Method used in node for wireless communication and apparatus

Abstract

The present application discloses a method used in a node for wireless communication and an apparatus. A first node receives a first higher layer message set, the first higher layer message set being used for configuring a first resource set and a first candidate resource set, and evaluates first radio link quality on the basis of the first resource set; and a physical layer of the first node indicates to a higher layer thereof a first candidate resource in the first candidate resource set. The evaluated first radio link quality is less than a second reference threshold; channel quality of the first candidate resource is equal to or greater than a first reference threshold; the first reference threshold depends on whether the channel quality of the first candidate resource is obtained on the basis of AI; when the channel quality of the first candidate resource is not obtained on the basis of the AI, the first reference threshold is a first threshold; and when the channel quality of the first candidate resource is obtained on the basis of the AI, the first reference threshold is a second threshold.

Description

A method and apparatus for use in a node for wireless communication

[0001] This application claims priority to Chinese Patent Application No. 202410931304.3, filed on July 11, 2024, entitled "A Method and Apparatus Used in a Node for Wireless Communication", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to transmission methods and apparatus in wireless communication systems, and more particularly to transmission schemes and apparatus in wireless communication systems. Background Technology

[0003] Multi-antenna technology is a key technology in 3GPP (3rd Generation Partner Project) LTE (Long-term Evolution) and NR (New Radio) systems. It gains additional spatial degrees of freedom by configuring multiple antennas at communication nodes, such as base stations or UEs (User Equipment). Multiple antennas, through beamforming, form beams pointing in a specific direction to improve communication quality. The degrees of freedom provided by multi-antenna systems can be used to improve transmission reliability and / or throughput. Since the beams formed by multiple antennas are relatively narrow, the communicating parties need to align the beams to provide communication quality. Starting with NR (New Radio) Release 15, 3GPP introduced a beam failure detection and recovery mechanism to quickly detect beam loss of synchronization and restore beam alignment, reducing the impact of beam loss of synchronization on system performance.

[0004] With the adoption of new technologies, the increase in the number of antennas, the diversification of application scenarios, and the increasing demands on system performance, traditional measurement and beam failure recovery methods incur significant redundancy overhead. Therefore, in NR R (release) 18, research on AI (Artificial Intelligence) / ML (Machine Learning) technologies was initiated to explore their impact on system performance and design. Compared to traditional methods, AI / ML offers advantages such as training-based and deployment-required features. Summary of the Invention

[0005] The applicant discovered through research that when AI / ML functionality is introduced, existing measurement mechanisms and candidate resource selection schemes may be unable to meet the needs of AI / ML. To address this issue, this application discloses a solution. It should be noted that while many embodiments of this application are specifically for AI / ML, this application is also applicable to other solutions, such as traditional candidate resource selection schemes. Furthermore, adopting a unified solution across different scenarios (including but not limited to AI / ML-based solutions and traditional candidate resource selection schemes) helps reduce hardware complexity and cost. Where there is no conflict, the embodiments and features in the embodiments of the first node of this application can be applied to the second node, and vice versa. Where there is no conflict, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0006] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS38 series.

[0007] As an example, the interpretation of the terms in this application is based on the definitions in the 3GPP specification protocol TS28 series.

[0008] This application discloses a method used in a first node of wireless communication, characterized by comprising:

[0009] Receive a first higher-level message set, which is used to configure a first resource set and a first candidate resource set; evaluate the quality of a first radio link based on the first resource set;

[0010] The physical layer of the first node indicates the first candidate resource in the first candidate resource set to its higher layers;

[0011] Wherein, 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 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.

[0012] As an example, the problem this application aims to solve includes: how to obtain channel information of candidate resources based on AI.

[0013] As an example, the problem this application aims to solve includes: how to support the selection of AI-based candidate resources.

[0014] As an example, in the above method, the reference threshold is adjusted based on whether the channel quality of the candidate resource is obtained based on AI, and a suitable candidate resource is selected, thereby improving the overall performance of the system.

[0015] As an example, the advantages of the above method include: better adaptability to various application scenarios and terminals, and improved flexibility and adaptability.

[0016] According to one aspect of this application, the first node is a user equipment.

[0017] According to one aspect of this application, the first node is a relay node.

