Method and apparatus in node used for wireless communication

Abstract

The invention discloses a method and an apparatus in a node used for wireless communication. A first node receives a first set of higher-layer messages, the first set of higher-layer messages being used to configure a first set of resources and a first set of candidate resources; evaluating the quality of a first wireless link according to the first resource set; the physical layer of the first node indicates a first candidate resource in the first candidate resource set to a higher layer thereof; the evaluated first wireless link quality is poorer than a second reference threshold value; the channel quality of the first candidate resource is equal to or greater than a first reference threshold; the first reference threshold is obtained depending on whether the channel quality of the first candidate resource is based on AI; when the channel quality of the first candidate resource is not obtained based on AI, the first reference threshold is the first threshold; when the channel quality of the first candidate resource is obtained based on AI, the first reference threshold is the second threshold.

Description

Technical Field

[0001] This application relates to a transmission method and apparatus in a wireless communication system, and particularly to a transmission scheme and apparatus in a wireless communication system. Background Art

[0002] Multiple-antenna technology is a key technology in 3GPP (3rd Generation Partner Project) LTE (Long-term Evolution) systems and NR (New Radio) systems. By configuring multiple antennas at a communication node, such as a base station or a UE (User Equipment), additional spatial degrees of freedom can be obtained. Multiple antennas form beams pointing in a specific direction through beamforming to improve communication quality. The degrees of freedom provided by a multiple-antenna system can be used to improve transmission reliability and / or throughput. Since the beams formed by multiple antennas are relatively narrow, both communication parties need to align the beams to provide communication quality. Starting from NR (New Radio) Release 15, 3GPP has introduced a beam failure detection and recovery mechanism to quickly detect beam out-of-step and restore beam alignment, reducing the impact of beam out-of-step on system performance.

[0003] With the adoption of new technologies, the increase in the number of antennas, the diversification of application scenarios, and the improvement of system performance requirements, etc., traditional measurement and beam failure recovery methods will bring a large amount of redundant overhead. Therefore, in NR Release 18, research on AI (Artificial Intelligence) / ML (Machine Learning) technology was initiated to explore its impact on system performance and system design. Compared with traditional processing methods, AI / ML has characteristics such as being based on training and requiring deployment. Summary of the Invention

[0004] The applicant has found through research that when AI / ML functions are introduced, the existing measurement mechanisms and candidate resource selection schemes may not be able to meet the requirements of AI / ML. In response to the above problems, the present application discloses a solution. It should be noted that although a large number of embodiments of the present application are directed to AI / ML, the present application is also applicable to other solutions, such as traditional candidate resource selection schemes. In addition, adopting a unified solution in different scenarios (including but not limited to AI / ML-based solutions and traditional candidate resource selection schemes) helps to reduce hardware complexity and cost. Without conflict, the embodiments and features in the first node of the present application can be applied to the second node, and vice versa. Without conflict, the embodiments and features in the embodiments of the present application can be combined with each other arbitrarily.

[0005] As an embodiment, the interpretation of the terms in the present application refers to the definitions in the 3GPP specification protocol series TS38.

[0006] As an embodiment, the interpretation of the terms in the present application refers to the definitions in the 3GPP specification protocol series TS28.

[0007] The present application discloses a method in a first node for wireless communication, characterized by including:

[0008] Receiving a first set of higher layer messages, the first set of higher layer messages being used to configure a first resource set and a first candidate resource set; evaluating a first radio link quality according to the first resource set;

[0009] The physical layer of the first node indicates a first candidate resource in the first candidate resource set to its higher layer;

[0010] Wherein, the first candidate resource set includes a plurality of candidate resources, the first candidate resource is one of the plurality of candidate resources; the evaluated first radio link quality is worse than a second reference threshold; the channel quality of the first candidate resource is equal to or greater than a first reference threshold; the first reference threshold is one of a first threshold or a second threshold, and the first reference threshold depends on whether the channel quality of the first candidate resource is obtained based on AI; when the channel quality of the first candidate resource is not obtained based on AI, the first reference threshold is the first threshold; when the channel quality of the first candidate resource is obtained based on AI, the first reference threshold is the second threshold.

[0011] As an embodiment, the problems to be solved by the present application include: how to obtain channel information of candidate resources based on AI.

[0012] As an embodiment, the problems to be solved by the present application include: how to support the selection of candidate resources based on AI.

[0013] As an embodiment, in the above method, according to whether the channel quality of the candidate resource is obtained based on AI, the reference threshold is adjusted, a suitable candidate resource is selected, and the overall performance of the system is improved.

[0014] As an embodiment, the advantages of the above method include: better adapting to various different application scenarios and terminals, and improving flexibility and adaptability.

[0015] According to one aspect of the present application, it is characterized in that the first node is a user equipment.

[0016] According to one aspect of the present application, it is characterized in that the first node is a relay node.

[0017] According to one aspect of the present application, it is characterized in that the channel quality of the first candidate resource not being obtained based on AI includes: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is the RSRP obtained by measuring the first candidate resource.

[0018] As an embodiment, the advantages of the above method include: having good backward compatibility.

[0019] According to one aspect of the present application, it is characterized in that the channel quality of the first candidate resource being obtained based on AI includes: the channel quality of the first candidate resource is obtained by prediction or inference.

[0020] As an embodiment, the advantages of the above method include: reducing the measurement of channel quality and reducing the overhead of RS resources.

[0021] As an embodiment, the advantages of the above method include: by supporting the way of obtaining the channel quality of candidate resources based on AI, more and more accurate channel quality information is obtained, and the performance of the system is improved.

[0022] According to one aspect of the present application, it is characterized in that the channel quality of the first candidate resource being obtained based on AI includes: the first node performs a first operation, the first operation is based on training or based on AI, and the channel quality of the first candidate resource depends on the output of the first operation.

[0023] As an embodiment, the AI (Artificial Intelligence) includes ML (Machine Learning).

[0024] As an embodiment, the benefits of the above method include: better adapting to various different application scenarios and terminals, and improving flexibility and adaptability.

[0025] According to one aspect of the present application, it is characterized in that the first operation is associated with the first type of identifier.

[0026] As an embodiment, the benefits of the above method include: determining the first operation through the first type of identifier, which simplifies the design.

[0027] According to one aspect of the present application, it is characterized in that whether the channel quality of the first candidate resource is RSRP depends on whether the channel quality of the first candidate resource is obtained based on AI; only when the channel quality of the first candidate resource is not obtained based on AI, the channel quality of the first candidate resource is RSRP.

[0028] As an embodiment, the benefits of the above method include: having little impact on the existing system and having good backward compatibility.

[0029] According to one aspect of the present application, it is characterized in that it includes:

[0030] The physical layer of the first node also indicates the channel quality of the first candidate resource to its higher layer.

[0031] As an embodiment, the benefits of the above method include: by indicating the channel quality of the candidate resource to the higher layer, it helps the first node select a more suitable candidate resource.

[0032] According to one aspect of the present application, it is characterized in that it includes:

[0033] The physical layer of the first node also indicates first information to its higher layer;

[0034] Wherein, the first information is used to indicate whether the channel quality of the first candidate resource is obtained based on AI, or the first information is used to indicate whether the channel quality of the first candidate resource is RSRP obtained by measuring the first candidate resource.

[0035] As an embodiment, the benefits of the above method include: determining how to obtain the channel quality of the first candidate resource through the first information, which helps the first node select a more suitable candidate resource.

[0036] According to one aspect of the present application, it is characterized in that it includes:

[0037] The physical layer of the first node sends a beam failure event indication to its higher layer;

[0038] When the value of the target counter is equal to or greater than the target threshold, beam failure recovery is triggered; the target counter is used for counting the beam failure event indication.

[0039] As an embodiment, the advantages of the above method include: by triggering beam failure recovery, the impact of beam failure on the system is reduced, and the reliability of transmission is ensured.

[0040] According to one aspect of the present application, it is characterized in that it includes:

[0041] Send a beam failure recovery request; receive a response to the beam failure recovery request;

[0042] Wherein, the beam failure recovery is triggered.

[0043] As an embodiment, the advantages of the above method include: having good backward compatibility.

[0044] As an embodiment, the first node is a terminal.

[0045] As an embodiment, the user equipment is a terminal.

[0046] The present application discloses a method in a second node for wireless communication, which is characterized in that it includes:

[0047] Send a first set of higher layer messages, the first set of higher layer messages is used to configure a first resource set and a first candidate resource set;

[0048] Wherein, the target receiver of the first set of higher layer messages evaluates the first radio link quality according to the first resource set; the physical layer of the target receiver of the first set of higher layer messages indicates a first candidate resource in the first candidate resource set to its higher layer;

[0049] The first candidate resource set includes a plurality of candidate resources, the first candidate resource is one of the plurality of candidate resources; the evaluated first radio link quality is worse than a second reference threshold; the channel quality of the first candidate resource is equal to or greater than a first reference threshold; the first reference threshold is one of a first threshold or a second threshold, and the first reference threshold depends on whether the channel quality of the first candidate resource is obtained based on AI; when the channel quality of the first candidate resource is not obtained based on AI, the first reference threshold is the first threshold; when the channel quality of the first candidate resource is obtained based on AI, the first reference threshold is the second threshold.

[0050] According to one aspect of the present application, it is characterized in that the second node is a base station.

[0051] According to one aspect of the present application, it is characterized in that the second node is a user equipment.

[0052] According to one aspect of the present application, it is characterized in that the second node is a relay node.

[0053] According to one aspect of the present application, it is characterized in that the channel quality of the first candidate resource not being based on AI includes: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is the RSRP obtained by measuring the first candidate resource.

[0054] According to one aspect of the present application, it is characterized in that the channel quality of the first candidate resource being based on AI includes: the channel quality of the first candidate resource is obtained by prediction or inference.

[0055] According to one aspect of the present application, it is characterized in that the channel quality of the first candidate resource being based on AI includes: the target receiver of the first higher layer message set performs a first operation, the first operation is based on training or based on AI, and the channel quality of the first candidate resource depends on the output of the first operation.

[0056] According to one aspect of the present application, it is characterized in that the first operation is associated with the first type of identifier.

[0057] According to one aspect of the present application, it is characterized in that whether the channel quality of the first candidate resource is RSRP depends on whether the channel quality of the first candidate resource is based on AI; only when the channel quality of the first candidate resource is not based on AI, the channel quality of the first candidate resource is RSRP.

[0058] According to one aspect of the present application, it includes:

[0059] The physical layer of the target receiver of the first higher layer message set also indicates the channel quality of the first candidate resource to its higher layer.

[0060] According to one aspect of the present application, it includes:

[0061] The physical layer of the target receiver of the first higher layer message set also indicates first information to its higher layer;

[0062] wherein, the first information is used to indicate whether the channel quality of the first candidate resource is based on AI, or the first information is used to indicate whether the channel quality of the first candidate resource is the RSRP obtained by measuring the first candidate resource.

[0063] According to one aspect of the present application, it is characterized by including:

[0064] The physical layer of the target receiver of the first higher layer message set sends a beam failure event indication to its higher layer;

[0065] When the value of the target counter is equal to or greater than the target threshold, beam failure recovery is triggered; the target counter is used for counting the beam failure event indication.

[0066] According to one aspect of the present application, it is characterized by including:

[0067] Receive a beam failure recovery request; send a response to the beam failure recovery request;

[0068] Wherein, the beam failure recovery is triggered.

[0069] The present application discloses a terminal, which is characterized in that the terminal includes: one or more processors and a memory;

[0070] The memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the terminal to execute the method in the first node.

[0071] The present application discloses a base station, which is characterized in that the base station includes: one or more processors and a memory;

[0072] The memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the base station to execute the method in the second node.

[0073] The present application discloses a first node for use in wireless communication, which is characterized by including:

[0074] A first processor, which receives a first higher layer message set, the first higher layer message set is used to configure a first resource set and a first candidate resource set; evaluate a first radio link quality according to the first resource set;

[0075] The physical layer of the first node indicates a first candidate resource in the first candidate resource set to its higher layer;

[0076] Among them, the first candidate resource set includes multiple candidate resources, and the first candidate resource is one of the multiple candidate resources; the evaluated first radio link quality is worse than a second reference threshold; the channel quality of the first candidate resource is equal to or greater than a first reference threshold; the first reference threshold is one of a first threshold and a second threshold, and the first reference threshold depends on whether the channel quality of the first candidate resource is obtained based on AI; when the channel quality of the first candidate resource is not obtained based on AI, the first reference threshold is the first threshold; when the channel quality of the first candidate resource is obtained based on AI, the first reference threshold is the second threshold.

[0077] This application discloses a second node used for wireless communication, which is characterized by including:

[0078] A second processor that sends a first set of higher layer messages, and the first set of higher layer messages is used to configure a first resource set and a first candidate resource set;

[0079] Among them, the target receiver of the first set of higher layer messages evaluates the first radio link quality according to the first resource set; the physical layer of the target receiver of the first set of higher layer messages indicates a first candidate resource in the first candidate resource set to its higher layer;

[0080] The first candidate resource set includes multiple candidate resources, and the first candidate resource is one of the multiple candidate resources; the evaluated first radio link quality is worse than a second reference threshold; the channel quality of the first candidate resource is equal to or greater than a first reference threshold; the first reference threshold is one of a first threshold and a second threshold, and the first reference threshold depends on whether the channel quality of the first candidate resource is obtained based on AI; when the channel quality of the first candidate resource is not obtained based on AI, the first reference threshold is the first threshold; when the channel quality of the first candidate resource is obtained based on AI, the first reference threshold is the second threshold.

[0081] As an embodiment, compared with traditional solutions, this application has the following advantages:

[0082] A flexible candidate resource selection scheme;

[0083] Enhanced overall system performance;

[0084] Lower air interface overhead;

[0085] More flexible and diverse input information;

[0086] Better flexibility and adaptability;

[0087] Enhanced reliability and robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Other features, objects, and advantages of the present application will become more apparent from the following detailed description of non - limiting embodiments read in conjunction with the accompanying drawings:

[0089] Figure 1 A flowchart showing a first set of higher - layer messages and a first candidate resource according to an embodiment of the present application;

[0090] Figure 2 A schematic diagram showing a network architecture according to an embodiment of the present application;

[0091] Figure 3 A schematic diagram showing an embodiment of a radio protocol architecture of a user plane and a control plane according to an embodiment of the present application;

[0092] Figure 4 A schematic diagram showing a first communication device and a second communication device according to an embodiment of the present application;

[0093] Figure 5 A flowchart showing a transmission between a first node and a second node according to an embodiment of the present application;

[0094] Figure 6 A schematic diagram showing that the channel quality of a first candidate resource is the RSRP obtained by measuring the first candidate resource according to an embodiment of the present application;

[0095] Figure 7 A schematic diagram showing that the channel quality of a first candidate resource is predicted or inferred according to an embodiment of the present application;

[0096] Figure 8 A schematic diagram showing that the channel quality of a first candidate resource depends on the output of a first operation according to an embodiment of the present application;

[0097] Figure 9 A schematic diagram showing that a first operation is associated with a first type of identifier according to an embodiment of the present application;

[0098] Figure 10 A schematic diagram showing that a first operation is training - based or AI - based according to an embodiment of the present application;

[0099] Figure 11 A schematic diagram showing the channel quality of a first candidate resource according to an embodiment of the present application;

[0100] Figure 12A schematic diagram showing that the physical layer of the first node according to an embodiment of the present application also indicates the channel quality of the first candidate resource to its higher layer;

[0101] Figure 13 A schematic diagram showing the first information according to an embodiment of the present application;

[0102] Figure 14 A schematic diagram showing beam failure event indication and beam failure recovery according to an embodiment of the present application;

[0103] Figure 15 A schematic diagram showing a beam failure recovery request according to an embodiment of the present application;

[0104] Figure 16 A schematic diagram showing the deployment of RAN (Radio Access Network) domain AI / ML functions according to an embodiment of the present application;

[0105] Figure 17 A schematic diagram showing the deployment of AI / ML functions of a UE according to an embodiment of the present application;

[0106] Figure 18 A schematic diagram showing a processing system based on artificial intelligence or machine learning according to an embodiment of the present application;

[0107] Figure 19 A schematic diagram showing artificial intelligence or machine learning according to an embodiment of the present application;

[0108] Figure 20 A block diagram showing the structure of a processing device in the first node according to an embodiment of the present application;

[0109] Figure 21 A block diagram showing the structure of a processing device in the second node according to an embodiment of the present application; Detailed implementation manners

[0110] The technical solutions of the present application will be further described in detail below with reference to the accompanying drawings. It should be noted that, without conflict, the embodiments and features in the embodiments of the present application can be combined with each other arbitrarily. Based on considerations such as performance, flexibility, complexity, overhead, and compatibility, those skilled in the art have the motivation to flexibly combine the embodiments in different drawings without conflict, for example, but not limited to, the embodiments in Figure 1 and the embodiments in Figure 5 - Figure 21 and the embodiments in Figure 5 and the embodiments in Figure 6 -Figure 21 in the embodiments, etc.

