MN may be a 6-GRAN node and the SN may be a 5-GRAN node. The MN may be a 5-GRAN node and the SN may be a 6-GRAN node. The MN may be a 5-GRAN node and the SN may be a 5-GRAN node.

[0081] Based on the predicted SCGFailureInfo received from the UE, MN may know that an SCG failure is likely to occur on the SCG side in the future and trigger an SCG change in advance. This can prevent an SCG failure. Alternatively, MN may send the predicted SCGFailureInfo received from the UE to SN. Based on the predicted SCGFailureInfo, SN may know that an SCG failure is likely to occur on the SCG side in the future and trigger an SCG change in advance. This can prevent an SCG failure.

[0082] AIML may predict the time instance in which a beam failure instance indication is received from the lower layer in the SCG. It may also predict the probability of receiving such an indication.

[0083] AIML may predict the time instance in which a predetermined number of beam failure instance indications are received from the lower layer in the SCG. It may also predict the probability of such reception. AIML may also predict the time instance in which the beam failure detection timer is activated in the SCG. It may also predict the probability of such activation.

[0084] AIML may predict the time instance in the SCG when the beam failure detection timer expires or stops. It may also predict the probability of such expiration or stoppage.

[0085] AIML may predict the time instance when BFI_COUNTER >= beamFailureInstanceMaxCount in SCG. It may also predict the probability of this.

[0086] AIML may predict the time instance in which beam failure recovery is triggered in the SCG. It may also predict the probability of this happening.

[0087] AIML may predict the target beam index in which a beam failure will occur. This could be the SSB index or the CSI-RS index. It may also predict the probability of such an occurrence.

[0088] The AIML model may reside on the UE side. The UE may report the above AIML predictions to the BS. Reporting may be done periodically or event-based. Event-based reporting may be done when the probability of RLF occurring is above a predetermined threshold (e.g., 60%, 70%, 80%, or 90%).

[0089] Figure 9 is a sequence diagram illustrating an example (7) of prediction by AIML in an embodiment of the present invention. In step S701, MN transmits an SCG failure prediction configuration to UE. In step S702, UE performs SCG failure prediction using the AIML model. In step S703, UE transmits predicted SCG failure information (SCGFailureInfo (predicted SCG RLF (i.e., T310 expiration) and time and location information for when the predicted SCG RLF occurs)) to MN. In step S704, UE, MN, and SN perform an SCG change triggered by MN.

[0090] Figure 10 is a sequence diagram illustrating an example (8) of prediction by AIML in an embodiment of the present invention. In step S801, MN transmits an SCG failure prediction configuration to UE. In step S802, UE performs SCG failure prediction using the AIML model. In step S803, UE transmits predicted SCG failure information (SCGFailureInfo (predicted SCG RLF (i.e., T310 expiration) and time and location information for when the predicted SCG RLF occurs)) to MN. In step S804, MN transmits predicted SCG failure information (SCGFailureInfo (predicted SCG RLF (i.e., T310 expiration) and time and location information for when the predicted SCG RLF occurs)) to SN. In step S805, UE, MN, and SN perform SCG changes triggered by SN.

[0091] Figure 11 is a sequence diagram illustrating an example (9) of prediction by AIML in an embodiment of the present invention. In step S901, MN transmits an SCG failure prediction configuration to UE. In step S902, UE performs SCG failure prediction using the AIML model. In step S903, UE transmits predicted SCG failure information (SCGFailureInfo (predicted SCG change failure (synchReconfigFailuerSCG))) and time and location information of the occurrence of said predicted SCG failure information (SCGFailureInfo (predicted SCG change failure (synchReconfigFailuerSCG))) to MN. In step S904, UE, MN, and SN perform an SCG change triggered by MN.

[0092] Figure 12 is a sequence diagram illustrating an example (10) of prediction by AIML in an embodiment of the present invention. In step S1001, MN transmits the SCG failure prediction configuration to UE. In step S1002, UE performs SCG failure prediction using the AIML model. In step S1003, UE transmits predicted SCG failure information (SCGFailureInfo (predicted SCG change failure (synchReconfigFailuerSCG))) and time and location information for when the predicted SCG failure information (SCGFailureInfo (predicted SCG change failure (synchReconfigFailuerSCG))) will occur to MN. In step S1004, MN transmits predicted SCG failure information (SCGFailureInfo (predicted SCG change failure (synchReconfigFailuerSCG))) and time and location information for when the predicted SCG failure information (SCGFailureInfo (predicted SCG change failure (synchReconfigFailuerSCG))) will occur to SN. In step S1005, UE, MN, and SN perform the SCG change triggered by SN.