[0018] According to one aspect of this application, the channel quality of the first candidate resource is not obtained based on AI, comprising: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is obtained by measuring the RSRP of the first candidate resource.

[0019] As an example, the advantages of the above method include good backward compatibility.

[0020] According to one aspect of this application, the channel quality of the first candidate resource is obtained based on AI, including: the channel quality of the first candidate resource is obtained through prediction or inference.

[0021] As an example, the advantages of the above method include: reducing the measurement of channel quality and reducing the overhead of RS resources.

[0022] As an example, the advantages of the above method include: by supporting the acquisition of channel quality of candidate resources based on AI, more and more accurate channel quality information is obtained, thereby improving the performance of the system.

[0023] According to one aspect of this application, the channel quality of the first candidate resource is obtained based on AI, comprising: the first node performing a first operation, the first operation being based on training or AI, and the channel quality of the first candidate resource depending on the output of the first operation.

[0024] As an example, the AI ​​(Artificial Intelligence) includes ML (Machine Learning).

[0025] As an example, the advantages of the above method include: better adaptability to various application scenarios and terminals, and improved flexibility and adaptability.

[0026] According to one aspect of this application, the first operation is associated with the first type of identifier.

[0027] As an example, the advantages of the above method include: determining the first operation through the first type of identifier simplifies the design.

[0028] According to one aspect of this application, the channel quality of the first candidate resource is RSRP, depending on whether the channel quality of the first candidate resource is obtained based on AI; the channel quality of the first candidate resource is RSRP only when the channel quality of the first candidate resource is not obtained based on AI.

[0029] As an example, the advantages of the above method include: minimal modification to existing systems and good backward compatibility.

[0030] According to one aspect of this application, it is characterized by comprising:

[0031] The physical layer of the first node also indicates the channel quality of the first candidate resource to its higher layers.

[0032] As an example, the advantages of the above method include: by indicating the channel quality of candidate resources to higher layers, it helps the first node select more suitable candidate resources.

[0033] According to one aspect of this application, it is characterized by comprising:

[0034] The physical layer of the first node also indicates the first information to its higher layers;

[0035] 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 obtained by measuring the RSRP of the first candidate resource.

[0036] As an example, the advantages of the above method include: determining how to obtain the channel quality of the first candidate resource through the first information helps the first node select a more suitable candidate resource.

[0037] According to one aspect of this application, it is characterized by comprising:

[0038] The physical layer of the first node sends a beam failure event indication to its higher layers;

[0039] 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 indicated by the beam failure event.

[0040] As an example, 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 guaranteed.

[0041] According to one aspect of this application, it is characterized by comprising:

[0042] Send a beam failure recovery request; receive a response to the beam failure recovery request;

[0043] Among them, the beam failure recovery is triggered.

[0044] As an example, the advantages of the above method include good backward compatibility.

[0045] As one example, the first node is a terminal.

[0046] As one example, the user equipment is a terminal.

[0047] This application discloses a method used in a second node for wireless communication, characterized by comprising:

[0048] Send a first higher-level message set, which is used to configure a first resource set and a first candidate resource set;

[0049] Wherein, the target receiver of the first higher-layer message set evaluates the quality of the first radio link based on 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;

[0050] 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 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.

[0051] According to one aspect of this application, the second node is a base station.

[0052] According to one aspect of this application, the second node is a user equipment.

[0053] According to one aspect of this application, the second node is a relay node.

[0054] According to one aspect of this application, the channel quality of the first candidate resource is not obtained based on AI, comprising: the first candidate resource is an RS resource, and the channel quality of the first candidate resource is obtained by measuring the RSRP of the first candidate resource.

[0055] According to one aspect of this application, the channel quality of the first candidate resource is obtained based on AI, including: the channel quality of the first candidate resource is obtained through prediction or inference.

[0056] According to one aspect of this application, the channel quality of the first candidate resource is obtained based on AI, including: the target receiver of the first higher-level message set performing a first operation, the first operation being based on training or AI, and the channel quality of the first candidate resource depending on the output of the first operation.

[0057] According to one aspect of this application, the first operation is associated with the first type of identifier.