[0111] Example 1

[0112] Embodiment 1 exemplifies a flowchart of a first set of higher layer messages and a first set of candidate resources according to an embodiment of the present application, as shown in the appendix Figure 1 as shown. In 100 shown in the appendix Figure 1 as shown, each box represents a step. In particular, the order of the steps in the box does not represent a specific temporal sequence between the respective steps.

[0113] In Embodiment 1, the first node receives a first set of higher layer messages in step 101; evaluates a first radio link quality according to the first resource set in step 102; the physical layer of the first node indicates a first candidate resource in the first set of candidate resources to its higher layer in step 103; wherein, the first set of higher layer messages is used to configure the first resource set and the first set of candidate resources; the first set of candidate resources includes a plurality of candidate resources, and the first candidate resource is one of the plurality of candidate resources; the evaluated first radio link quality is worse than a second reference threshold; the channel quality of the first candidate resource is equal to or greater than a first reference threshold; the first reference threshold is one of a first threshold or a second threshold, and the first reference threshold depends on whether the channel quality of the first candidate resource is obtained based on AI; when the channel quality of the first candidate resource is not obtained based on AI, the first reference threshold is the first threshold; when the channel quality of the first candidate resource is obtained based on AI, the first reference threshold is the second threshold.

[0114] As an embodiment, the first set of higher layer messages includes at least one higher layer message.

[0115] As an embodiment, the first set of higher layer messages includes RRC messages.

[0116] As an embodiment, the first set of higher layer messages includes at least RRC messages among RRC messages or MAC CE messages.

[0117] As an embodiment, the first set of higher layer messages includes RRC messages and MAC CE messages.

[0118] As an embodiment, the first set of higher layer messages includes some or all fields in one or more RRC IEs.

[0119] As an embodiment, the first set of higher layer messages includes some or all fields in one RRC IE.

[0120] As an example, the first set of higher layer messages includes some fields in RRC IE RadioLinkMonitoringConfig.

[0121] As an example, the first set of higher layer messages includes the field in RRC IE whose name includes failureDetectionResourcesToAddModList.

[0122] As an example, the first set of higher layer messages includes the failureDetectionResourcesToAddModList field in RRC IE RadioLinkMonitoringConfig.

[0123] As an example, the first set of higher layer messages includes the failureDetectionSet1 field and the failureDetectionSet2 field in RRC IE RadioLinkMonitoringConfig.

[0124] As an example, the first set of higher layer messages includes the field in RRC IE whose name includes failureDetectionSet1 and the field in RRC IE whose name includes failureDetectionSet2.

[0125] As an example, the first set of higher layer messages includes at least one field in RRC IE whose name includes failureDetectionSet.

[0126] As an example, the name of the RRC message in the first set of higher layer messages includes failureDetectionResources.

[0127] As an example, the name of the RRC message in the first set of higher layer messages includes failureDetectionSet.

[0128] As an example, the RRC message in the first set of higher layer messages includes the failureDetectionSet1 field and the failureDetectionSet2 field in RRC IE RadioLinkMonitoringConfig, and the MAC CE message in the first set of higher layer messages includes the BFD-RS Indication MAC CE.

[0129] As an example, the MAC CE message in the first set of higher layer messages is the BFD-RS Indication MAC CE.

[0130] As an example, the name of the MAC CE message in the first set of higher layer messages includes BFD-RS Indication MAC CE.

[0131] As an example, the name of the MAC CE message in the first set of higher layer messages includes BFD.

[0132] As an example, the first set of higher layer messages includes the failureDetectionSet1 field and the failureDetectionSet2 field in RRC IE RadioLinkMonitoringConfig, and the BFD-RS Indication MAC CE.

[0133] Typically, when the number of RS resources indicated by the failureDetectionSet1 field or the failureDetectionSet2 field in RRC IE RadioLinkMonitoringConfig is greater than 2, the BFD-RS Indication MAC CE activates one or two RS resources from failureDetectionSet1 or failureDetectionSet2.

[0134] As an example, for the specific definitions of the RRC IE RadioLinkMonitoringConfig, the failureDetectionResourcesToAddModList field, the failureDetectionSet1 field, and the failureDetectionSet2 field, refer to Section 6.3.2 of 3GPP TS38.331.

[0135] As an example, for the specific definition of the BFD-RS Indication MAC CE, refer to Section 5.18.25 of 3GPP TS38.321.

[0136] As an example, for the specific definition of the IE RadioLinkMonitoringConfig, refer to Section 6.3.2 of 3GPP TS38.331.

[0137] As an example, the first set of higher layer messages includes some fields in RRC IE BeamFailureRecoveryConfig.

[0138] As an example, the first set of higher layer messages includes the candidateBeamRSList field in RRC IE BeamFailureRecoveryConfig.

[0139] As an example, the first set of higher layer messages includes the candidateBeamRSListExt field in RRC IE BeamFailureRecoveryConfig.

[0140] As an example, the first set of higher layer messages includes the candidateBeamRSSCellList field in RRC IE BeamFailureRecoveryConfig.

[0141] As an example, the first set of higher layer messages includes a field in RRC IE whose name includes candidateBeamRSList.

[0142] As an example, the first set of higher layer messages includes a field in RRC IE whose name includes candidateBeam.

[0143] As an example, the first set of higher layer messages includes one of the higher layer parameters candidateBeamRSList, candidateBeamRSListExt, or candidateBeamRSSCellList.

[0144] As an example, for the specific definitions of candidateBeamRSList, candidateBeamRSListExt, and candidateBeamRSSCellList, refer to Section 6 of 3GPP TS38.213.

[0145] As an example, the first resource set includes at least one RS resource.

[0146] As an example, the first resource set consists of at least one RS resource.

[0147] As an example, the first resource set is used for Beam Failure Detection (BFD).

[0148] As an example, the first resource set is used for failure monitoring.

[0149] As an example, the first resource set is

[0150] As an example, the first resource set is

[0151] As an example, the first resource set is

[0152] As an example, the first resource set is at least one of

[0153] As an example, For the specific definition, see Section 6 of 3GPP TS38.213.

[0154] As an example, the first resource set includes at least one RS resource, and the at least one RS resource in the first resource set includes at least one of CSI-RS (Channel State Information-Reference Signal) resource or SS / PBCH (Synchronization Signal / Physical Broadcast CHannel) block resources.

[0155] As an example, the first resource set includes at least one RS resource, and any RS resource in the first resource set is an SS / PBCH block resource.

[0156] As an example, the first resource set includes at least one RS resource, and any RS resource in the first resource set is a CSI-RS resource.

[0157] As an example, the first resource set includes at least one RS resource, and any RS resource in the first resource set is a periodic CSI-RS resource.

[0158] As an example, the first higher layer message set is used to configure the index of each RS resource in the first resource set.

[0159] As an example, the first higher layer message set is used to configure the index of each RS resource in the first candidate resource set.

[0160] As an example, the index of an RS resource is used to identify the RS resource.

[0161] As an example, the index of a RS resource is the configuration index of the RS resource.

[0162] As an example, the index of a RS resource includes the configuration index of the RS resource.

[0163] As an example, the index of an SS / PBCH block resource is used to identify the SS / PBCH block resource.

[0164] As an example, the index of an SS / PBCH block resource is used to identify the configuration of the SS / PBCH block resource.

[0165] As an example, the index of a periodic CSI-RS resource is the configuration index of the periodic CSI-RS resource.

[0166] As an example, the index of a periodic CSI-RS resource includes the configuration index of the periodic CSI-RS resource.

[0167] As an example, the index of a CSI-RS resource is NZP-CSI-RS-ResourceId.

[0168] As an example, the index of a CSI-RS resource is csi-RS-Index.

[0169] As an example, the index of an SS / PBCH block resource is SSB-Index.

[0170] As an example, the index of an SS / PBCH block resource is ssb-Index.

[0171] As an example, each RS resource in the first resource set depends on the configuration of the first set of higher layer messages.

[0172] As an example, the first set of higher layer messages includes the index of each RS resource included in the first resource set.

[0173] As an example, the first set of higher layer messages includes RRC messages and MAC CE messages; the RRC messages in the first set of higher layer messages are used to configure a target RS resource pool for a first BWP, and the MAC CE messages in the first set of higher layer messages are used to activate the first resource set from the target RS resource pool.

[0174] As an example, the first higher layer message set includes RRC messages and MAC CE messages; the first resource set belongs to a target RS resource pool, the RRC messages in the first higher layer message set include the index of each RS resource included in the target RS resource pool, and the MAC CE messages in the first higher layer message set activate the first resource set from the target RS resource pool.

[0175] As an example, the first higher layer message set includes RRC messages and MAC CE messages; the first resource set belongs to a target RS resource pool, the RRC messages in the first higher layer message set include the index of each RS resource included in the target RS resource pool, and the MAC CE messages in the first higher layer message set activate the first resource set from the target RS resource pool.

[0176] As an example, the first higher layer message set is used to configure a first CORESET pool, the first CORESET pool includes at least one CORESET; the first resource set depends on at least one TCI state of at least one CORESET in the first CORESET pool.

[0177] As a sub - example of the above example, the first higher layer message set includes some fields in IE PDCCH - Config.

[0178] As a sub - example of the above example, the first higher layer message set includes the controlResourceSetToAddModList field in IE PDCCH - Config.

[0179] As a sub - example of the above example, the first higher layer message set includes a field in IE PDCCH - Config whose name includes controlResourceSetToAddModList.

[0180] As a sub - example of the above example, the first higher layer message set includes a field in IE PDCCH - Config whose name includes controlResourceSet.

[0181] As an example, the meaning of the sentence "the first resource set depends on at least one TCI state of at least one CORESET in the first CORESET pool" includes: the first resource set is determined according to the RS indexes of at least one RS resource indicated by at least one TCI state of at least one CORESET in the first CORESET pool.

[0182] As an example, the meaning of the sentence "the first resource set depends on at least one TCI state of at least one CORESET in the first CORESET pool" includes: the first resource set is determined by the RS indexes configured with QCL type 'typeD' among at least one RS resource indicated by at least one TCI state of at least one CORESET in the first CORESET pool.

[0183] As an example, the meaning of the sentence "the first resource set depends on at least one TCI state of at least one CORESET in the first CORESET pool" includes: the first resource set includes at least one RS resource indicated by at least one TCI state of at least one CORESET in the first CORESET pool.

[0184] As an example, the meaning of the sentence "the first resource set depends on at least one TCI state of at least one CORESET in the first CORESET pool" includes: the first resource set includes the RS resources configured with QCL type 'typeD' among at least one RS resource indicated by at least one TCI state of at least one CORESET in the first CORESET pool.

[0185] As an example, evaluating the first radio link quality according to the first resource set is used for beam failure monitoring.

[0186] As an example, for the specific process of beam failure monitoring, refer to Section 6 of 3GPP TS38.213.

[0187] As an example, for the specific process of beam failure monitoring, refer to Section 5.17 of 3GPP TS38.321.

[0188] As an example, evaluating the first radio link quality according to the first resource set includes: determining whether the first radio link quality is worse than a second reference threshold.

[0189] As an example, evaluating the first radio link quality according to the first resource set includes: evaluating the first radio link quality according to the measurement of the first resource set.

[0190] As an example, the first radio link quality is RSRP.

[0191] As an example, the first radio link quality is L1-RSRP.

[0192] As an example, the first radio link quality is SINR.

[0193] As an example, the first radio link quality is L1 - SINR.

[0194] As an example, the first radio link quality is BLER.

[0195] As an example, the first radio link quality is hypothetical BLER.

[0196] As an example, the first radio link quality is one of RSRP, L1 - RSRP, SINR or L1 - SINR; the evaluated first radio link quality being worse than a second reference threshold includes: the evaluated first radio link quality being less than the second reference threshold.

[0197] As a sub - example of the above example, the unit of the second reference threshold is dBm or dB.

[0198] As an example, the first radio link quality is BLER; the evaluated first radio link quality being worse than a second reference threshold includes: the evaluated first radio link quality being greater than the second reference threshold.

[0199] As a sub - example of the above example, the second reference threshold is a BLER threshold.

[0200] As an example, the first radio link quality is hypothetical BLER; the evaluated first radio link quality being worse than a second reference threshold includes: the evaluated first radio link quality being greater than the second reference threshold.

[0201] As an example, the physical layer of the first node indicates to its higher layer at least one candidate resource in the first candidate resource set, and the channel quality of any candidate resource in the at least one candidate resource is equal to or greater than the first reference threshold.

[0202] As an example, the physical layer of the first node indicates to its higher layer a plurality of candidate resources in the first candidate resource set, and the channel quality of any candidate resource in the plurality of candidate resources is equal to or greater than the first reference threshold.

[0203] As an example, the physical layer of the first node indicates to its higher layer the index of the candidate resources in the first candidate resource set.

[0204] As an example, the physical layer of the first node indicates to its higher layer the channel quality of the candidate resources in the first candidate resource set.

[0205] As a sub - embodiment of the above - mentioned embodiment, the channel quality is RSRP, SINR, BLER, or hypothetical BLER.

[0206] As an embodiment, the physical layer of the first node indicates to its higher layer the number of candidate resources in the first candidate resource set that meet the first condition, where the first condition includes that the channel quality is equal to or greater than the first reference threshold.

[0207] As an embodiment, the second reference threshold is a real number.

[0208] As an embodiment, the second reference threshold is a non - negative real number.

[0209] As an embodiment, the second reference threshold is a non - negative real number not greater than 1.

[0210] As an embodiment, the second reference threshold is Qout_LR.