[0093] Figure 13 is a sequence diagram illustrating an example (11) of prediction by AIML in an embodiment of the present invention. In step S1101, MN transmits an SCG failure prediction configuration to UE. In step S1102, UE performs SCG failure prediction using the AIML model. In step S1103, UE transmits predicted SCG failure information (Predicted SCGFailureInfo, which may include predicted SCG beam failure and time and location information of the predicted SCG beam failure) to MN. In step S1104, UE, MN, and SN perform an SCG change triggered by MN.

[0094] Figure 14 is a sequence diagram illustrating an example (12) of prediction by AIML in an embodiment of the present invention. In step S1201, MN transmits an SCG failure prediction configuration to UE. In step S1202, UE performs SCG failure prediction using the AIML model. In step S1203, UE transmits predicted SCG failure information (Predicted SCGFailureInfo (which may include predicted SCG beam failure and time and location information for the predicted SCG beam failure)) to MN. In step S1204, MN transmits predicted SCG failure information (Predicted SCGFailureInfo (which may include predicted SCG beam failure and time and location information for the predicted SCG beam failure)) to SN. In step S1205, UE, MN, and SN perform the SCG change triggered by SN.

[0095] Based on the above operation, when 6G is introduced and inter-RAT measurements are performed between 5G-RAT and 6G-RAT, it is expected that using prediction results from AIML will reduce resource consumption and help facilitate a smooth transition from 6G to 5G.

[0096] In other words, it is possible to achieve dual connectivity by applying artificial intelligence and machine learning models to InterRAT (Radio Access Technology) measurements.

[0097] (Device Configuration) Next, an example of the functional configuration of the base station 10, network node 30, and terminal 20 that perform the processing and operations described above will be explained. The base station 10, network node 30, and terminal 20 include the functions to perform the embodiments described above. However, the base station 10, network node 30, and terminal 20 may each be equipped with only some of the functions in the embodiments.

[0098] <Base Station 10 and Network Node 30> Figure 15 shows an example of the functional configuration of a base station 10 and a network node 30. As shown in Figure 15, 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 Figure 15 is merely an example. The functional classifications and names of the functional units can be anything as long as they can perform the operations according to the embodiment of the present invention. The network node 30 may have the same functional configuration as the base station 10. Furthermore, a network node 30 having multiple different functions on the system architecture may be composed of multiple network nodes 30 separated by function.

[0099] The transmitting unit 110 includes the function of generating a signal to be transmitted to the terminal 20 or other network node 30 and transmitting the signal by wire or wireless. The receiving unit 120 includes the function of receiving various signals transmitted from the terminal 20 or other network node 30 and obtaining information from the received signal, for example, information from a higher layer. A communication unit including the transmitting unit 110 and the receiving unit 120 may be configured.

[0100] The transmitting unit 110 includes the function of generating a signal to be transmitted to the terminal 20 and transmitting the signal wirelessly. The transmitting unit 110 also transmits setting information, instructions, and notifications related to the low-power wake-up signal to the terminal 20. The transmitting unit 110 also transmits notifications to the terminal regarding the switching of monitoring operations. The receiving unit 120 includes the function of receiving various signals transmitted from the terminal 20 and obtaining information from the received signals, for example, information from a higher layer. The transmitting unit 110 also has the function of transmitting PSS, SSS, PBCH, DL / UL control signals, etc. to the terminal 20. The receiving unit 120 also receives inter-network node messages from other network nodes.

[0101] The setting unit 130 stores pre-configured setting information and various setting information to be transmitted to the terminal 20. The contents of the setting information include, for example, information related to the operation described in the embodiment.

[0102] The control unit 140 performs control related to setting, instructing, and notifying the operations described in the embodiment. The signal transmission function unit of the control unit 140 may be included in the transmission unit 110, and the signal reception function unit of the control unit 140 may be included in the reception unit 120.