[0058] According to one aspect of this application, the channel quality of the first candidate resource is RSRP, depending on whether the channel quality of the first candidate resource is obtained based on AI; the channel quality of the first candidate resource is RSRP only when the channel quality of the first candidate resource is not obtained based on AI.

[0059] According to one aspect of this application, it is characterized by comprising:

[0060] The physical layer of the target receiver of the first higher-level message set also indicates the channel quality of the first candidate resource to its higher layers.

[0061] According to one aspect of this application, it is characterized by comprising:

[0062] The physical layer of the target receiver of the first higher-level message set also indicates the first information to its higher layer;

[0063] 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 obtained by measuring the RSRP of the first candidate resource.

[0064] According to one aspect of this application, it is characterized by comprising:

[0065] The physical layer of the target receiver in the first higher-level message set sends a beam failure event indication to its higher layer;

[0066] 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 indicated by the beam failure event.

[0067] According to one aspect of this application, it is characterized by comprising:

[0068] Receive a beam failure recovery request; send a response to the beam failure recovery request;

[0069] Among them, the beam failure recovery is triggered.

[0070] This application discloses a terminal, characterized in that the terminal includes: one or more processors and a memory;

[0071] The memory is coupled to the one or more processors and is used to store computer program code, which includes computer instructions. The one or more processors invoke the computer instructions to cause the terminal to execute the method in the first node.

[0072] This application discloses a base station, characterized in that the base station includes: one or more processors and a memory;

[0073] The memory is coupled to the one or more processors and is used to store computer program code, which includes computer instructions. The one or more processors invoke the computer instructions to cause the base station to perform the method in the second node.

[0074] This application discloses a first node used for wireless communication, characterized in that it comprises:

[0075] A first processor receives a first higher-level message set, the first higher-level message set being used to configure a first resource set and a first candidate resource set; and evaluates the quality of a first radio link based on the first resource set.

[0076] The physical layer of the first node indicates the first candidate resource in the first candidate resource set to its higher layers;

[0077] Wherein, 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 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.

[0078] This application discloses a second node used for wireless communication, characterized in that it comprises:

[0079] The second processor sends a first higher-level message set, which is used to configure a first resource set and a first candidate resource set.

[0080] Wherein, the target receiver of the first higher-layer message set evaluates the quality of the first radio link based on 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;

[0081] 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 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.

[0082] As an example, compared with conventional solutions, this application has the following advantages:

[0083] Flexible selection scheme for candidate resources;

[0084] Enhanced overall system performance;

[0085] Lower air interface overhead;

[0086] More flexible and diverse input information;

[0087] Better flexibility and adaptability;

[0088] Enhanced reliability and robustness. Attached Figure Description

[0089] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0090] Figure 1 illustrates a flowchart of a first higher-level message set and a first candidate resource according to an embodiment of this application;

[0091] Figure 2 shows a schematic diagram of a network architecture according to an embodiment of this application;

[0092] Figure 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for the user plane and control plane according to an embodiment of this application;

[0093] Figure 4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of this application;

[0094] Figure 5 illustrates a flowchart of the transmission between a first node and a second node according to an embodiment of this application;

[0095] Figure 6 illustrates a schematic diagram of the channel quality of a first candidate resource according to an embodiment of this application, obtained by measuring the RSRP of the first candidate resource.

[0096] Figure 7 illustrates a schematic diagram showing that the channel quality of a first candidate resource according to an embodiment of this application is predicted or inferred.

[0097] Figure 8 illustrates a schematic diagram of the channel quality of a first candidate resource depending on the output of a first operation according to an embodiment of this application;

[0098] Figure 9 illustrates a schematic diagram of a first operation being associated with a first type of identifier according to an embodiment of this application;

[0099] Figure 10 illustrates a schematic diagram of a first operation based on training or AI according to an embodiment of this application;

[0100] Figure 11 shows a schematic diagram of the channel quality of a first candidate resource according to an embodiment of this application;

[0101] Figure 12 illustrates a schematic diagram in which the physical layer of a first node according to an embodiment of the present application further indicates the channel quality of a first candidate resource to its higher layers;

[0102] Figure 13 shows a schematic diagram of first information according to an embodiment of this application;