[0211] As an embodiment, the second reference threshold is one of Qout_LR, Qout_LR_SSB, or Qout_LR_CSI - RS.

[0212] As an embodiment, the definitions of Qout_LR, Qout_LR_SSB, and Qout_LR_CSI - RS can be found in 3GPP TS38.133.

[0213] As an embodiment, the first candidate resource set is

[0214] As an embodiment, the first candidate resource set is

[0215] As an embodiment, the first candidate resource set is

[0216] As an embodiment, the first candidate resource set is at least one of

[0217] As an embodiment, The specific definition of can be found in Section 6 of 3GPP TS38.213.

[0218] As an embodiment, the first candidate resource set includes a plurality of RS resources, and any one of the plurality of candidate resources is an RS resource.

[0219] As an embodiment, the RS resource in this application is a CSI - RS resource.

[0220] As an example, the RS resource in the present application is a CSI-RS resource or an SS / PBCH block resource.

[0221] As an example, the first candidate resource set consists of a plurality of RS resources, and any one of the plurality of candidate resources is an RS resource.

[0222] As an example, the first candidate resource set includes at least one of at least one RS resource or at least one beam; any one of the plurality of candidate resources is an RS resource or a beam.

[0223] As an example, the first candidate resource set includes at least one of at least one RS resource, at least one training data set, at least one radio resource, or at least one beam; any one of the plurality of candidate resources is at least one of an RS resource, a training data set, a radio resource, or a beam.

[0224] As an example, the radio resource includes at least one of a time domain resource, a frequency domain resource, a code domain resource, or a space domain resource.

[0225] As an example, when the channel quality of any one of the candidate resources in the first candidate resource set is not obtained based on AI, the first candidate resource set consists of at least one RS resource; when the channel quality of at least one candidate resource in the first candidate resource set is obtained based on AI, the first candidate resource set includes at least one of at least one RS resource, at least one training data set, at least one radio resource, or at least one beam.

[0226] As an example, the channel quality of the first candidate resource is RSRP.

[0227] As an example, the RSRP includes L1-RSRP.

[0228] As an example, the channel quality of the first candidate resource is SINR.

[0229] As an example, the SINR includes L1-SINR.

[0230] As an example, the channel quality of the first candidate resource is BLER.

[0231] As an example, the channel quality of the first candidate resource is a hypothetical BLER.

[0232] As an example, the channel quality of the first candidate resource is RSRP, SINR, BLER, or hypothetical BLER.

[0233] As an example, regardless of whether the channel quality of the first candidate resource is obtained based on AI, the channel quality of the first candidate resource is RSRP.

[0234] As an example, the first reference threshold is a real number.

[0235] As an example, the first reference threshold is Q in,LR 。

[0236] As an example, Q in,LR For the definition of Q, refer to Section 6 of 3GPP TS38.213.

[0237] As an example, the first threshold is a real number.

[0238] As an example, the first threshold is configurable.

[0239] As an example, the first threshold is indicated by a higher layer parameter.

[0240] As an example, the first threshold is indicated by the higher layer parameters rsrp-ThresholdSSB or rsrp-ThresholdBFR.

[0241] As an example, for the specific definitions of rsrp-ThresholdSSB and rsrp-ThresholdBFR, refer to Section 6 of 3GPP TS38.213.

[0242] As an example, the second threshold is a real number.

[0243] As an example, the second threshold is configurable.

[0244] As an example, the second threshold is indicated by a higher layer parameter.

[0245] As an example, the second threshold and the first threshold are in a linear relationship.

[0246] As an example, the first threshold and the second threshold are indicated by different higher layer parameters respectively.

[0247] As an example, the first threshold and the second threshold are configured separately.

[0248] As an embodiment, the second threshold is equal to the sum of the first threshold and a first offset.

[0249] As a sub - embodiment of the above - mentioned embodiment, the first offset is configured.

[0250] As a sub - embodiment of the above - mentioned embodiment, the first offset is reported by the first node.

[0251] As a sub - embodiment of the above - mentioned embodiment, the first offset is predefined.

[0252] As a sub - embodiment of the above - mentioned embodiment, the first offset is a real number.

[0253] As an embodiment, the first threshold and the second threshold are different.

[0254] As an embodiment, the second threshold is less than the first threshold.

[0255] As an embodiment, in the above - mentioned method, the threshold adopted in the AI - based manner is less than the threshold adopted in the non - AI - based manner.

[0256] As an embodiment, the advantages of the above - mentioned method include: increasing the probability of selecting appropriate resources.

[0257] As an embodiment, the advantages of the above - mentioned method include: being particularly applicable to the case where the channel quality obtained based on AI is lower than the actual channel quality.

[0258] As an embodiment, the second threshold is greater than the first threshold.

[0259] In the above - mentioned method, the threshold adopted in the AI - based manner is greater than the threshold adopted in the non - AI - based manner.

[0260] As an embodiment, the advantages of the above - mentioned method include: reducing the probability of selecting inappropriate resources caused by errors in AI prediction or inference.

[0261] As an embodiment, the advantages of the above - mentioned method include: being particularly applicable to the case where the channel quality obtained based on AI is higher than the actual channel quality.

[0262] As an embodiment, a higher - layer parameter is used to indicate whether the channel quality of the first candidate resource is obtained based on AI.

[0263] As an embodiment, a higher - layer parameter is used to indicate whether the channel quality of the first candidate resource is allowed to be obtained based on AI.

[0264] As an example, higher layer parameters are used to indicate whether the channel quality of at least one candidate resource in the first candidate resource set is allowed to be obtained based on AI.

[0265] As an example, the first set of higher layer messages is used to indicate whether the channel quality of the first candidate resource is obtained based on AI.

[0266] As an example, the first set of higher layer messages is used to indicate whether the channel quality of the first candidate resource is allowed to be obtained based on AI.

[0267] As an example, the first set of higher layer messages is used to indicate whether the channel quality of at least one candidate resource in the first candidate resource set is allowed to be obtained based on AI.

[0268] As an example, whether the channel quality of the first candidate resource is obtained based on AI depends on whether the first node receives the first higher layer parameter; only when the first node receives the first higher layer parameter, the channel quality of the first candidate resource is obtained based on AI.

[0269] As an example, whether the first node supports obtaining the channel quality of at least one candidate resource in the first candidate resource set based on AI depends on whether the first node receives the first higher layer parameter; only when the first node receives the first higher layer parameter, the first node supports obtaining the channel quality of at least one candidate resource in the first candidate resource set based on AI.

[0270] As an example, the first node indicates whether it supports obtaining the channel quality of at least one candidate resource in the first candidate resource set based on AI through capability reporting.

[0271] As an example, whether the channel quality of the first candidate resource is obtained based on AI depends on whether the first candidate resource is measured; when the first candidate resource is not measured, the channel quality of the first candidate resource is obtained based on AI; when the first candidate resource is measured, the channel quality of the first candidate resource is not obtained based on AI.

[0272] As an example, the unmeasured includes: not being expected to be measured.

[0273] As an example, whether the channel quality of the first candidate resource is obtained based on AI depends on whether the first candidate resource includes resources other than RS resources; when the first candidate resource includes resources other than RS resources, the channel quality of the first candidate resource is obtained based on AI; when the first candidate resource is an RS resource, the channel quality of the first candidate resource is not obtained based on AI.

[0274] As an example, the resources other than the RS resources include beams.

[0275] As an example, the resources other than the RS resources include at least one of a training data set, an air interface resource, or a beam.

[0276] As an example, the first candidate resource set is used for candidate beam detection.

[0277] As an example, the first candidate resource set is used to select a new candidate beam from the first candidate resource set during beam failure recovery.

[0278] As an example, the first candidate resource set is used for candidate beam detection; during an evaluation period, the first node evaluates whether the channel quality of each candidate resource therein is better than a first reference threshold, or the first node evaluates whether the channel quality of each candidate resource therein is equal to or better than the first reference threshold.

[0279] As an example, the evaluation period is T Evaluate_CBD_SSB or T Evaluate_CBD_CSI-RS .

[0280] As an example, a candidate resource in the first candidate resource set is an SS / PBCH block resource, and the channel quality is the L1-RSRP obtained based on the candidate resource.

[0281] As an example, a candidate resource in the first candidate resource set is a CSI-RS resource, and the channel quality is obtained by subtracting a first power value from the L1-RSRP obtained based on the candidate resource. The first power value is the power offset of the candidate resource with respect to the SS / PBCH block resource; the units of the L1-RSRP, the first power value, the power of the candidate resource, and the power of the SS / PBCH block resource are all dB.

[0282] As an example, the channel quality is L1-RSRP; when the channel quality is greater than the first reference threshold, the channel quality is better than the first reference threshold; when the channel quality is less than the first reference threshold, the channel quality is worse than the first reference threshold.

[0283] As an example, the channel quality is L1-RSRP; when the channel quality of a candidate resource in the first candidate resource set is better than the first reference threshold, the physical layer of the first node sends the configuration index and L1-RSRP of the candidate resource to its higher layer.

[0284] As an example, the channel quality is L1-RSRP; when the channel quality evaluated according to a candidate resource in the first candidate resource set is equal to or better than the first reference threshold, the physical layer of the first node sends the configuration index and L1-RSRP of the candidate resource to its higher layer.

[0285] As an example, the first power value is configured by the higher layer parameter powerControlOffsetSS.

[0286] Example 2

[0287] Embodiment 2 exemplifies a schematic diagram of a network architecture according to an embodiment of the present application, as shown in the appendix Figure 2 as follows.

[0288] Appendix Figure 2Describes the network architecture 200. The network architecture 200 is a 5G NR (New Radio) / LTE (Long-Term Evolution) / LTE-A (Long-Term Evolution Advanced) system, or the network architecture 200 is a 5G+ network architecture, or the network architecture 200 is a 6G network architecture, or the network architecture 200 is a network architecture adopted in the future continuous evolution of 3GPP; the network architecture 200 can be referred to as 5GS (5G System) / EPS (Evolved Packet System), or the network architecture 200 can be referred to as 6GS (6G System); the network architecture 200 includes at least one of UE (User Equipment) 201, RAN (Radio Access Network) 202, core network 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet service 230. The network architecture 200 can be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. As shown, the network architecture 200 provides packet switching services. However, those skilled in the art will easily understand that the various concepts presented throughout this application can be extended to networks providing circuit switching services or other cellular networks. The RAN includes node 203. The RAN may also include other nodes 204. Node 203 provides user and control plane protocol termination towards UE 201. Node 203 can be connected to other nodes 204 via the Xn interface (e.g., backhaul) / X2 interface. Node 203 can also be referred to as a base station, base transceiver station, radio base station, radio transceiver, transceiver function, basic service set (BSS), extended service set (ESS), TRP (Transmit Receive Point), or some other appropriate term. The core network 210 is 5GC (5G Core Network) / EPC (Evolved Packet Core), or the core network 210 is 6GC; node 203 provides an access point for UE 201 to the core network 210.Examples of the UE201 include cellular phones, smart phones, Session Initiation Protocol (SIP) phones, laptop computers, personal digital assistants (PDAs), satellite radios, non-terrestrial base station communications, satellite mobile communications, global positioning systems, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband Internet of Things devices, machine type communication devices, land vehicles, automobiles, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to the UE201 as a mobile station, subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable term. The node 203 is connected to the core network 210 through the S1 / NG interface. The core network 210 includes a Mobility Management Entity (MME) / Authentication Management Field (AMF) / Session Management Function (SMF) 211, other MME / AMF / SMFs 214, a Service Gateway (S-GW) / User Plane Function (UPF) 212, and a Packet Date Network Gateway (P-GW) / UPF 213. The MME / AMF / SMF 211 is a control node that processes the signaling between the UE201 and the core network 210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user Internet Protocol (IP) packets are transmitted through the S-GW / UPF 212, which is itself connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation and other functions. The P-GW / UPF 213 is connected to the Internet service 230. The Internet service 230 includes the operator's corresponding Internet protocol services, which may specifically include the Internet, intranet, IP Multimedia Subsystem (IMS), and packet switching services.

[0289] As an embodiment, the first node includes the UE201.

[0290] As an embodiment, the second node includes the node 203.

[0291] As an example, the radio link between the UE 201 and the node 203 includes a cellular network link.

[0292] As an example, the sender of the first set of higher layer messages includes the node 203.

[0293] As an example, the receiver of the first set of higher layer messages includes the UE 201.

[0294] As an example, the sender of the beam failure event indication includes the UE 201.

[0295] As an example, the trigger of the beam failure recovery includes the UE 201.

[0296] As an example, the executor of the first operation includes the UE 201.

[0297] As an example, the deployer of the first operation includes the UE 201.

[0298] As an example, the first candidate resource is indicated to the UE 201.

[0299] As an example, the channel quality of the first candidate resource is indicated to the UE 201.

[0300] As an example, the first information is indicated to the UE 201.

[0301] As an example, the sender of the beam failure recovery request includes the UE 201.

[0302] As an example, the receiver of the beam failure recovery request includes the node 203.

[0303] As an example, the receiver of the response to the beam failure recovery request includes the UE 201.

[0304] As an example, the sender of the response to the beam failure recovery request includes the node 203.

[0305] Example 3

[0306] Example 3 illustrates a schematic diagram of an embodiment of a radio protocol architecture for a user plane and a control plane according to an embodiment of the present application, as shown in the appendix Figure 3 as shown.

[0307] Example 3 shows a schematic diagram of an embodiment of a radio protocol architecture for a user plane and a control plane according to an embodiment of the present application, as shown in the appendix Figure 3as shown Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300 Figure 3The radio protocol architecture of the control plane 300 for between a first communication node device (UE, gNB or RSU in V2X) and a second communication node device (gNB, UE or RSU in V2X), or between two UEs, is shown with three layers: Layer 1, Layer 2, and Layer 3. Layer 1 (L1 layer) is the lowest layer and implements various PHY (Physical Layer) signal processing functions. Layer 1 will be referred to as PHY301 herein. Layer 2 (L2 layer) 305 is above PHY301 and is responsible for the link between the first communication node device and the second communication node device, or between two UEs. L2 layer 305 includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, and these sublayers terminate at the second communication node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security by encrypting data packets, and provides handover support for the first communication node device between the second communication node devices. The RLC sublayer 303 provides segmentation and reassembly of upper layer data packets, retransmission of lost data packets, and reordering of data packets to compensate for disordered reception due to HARQ. The MAC sublayer 302 provides multiplexing between logical and transport channels. The MAC sublayer 302 is also responsible for allocating various radio resources (e.g., resource blocks) in a cell between the first communication node devices. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in Layer 3 (L3 layer) of the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring the lower layers using RRC signaling between the second communication node device and the first communication node device. The radio protocol architecture of the user plane 350 includes Layer 1 (L1 layer) and Layer 2 (L2 layer). For the radio protocol architecture for the first communication node device and the second communication node device in the user plane 350, the physical layer 351, the PDCP sublayer 354 in the L2 layer 355, the RLC sublayer 353 in the L2 layer 355, and the MAC sublayer 352 in the L2 layer 355 are generally the same as the corresponding layers and sublayers in the control plane 300, but the PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead.The L2 layer 355 in the user plane 350 further includes an SDAP (Service Data Adaptation Protocol) sub-layer 356. The SDAP sub-layer 356 is responsible for the mapping between QoS flows and data radio bearers (DRBs) to support the diversity of services. Although not shown, the first communication node device may have several upper layers above the L2 layer 355, including a network layer (e.g., IP layer) terminated at the P-GW on the network side and an application layer terminated at the other end of the connection (e.g., a remote UE, server, etc.).