[0103] <Terminal 20> Figure 16 is a diagram showing an example of the functional configuration of terminal 20 in an embodiment of the present invention. As shown in Figure 16, 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 Figure 16 is merely an example. Any functional classification and functional unit name is acceptable as long as it can perform the operations according to the embodiment of the present invention. The transmitting unit 210 and the receiving unit 220 may be collectively referred to as the communication unit.

[0104] The transmitting unit 210 creates a transmission signal from the transmission data and transmits the transmission signal wirelessly. The transmitting unit 210 also transmits capability information related to the low-power wake-up signal to the base station 10. The receiving unit 220 wirelessly receives various signals and acquires signals from higher layers from the received physical layer signals. The receiving unit 220 also has the function of receiving PSS, SSS, PBCH, DL / UL / SL control signals, etc. transmitted from the base station 10. The receiving unit 220 also receives setting information, instructions, and notifications related to paging notification information and the low-power wake-up signal from the base station 10. For example, the receiving unit 220 receives the low-power wake-up signal from the base station 10. The setting unit 230 stores various setting information received from the base station 10 by the receiving unit 220. The setting unit 230 also stores pre-set setting information. The content of the setting information is, for example, information related to the operation described in the embodiment.

[0105] As described in the embodiment, the control unit 240 performs control related to setting, instructing, and notifying the operations described in the embodiment. The signal transmission function unit of the control unit 240 may be included in the transmission unit 210, and the signal reception function unit of the control unit 240 may be included in the reception unit 220.

[0106] (Hardware Configuration) The block diagrams (Figures 15 and 16) used in the description of the above embodiments show functional units. These functional blocks (components) are realized by hardware, software, or a combination thereof. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or it may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wired, wireless, etc.). A functional block may be realized by combining the above one device or the above multiple devices with software.

[0107] For example, a base station, terminal, network node, etc. in one embodiment of the present disclosure may function as a computer that processes the wireless communication method of the present disclosure. Figure 17 is a diagram showing an example of the hardware configuration of a base station and terminal according to one embodiment of the present disclosure. The base station 10 and terminal 20 described above may be physically configured as a computer device including a processor 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc.

[0108] In this disclosure, any two terms selected from the set of terms such as circuit, device, unit, module, chip, and means may be used interchangeably. The hardware configuration of the base station 10 and terminal 20 may include one or more of the devices shown in the figures, or it may be configured to omit some of the devices.

[0109] Each function in the base station 10 and terminal 20 is realized by loading predetermined software (programs) onto hardware such as the processor 1001 and memory 1002, which allows the processor 1001 to perform calculations and control communication by the communication device 1004, or to control the reading, writing, or both of the reading and writing of data in the memory 1002 and storage 1003.

[0110] The processor 1001 controls the entire computer, for example, by running the operating system. The processor 1001 may be composed of a central processing unit (CPU) that includes interfaces with peripheral devices, control devices, arithmetic units, registers, etc. For example, the baseband signal processing unit, call processing unit, etc., may be implemented by the processor 1001. Although only one processor 1001 is shown in the diagram, there may be multiple processors.

[0111] Furthermore, the processor 1001 reads programs (program code), 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 accordingly. The program used is one that causes the computer to execute at least a part of the operations described in the above embodiment. 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 other functional blocks may be implemented similarly. The above-described various processes have been explained as being executed by one processor 1001, but they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may be transmitted from the network via a telecommunications line, or provided to the computer device via the communication device 1004, for example.

[0112] Furthermore, this disclosure also provides a computer program product, which includes a computer program. This computer program product may implement the steps of the method described in the above embodiments when the computer program is executed by a computer (for example, a processor 1001).

[0113] The memory 1002 is a computer-readable recording medium and may be composed of, for example, ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), or at least a combination of these. The memory 1002 may also be called a register, cache, main memory, etc. The memory 1002 can store executable programs (program code), software modules, etc., for implementing a wireless communication method according to one embodiment of the present disclosure.