[0103] Figure 14 illustrates a schematic diagram of beam failure event indication and beam failure recovery according to an embodiment of this application;

[0104] Figure 15 illustrates a schematic diagram of a beam failure recovery request according to an embodiment of this application;

[0105] Figure 16 illustrates a schematic diagram of the deployment of AI / ML functions in a RAN (Radio Access Network) domain according to an embodiment of this application;

[0106] Figure 17 shows a schematic diagram of the AI / ML function deployment of a UE according to an embodiment of this application;

[0107] Figure 18 shows a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application;

[0108] Figure 19 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of this application;

[0109] Figure 20 shows a structural block diagram of a processing apparatus for a first node according to an embodiment of the present application;

[0110] Figure 21 shows a structural block diagram of a processing apparatus for a second node according to an embodiment of this application; Detailed Implementation

[0111] The technical solutions of this application will be further described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Considering performance, flexibility, complexity, overhead, and compatibility, those skilled in the art are motivated to flexibly combine the embodiments in different drawings without conflict, such as, but not limited to, the embodiments in Figure 1 and the embodiments in Figures 5-21, the embodiments in Figure 5 and the embodiments in Figures 6-21, etc.

[0112] Example 1

[0113] Example 1 illustrates a flowchart of a first higher-level message set and a first candidate resource according to an embodiment of this application, as shown in Figure 1. In Figure 1, each block represents a step. In particular, the order of the steps in the blocks does not represent a specific temporal sequence between the steps.

[0114] In Embodiment 1, the first node receives a first higher-layer message set in step 101; evaluates the quality of a first radio link based on the first resource set in step 102; and in step 103, the physical layer of the first node indicates a first candidate resource in the first candidate resource set to its higher layers. The first higher-layer message set is used to configure the first resource set and the first candidate resource set. The first candidate resource set includes multiple candidate resources, and the first candidate resource is one of the multiple candidate resources. The evaluated quality of the first radio link 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 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.

[0115] As one embodiment, the first higher-level message set includes at least one higher-level message.

[0116] As one example, the first higher-level message set includes RRC messages.

[0117] As an example, the first higher-level message set includes at least one RRC message from either RRC messages or MAC CE messages.

[0118] As an example, the first higher-level message set includes RRC messages and MAC CE messages.

[0119] As one embodiment, the first higher-level message set includes some or all of the fields in one or more RRC IEs.

[0120] As one embodiment, the first higher-level message set includes some or all of the fields in an RRC IE.

[0121] As one embodiment, the first higher-level message set includes a portion of the fields in RRC IE RadioLinkMonitoringConfig.

[0122] As an example, the first higher-level message set includes a field in the RRC IE whose name includes failureDetectionResourcesToAddModList.

[0123] As an example, the first higher-level message set includes the failureDetectionResourcesToAddModList field in RRC IE RadioLinkMonitoringConfig.

[0124] As an example, the first higher-level message set includes the failureDetectionSet1 field and the failureDetectionSet2 field in RRC IE RadioLinkMonitoringConfig.

[0125] As an example, the first higher-level message set includes a field in the RRC IE whose name includes failureDetectionSet1 and a field whose name includes failureDetectionSet2.

[0126] As an example, the first higher-level message set includes at least one field in the RRC IE whose name includes failureDetectionSet.

[0127] As an example, the name of the RRC message in the first higher-level message set includes failureDetectionResources.

[0128] As an example, the name of the RRC message in the first higher-level message set includes failureDetectionSet.

[0129] As an example, the RRC message in the first higher-level message set includes the failureDetectionSet1 field and the failureDetectionSet2 field in RRC IE RadioLinkMonitoringConfig, and the MAC CE message in the first higher-level message set includes BFD-RS Indication MAC CE.

[0130] As an example, the MAC CE message in the first higher-level message set is a BFD-RS Indication MAC CE.

[0131] As an example, the name of the MAC CE message in the first higher-level message set includes BFD-RS Indication MAC CE.

[0132] As an example, the name of the MAC CE message in the first higher-level message set includes BFD.

[0133] As an example, the first higher-level message set includes the failureDetectionSet1 and failureDetectionSet2 fields in the RRC IE RadioLinkMonitoringConfig, as well as the BFD-RS Indication MAC CE.