[0308] As an embodiment, the Figure 3 radio protocol architecture in is applicable to the first node.

[0309] As an embodiment, the Figure 3 radio protocol architecture in is applicable to the second node.

[0310] As an embodiment, the higher layer in this application refers to the layer above the physical layer.

[0311] As an embodiment, the first set of higher layer messages is generated in the RRC sub-layer 306.

[0312] As an embodiment, the first set of higher layer messages is generated in the MAC sub-layer 302 or the MAC sub-layer 352.

[0313] As an embodiment, the first set of higher layer messages is generated in the RRC sub-layer 306 and the MAC sub-layer 302.

[0314] As an embodiment, the beam failure event indication is generated in the PHY301 or the PHY351.

[0315] As an embodiment, the target counter is generated in the MAC sub-layer 302 or the MAC sub-layer 352.

[0316] As an embodiment, the channel quality information of the first candidate resource is generated in the PHY301 or the PHY351.

[0317] As an embodiment, the first information is generated in the PHY301 or the PHY351.

[0318] As an embodiment, the beam failure recovery request is generated in the PHY301 or the PHY351.

[0319] As an example, the beam failure recovery request is generated at the MAC sublayer 302 or the MAC sublayer 352.

[0320] As an example, the response to the beam failure recovery request is generated at the PHY 301 or the PHY 351.

[0321] As an example, the response to the beam failure recovery request is generated at the MAC sublayer 302 or the MAC sublayer 352.

[0322] Example 4

[0323] Example 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application, as shown in the Figure 4 appendix. The Figure 4 appendix is a block diagram of a first communication device 410 and a second communication device 450 that communicate with each other in an access network.

[0324] The first communication device 410 includes a controller / processor 475, a memory 476, a receiving processor 470, a transmitting processor 416, a multi-antenna receiving processor 472, a multi-antenna transmitting processor 471, a transmitter / receiver 418, and an antenna 420.

[0325] The second communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmitting processor 468, a receiving processor 456, a multi-antenna transmitting processor 457, a multi-antenna receiving processor 458, a transmitter / receiver 454, and an antenna 452.

[0326] In the transmission from the first communication device 410 to the second communication device 450, at the first communication device 410, upper layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements the functionality of the L2 layer. In the DL (DownLink), the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation to the second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the second communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for the L1 layer (i.e., the physical layer). The transmit processor 416 implements encoding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, and constellation mapping based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The multi-antenna transmit processor 471 performs digital spatial precoding on the encoded and modulated symbols, including codebook-based precoding and non-codebook-based precoding, and beamforming processing, to generate one or more parallel streams. The transmit processor 416 then maps each parallel stream to subcarriers, multiplexes the modulated symbols with reference signals (e.g., pilots) in the time domain and / or frequency domain, and then uses the inverse fast Fourier transform (IFFT) to generate a physical channel carrying time-domain multi-carrier symbol streams. Subsequently, the multi-antenna transmit processor 471 performs transmit analog precoding / beamforming operations on the time-domain multi-carrier symbol streams. Each transmitter 418 converts the baseband multi-carrier symbol streams provided by the multi-antenna transmit processor 471 into radio frequency streams, and then provides them to different antennas 420.

[0327] In the transmission from the first communication device 410 to the second communication device 450, at the second communication device 450, each receiver 454 receives signals through its respective antenna 452. Each receiver 454 recovers the information modulated onto the radio frequency carrier and converts the radio frequency stream into a baseband multi-carrier symbol stream for providing to the receive processor 456. The receive processor 456 and the multi-antenna receive processor 458 implement various signal processing functions of the L1 layer. The multi-antenna receive processor 458 performs receive analog precoding / beamforming operations on the baseband multi-carrier symbol stream from the receivers 454. The receive processor 456 uses the fast Fourier transform (FFT) to convert the baseband multi-carrier symbol stream after the receive analog precoding / beamforming operations from the time domain to the frequency domain. In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receive processor 456, where the reference signal will be used for channel estimation, and the data signal recovers any parallel streams destined for the second communication device 450 after multi-antenna detection in the multi-antenna receive processor 458. The symbols on each parallel stream are demodulated and recovered in the receive processor 456, and soft decisions are generated. Subsequently, the receive processor 456 decodes and deinterleaves the soft decisions to recover the upper layer data and control signals transmitted by the first communication device 410 on the physical channel. Subsequently, the upper layer data and control signals are provided to the controller / processor 459. The controller / processor 459 implements the functions of the L2 layer. The controller / processor 459 may be associated with a memory 460 that stores program code and data. The memory 460 may be referred to as a computer-readable medium. In the DL, the controller / processor 459 provides demultiplexing between the transmission and the logical channel, packet reassembly, decryption, header decompression, control signal processing to recover the upper layer data packets from the core network. Subsequently, the upper layer data packets are provided to all protocol layers above the L2 layer. Various control signals may also be provided to the L3 for L3 processing. The controller / processor 459 is also responsible for error detection using the acknowledgment (ACK) and / or negative acknowledgment (NACK) protocols to support HARQ operations.

[0328] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, a data source 467 is used to provide upper layer data packets to a controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmit function at the first communication device 410 described in DL, the controller / processor 459 performs header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on the radio resource allocation of the first communication device 410, and performs L2 layer functions for the user plane and the control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. A transmit processor 468 performs modulation mapping and channel coding processing. A multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing. Subsequently, the transmit processor 468 modulates the generated parallel streams into multi-carrier / single-carrier symbol streams, and after analog precoding / beamforming operations in the multi-antenna transmit processor 457, provides them to different antennas 452 via a transmitter 454. Each transmitter 454 first converts the baseband symbol stream provided by the multi-antenna transmit processor 457 into a radio frequency symbol stream and then provides it to the antenna 452.

[0329] In the transmission from the second communication device 450 to the first communication device 410, the functions at the first communication device 410 are similar to the receive functions at the second communication device 450 described in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives radio frequency signals through its corresponding antenna 420, converts the received radio frequency signals into baseband signals, and provides the baseband signals to a multi-antenna receive processor 472 and a receive processor 470. The receive processor 470 and the multi-antenna receive processor 472 jointly perform L1 layer functions. A controller / processor 475 performs L2 layer functions. The controller / processor 475 may be associated with a memory 476 that stores program code and data. The memory 476 may be referred to as a computer-readable medium. The controller / processor 475 provides demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover upper layer data packets from the second communication device 450. The upper layer data packets from the controller / processor 475 may be provided to the core network. The controller / processor 475 is also responsible for error detection using the ACK and / or NACK protocols to support HARQ operations.

[0330] As an example, the second communication device 450 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used together with the at least one processor. The second communication device 450 is at least configured to: receive a first set of higher layer messages, the first set of higher layer messages being used to configure a first set of resources and a first set of candidate resources; evaluate a first radio link quality according to the first set of resources; the physical layer of the first node indicates a first candidate resource in the first set of candidate resources to its higher layer; wherein, the first set of candidate resources includes a plurality of candidate resources, and the first candidate resource is one of the plurality of candidate resources; the evaluated first radio link quality is worse than a second reference threshold; the channel quality of the first candidate resource is equal to or greater than a first reference threshold; the first reference threshold is one of a first threshold or a second threshold, and the first reference threshold depends on whether the channel quality of the first candidate resource is obtained based on AI; when the channel quality of the first candidate resource is not obtained based on AI, the first reference threshold is the first threshold; when the channel quality of the first candidate resource is obtained based on AI, the first reference threshold is the second threshold.

[0331] As an example, the second communication device 450 includes: a memory storing a computer-readable instruction program, the computer-readable instruction program generating actions when executed by at least one processor, the actions including: receiving a first set of higher layer messages, the first set of higher layer messages being used to configure a first set of resources and a first set of candidate resources; evaluate a first radio link quality according to the first set of resources; the physical layer of the first node indicates a first candidate resource in the first set of candidate resources to its higher layer; wherein, the first set of candidate resources includes a plurality of candidate resources, and the first candidate resource is one of the plurality of candidate resources; the evaluated first radio link quality is worse than a second reference threshold; the channel quality of the first candidate resource is equal to or greater than a first reference threshold; the first reference threshold is one of a first threshold or a second threshold, and the first reference threshold depends on whether the channel quality of the first candidate resource is obtained based on AI; when the channel quality of the first candidate resource is not obtained based on AI, the first reference threshold is the first threshold; when the channel quality of the first candidate resource is obtained based on AI, the first reference threshold is the second threshold.

[0332] As an example, the first communication device 410 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The first communication device 410 is at least configured to: send a first set of higher layer messages, the first set of higher layer messages being used to configure a first set of resources and a first set of candidate resources; wherein, a target receiver of the first set of higher layer messages evaluates a first radio link quality according to the first set of resources; a physical layer of the target receiver of the first set of higher layer messages indicates a first candidate resource in the first set of candidate resources to its higher layer; the first set of candidate resources includes a plurality of candidate resources, the first candidate resource being one of the plurality of candidate resources; the evaluated first radio link quality is worse than a second reference threshold; a channel quality of the first candidate resource is equal to or greater than a first reference threshold; the first reference threshold is one of a first threshold or a second threshold, the first reference threshold depending on whether the channel quality of the first candidate resource is obtained based on AI; when the channel quality of the first candidate resource is not obtained based on AI, the first reference threshold is the first threshold; when the channel quality of the first candidate resource is obtained based on AI, the first reference threshold is the second threshold.

[0333] As an example, the first communication device 410 includes: a memory storing a computer-readable instruction program, the computer-readable instruction program generating actions when executed by at least one processor, the actions including: sending a first set of higher layer messages, the first set of higher layer messages being used to configure a first set of resources and a first set of candidate resources; wherein, a target receiver of the first set of higher layer messages evaluates a first radio link quality according to the first set of resources; a physical layer of the target receiver of the first set of higher layer messages indicates a first candidate resource in the first set of candidate resources to its higher layer; the first set of candidate resources includes a plurality of candidate resources, the first candidate resource being one of the plurality of candidate resources; the evaluated first radio link quality is worse than a second reference threshold; a channel quality of the first candidate resource is equal to or greater than a first reference threshold; the first reference threshold is one of a first threshold or a second threshold, the first reference threshold depending on whether the channel quality of the first candidate resource is obtained based on AI; when the channel quality of the first candidate resource is not obtained based on AI, the first reference threshold is the first threshold; when the channel quality of the first candidate resource is obtained based on AI, the first reference threshold is the second threshold.

[0334] As an example, the first node in the present application includes the second communication device 450.

[0335] As an example, the second node in the present application includes the first communication device 410.

[0336] As an example, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, the data source 467} is used to receive the first set of higher layer messages; at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, the memory 476} is used to send the first set of higher layer messages.

[0337] As an example, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, the data source 467} is used to receive the reference signal in the first resource set; at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, the memory 476} is used to send the reference signal in the first resource set.

[0338] As an example, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, the data source 467} is used to receive the reference signal in the first candidate resource set; at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, the memory 476} is used to send the reference signal in the first candidate resource set.

[0339] As an example, at least one of {the antenna 452, the receiver / transmitter 454, the receiving processor 456, the transmitting processor 468, the multi-antenna receiving processor 458, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460, the data source 467} is used to send the beam failure event indication.

[0340] As an example, at least one of {the antenna 452, the receiver / transmitter 454, the receive processor 456, the transmit processor 468, the multi-antenna receive processor 458, the multi-antenna transmit processor 457, the controller / processor 459, the memory 460, the data source 467} is used to trigger the beam failure recovery.

[0341] As an example, at least one of {the antenna 452, the receiver / transmitter 454, the receive processor 456, the transmit processor 468, the multi-antenna receive processor 458, the multi-antenna transmit processor 457, the controller / processor 459, the memory 460, the data source 467} is used to perform the first operation in this application.

[0342] As an example, at least one of {the antenna 452, the receiver / transmitter 454, the receive processor 456, the transmit processor 468, the multi-antenna receive processor 458, the multi-antenna transmit processor 457, the controller / processor 459, the memory 460, the data source 467} is used to indicate the first candidate resource in the first candidate resource set.

[0343] As an example, at least one of {the antenna 452, the receiver / transmitter 454, the receive processor 456, the transmit processor 468, the multi-antenna receive processor 458, the multi-antenna transmit processor 457, the controller / processor 459, the memory 460, the data source 467} is used to indicate the channel quality of the first candidate resource.

[0344] As an example, at least one of {the antenna 452, the receiver / transmitter 454, the receive processor 456, the transmit processor 468, the multi-antenna receive processor 458, the multi-antenna transmit processor 457, the controller / processor 459, the memory 460, the data source 467} is used to indicate the first information.

[0345] As an example, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, the data source 467} is used to receive a response to the beam failure recovery request; at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, the memory 476} is used to send a response to the beam failure recovery request.

[0346] As an example, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, the data source 467} is used to send a beam failure recovery request; at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, the memory 476} is used to receive a beam failure recovery request.

[0347] Example 5

[0348] Embodiment 5 exemplifies a flowchart of a transmission between a first node and a second node according to an embodiment of the present application; as shown in the appendix Figure 5 shown. In the appendix Figure 5 the second node U1 and the first node U2 are communication nodes transmitted through an air interface. In the appendix Figure 5 the steps in blocks F51 to F57 are optional respectively.

[0349] For the second node U1, a first set of higher layer messages is sent in step S511; a beam failure recovery request is received in step S5101; a response to the beam failure recovery request is sent in step S5102.

[0350] For the first node U2, in step S521, it receives a first set of higher layer messages; in step S522, it evaluates a first radio link quality according to the first resource set; in step S5201, the physical layer of the first node sends a beam failure event indication to its higher layer; in step S5202, it triggers beam failure recovery; in step S5203, it performs a first operation; in step S523, the physical layer of the first node indicates a first candidate resource in the first candidate resource set to its higher layer; in step S5204, the physical layer of the first node also indicates the channel quality of the first candidate resource to its higher layer; in step S5205, the physical layer of the first node also indicates first information to its higher layer; in step S5206, it sends a beam failure recovery request; in step S5207, it receives a response to the beam failure recovery request.

[0351] In Embodiment 5, the first set of higher layer messages is used to configure a first resource set and a first candidate resource set; a first radio link quality is evaluated according to the first resource set; the physical layer of the first node indicates a first candidate resource in the first candidate resource set to its higher layer; the first candidate resource set includes a plurality of candidate resources, and the first candidate resource is one of the plurality of candidate resources; the evaluated first radio link quality is worse than a second reference threshold; the channel quality of the first candidate resource is equal to or greater than a first reference threshold; the first reference threshold is one of a first threshold or a second threshold, and the first reference threshold depends on whether the channel quality of the first candidate resource is obtained based on AI; when the channel quality of the first candidate resource is not obtained based on AI, the first reference threshold is the first threshold; when the channel quality of the first candidate resource is obtained based on AI, the first reference threshold is the second threshold.

[0352] As an embodiment, the first node U2 is the first node in this application.

[0353] As an embodiment, the second node U1 is the second node in this application.