[0114] The storage 1003 is a computer-readable recording medium and may consist of, for example, an optical disc such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., Compact Disc, Digital Multipurpose Disc, Blu-ray® Disc), a smart card, flash memory (e.g., card, stick, key drive), a floppy® disk, a magnetic strip, or a combination of at least two of these. The storage 1003 may also be called an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, server, or other suitable medium including memory 1002, storage 1003, or both memory 1002 and storage 1003.

[0115] The communication device 1004 is hardware (transceiver / receiver device) for communicating between computers via a wired network, a wireless network, or both wired and wireless networks, and is also referred to as a network device, network controller, network card, or communication module, for example. The communication device 1004 may include a high-frequency switch, duplexer, filter, frequency synthesizer, or a combination of at least two of these. For example, a transmit / receive antenna, amplifier section, transmit / receive section, or transmission path interface may be implemented by the communication device 1004. The transmit / receive section may be implemented in a physically or logically separated manner, consisting of a transmit section and a receive section.

[0116] The input device 1005 is an input device that accepts input from an external source (e.g., a keyboard, mouse, microphone, switch, button, sensor, or a combination of at least two of these). The output device 1006 is an output device that outputs to an external source (e.g., a display, speaker, LED lamp, or a combination of at least two of these). The input device 1005 and the output device 1006 may be configured as an integrated unit (e.g., a touch panel).

[0117] Furthermore, each device, such as the processor 1001 and memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or different buses may be configured for each device.

[0118] Furthermore, the base station 10 and terminal 20 may be configured with hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), an FPGA (Field Programmable Gate Array), a GPU (Graphics Processing Unit), an NPU (Neural Processing Unit), or a combination of at least two of these, and some or all of each functional block may be realized by such hardware. For example, the processor 1001 may be implemented using at least one of these hardware components.

[0119] <Configuration relating to this embodiment> (1) A terminal having: a receiving unit that receives a failure prediction setting in a Secondary Cell Group (SCG) from a master node performing dual connectivity; a control unit that performs a failure prediction in the SCG based on the failure prediction setting in the SCG using an artificial intelligence and machine learning model; and a transmitting unit that transmits the failure in the SCG predicted by the model to the master node. (2) The terminal according to paragraph 1, wherein the control unit performs SCG changes triggered on the master node based on the predicted failure in the SCG with the master node and the secondary node. (3) The terminal according to paragraph 1, wherein the control unit performs SCG changes triggered on the secondary node based on the predicted failure in the SCG with the master node and the secondary node. (4) The terminal according to paragraph 1, wherein the control unit performs a prediction of continuous synchronization out-of-sync time instances in the SCG using the model. (Clause 5) The terminal described in Clause 1, wherein the control unit performs the prediction of time instances of beam fault recovery failure in the SCG using the model. (Clause 6) A communication method in which a terminal performs the following steps: receiving a fault prediction setting in the SCG (Secondary Cell Group) from a master node performing dual connectivity; performing the prediction of fault occurrence in the SCG using artificial intelligence and machine learning models based on the fault prediction setting in the SCG; and transmitting the fault occurrence in the SCG predicted by the model to the master node.

[0120] In any of the above configurations, dual connectivity can be achieved by applying artificial intelligence and machine learning models to cell group measurements. Furthermore, according to paragraphs 2 through 5, when 6G is introduced and inter-RAT measurements are performed during dual connectivity between 5G-RAT and 6G-RAT, it is expected that using AIML prediction results will reduce resource consumption and help facilitate a smooth transition from 6G to 5G.

[0121] (Supplement to Embodiments) Embodiments of the present invention have been described above, but the disclosed invention is not limited to such embodiments, and those skilled in the art will understand various modifications, alterations, alternatives, substitutions, etc. Specific numerical examples have been used to facilitate understanding of the invention, but 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, and matters described in two or more items may be combined as needed, and matters described in one item may be applied to matters described in another item (as long as they do not contradict each other). The boundaries of functional units or processing units in the functional block diagram do not necessarily correspond to the boundaries of physical parts. The operation of multiple functional units may be physically performed by one part, or the operation of one functional unit may be physically performed by multiple parts. The processing procedures described in the embodiments may be rearranged as long as they do not contradict each other. For the convenience of explaining the processing, the base station 10 and terminal 20 have been described using functional block diagrams, but such devices may be realized in hardware, software, or a combination thereof. The software operated by the processor of the base station 10 according to an embodiment of the present invention and the software operated by the processor of the terminal 20 according to an embodiment of the present invention may be stored in any suitable storage medium such as random access memory (RAM), flash memory, read-only memory (ROM), EPROM, EEPROM, register, hard disk (HDD), removable disk, CD-ROM, database, server, or other appropriate storage medium.