[0134] Typically, when the number of RS resources indicated by the failureDetectionSet1 or failureDetectionSet2 field in the RRC IE RadioLinkMonitoringConfig is greater than 2, the BFD-RS Indication MAC CE activates one or two RS resources from failureDetectionSet1 or failureDetectionSet2.

[0135] As an example, the specific definitions of the RRC IE RadioLinkMonitoringConfig, failureDetectionResourcesToAddModList field, failureDetectionSet1 field, and failureDetectionSet2 field can be found in section 6.3.2 of 3GPP TS38.331.

[0136] As an example, the specific definition of BFD-RS Indication MAC CE can be found in section 5.18.25 of 3GPP TS38.321.

[0137] As an example, the specific definition of IE RadioLinkMonitoringConfig can be found in section 6.3.2 of 3GPP TS38.331.

[0138] As one embodiment, the first higher-level message set includes a portion of the domains in RRC IE BeamFailureRecoveryConfig.

[0139] As an example, the first higher-level message set includes the candidateBeamRSList field in RRC IE BeamFailureRecoveryConfig.

[0140] As one example, the first higher-level message set includes the candidateBeamRSListExt field in RRC IE BeamFailureRecoveryConfig.

[0141] As an example, the first higher-level message set includes the candidateBeamRSSCellList field in RRC IE BeamFailureRecoveryConfig.

[0142] As an example, the first higher-level message set includes a field in the RRC IE whose name includes candidateBeamRSList.

[0143] As an example, the first higher-level message set includes a field in the RRC IE whose name includes candidateBeam.

[0144] As an example, the first higher-level message set includes one of the higher-level parameters candidateBeamRSList, candidateBeamRSListExt, or candidateBeamRSSCellList.

[0145] As an example, the specific definitions of candidateBeamRSList, candidateBeamRSListExt, and candidateBeamRSSCellList can be found in Chapter 6 of 3GPP TS38.213.

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

[0147] As an example, the first resource set consists of at least one RS resource.

[0148] As an example, the first resource set is used for beam failure detection (BFD).

[0149] As an example, the first resource set is used for failure monitoring.

[0150] As one embodiment, the first resource set is

[0151] As one embodiment, the first resource set is

[0152] As one embodiment, the first resource set is

[0153] As one embodiment, the first resource set is or At least one of them.

[0154] As an example, For a specific definition, please refer to Chapter 6 of 3GPP TS38.213.

[0155] 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) resources or SS / PBCH (Synchronization Signal / Physical Broadcast CHannel) block resources.

[0156] 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.

[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 CSI-RS resource.

[0158] 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.

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

[0160] As an example, the first higher-level message set is used to configure the index of each RS resource in the first candidate resource set.

[0161] As an example, the index of an RS resource is used to identify the RS resource.

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

[0163] As an example, the index of an RS resource includes the configuration index of the RS resource.

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

[0165] As an example, an index of an SS / PBCH block resource is used to identify the configuration of the SS / PBCH block resource.

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

[0167] As an example, an index of a periodic CSI-RS resource includes a configuration index of the periodic CSI-RS resource.

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

[0169] As an example, an index for a CSI-RS resource is a csi-RS-Index.

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

[0171] As an example, the index of an SS / PBCH block resource is the ssb-Index.

[0172] As an example, each RS resource in the first resource set depends on the configuration of the first higher-level message set.

[0173] As one embodiment, the first higher-level message set includes an index of each RS resource included in the first resource set.

[0174] As one embodiment, the first higher-level message set includes RRC messages and MAC CE messages; the RRC messages in the first higher-level message set are used to configure a target RS resource pool for the first BWP, and the MAC CE messages in the first higher-level message set are used to activate the first resource set from the target RS resource pool.

[0175] As an example, the first higher-level message set includes RRC messages and MAC CE messages; the first resource set belongs to the target RS resource pool, the RRC messages in the first higher-level 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-level message set activate the first resource set from the target RS resource pool.

[0176] As an example, the first higher-level message set includes RRC messages and MAC CE messages; the first resource set belongs to the target RS resource pool, the RRC messages in the first higher-level 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-level message set activate the first resource set from the target RS resource pool.