[0354] As an embodiment, the air interface between the second node U1 and the first node U2 includes a radio interface between a base station device and a user equipment.

[0355] As an embodiment, the air interface between the second node U1 and the first node U2 includes a radio interface between a relay node device and a user equipment.

[0356] As an embodiment, the air interface between the second node U1 and the first node U2 includes a radio interface between user equipments.

[0357] As an example, the second node U1 is the serving cell maintenance base station of the first node U2.

[0358] As an example, the AI training function in the RAN (Radio Access Network) domain is located in the 3GPP RAN domain-specific management function, while the AI inference function is located in the UE.

[0359] As an example, the RAN domain-specific management function provides the management capabilities for the AI training function and the AI inference function.

[0360] As an example, the AI training function is located in the RAN domain-specific management function, and the AI inference function is located locally in the gNB.

[0361] As an example, the management capability of the AI training function is provided by the RAN domain-specific management function, and the management capability of the AI inference is provided locally by the gNB.

[0362] As an example, MnF refers to Management Function.

[0363] As an example, both the AI training function and the AI inference function are located in the UE, where the UE provides the capabilities for training and inference.

[0364] As an example, the RAN domain-specific management function provides the management capabilities for the AI training function and the AI inference function.

[0365] As an example, both the AI training function and the AI inference function are located in the gNB.

[0366] As an example, the management capabilities of both the AI training function and the AI inference function are provided locally by the gNB.

[0367] As an example, the Figure 5 steps in block F51 in the appendix exist; the method in the first node for wireless communication includes: the physical layer of the first node sending a beam failure event indication to its higher layer.

[0368] As an example, the appendix Figure 5The steps in block F52 therein exist; the method in the first node for wireless communication includes: triggering beam failure recovery when the value of the target counter is equal to or greater than the target threshold; the target counter is used for counting the beam failure events indication.

[0369] As an embodiment, append Figure 5 The steps in block F53 therein exist.

[0370] As an embodiment, when the channel quality of the first candidate resource is obtained based on AI, append Figure 5 The steps in block F53 therein exist; when the channel quality of the first candidate resource is not obtained based on AI, append Figure 5 The steps in block F53 therein do not exist.

[0371] As an embodiment, append Figure 5 The steps in block F53 therein exist; the method in the first node for wireless communication includes: performing a first operation, the first operation is training-based or AI-based, and the channel quality of the first candidate resource depends on the output of the first operation.

[0372] As an embodiment, append Figure 5 The steps in block F54 therein exist; the method in the first node for wireless communication includes: the physical layer of the first node also indicates the channel quality of the first candidate resource to its higher layer.

[0373] As an embodiment, append Figure 5 The steps in block F55 therein exist; the method in the first node for wireless communication includes: the physical layer of the first node also indicates first information to its higher layer.

[0374] As an embodiment, the indication of the channel quality of the first candidate resource is earlier than the indication of the first information.

[0375] As an embodiment, the indication of the channel quality of the first candidate resource is not earlier than the indication of the first information.

[0376] As an embodiment, append Figure 5 The steps in block F56 therein exist; the method in the first node for wireless communication includes: sending a beam failure recovery request.

[0377] As an embodiment, append Figure 5 The steps in block F56 therein exist; the method in the second node for wireless communication includes: receiving a beam failure recovery request.

[0378] As an example, the steps in block F57 in Figure 5 exist; the method in the first node for wireless communication includes: receiving a response to the beam failure recovery request.

[0379] As an example, the steps in block F57 in Figure 5 exist; the method in the second node for wireless communication includes: sending a response to the beam failure recovery request.

[0380] As an example, the first set of higher layer messages is transmitted on the PDSCH (Physical Downlink Shared Channel).

[0381] As an example, the beam failure recovery request is transmitted on the PUSCH (Physical Uplink Shared Channel).

[0382] As an example, the beam failure recovery request is transmitted on the PUCCH (Physical Uplink Control Channel).

[0383] As an example, the response to the beam failure recovery request is transmitted on the PDSCH (Physical Downlink Shared Channel).

[0384] As an example, the response to the beam failure recovery request is transmitted on the PDCCH (Physical Downlink Control Channel).

[0385] Example 6

[0386] Embodiment 6 exemplifies a schematic diagram in which the channel quality of a first candidate resource according to an embodiment of the present application is the RSRP obtained by measuring the first candidate resource; as shown in Figure 6 the figure. In Embodiment 6, the channel quality of the first candidate resource is not based on AI, including: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is the RSRP obtained by measuring the first candidate resource.

[0387] As an example, the channel quality of the first candidate resource not being obtained based on AI includes: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is the RSRP obtained by measuring the first candidate resource.

[0388] As an example, the channel quality of the first candidate resource not being obtained based on AI includes: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is the L1-RSRP obtained by measuring the first candidate resource.

[0389] As an example, the channel quality of the first candidate resource not being obtained based on AI includes: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is the SINR obtained by measuring the first candidate resource.

[0390] As an example, the channel quality of the first candidate resource not being obtained based on AI includes: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is the L1-SINR obtained by measuring the first candidate resource.

[0391] As an example, the channel quality of the first candidate resource not being obtained based on AI includes: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is the BLER obtained by measuring the first candidate resource.

[0392] As an example, the channel quality of the first candidate resource not being obtained based on AI includes: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is the hypothetical BLER obtained by measuring the first candidate resource.

[0393] As an example, the channel quality of the first candidate resource not being obtained based on AI includes: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is the RSRP, SINR, BLER, or hypothetical BLER obtained by measuring the first candidate resource.

[0394] Example 7

[0395] Example 7 illustrates a schematic diagram in which the channel quality of the first candidate resource according to an embodiment of the present application is predicted or inferred; as shown in the appendix Figure 7 As shown. In Example 7, the channel quality of the first candidate resource being obtained based on AI includes: the channel quality of the first candidate resource is obtained by prediction or inference.

[0396] As an example, the prediction includes AI prediction.

[0397] As an example, the reasoning includes AI reasoning.

[0398] As an example, the channel quality of the first candidate resource is obtained based on AI, including that the acquisition of the channel quality of the first candidate resource uses an AI model.

[0399] As an example, the channel quality of the first candidate resource is not obtained based on AI, including that the acquisition of the channel quality of the first candidate resource does not use an AI model.

[0400] As an example, the channel quality of the first candidate resource is obtained based on AI, including that the channel quality of the first candidate resource includes information based on artificial intelligence or machine learning.

[0401] As an example, the channel quality of the first candidate resource is obtained based on AI, including that the channel quality of the first candidate resource includes information generated based on a Neural Network.

[0402] As an example, the channel quality of the first candidate resource is obtained based on AI, including that the channel quality of the first candidate resource includes information generated based on a CNN (Conventional Neural Networks).

[0403] As an example, the channel quality of the first candidate resource is not obtained based on AI, including that the channel quality of the first candidate resource does not include information based on artificial intelligence or machine learning.

[0404] As an example, the channel quality of the first candidate resource is not obtained based on AI, including that the channel quality of the first candidate resource does not include information generated based on a Neural Network.

[0405] As an example, the channel quality of the first candidate resource is not obtained based on AI, including that the channel quality of the first candidate resource does not include information generated based on a CNN.

[0406] As an example, the channel quality of the first candidate resource is obtained through prediction or inference, including that the first node obtains the channel quality of the first candidate resource through prediction or inference.

[0407] As an example, the channel quality of the first candidate resource obtained by prediction or inference includes: the channel quality of the first candidate resource is not obtained based on the measurement of RS resources.

[0408] As an example, the channel quality of the first candidate resource not being obtained based on the measurement of RS resources includes: the channel quality of the first candidate resource is not expected to be obtained based on the measurement of RS resources.

[0409] As an example, the specific algorithm for obtaining the channel quality of the first candidate resource based on AI is determined by the manufacturer of the first node itself, or is implementation-related.

[0410] As an example, the channel quality of the first candidate resource is one of RSRP, L1-RSRP, SINR, or L1-SINR; the channel quality of the first candidate resource obtained based on AI is the RSRP, L1-RSRP, SINR, or L1-SINR predicted or inferred for the first candidate resource.

[0411] As an example, the channel quality of the first candidate resource is one of RSRP, L1-RSRP, SINR, or L1-SINR; the channel quality of the first candidate resource obtained based on AI is the average value of the RSRP, L1-RSRP, SINR, or L1-SINR predicted or inferred for at least one transmission opportunity of the first candidate resource.

[0412] As an example, the channel quality of the first candidate resource is one of RSRP, L1-RSRP, SINR, or L1-SINR; the channel quality of the first candidate resource obtained based on AI is the minimum value of the RSRP, L1-RSRP, SINR, or L1-SINR predicted or inferred for at least one transmission opportunity of the first candidate resource.

[0413] As an example, the channel quality of the first candidate resource is BLER; the channel quality of the first candidate resource obtained based on AI is the BLER predicted or inferred for the first candidate resource.

[0414] As an example, the channel quality of the first candidate resource is BLER; the channel quality of the first candidate resource obtained based on AI is the average value of the BLER predicted or inferred for at least one transmission opportunity of the first candidate resource.

[0415] As an example, the channel quality of the first candidate resource is the BLER; the channel quality of the first candidate resource obtained based on AI is the maximum value of the BLER obtained by predicting or inferring the transmission opportunity of the first candidate resource at least once.

[0416] As an example, the channel quality of the first candidate resource is a hypothetical BLER; the first radio link quality evaluated based on AI is a hypothetical BLER obtained by predicting or inferring the first candidate resource.

[0417] As an example, the channel quality of the first candidate resource is a hypothetical BLER; the first radio link quality evaluated based on AI is the average value of the hypothetical BLER obtained by predicting or inferring the transmission opportunity of the first candidate resource at least once.

[0418] As an example, the channel quality of the first candidate resource is a hypothetical BLER; the first radio link quality evaluated based on AI is the maximum value of the hypothetical BLER obtained by predicting or inferring the transmission opportunity of the first candidate resource at least once.

[0419] Example 8

[0420] Embodiment 8 exemplifies a schematic diagram of the channel quality of the first candidate resource depending on the output of the first operation according to an embodiment of the present application; as shown in the appendix Figure 8 As shown. In Embodiment 8, the channel quality of the first candidate resource obtained based on AI includes: the first node performs a first operation, the first operation is based on training or based on AI, and the channel quality of the first candidate resource depends on the output of the first operation.

[0421] As an example, the channel quality of the first candidate resource obtained based on AI includes: the first node performs a first operation, the first operation is based on training or based on AI, and the channel quality of the first candidate resource depends on the output of the first operation; the channel quality of the first candidate resource not obtained based on AI includes: the acquisition of the channel quality of the first candidate resource does not include the first node performing the first operation.

[0422] As an example, the channel quality of the first candidate resource obtained based on AI includes: the first higher layer message set indicates that the channel quality of the first candidate resource is obtained based on AI by indicating a first type of identifier.

[0423] As an example, the channel quality of the first candidate resource is obtained based on AI, including: the channel quality of the first candidate resource is obtained using an AI model identified by a first type of identifier.

[0424] As an example, the channel quality of the first candidate resource is obtained based on AI, including: the channel quality of the first candidate resource is used for an AI function identified by a first type of identifier.

[0425] As an example, the channel quality of the first candidate resource is obtained based on AI, including: the channel quality of the first candidate resource is obtained within an AI entity identified by a first type of identifier.

[0426] As an example, the output of the first operation is used to generate the channel quality of the first candidate resource.

[0427] As an example, the channel quality of the first candidate resource includes the output of the first operation.

[0428] As an example, the channel quality of the first candidate resource includes the post - processed output of the first operation.

[0429] As an example, the channel quality of the first candidate resource includes the truncated and / or quantized output of the first operation.

[0430] As an example, after the output of the first operation is post - processed, it is used to generate the channel quality of the first candidate resource.

[0431] As an example, after the output of the first operation is truncated and / or quantized, it is used to generate the channel quality of the first candidate resource.

[0432] As an example, part or all of the output of the first operation, after being post - processed, is used to generate the channel quality of the first candidate resource.

[0433] As an example, part or all of the output of the first operation, after being truncated and / or quantized, is used to generate the channel quality of the first candidate resource.

[0434] As an example, how the output of the first operation is used to generate the channel quality of the first candidate resource is determined by the manufacturer of the first node or is implementation - related. These are some typical but non - restrictive implementation manners.

[0435] Example 9

[0436] Example 9 illustrates a schematic diagram in which a first operation according to an embodiment of the present application is associated with a first type of identifier; as shown in the attached Figure 9 figure. In Example 9, the first operation is associated with the first type of identifier.

[0437] As an embodiment, the first operation is identified by the first type of identifier.

[0438] As an embodiment, the AI model used by the first operation is identified by the first type of identifier.

[0439] As an embodiment, the AI entity to which the first operation belongs is identified by the first type of identifier.

[0440] As an embodiment, the AI entity that executes the first operation is identified by the first type of identifier.

[0441] As an embodiment, the first operation is for an AI function identified by the first type of identifier.

[0442] As an embodiment, the advantages of the above method include that by identifying an AI entity or function with the first type of identifier, the design is simplified and the understanding of different AI entities or functions is unified among multiple nodes.

[0443] As an embodiment, the first type of identifier is a model identifier.

[0444] As an embodiment, the first type of identifier is used to identify an AI model.

[0445] As an embodiment, the first type of identifier is used by the first node to determine an AI model.

[0446] As an embodiment, the first type of identifier is used by the first node to determine the AI model used by the first operation.

[0447] As an embodiment, the advantages of the above method include that by identifying an AI model / entity / function with the first type of identifier, the design is simplified and the understanding of different AI entities / functions is unified among multiple nodes.

[0448] As an embodiment, the first type of identifier is used to identify or indicate a reference resource set, and measurements for the reference resource set are used to obtain the training data set for the first operation.

[0449] As an embodiment, the first type of identifier is used to identify the configuration information of the reference resource set, and measurements for the reference resource set are used to obtain the training data set for the first operation.

[0450] As an example, the training for obtaining the first operation is identified by the first type of identifier.

[0451] As an example, the data set for the training of the first operation is identified by the first type of identifier.

[0452] As an example, the benefits of the above method include that by identifying an AI training or an AI training data set to recognize the inferences generated by this AI training or AI training data set, a consensus is established among different AI functions, further simplifying the design.

[0453] As an example, the first type of identifier is a non - negative integer.

[0454] As an example, the first type of identifier is a string.

[0455] As an example, the first type of identifier is used to identify an AI model.

[0456] As an example, the first type of identifier is used to identify an AI entity.

[0457] As an example, the first type of identifier is used to identify an AI function.

[0458] As an example, the benefits of the above method include that by using the first type of identifier to identify an AI entity or function, the design is simplified and the understanding of different AI entities or functions is unified among multiple nodes.

[0459] As an example, the first type of identifier is a model identifier.

[0460] As an example, the first type of identifier is used to identify an AI model.

[0461] As an example, the first type of identifier is used by the first node to determine an AI model.

[0462] As an example, the first type of identifier is used by the first node to determine the AI model adopted by the first reference operation.

[0463] As an example, the benefits of the above method include that by using the first type of identifier to identify an AI model / entity / function, the design is simplified and the understanding of different AI entities / functions is unified among multiple nodes.

[0464] As an example, the first type of identifier is used to identify or indicate a resource set.