[0122] Each aspect / embodiment described in this disclosure is an LTE (Long Term Evolution), LTE-A (LTE-Advanced), IMT-Advanced (International Mobile Telecommunications-Advanced), 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), 5G-A (5G-Advanced), 6G (6th generation mobile communication system), xG (xth generation mobile communication system (where x is, for example, an integer or decimal)), FRA (Future Radio Access), NR (new Radio), NX (New radio access), FX (Future generation radio access), O-RAN (Open Radio Access Network), W-CDMA (Wideband Code Division Multiple Access) (registered trademark), GSM (Global System for Mobile communications) (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE (Institute of Electrical and Electronics Technology) Engineers) 802.11, IEEE 802.11x (where x is any string such as b, a, g, n, ac, ax, be, bn, etc., and x=n is also called Wi-Fi 4, x=ac is Wi-Fi 5, x=ax is Wi-Fi 6 or Wi-Fi 6E, x=be is Wi-Fi 7, x=bn is Wi-Fi 8, etc. Note that Wi-Fi is a registered trademark.This may be applied to systems based on technologies such as IEEE 802.16 (WiMAX®), IEEE 802.20, UWB (Ultra-WideBand), Bluetooth®, network virtualization technologies (e.g., NFV (Network Function Virtualization), SFC (Service Function Chaining), SDN (Software Defined Networking)), or LPWA (Low Power Wide Area). Furthermore, each aspect / embodiment described in this disclosure may be applied to systems based on a combination of at least two of these technologies. Of course, "based on" means not only systems that utilize the technology, but also systems that utilize extensions or modifications of the technology.

[0123] In this disclosure, any two terms selected from the set of terms such as "Base Station (BS)", "Radio Base 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", "Ground 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.

[0124] Each cell contained within a base station may be referred to as a macrocell, small cell, femtocell, picocell, serving cell, or supercell, among other terms. In this disclosure, any two terms selected from the set of terms such as “cell,” “sector,” “cell group,” “carrier,” “component carrier,” “cluster,” “bandwidth part (BWP),” and “carrier bandwidth” may be used interchangeably.

[0125] In this disclosure, any two terms selected from the set of terms such as "Mobile Station (MS)", "user terminal", "User Equipment (UE)", "Device", "module", and "Terminal" may be used interchangeably.

[0126] The terminal may be called 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 appropriate term.

[0127] A base station and a terminal may each consist of one or more devices. Devices that constitute at least a part of each base station and terminal may be called transmitting devices, receiving devices, communication devices, etc. Furthermore, the devices constituting at least a part of each base station and terminal may be, but are not limited to, objects themselves such as vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, handcarts, rickshaws, ships and other watercraft, airplanes, rockets, unmanned aerial vehicles, stratospheric base stations (e.g., High Altitude Platform Stations (HAPS)), artificial satellites (e.g., Low Earth Orbit (LEO) satellites, Middle Earth Orbit (MEO) satellites, Geostationary Earth Orbit (GEO) satellites), drones (registered trademark), multicopters, quadcopters, balloons, and IoT (Internet of Things) devices (e.g., smart meters, sensors), or include, but are not limited to, objects or devices mounted on such objects. Furthermore, the object in question may be a moving object (hereinafter referred to as a "moving object"; this does not exclude the possibility of the moving object being in a stationary state) or a fixedly positioned object (hereinafter referred to as a "non-moving object").

[0128] Furthermore, the term "base station" in this disclosure may be interpreted as "terminal." For example, the various aspects / embodiments of this disclosure may be applied to configurations 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), V2X (Vehicle-to-Everything), etc.) or communication over a non-terrestrial network (NTN). In this case, the terminal 20 may have at least some of the functions of the base station 10 described above. Also, terms such as "uplink" and "downlink" may be interpreted as terms corresponding to terminal-to-terminal communication (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 interpreted as a sidelink channel.