[0177] As an example, the first higher-level message set is used to configure a first CORESET pool, which 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.

[0178] As a sub-implementation of the above embodiments, the first higher-level message set includes a portion of the domains in IE PDCCH-Config.

[0179] As a sub-implementation of the above embodiments, the first higher-level message set includes the controlResourceSetToAddModList field in IE PDCCH-Config.

[0180] As a sub-implementation of the above embodiments, the first higher-level message set includes a field in IE PDCCH-Config whose name includes controlResourceSetToAddModList.

[0181] As a sub-implementation of the above embodiments, the first higher-level message set includes a field in IE PDCCH-Config whose name includes controlResourceSet.

[0182] As an example, the sentence "the first resource set depends on at least one TCI state of at least one CORESET in the first CORESET pool" means that the first resource set is determined by the RS index of 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 sentence "the first resource set depends on at least one TCI state of at least one CORESET in the first CORESET pool" means that the first resource set is determined by an RS index configured with QCL type 'typeD' in 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 sentence "the first resource set depends on at least one TCI state of at least one CORESET in the first CORESET pool" means that 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.

[0185] As an example, the sentence "the first resource set depends on at least one TCI state of at least one CORESET in the first CORESET pool" means that the first resource set includes at least one RS resource in the first CORESET pool that is configured with QCL type 'typeD', indicating at least one TCI state of at least one CORESET in the first CORESET pool.

[0186] As an example, the evaluation of the first wireless link quality based on the first resource set is used for beam failure detection.

[0187] As an example, the specific procedures for beam failure monitoring can be found in Chapter 6 of 3GPP TS38.213.

[0188] As an example, the specific procedures for beam failure monitoring can be found in section 5.17 of 3GPP TS38.321.

[0189] As one embodiment, the step of evaluating the quality of the first wireless link based on the first resource set includes: determining whether the quality of the first wireless link is worse than a second reference threshold.

[0190] As one embodiment, evaluating the quality of the first wireless link based on the first resource set includes: evaluating the quality of the first wireless link based on measurements of the first resource set.

[0191] As an example, the quality of the first wireless link is RSRP.

[0192] As an example, the quality of the first wireless link is L1-RSRP.

[0193] As an example, the quality of the first wireless link is SINR.

[0194] As an example, the quality of the first wireless link is L1-SINR.

[0195] As an example, the quality of the first wireless link is BLER.

[0196] As an example, the quality of the first wireless link is hypothetical BLER.

[0197] As one embodiment, the first wireless link quality is one of RSRP, L1-RSRP, SINR, or L1-SINR; the evaluation of the first wireless link quality being worse than the second reference threshold includes: the evaluation of the first wireless link quality being less than the second reference threshold.

[0198] As a sub-implementation of the above embodiments, the unit of the second reference threshold is dBm or dB.

[0199] As one embodiment, the first wireless link quality is BLER; the evaluation of the first wireless link quality being worse than the second reference threshold includes: the evaluation of the first wireless link quality being greater than the second reference threshold.

[0200] As a sub-implementation of the above embodiments, the second reference threshold is the BLER threshold.

[0201] As an example, the first wireless link quality is a hypothetical BLER; the evaluation of the first wireless link quality being worse than the second reference threshold includes: the evaluation of the first wireless link quality being greater than the second reference threshold.

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

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

[0205] As an example, the physical layer of the first node indicates the channel quality of the candidate resources in the first candidate resource set to its higher layers.

[0206] As a sub-example of the above embodiments, the channel quality is RSRP, SINR, BLER, or hypothetical BLER.

[0207] As an example, the physical layer of the first node indicates to its higher layers the number of candidate resources in the first candidate resource set that satisfy a first condition, the first condition including channel quality equal to or greater than a first reference threshold.

[0208] As an example, the second reference threshold is a real number.

[0209] As an example, the second reference threshold is a non-negative real number.

[0210] As an example, the second reference threshold is a non-negative real number that is no greater than 1.

[0211] As an example, the second reference threshold is Qout_LR.

[0212] As an example, the second reference threshold is one of Qout_LR, Qout_LR_SSB, or Qout_LR_CSI-RS.