[0465] As an example, the first type of identifier is used to identify or indicate a resource set, and the measurement of the resource set is used to obtain a training data set.

[0466] As an example, the first type of identifier is used to identify or indicate a resource set.

[0467] As an example, the first type of identifier is used to identify or indicate a training data set.

[0468] As an example, the benefits of the above method include that by identifying an AI training or an AI training data set, inferences generated by this AI training or AI training data set are recognized, establishing a consensus among different AI functions and further simplifying the design.

[0469] Example 10

[0470] Example 10 illustrates a schematic diagram of a first operation based on training or based on AI according to an embodiment of the present application; as shown in the appendix Figure 10 In Example 10, the first operation is based on training or based on AI.

[0471] As an example, the first operation is based on training or AI.

[0472] As an example, the first operation includes inference.

[0473] As an example, the inference includes AI inference.

[0474] As an example, the first operation includes an AI entity.

[0475] As an example, the first operation includes an AI entity for inference.

[0476] As an example, the first operation includes a part of an AI entity.

[0477] As an example, the first operation includes a part of an AI entity for inference.

[0478] As an example, the first operation includes an inference for obtaining the first information report.

[0479] As an example, the inference includes: AI (Artificial Intelligence) inference.

[0480] As an example, the first operation is used for an AI function.

[0481] As an example, the first operation is performed by the physical layer of the first node.

[0482] As an example, the first operation is performed by a higher layer of the first node.

[0483] As an example, the model of the first operation is obtained through training.

[0484] As an example, the training of the first operation is performed by the first node.

[0485] As an example, the training of the first operation is performed by the sender of the first information set.

[0486] As an example, the training of the first operation is performed by the core network.

[0487] As an example, the training of the first operation is performed by an AI training producer.

[0488] As an example, the training of the first operation is performed by an MDA function (Management Data Analytics Function).

[0489] As an example, the training of the first operation is performed by the MDA function located at the first node.

[0490] As an example, the training of the first operation is performed by the MDA function located at the sender of the first information set.

[0491] As an example, the training of the first operation is performed by an NWDAF (Network Data Analytics Function).

[0492] As an example, the training of the first operation is performed by an MDAS (Management Data Analytics Service) producer.

[0493] As an example, the training of the first operation is performed by an MnS (Management Service) producer.

[0494] As an example, the first operation needs to be deployed.

[0495] As an example, the first operation is obtained through loading.

[0496] As an example, the first operation is obtained by loading from the serving cell of the first node.

[0497] As an example, the first operation is obtained by loading from the maintenance base station of the serving cell of the first node.

[0498] As an example, the first node deploys the first operation.

[0499] As an example, the first operation does not require deployment.

[0500] As an example, the first operation is obtained by loading from the core network.

[0501] As an example, the first operation is based on artificial intelligence or machine learning.

[0502] As an example, the first operation is based on a Neural Network.

[0503] As an example, the first operation is based on CNN (Conventional Neural Networks).

[0504] As an example, the first operation includes preprocessing.

[0505] As an example, the first operation includes postprocessing.

[0506] As an example, the postprocessing includes DFT.

[0507] As an example, the postprocessing includes quantization.

[0508] As an example, the postprocessing includes one or more of a transformation from the angular domain to the spatial domain, a transformation from the spatial domain to the angular domain, a transformation from the time domain to the frequency domain, and a transformation from the frequency domain to the time domain.

[0509] As an example, the postprocessing includes truncation and / or padding.

[0510] As an example, the first operation includes one or more of convolution, pooling, concatenation, and activation.

[0511] As an example, the first operation includes a fully connected layer.

[0512] As an example, the first operation includes a pooling layer.

[0513] As an example, the first operation includes at least one convolutional layer.

[0514] As an example, the first operation includes at least one encoding layer.

[0515] As an example, an encoding layer includes at least one convolutional layer and a pooling layer.

[0516] As an example, in the convolutional layer, at least one convolutional kernel is used to convolve the input to generate a corresponding feature map, and at least one feature map output by the convolutional layer is reshaped into a vector and input to the fully connected layer; the fully connected layer converts the vector into an output.

[0517] As an example, some or all of the convolutional kernel size, number of convolutional layers, convolutional stride, pooling kernel size, pooling kernel stride, pooling function, activation function, and number of feature maps of the first operation are obtained through training.

[0518] As an example, some or all of the convolutional kernel, pooling kernel, pooling function, activation function, parameters of the pooling function, and parameters of the activation function of the first operation are obtained through training.

[0519] Without loss of generality, the parameters or AI model adopted by the first operation are determined by the manufacturer of the first node itself.

[0520] As an example, the first operation includes positioning based on artificial intelligence or machine learning.

[0521] As an example, the first operation includes positioning assisted by artificial intelligence or machine learning.

[0522] As an example, the first node is a consumer.

[0523] As an example, the first node is a consumer of the AI function.

[0524] As an example, the first node is a user of AI inference.

[0525] As an example, the first node is a user of AI training.

[0526] As an example, the first node is a user of MnS (Management Service).

[0527] As an example, the first node is a producer of AI inference.

[0528] As an example, the first node is a producer of AI training.

[0529] As an example, the first operation includes preprocessing.

[0530] As an example, the preprocessing includes DFT (Discrete Fourier Transform).

[0531] As an example, the preprocessing includes one or more of matrix decomposition, matrix transformation, and projection.

[0532] As an example, the preprocessing includes one or more of quantization, transformation from the spatial domain to the angular domain, transformation from the angular domain to the spatial domain, transformation from the frequency domain to the time domain, and transformation from the time domain to the frequency domain.

[0533] As an example, the preprocessing includes truncation and / or padding.

[0534] As an example, the preprocessing includes mapping.

[0535] As an example, the preprocessing includes mapping to a vector.

[0536] As an example, the preprocessing includes a label.

[0537] As an example, the label means marking with a label.

[0538] As an example, the first node deploys the first operation.

[0539] As an example, the deployment includes obtaining the first operation.

[0540] As an example, the deployment includes obtaining an AI entity.

[0541] As an example, the deployment includes obtaining an AI entity that executes the first operation.

[0542] As an example, the deployment includes obtaining an AI entity that includes an AI function for executing the first operation.

[0543] As an example, the deployment includes loading the first operation.

[0544] As an example, the deployment includes making a request to load the first operation.

[0545] As an example, the first operation is obtained by loading from the serving cell of the first node.

[0546] As an example, the first operation is obtained by loading from the maintenance base station of the serving cell of the first node.

[0547] As an example, the first operation is obtained by loading from the core network.

[0548] As an example, the deployment is completed by an AI function.

[0549] As an example, the deployment is completed by an AI function deployed on the first node.

[0550] As an example, the deployment is completed by an AI deployment function.

[0551] As an example, the deployment is completed by an AI deployment function deployed on the first node.

[0552] As an example, the deployment is completed by an AI inference function.

[0553] As an example, the deployment is completed by an AI inference function deployed on the first node.

[0554] As an example, the deployment is completed by an AI entity.

[0555] As an example, the deployment is completed by an AI entity deployed on the first node.

[0556] As an example, the deployment is completed by an AI entity with a deployment function.

[0557] As an example, the deployment is completed by an AI entity with a deployment function deployed on the first node.

[0558] As an example, the deployment is completed by an AI entity with an inference function.

[0559] As an example, the deployment is completed by an AI entity with an inference function deployed on the first node.

[0560] Example 11

[0561] Example 11 illustrates a schematic diagram of the channel quality of a first candidate resource according to an embodiment of the present application; as shown in the appendix Figure 11 As shown. In Example 11, whether the channel quality of the first candidate resource is RSRP depends on whether the channel quality of the first candidate resource is obtained based on AI; only when the channel quality of the first candidate resource is not obtained based on AI, the channel quality of the first candidate resource is RSRP.

[0562] As an embodiment, the channel quality of the first candidate resource is RSRP, SINR, BLER, or hypothetical BLER; when the channel quality of the first candidate resource is not obtained based on AI, the channel quality of the first candidate resource is RSRP; when the channel quality of the first candidate resource is obtained based on AI, the channel quality of the first candidate resource is SINR, BLER, or hypothetical BLER.

[0563] As an embodiment, the channel quality of the first candidate resource is RSRP or SINR; when the channel quality of the first candidate resource is not obtained based on AI, the channel quality of the first candidate resource is RSRP; when the channel quality of the first candidate resource is obtained based on AI, the channel quality of the first candidate resource is SINR.

[0564] As an embodiment, the channel quality of the first candidate resource is RSRP or BLER; when the channel quality of the first candidate resource is not obtained based on AI, the channel quality of the first candidate resource is RSRP; when the channel quality of the first candidate resource is obtained based on AI, the channel quality of the first candidate resource is BLER.

[0565] As an embodiment, the channel quality of the first candidate resource is RSRP or hypothetical BLER; when the channel quality of the first candidate resource is not obtained based on AI, the channel quality of the first candidate resource is RSRP; when the channel quality of the first candidate resource is obtained based on AI, the channel quality of the first candidate resource is hypothetical BLER.

[0566] Example 12

[0567] Example 12 illustrates a schematic diagram of the physical layer of a first node according to an embodiment of the present application further indicating the channel quality of a first candidate resource to its higher layer; as shown in the appendix Figure 12 As shown. In Example 12, the physical layer of the first node further indicates the channel quality of the first candidate resource to its higher layer.

[0568] As an embodiment, the physical layer of the first node further indicates the channel quality of the first candidate resource to its higher layer, and the channel quality of the first candidate resource is RSRP, L1-RSRP, SINR, L1-SINR, BLER, or hypothetical BLER.

[0569] As an embodiment, the physical layer of the first node indicates the channel quality of the first candidate resource and the index of the first candidate resource to its higher layer.

[0570] As an embodiment, the physical layer of the first node further indicates the channel quality of the first candidate resource to its higher layer, and the channel quality of the first candidate resource is the RSRP measured for the first candidate resource.

[0571] As an embodiment, the physical layer of the first node further indicates the channel quality of the first candidate resource to its higher layer, and the channel quality of the first candidate resource is obtained through prediction or inference.

[0572] Example 13

[0573] Example 13 illustrates a schematic diagram of a first piece of information according to an embodiment of the present application; as shown in the appendix Figure 13 As shown. In Example 13, the physical layer of the first node further indicates first information to its higher layer; wherein, the first information is used to indicate whether the channel quality of the first candidate resource is obtained based on AI, or the first information is used to indicate whether the channel quality of the first candidate resource is the RSRP obtained by measuring the first candidate resource.

[0574] As an embodiment, the physical layer of the first node further indicates first information to its higher layer; the first information is used to indicate whether the channel quality of the first candidate resource is obtained based on AI.

[0575] As an embodiment, the physical layer of the first node further indicates first information to its higher layer; the first information is used to indicate whether the channel quality of the first candidate resource is the RSRP obtained by measuring the first candidate resource.

[0576] As an example, the first information includes a first field, and the first field included in the first information indicates whether the channel quality of the first candidate resource is obtained based on AI.

[0577] As an example, the first field included in the first information includes one bit. When the first field included in the first information is 1, the channel quality of the first candidate resource is obtained based on AI; when the first field included in the first information is 0, the channel quality of the first candidate resource is not obtained based on AI.

[0578] As an example, the first field included in the first information includes one bit. When the first field included in the first information is 0, the channel quality of the first candidate resource is obtained based on AI; when the first field included in the first information is 1, the channel quality of the first candidate resource is not obtained based on AI.

[0579] As an example, the first information includes a second field, and the second field included in the first information indicates whether the channel quality of the first candidate resource is the RSRP obtained by measuring the first candidate resource.

[0580] As an example, the second field included in the first information includes one bit. When the second field included in the first information is 1, the channel quality of the first candidate resource is the RSRP obtained by measuring the first candidate resource; when the second field included in the first information is 0, the channel quality of the first candidate resource is not the RSRP obtained by measuring the first candidate resource.

[0581] As an example, the second field included in the first information includes one bit. When the second field included in the first information is 0, the channel quality of the first candidate resource is the RSRP obtained by measuring the first candidate resource; when the second field included in the first information is 1, the channel quality of the first candidate resource is not the RSRP obtained by measuring the first candidate resource.

[0582] Example 14

[0583] Example 14 illustrates a schematic diagram of beam failure event indication and beam failure recovery according to an embodiment of the present application; as shown in the appendix Figure 14 shown. In the appendix Figure 14Among the 140 shown in [Figure], in step 141, the first node's physical layer sends a beam failure event indication to its higher layer; in step 142, beam failure recovery is triggered. In Embodiment 14, the first node's physical layer sends a beam failure event indication to its higher layer; when the value of the target counter is equal to or greater than the target threshold, beam failure recovery is triggered; the target counter is used for counting the beam failure event indication.

[0584] As an embodiment, whenever the evaluated first radio link quality is worse than a second reference threshold, the first node's physical layer sends a beam failure event indication to its higher layer.

[0585] As an embodiment, the beam failure event indication refers to: beam failure instance indication.

[0586] As an embodiment, the first radio link quality is the radio link quality for the first serving cell, the beam failure event indication is for the first serving cell, the target counter is used for counting the beam failure event indication for the first serving cell, and the beam failure recovery is for the first serving cell.

[0587] As an embodiment, the first radio link quality is the radio link quality for the first resource set, the beam failure event indication is the beam failure event indication for the first resource set, the target counter is used for counting the beam failure event indication for the first resource set, and the beam failure recovery is for the first resource set.

[0588] As an embodiment, the first resource set is configured for the first BWP, and the first BWP is a BWP of the first serving cell; the first radio link quality is the radio link quality for the first serving cell, the beam failure event indication is for the first serving cell, the target counter is used for counting the beam failure event indication for the first serving cell, and the beam failure recovery is for the first serving cell.

[0589] As a sub - embodiment of the above - mentioned embodiment, the first resource set is

[0590] As an example, the first resource set is one of two resource sets configured for a first BWP, where the first BWP is a BWP of a first serving cell; the first radio link quality is the radio link quality for the first resource set, the beam failure event indication is the beam failure event indication for the first resource set, the target counter is used for counting the beam failure event indication for the first resource set, and the beam failure recovery is for the first resource set.

[0591] As a sub - example of the above example, the first resource set is

[0592] Typically, the meaning of the sentence "when the value of the target counter is equal to or greater than the target threshold" means: when and only when the value of the target counter is equal to or greater than the target threshold.

[0593] Typically, the meaning of the sentence "when the value of the target counter is equal to or greater than the target threshold" means: in response to the value of the target counter being equal to or greater than the target threshold.

[0594] Typically, the first node maintains the target counter at the MAC layer.

[0595] Typically, the MAC entity of the first node maintains the target counter.

[0596] Typically, whenever the MAC entity of the first node receives a beam failure event indication from the physical layer, it starts or restarts the target timer, and the value of the target counter is incremented by 1.

[0597] Typically, the target counter is BFI_COUNTER.

[0598] Typically, when the target timer expires, the target counter is set to 0.

[0599] Typically, the target timer is beamFailureDetectionTimer.

[0600] As an example, the target counter is BFI_COUNTER.

[0601] As an example, the initial value of the target counter is 0.

[0602] As an example, the target threshold is a positive integer.

[0603] As an example, the target threshold is beamFailureInstanceMaxCount.

[0604] As an example, the target threshold is configured by RRC parameters.