[0129] Furthermore, this disclosure is also applicable when at least some of the equipment constituting the base station and terminal operates outside of the ground (for example, in the atmosphere or in outer space).

[0130] Furthermore, the term "terminal" in this disclosure may be interpreted as "base station." In this case, the base station 10 may be configured to have the same functions as the terminal 20 described above.

[0131] Information notification is not limited to the embodiments described herein and may be carried out by other means. For example, information notification may be carried out by physical layer signaling (e.g., DCI (Downlink Control Information), UCI (Uplink Control Information)), upper layer signaling (e.g., RRC (Radio Resource Control) signaling, MAC (Medium Access Control) signaling, broadcast information (MIB (Master Information Block), SIB (System Information Block))), other signals, or at least two combinations thereof. Physical layer signaling may also be called L1 (Layer 1) control information. MAC signaling may be called, for example, a MAC control element (CE) or a MAC PDU (Protocol Data Unit). RRC signaling may also be called an RRC message or an information element (IE) within the RRC message. The RRC message may be a message used for, for example, controlling an RRC connection (e.g., setup, reconfiguration, establishment, reestablishment, release, or resume), mobility, measurement reporting, or notification of terminal capability, or it may be an information element within the message. Furthermore, the notification of information may be explicit or implicit. An explicit notification of information is the notification of the information itself, while an implicit notification of information may be the notification of information other than the information in question, or the fulfillment of certain conditions which constitutes notification of the information in question.Furthermore, notification of information may include not only notification between the same layer of different devices (e.g., between lower layers or upper layers of base station 10 and terminal 20), but also notification between different layers within the same or different devices (e.g., between lower and upper layers within base station 10 or terminal 20). Also, notification of information from one device to another may be carried out via one or more devices. With respect to any information described in this disclosure (e.g., variables, constants, parameters, settings), information indicating / specifying (or relating to) the value of such arbitrary information may be notified from any first device (e.g., terminal / base station) to any second device (e.g., base station / terminal), even if not specifically stated in the embodiments described above.

[0132] The processing procedures, sequences, flowcharts, etc., of each aspect / embodiment described in this disclosure may be reordered, provided they are consistent. For example, the methods described in this disclosure present various step elements using exemplary order and are not limited to the specific order presented.

[0133] The specific operations described in this disclosure as being performed by a base station may, in some cases, be performed by its upper node, or by a part of it (e.g., CU, RU, or DU). It is clear that various operations performed for communication with terminals in a RAN or core network may be performed by at least a part of the base station and other network nodes other than the base station. The other network nodes may be a single node or a combination of multiple nodes. A network node is, for example, a node provided in various core networks such as EPC (Evolved Packet Core) or 5GC (5G Core Network), and provides one or more network functions (NFs).

[0134] Furthermore, in this disclosure, the action of "a terminal receiving information from a base station" is accompanied by the actions of "a base station transmitting the information to the terminal," "a base station generating the information," or both. Similarly, the action of "a terminal transmitting information to a base station" is accompanied by the action of "a base station receiving the information from the terminal." In addition, actions such as "a terminal is configured to..." or "a terminal is configured to..." may include the actions of "a base station transmitting configuration information regarding the terminal's settings" and "the terminal setting a predetermined action based on the configuration information."

[0135] Each aspect / embodiment described herein may be used individually, in combination, or switched between as needed during execution.

[0136] The above description of the disclosure is illustrative, and the present invention is not limited to the embodiments described herein. The disclosure can be implemented as modified and altered forms without departing from the spirit of the invention. The disclosure and its modifications and altered forms are included within the scope of the present invention and its equivalents.

[0137] Any reference to elements using the designations “first,” “second,” etc., as used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Accordingly, references to the first and second elements do not imply that only two elements may be employed, or that the first element must precede the second element in any way.

[0138] Radio resources can be defined by a combination of units of resources in one or more domains, such as the time domain, frequency domain, spatial domain, code domain, and power domain.