[0213] As an example, the definitions of Qout_LR, Qout_LR_SSB and Qout_LR_CSI-RS can be found in 3GPP TS38.133.

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

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

[0216] As one embodiment, the first candidate resource set is

[0217] As one embodiment, the first candidate resource set is or At least one of them.

[0218] As an example, For a specific definition, please refer to Chapter 6 of 3GPP TS38.213.

[0219] As an example, the first candidate resource set includes a plurality of RS resources, and any one of the plurality of candidate resources is an RS resource.

[0220] As an example, the RS resource described in this application is a CSI-RS resource.

[0221] As an example, the RS resource in this application is a CSI-RS resource or an SS / PBCH block resource.

[0222] As an example, the first candidate resource set consists of multiple RS resources, and any one of the multiple candidate resources is an RS resource.

[0223] As one embodiment, 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.

[0224] As one embodiment, the first candidate resource set includes at least one of at least an RS resource, at least one training dataset, at least one air interface resource, or at least one beam; any candidate resource among the plurality of candidate resources is at least one of an RS resource, a training dataset, an air interface resource, or a beam.

[0225] As an example, the air interface resources include at least one of time domain resources, frequency domain resources, code domain resources, or spatial domain resources.

[0226] As an example, when the channel quality of any candidate resource 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 RS resource, at least one training dataset, at least one air interface resource, or at least one beam.

[0227] As an example, the channel quality of the first candidate resource is RSRP.

[0228] As an example, the RSRP includes L1-RSRP.

[0229] As an example, the channel quality of the first candidate resource is SINR.

[0230] As an example, the SINR includes L1-SINR.

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

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

[0233] As an example, the channel quality of the first candidate resource is RSRP, SINR, BLER, or hypothetical BLER.

[0234] 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.

[0235] As an example, the first reference threshold is a real number.

[0236] As an example, the first reference threshold is Q. in,LR .

[0237] As an example, Q in,LR For the definition, please refer to Chapter 6 of 3GPP TS38.213.

[0238] As an example, the first threshold is a real number.

[0239] As an example, the first threshold is configurable.

[0240] As an example, the first threshold is indicated by a higher-level parameter.

[0241] As an example, the first threshold is indicated by a higher-level parameter rsrp-ThresholdSSB or rsrp-ThresholdBFR.

[0242] As an example, the specific definitions of rsrp-ThresholdSSB and rsrp-ThresholdBFR can be found in Chapter 6 of 3GPP TS38.213.

[0243] As an example, the second threshold is a real number.

[0244] As one example, the second threshold is configurable.

[0245] As one example, the second threshold is indicated by a higher-level parameter.

[0246] As one example, the second threshold and the first threshold are linearly related.

[0247] As an example, the first threshold and the second threshold are indicated by different higher-level parameters.

[0248] As an example, the first threshold and the second threshold are configured separately.

[0249] As an example, the second threshold is equal to the sum of the first threshold and the first offset.

[0250] As a sub-implementation of the above embodiments, the first offset is configured.

[0251] As a sub-implementation of the above embodiment, the first offset is reported by the first node.

[0252] As a sub-implementation of the above embodiments, the first offset is predefined.

[0253] As a sub-implementation of the above embodiment, the first offset is a real number.

[0254] As one example, the first threshold and the second threshold are different.

[0255] As an example, the second threshold is less than the first threshold.

[0256] As an example, in the above method, the threshold used by the AI-based approach is lower than the threshold used by the non-AI-based approach.

[0257] As an example, the advantages of the above method include: increasing the probability of selecting a suitable resource.

[0258] As an example, the advantages of the above method include: it is particularly suitable for situations where the channel quality obtained based on AI is lower than the actual channel quality.

[0259] As one example, the second threshold is greater than the first threshold.

[0260] In the above methods, the threshold used by the AI-based approach is greater than the threshold used by the non-AI-based approach.

[0261] As an example, the advantages of the above method include reducing the probability of selecting inappropriate resources due to errors in AI prediction or inference.

[0262] As an example, the advantages of the above method include: it is particularly suitable for situations where the channel quality obtained based on AI is higher than the actual channel quality.

[0263] As an example, higher-level parameters are used to indicate whether the channel quality of the first candidate resource is obtained based on AI.