[0605] As an example, the RRC parameters configuring the target threshold include all or part of the information in the beamFailureInstanceMaxCount field of RadioLinkMonitoringConfigIE.

[0606] As an example, the target timer is beamFailureDetectionTimer.

[0607] As an example, the initial value of the target timer is a positive integer.

[0608] As an example, the initial value of the target timer is a positive real number.

[0609] As an example, the unit of the initial value of the target timer is the Qout,LR reporting period of the beam failure detection RS.

[0610] As an example, the initial value of the target timer is configured by the higher layer parameter beamFailureDetectionTimer.

[0611] As an example, the initial value of the target timer is configured by an IE.

[0612] As an example, the name of the IE configuring the initial value of the target timer includes RadioLinkMonitoring.

[0613] Example 15

[0614] Example 15 illustrates a schematic diagram of a beam failure recovery request according to an embodiment of the present application; as shown in the appendix Figure 15 As shown. In Example 15, the first node sends a beam failure recovery request in step 151; and receives a response to the beam failure recovery request in step 152; wherein the beam failure recovery is triggered.

[0615] As an example, the beam failure recovery includes the first node sending a beam failure recovery request and the sender of the first higher layer message set sending a response to the beam failure recovery request.

[0616] As an example, the Beam Failure Recovery (BFR) includes a random access procedure, the beam failure recovery request includes a random access preamble, and the response to the beam failure recovery request includes a PDCCH.

[0617] As an example, the beam failure recovery is scheduling request-based, and the beam failure recovery request includes a scheduling request (SR) for beam failure recovery.

[0618] As an example, the beam failure recovery request includes a random access preamble, and the random access preamble corresponds to a second candidate resource in the first candidate resource set.

[0619] As an example, the random access preamble is a contention-based RandomAccessPreamble.

[0620] As an example, the random access preamble is a contention-freeRandom AccessPreamble.

[0621] As an example, the Beam Failure Recovery (BFR) includes a contention-based random access procedure.

[0622] As an example, the Beam Failure Recovery (BFR) includes a contention-free random access procedure.

[0623] As an example, the beam failure recovery is scheduling request-based.

[0624] As an example, the beam failure recovery includes the first node triggering a scheduling request (SR) for beam failure recovery.

[0625] As an example, the beam failure recovery request includes a first MAC CE, and a first HARQ process is used for the transmission of the first MAC CE; the response to the beam failure recovery request includes a first PDCCH, and the first PDCCH indicates an uplink grant for a new transmission for the first HARQ process.

[0626] As an example, the name of the first MAC CE includes BFR.

[0627] As an example, the first MAC CE is a BFR MAC CE or a Truncated BFR MAC CE.

[0628] As an example, the first MAC CE is an Enhanced BFR MAC CE or a Truncated Enhanced BFR MAC CE.

[0629] As an example, the first MAC CE indicates a second candidate resource in the first candidate resource set.

[0630] As an example, whenever the evaluated first radio link quality is worse than a second reference threshold, the physical layer of the first node sends a beam failure event indication to its higher layer, and the physical layer of the first node indicates a candidate resource in the first candidate resource set to its higher layer; the second candidate resource is one of all the candidate resources indicated by the physical layer of the first node to its higher layer.

[0631] As an example, the higher layer of the first node selects a second candidate resource in the first candidate resource set and indicates the second candidate resource to its physical layer.

[0632] As an example, the second candidate resource is the first candidate resource.

[0633] As an example, the second candidate resource is not the first candidate resource.

[0634] As an example, the beam failure recovery request includes a MAC CE whose name includes BFR.

[0635] As an example, for the beam failure recovery procedure, refer to Section 5.17 of 3GPP TS 38.321.

[0636] As an example, for the beam failure recovery procedure, refer to Section 6 of 3GPP TS 38.213.

[0637] Example 16

[0638] Example 16 illustrates a schematic diagram of the deployment of RAN (Radio Access Network) domain AI / ML functions according to an embodiment of the present application; as shown in the appendix Figure 16 As shown. The gNB in Example 16 can be replaced by network devices such as eNB or 6G base stations.

[0639] AI / ML-related functions include ML training functions (also known as AI training, or AI / ML training), ML testing functions, ML inference (also known as AI inference, or AI / ML inference) functions, etc. The ML training function, ML testing function, and ML inference function can be deployed independently or co-located. The deployment of AI / ML-related functions can be achieved through software, such as the download and / or running of executable files; it can also be achieved through a combination of software and hardware, such as accelerating specific computing units through hardware to improve the operation speed or save power consumption.

[0640] For the ML training function, it can be deployed in a cross-domain management system or a domain-specific management system; the domain-specific management system is used to manage the RAN domain or the CN (Core Network) domain. For example, the ML training function for MDA (Management Data Analytics) can be deployed in MDAF (MDA function); the ML training for network data analysis can be deployed in NWDAF (Network Data Analytics Function), that is, the ML training function is MTLF (Model Training logical function).

[0641] For the ML inference function, it can also be deployed in a cross-domain management system or a domain-specific management system; for example, the ML inference function is MDAF, or the ML inference function is AnLF (Analytics logical function) located in NWDAF.

[0642] Similarly, the ML testing function can also be deployed in a cross-domain management system or a domain-specific management system.

[0643] In Embodiment 16, the RAN domain ML training function 1402 is located in the RAN domain management function 1403; while the ML inference function is located in the base station, that is, the AI / ML inference function 1404 is located in the gNB 1405, the AI / ML inference function 1406 is located in the gNB 1407,....

[0644] Appendix Figure 16 In, the management of the ML inference functions of multiple base stations is completed by the RAN domain management function 1403, that is, data interaction is performed with the RAN domain MnS (Management Service) consumer / cross-domain management 1401 (as shown by the dotted arrow in Appendix Figure 14 ).

[0645] Optionally, the management of the ML inference function can also be completed by the base station itself, that is, each base station can independently perform data interaction with the RAN domain MnS consumer / cross-domain management 1401.

[0646] It should be noted that Embodiment 16 is merely a non-limiting implementation; optionally, the ML training function of the RAN domain may also be deployed in the base station; or optionally, some base stations deploy the ML inference function and the ML training function of the RAN domain, while some base stations only deploy the ML inference function.

[0647] As an embodiment, one gNB (or base station) in Embodiment 16 is the second node of the present application.

[0648] As an embodiment, the second processor in the present application includes an AL / ML inference function in Appendix Figure 16 , that is, 1404 or 1406.

[0649] Example 17

[0650] Embodiment 17 illustrates a schematic diagram of the deployment of the AI / ML functions of a UE according to an embodiment of the present application; as shown in Appendix Figure 17 . The RAN domain ML training function 1505 in Appendix Figure 17 is optional.

[0651] The UE function 1504 is deployed in the first node of the present application, and the UE function 1504 includes an AI / ML inference function 1506; the AI / ML inference function 1506 performs inference using an ML model (also referred to as an AI model); an ML model usually needs to be trained before being used for AI / ML inference.

[0652] As an embodiment, the first information reporting in the present application is obtained through the inference of the AI / ML inference function 1506.

[0653] As an example, the first processor in the present application includes an attached Figure 17 AL / ML inference function 1506 in

[0654] As an example, the UE function 1504 includes a RAN domain ML training function 1505. The RAN domain ML training function 1505 runs training data through an ML model to derive relevant losses, and adjusts the parameters of the ML model based on the calculated losses. The ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[0655] The above embodiments can reduce the complexity of the base station, or save the radio interface resources caused by reporting training data. However, the above embodiments place relatively high requirements on the processing capabilities of the UE side.

[0656] Optionally, the UE function 1504 further includes a CN domain ML training function ( Figure 17 not included in

[0657] Optionally, the UE function 1504 further includes an AI / ML deployment function - Figure 17 not included in

[0658] As an example, the first node indicates whether it supports the ML training function (RAN domain or CN domain) through capability reporting. The capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0659] As an example, the ML model and related metadata are loaded by the first node from a network device or a remote server.

[0660] Optionally, the UE function 1504 is an MnS (Management Service) producer, and provides data to the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 for management or analysis (as shown by the double arrow 1507).

[0661] Optionally, the UE function 1504 is an MnS consumer that loads data for AI / ML-related management, such as management data requests, ML model activation, and / or ML training, etc. (as shown by the double arrow 1507), from the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503.

[0662] As an example, the ML model is based on a Neural Network.

[0663] As an example, the ML model is based on a CNN (Conventional Neural Networks, Convolutional Neural Network).

[0664] As an example, the ML model is based on a Transformer architecture.

[0665] Example 18

[0666] Embodiment 18 exemplifies a schematic diagram of an artificial intelligence or machine learning-based processing system according to an embodiment of the present application; as shown in the appendix Figure 18 shown. Appendix Figure 18 (a) includes a third processor, a fourth processor, and a fifth processor, appendix Figure 18 (b) includes a third processor, a fourth processor, a fifth processor, and a sixth processor.

[0667] In Embodiment 18(a), the third processor sends a first data set to the fourth processor and a second data set to the fifth processor; the fourth processor generates a target first type of parameter set based on the first data set, and the fourth processor sends the generated target first type of parameter set to the fifth processor; the fifth processor processes the second data set using the target first type of parameter set to obtain a first type of output. In appendix Figure 18 (a), the first type of feedback is optional.

[0668] In Embodiment 18(b), the third processor sends a first data set to the fourth processor and a second data set to the fifth processor; the fourth processor generates a target first type of parameter set based on the first data set, and the fourth processor sends the generated target first type of parameter set to the fifth processor; the fifth processor processes the second data set using the target first type of parameter set to obtain a first type of output, and the fifth processor sends the first type of output to the sixth processor. In appendix Figure 18(b), the first type of feedback and the second type of feedback are optional.

[0669] As an example, attached Figure 18 In (a), the fifth processor sends the first type of output to the second node in this application.

[0670] As an example, attached Figure 18 In (a), a single - side AI model is used for beam prediction or channel information prediction, and the fifth processor performs the first operation, which is used for beam prediction or channel information prediction.

[0671] As an example, attached Figure 18 In (a), a single - side AI model is used to obtain the channel quality of the first candidate resource, and the fifth processor performs the first operation, and the channel quality of the first candidate resource depends on the output of the first operation.

[0672] As an example, the AI includes ML (Machine Learning) inference.

[0673] As an example, the fifth processor performs the first operation.

[0674] As an example, the fifth processor sends the first type of feedback to the fourth processor, and the first type of feedback is used to trigger the recalculation or update of the target first - type parameter group.

[0675] As an example, the sixth processor sends the second type of feedback to the third processor, and the second type of feedback is used to generate the first data set or the second data set, or the second type of feedback is used to trigger the sending of the first data set or the sending of the second data set.

[0676] As an example, the third processor generates the first data set and the second data set based on the measurement of the first - type wireless signal, and the first - type wireless signal includes downlink RS.

[0677] As an example, the fifth processor belongs to the first node, and the sixth processor belongs to the second node.

[0678] As an example, the second data set includes the input of the first operation.

[0679] As an example, the second data set includes information obtained based on the first configuration and the M1 configurations.

[0680] As an example, the first data set includes training data.

[0681] As an example, the fourth processor belongs to the producer of the first operation.

[0682] As an example, the fourth processor includes an AI training producer.

[0683] As an example, the fourth processor includes an AI training function.

[0684] As an example, the fourth processor is used for model training, and the trained model is described by the target first type of parameter group.

[0685] As an example, the fourth processor belongs to the first node.

[0686] The above example avoids transmitting the first data set to the second node.

[0687] As an example, the fourth processor belongs to the second node.

[0688] The above example supports joint training and optimizes the system performance.

[0689] As an example, the fourth processor belongs to the core network.

[0690] The above example supports full network joint training and further optimizes the system performance.

[0691] As an example, the second data set includes inference data.

[0692] As an example, the fifth processor includes an AI inference producer.

[0693] As an example, the fifth processor includes an AI inference function.

[0694] As an example, the fifth processor belongs to the first node.

[0695] As an example, the fifth processor constructs a model according to the target first type of parameter group, and then inputs the second data set into the constructed model to obtain the first type of output.

[0696] As an example, the first operation is described by the target first type of parameter group.

[0697] As an example, the target first type of parameter group is used to construct the first operation.

[0698] As an example, the fifth processor generates a recovery data set according to the first type of output, and the error between the recovery data set and the second data set is used to generate the first type of feedback.

[0699] As an example, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model does not meet the requirements, the fourth processor recalculates the target first type of parameter group.

[0700] As an example, when the error is too large or there is no update for too long, the performance of the trained model is considered not to meet the requirements.

[0701] As an example, the target first type of parameter group includes one or more of: convolution kernel size, number of convolution layers, convolution stride, pooling kernel size, pooling kernel stride, pooling function, activation function, or number of feature maps.

[0702] As an example, the target first type of parameter group includes one or more of: convolution kernel, pooling kernel, pooling function, activation function, parameters of the pooling function, or parameters of the activation function.

[0703] Example 19

[0704] Example 19 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of the present application; as shown in the appendix Figure 19 shown. Appendix Figure 19 Includes a third operation, a fourth operation, a fifth operation, a sixth operation, and a seventh operation. In Example 19, the third operation and the fourth operation belong to the first stage, the fifth operation belongs to the second stage, the sixth operation belongs to the third stage, and the seventh operation belongs to the fourth stage. In the appendix Figure 19 The arrowed lines represent the order of the process.

[0705] As an example, the third operation includes AI training, the fourth operation includes AI testing, the fifth operation includes AI emulation, the sixth operation includes AI entity loading, and the seventh operation includes AI inference.

[0706] As an example, the first stage includes a training phase, the second stage includes an emulation phase, the third stage includes a deployment phase, and the fourth stage includes an inference phase.

[0707] As an example, the first stage includes AI model training.

[0708] As an example, the first stage includes AI model training and AI testing.

[0709] As an example, the AI includes ML (Machine Learning) inference.

[0710] As an example, the AI model training includes the initial training and re-training of one or a group of AI entities.

[0711] As an example, the AI model training depends on training data.

[0712] As an example, the AI model training includes AI entity validation.

[0713] As an example, the AI entity validation is used to evaluate the performance of the AI entity.

[0714] As an example, the AI entity validation depends on validation data.

[0715] As an example, if the result of the AI entity validation does not meet the expectation, the AI model will be re-trained.

[0716] As an example, the AI testing includes testing the validated AI entity to evaluate the performance of the trained AI model.

[0717] As an example, if the result of the AI testing meets the expectation, the AI entity proceeds to the next stage; otherwise, the AI model will be re-trained.

[0718] As an example, the AI testing depends on test data.

[0719] As an example, the second stage includes AI emulation, and the AI emulation performs inference of the AI entity in an emulation environment.

[0720] As an example, the AI simulation estimates the performance of AI entity inferences in a simulation environment before using the AI entity.

[0721] As an example, the second stage is optional.

[0722] As an example, the third stage includes AI entity loading, which is to obtain a trained AI entity to obtain the desired AI inference function.

[0723] As an example, the third stage is optional.

[0724] As an example, when the training function and the inference function are co-located, the third stage is no longer required.

[0725] As an example, the fourth stage includes AI inference.

[0726] As an example, the seventh operation includes the first operation.