[0139] For example, resources in the time domain may be defined by one or more time units. These one or more time units include, but are not limited to, radio frames, subframes, slots, symbols, transmission time intervals (TTIs), or at least two combinations thereof. Furthermore, these time units may be fixed-length time units independent of numerology, variable-length time units dependent on numerology, or both. Examples of fixed-length time units include, but are not limited to, subframes consisting of one or more slots, and radio frames containing multiple subframes. Examples of variable-length time units include, but are not limited to, symbols and slots containing a fixed number of symbols. A certain time unit may be divided into shorter time units. Examples of shorter time units include, but are not limited to, mini-slots consisting of fewer symbols than the number of symbols that make up a slot. The time units described above may include, for example, time units used as units for scheduling, link adaptation, etc. Furthermore, any time unit in this disclosure may be interpreted as being interchangeable with other time units.

[0140] Numerology refers to parameters that define the physical layer structure, and may be parameters based on at least one of the following: subcarrier spacing (SCS), symbol length, cyclic prefix length, and sampling time.

[0141] Resources in the frequency domain may be defined, for example, by one or more frequency units. Such 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 names of frequency units are not limited to these. Furthermore, the number of subcarriers included in a frequency unit may be a fixed number regardless of the neurology, or a variable number that changes depending on the neurology. For example, an RB consists 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 the neurology, for example, 12, but is not limited to this. Similarly, a BWP may consist of, for example, one or more consecutive RBs within a carrier bandwidth, but is not limited to this. For terminal 20, one or more BWPs may be set within one carrier, and at least one of the BWPs may be activated. Furthermore, any frequency unit in this disclosure may be interpreted as another frequency unit.

[0142] Furthermore, resources in both the time domain and the frequency domain may be defined, for example, by one or more time / frequency units composed of time units and frequency units. Such time / frequency units are, but are not limited to, resource elements (REs) consisting of one symbol and one subcarrier, resource element groups (REGs) consisting of a predetermined number of REs, and control resource sets (CORESETs) consisting of a predetermined number of symbols and a predetermined number of RBs.

[0143] Furthermore, resources in the spatial domain may be defined, for example, by one or more spatial units. Such spatial units include, but are not limited to, beams, MIMO (Multi Input Multi Output) layers, antenna ports, or at least two combinations thereof.

[0144] Furthermore, resources in the code domain may be defined, for example, by one or more code units. Such code units may be, but are not limited to, cyclic shifts (CS), orthogonal cover codes (OCC), or combinations thereof.

[0145] In this disclosure, if articles are added through translation, such as a, an, and the in English, this disclosure may include the fact that the noun following these articles is plural.

[0146] In the configuration of each of the above devices, "means" may be replaced with "part," "circuit," "device," etc.

[0147] 10 Base station 110 Transmitter 120 Receiver 130 Setting unit 140 Control unit 20 Terminal 210 Transmitter 220 Receiver 230 Setting unit 240 Control unit 1001 Processor 1002 Storage device 1003 Auxiliary storage device 1004 Communication device 1005 Input device 1006 Output device

Claims

1. A terminal comprising: a receiving unit that receives fault prediction settings in a Secondary Cell Group (SCG) from a master node performing dual connectivity; a control unit that performs fault prediction in the SCG based on the fault prediction settings in the SCG using artificial intelligence and machine learning models; and a transmitting unit that transmits the fault in the SCG predicted by the models to the master node.

2. The terminal according to claim 1, wherein the control unit executes SCG changes triggered on the master node based on the predicted occurrence of a failure in the SCG with the master node and the secondary node.

3. The terminal according to claim 1, wherein the control unit executes SCG changes triggered on the secondary node based on the predicted occurrence of a failure in the SCG with the master node and the secondary node.

4. The terminal according to claim 1, wherein the control unit performs the prediction of continuous synchronization out-of-sync time instances in the SCG using the model.

5. The terminal according to claim 1, wherein the control unit performs prediction of time instances of beam fault recovery failure in the SCG using the model.

6. A communication method in which a terminal performs the following steps: receiving a failure prediction setting in a Secondary Cell Group (SCG) from a master node performing dual connectivity; performing a failure prediction in the SCG based on the failure prediction setting in the SCG using artificial intelligence and machine learning models; and transmitting the failure in the SCG predicted by the models to the master node.

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