[0727] Example 20

[0728] Example 20 illustrates a structural block diagram of a processing device in a first node according to an example of the present application; as shown in the appendix Figure 20 shown. In the appendix Figure 20 In it, the processing device 2000 in the first node includes a first processor 2001.

[0729] As an example, the first node is a user equipment.

[0730] As an example, the user equipment is a terminal.

[0731] As an example, the first node is a relay node device.

[0732] As an example, the first processor 2001 includes at least one of {antenna 452, receiver / transmitter 454, receive processor 456, transmit processor 468, multi-antenna receive processor 458, multi-antenna transmit processor 457, controller / processor 459, memory 460, data source 467} in Example 4.

[0733] In Example 20, the first processor 2001 receives a first set of higher layer messages, which are used to configure a first set of resources and a first set of candidate resources; and evaluates a first radio link quality according to the first set of resources.

[0734] In Embodiment 20, the physical layer of the first node indicates a first candidate resource in the first candidate resource set to a higher layer of the first processor 2001;

[0735] In Embodiment 20, the first candidate resource set includes a plurality of candidate resources, and the first candidate resource is one of the plurality of candidate resources; the evaluated first radio link quality is worse than a second reference threshold; the channel quality of the first candidate resource is equal to or greater than a first reference threshold; the first reference threshold is one of a first threshold or a second threshold, and the first reference threshold depends on whether the channel quality of the first candidate resource is obtained based on AI; when the channel quality of the first candidate resource is not obtained based on AI, the first reference threshold is the first threshold; when the channel quality of the first candidate resource is obtained based on AI, the first reference threshold is the second threshold.

[0736] As an embodiment, that the channel quality of the first candidate resource is not obtained based on AI includes: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is the RSRP obtained by measuring the first candidate resource.

[0737] As an embodiment, that the channel quality of the first candidate resource is obtained based on AI includes: the channel quality of the first candidate resource is obtained by prediction or inference.

[0738] As an embodiment, that the channel quality of the first candidate resource is obtained based on AI includes: the first node performs a first operation, the first operation is based on training or based on AI, and the channel quality of the first candidate resource depends on the output of the first operation.

[0739] As an embodiment, the AI (Artificial Intelligence) includes ML (Machine Learning).

[0740] As an embodiment, the first operation is associated with the first type of identifier.

[0741] As an embodiment, whether the channel quality of the first candidate resource is RSRP depends on whether the channel quality of the first candidate resource is obtained based on AI; only when the channel quality of the first candidate resource is not obtained based on AI, the channel quality of the first candidate resource is RSRP.

[0742] As an embodiment, it includes:

[0743] The first processor 2001, and the physical layer of the first node also indicates the channel quality of the first candidate resource to its higher layer.

[0744] As an embodiment, it includes:

[0745] The first processor 2001, and the physical layer of the first node also indicates first information to its higher layer;

[0746] Wherein, the first information is used to indicate whether the channel quality of the first candidate resource is obtained based on AI, or the first information is used to indicate whether the channel quality of the first candidate resource is the RSRP obtained by measuring the first candidate resource.

[0747] As an embodiment, it includes:

[0748] The first processor 2001, and the physical layer of the first node sends a beam failure event indication to its higher layer;

[0749] The first processor 2001 triggers beam failure recovery when the value of the target counter is equal to or greater than the target threshold; the target counter is used for counting the beam failure event indication.

[0750] As an embodiment, it includes:

[0751] The first processor 2001 sends a beam failure recovery request; receives a response to the beam failure recovery request;

[0752] Wherein, the beam failure recovery is triggered.

[0753] As an embodiment, it includes:

[0754] The first processor 2001 deploys the first operation.

[0755] As an embodiment, the first processor 2001 receives a signal in the first resource set.

[0756] As an embodiment, the first processor 2001 receives a reference signal in the first resource set, and the first resource set includes one or more RS resources.

[0757] As an embodiment, the first processor 2001 receives a signal in the first candidate resource set.

[0758] As an embodiment, the first processor 2001 receives a reference signal in the first candidate resource set, and the first candidate resource set includes one or more RS resources.

[0759] As an example, the first operation is training-based or AI-based.

[0760] As an example, the first operation requires deployment.

[0761] As an example, the first operation is obtained by loading.

[0762] Example 21

[0763] Embodiment 21 illustrates a structural block diagram of a processing device in a second node according to an embodiment of the present application; as shown in the appendix Figure 21 shown. In the appendix Figure 21 the processing device 2100 in the second node includes a second processor 2101.

[0764] As an example, the second node is a base station device.

[0765] As an example, the second node is a user equipment.

[0766] As an example, the second node is a relay node device.

[0767] As an example, the second processor 2101 includes at least one of {antenna 420, receiver / transmitter 418, receive processor 470, transmit processor 416, multi-antenna receive processor 472, multi-antenna transmit processor 471, controller / processor 475, memory 476} in Embodiment 4.

[0768] In Embodiment 21, the second processor 2101 sends a first set of higher layer messages, and the first set of higher layer messages is used to configure a first resource set and a first candidate resource set.

[0769] In Embodiment 21, a target receiver of the first higher layer message set evaluates a first radio link quality according to the first resource set; a physical layer of the target receiver of the first higher layer message set indicates a first candidate resource in the first candidate resource set to its higher layer; the first candidate resource set includes a plurality of candidate resources, and the first candidate resource is one of the plurality of candidate resources; the evaluated first radio link quality is worse than a second reference threshold; a channel quality of the first candidate resource is equal to or greater than a first reference threshold; the first reference threshold is one of a first threshold or a second threshold, and the first reference threshold depends on whether the channel quality of the first candidate resource is obtained based on AI; when the channel quality of the first candidate resource is not obtained based on AI, the first reference threshold is the first threshold; when the channel quality of the first candidate resource is obtained based on AI, the first reference threshold is the second threshold.

[0770] As an embodiment, the channel quality of the first candidate resource not being obtained based on AI includes: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is an RSRP obtained by measuring the first candidate resource.

[0771] As an embodiment, the channel quality of the first candidate resource being obtained based on AI includes: the channel quality of the first candidate resource is obtained by prediction or inference.

[0772] As an embodiment, the channel quality of the first candidate resource being obtained based on AI includes: the target receiver of the first higher layer message set performs a first operation, the first operation is based on training or based on AI, and the channel quality of the first candidate resource depends on an output of the first operation.

[0773] As an embodiment, the first operation is associated with the first type of identifier.

[0774] As an embodiment, whether the channel quality of the first candidate resource is an RSRP depends on whether the channel quality of the first candidate resource is obtained based on AI; only when the channel quality of the first candidate resource is not obtained based on AI, the channel quality of the first candidate resource is an RSRP.

[0775] As an embodiment, it includes:

[0776] The first processor 2001, and the physical layer of the target receiver of the first higher layer message set further indicates the channel quality of the first candidate resource to its higher layer.

[0777] As an embodiment, it includes:

[0778] For the first processor 2001, the physical layer of the target receiver of the first higher layer message set also indicates first information to its higher layer;

[0779] Wherein, the first information is used to indicate whether the channel quality of the first candidate resource is obtained based on AI, or the first information is used to indicate whether the channel quality of the first candidate resource is the RSRP obtained by measuring the first candidate resource.

[0780] As an embodiment, it includes:

[0781] For the first processor 2001, the physical layer of the target receiver of the first higher layer message set sends a beam failure event indication to its higher layer;

[0782] For the first processor 2001, when the value of the target counter is equal to or greater than the target threshold, beam failure recovery is triggered; the target counter is used for counting the beam failure event indication.

[0783] As an embodiment, it includes:

[0784] The second processor 2101 receives a beam failure recovery request; sends a response to the beam failure recovery request;

[0785] Wherein, the beam failure recovery is triggered.

[0786] As an embodiment, the second processor 2101 sends a signal in the first resource set.

[0787] As an embodiment, the second processor 2101 sends a reference signal in the first resource set, and the first resource set includes one or more RS resources.

[0788] As an embodiment, the second processor 2101 sends a signal in the first candidate resource set.

[0789] As an embodiment, the second processor 2101 sends a reference signal in the first candidate resource set, and the first candidate resource set includes one or more RS resources.

[0790] As an embodiment, the first operation is training-based or AI-based.

[0791] As an embodiment, the first operation requires deployment.

[0792] As an embodiment, the first operation is obtained by loading.

[0793] Those of ordinary skill in the art can understand that all or part of the steps in the above methods can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a hard disk, or an optical disc, etc. Optionally, all or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module unit in the above embodiments can be implemented in a hardware form or in the form of a software functional module. This application is not limited to any specific form of the combination of software and hardware. The user equipment, terminal, and UE in this application include, but are not limited to, unmanned aerial vehicles, communication modules on unmanned aerial vehicles, remote control aircraft, aircraft, small aircraft, mobile phones, tablet computers, notebooks, vehicle-mounted communication devices, transportation vehicles, vehicles, RSUs, wireless sensors, network cards, Internet of Things terminals, RFID terminals, NB-IOT terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, network cards, vehicle-mounted communication devices, low-cost mobile phones, low-cost tablet computers, and other wireless communication devices. The base stations or system devices in this application include, but are not limited to, macrocell base stations, microcell base stations, small cell base stations, home base stations, relay base stations, eNBs, gNBs, TRPs (Transmitter Receiver Points), GNSSs, relay satellites, satellite base stations, aerial base stations, RSUs (Road Side Units), unmanned aerial vehicles, test devices, such as transceiver devices or signaling testers that simulate some functions of base stations, and other wireless communication devices.

[0794] Those skilled in the art should understand that the present invention can be implemented in other specified forms without departing from its core or basic characteristics. Therefore, the currently disclosed embodiments should be regarded as descriptive rather than restrictive in any case. The scope of the invention is determined by the appended claims rather than the previous description, and all modifications within the equivalent meaning and scope are considered to be included therein.

Claims

1. A method in a first node for wireless communication, characterized in that: include: receiving a first set of higher-layer messages, the first set of higher-layer messages being used to configure a first set of resources and a first set of candidate resources; evaluating a first radio link quality according to the first resource set; The physical layer of the first node indicates a first candidate resource in the first set of candidate resources to a higher layer thereof; The first candidate resource set includes multiple candidate resources, the first candidate resource is one of the multiple candidate resources; the evaluated first wireless link quality is worse than a second reference threshold; the channel quality of the first candidate resource is equal to or greater than the first reference threshold; The first reference threshold is one of the first threshold or the second threshold, and the first reference threshold depends on whether the channel quality of the first candidate resource is obtained based on AI; when the channel quality of the first candidate resource is not obtained based on AI, the first reference threshold is the first threshold; when the channel quality of the first candidate resource is obtained based on AI, the first reference threshold is the second threshold.

2. The method in the first node according to claim 1, characterized in that: The channel quality of the first candidate resource is not obtained based on AI, including: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is RSRP obtained by measuring the first candidate resource.

3. The method in the first node according to claim 1 or 2, characterized in that: The channel quality of the first candidate resource is obtained based on AI, including: the channel quality of the first candidate resource is obtained by prediction or inference.

4. The method in the first node according to any one of claims 1 to 3, characterized in that: The channel quality of the first candidate resource is obtained based on AI, including: the first node performs a first operation, the first operation is based on training or AI, and the channel quality of the first candidate resource depends on an output of the first operation.

5. The method in the first node according to claim 4, characterized in that: The first operation is associated with the first type identification.

6. The method in the first node according to any one of claims 1 to 5, characterized in that: Whether the channel quality of the first candidate resource is RSRP depends on whether the channel quality of the first candidate resource is obtained based on AI; only when the channel quality of the first candidate resource is not obtained based on AI, the channel quality of the first candidate resource is RSRP.

7. The method in the first node according to any one of claims 1 to 6, characterized in that: include: The physical layer of the first node also indicates the channel quality of the first candidate resource to its higher layer.

8. The method in the first node according to any one of claims 1 to 7, characterized in that: include: The physical layer of the first node also indicates first information to its higher layer; The first information is used to indicate whether the channel quality of the first candidate resource is obtained based on AI, or the first information is used to indicate whether the channel quality of the first candidate resource is obtained by measuring the RSRP of the first candidate resource.

9. The method in the first node according to any one of claims 1 to 8, characterized in that: include: The physical layer of the first node sends a beam failure event indication to its higher layer; When the value of the target counter is equal to or greater than a target threshold, beam failure recovery is triggered; the target counter is used to count the beam failure event indications.

10. A terminal, characterized in that: The terminal includes: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions. The one or more processors call the computer instructions to enable the terminal to execute the method according to any one of claims 1 to 9.

11. A method in a second node for wireless communication, characterized in that: include: Sending a first higher layer message set, the first higher layer message set being used to configure a first resource set and a first candidate resource set; The target receiver of the first higher-layer message set evaluates the first radio link quality according to the first resource set; the physical layer of the target receiver of the first higher-layer message set indicates the first candidate resource in the first candidate resource set to its higher layer; The first candidate resource set includes multiple candidate resources, and the first candidate resource is one of the multiple candidate resources; the evaluated first wireless link quality is worse than a second reference threshold; the channel quality of the first candidate resource is equal to or greater than the first reference threshold; the first reference threshold is one of the first threshold or the second threshold, and the first reference threshold depends on whether the channel quality of the first candidate resource is obtained based on AI; when the channel quality of the first candidate resource is not obtained based on AI, the first reference threshold is the first threshold; when the channel quality of the first candidate resource is obtained based on AI, the first reference threshold is the second threshold.

12. The method in the second node according to claim 11, characterized in that: The channel quality of the first candidate resource is not obtained based on AI, including: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is RSRP obtained by measuring the first candidate resource.

13. The method in the second node according to claim 11 or 12, characterized in that: The channel quality of the first candidate resource is obtained based on AI, including: the channel quality of the first candidate resource is obtained by prediction or inference.

14. The method in the second node according to any one of claims 11 to 13, characterized in that: The channel quality of the first candidate resource is obtained based on AI and includes: the target receiver of the first higher-layer message set performs a first operation, the first operation is based on training or based on AI, and the channel quality of the first candidate resource depends on the output of the first operation.

15. The method in the second node according to claim 14, characterized in that: The first operation is associated with the first type identification.

16. The method in the second node according to any one of claims 11 to 15, characterized in that: Whether the channel quality of the first candidate resource is RSRP depends on whether the channel quality of the first candidate resource is obtained based on AI; only when the channel quality of the first candidate resource is not obtained based on AI, the channel quality of the first candidate resource is RSRP.

17. The method in the second node according to any one of claims 11 to 16, characterized in that: include: The physical layer of the target recipient of the first higher layer message set also indicates the channel quality of the first candidate resource to its higher layer.

18. The method in the second node according to any one of claims 11 to 17, characterized in that: include: The physical layer of the target recipient of the first higher layer message set also indicates first information to its higher layer; The first information is used to indicate whether the channel quality of the first candidate resource is obtained based on AI, or the first information is used to indicate whether the channel quality of the first candidate resource is obtained by measuring the RSRP of the first candidate resource.

19. The method in the second node according to any one of claims 11 to 18, characterized in that: include: The physical layer of the target recipient of the first higher layer message set sends a beam failure event indication to its higher layer; When the value of the target counter is equal to or greater than a target threshold, beam failure recovery is triggered; the target counter is used to count the beam failure event indications.

20. A base station, characterized in that: The base station includes: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the base station to perform the method according to any one of claims 11 to 19.

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