[0264] As an example, higher-level parameters are used to indicate whether the channel quality of the first candidate resource is allowed to be obtained based on AI.

[0265] As an example, higher-level 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.

[0266] As an example, the first higher-level message set is used to indicate whether the channel quality of the first candidate resource is obtained based on AI.

[0267] As an example, the first higher-level message set is used to indicate whether the channel quality of the first candidate resource is allowed to be obtained based on AI.

[0268] As an example, the first higher-level message set 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.

[0269] As an example, whether the channel quality of the first candidate resource is obtained based on AI depends on whether the first node receives a first higher-level parameter; the channel quality of the first candidate resource is obtained based on AI only when the first node receives the first higher-level parameter.

[0270] As one embodiment, 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 a first higher-level parameter; the first node supports obtaining the channel quality of at least one candidate resource in the first candidate resource set based on AI only when the first node receives the first higher-level parameter.

[0271] 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.

[0272] As one embodiment, 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.

[0273] As one example, the term "not being measured" includes: not being expected to be measured.

[0274] 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.

[0275] As one example, resources other than RS resources include beams.

[0276] As an example, the resources other than the RS resources include at least one of training datasets, air interface resources, or beams.

[0277] As an example, the first set of candidate resources is used for candidate beam detection.

[0278] As one embodiment, the first candidate resource set is used to select a new candidate beam from the first candidate resource set during beam failure recovery.

[0279] As an example, the first set of candidate resources is used for candidate beam monitoring; 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.

[0280] As an example, the evaluation period is TEvaluate_CBD_SSB or TEvaluate_CBD_CSI-RS.

[0281] As an example, one of the candidate resources in the first candidate resource set is an SS / PBCH block resource, and the channel quality is based on the L1-RSRP obtained from the one candidate resource.

[0282] As an example, one of the candidate resources 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 from the candidate resource. The first power value is the power offset of the candidate resource relative to the SS / PBCH block resource. The units of L1-RSRP, the first power value, the power of the candidate resource, and the power of the SS / PBCH block resource are all dB.

[0283] 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.

[0284] 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.

[0285] As an example, the channel quality is L1-RSRP; when the channel quality evaluated based on a candidate resource in the first candidate resource set is equal to or better than a first reference threshold, the physical layer of the first node sends the configuration index and L1-RSRP of the candidate resource to its higher layers.

[0286] As an example, the first power value is configured by the higher-level parameter powerControlOffsetSS.

[0287] Example 2

[0288] Example 2 illustrates a schematic diagram of a network architecture according to an embodiment of this application, as shown in Figure 2.

[0289] Figure 2 illustrates network architecture 200. Network architecture 200 is a 5G NR (New Radio) / LTE (Long-Term Evolution) / LTE-A (Long-Term Evolution Advanced) system, or a 5G+ network architecture, or a 6G network architecture, or a network architecture adopted in future evolutions by 3GPP; network architecture 200 may be referred to as 5GS (5G System) / EPS (Evolved Packet System), or 6GS (6G System); 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 interconnect with other access networks, but these entities / interfaces are not shown for simplicity. As shown, the network architecture 200 provides packet-switched services; however, those skilled in the art will readily understand that the various concepts presented throughout this application can be extended to networks providing circuit-switched 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 toward UE 201. Node 203 may be connected to other nodes 204 via an Xn interface (e.g., backhaul) / X2 interface. Node 203 may 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 (transmitter-receiver node), or some other suitable term. The core network 210 is a 5GC (5G Core Network) / EPC (Evolved Packet Core), or the core network 210 is a 6GC; node 203 provides UE 201 with an access point to the core network 210.Examples of UE201 include cellular phones, smartphones, Session Initiation Protocol (SIP) phones, laptops, 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 IoT 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 UE201 as a mobile station, subscriber station, mobile unit, subscriber unit, radio unit, remote unit, mobile device, radio device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handheld device, user agent, mobile client, client, or any other suitable term. Node 203 is connected to the core network 210 via an S1 / NG interface. The core network 210 includes an MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MMEs / AMFs / SMFs 214, an S-GW (Service Gateway) / UPF (User Plane Function) 212, and a P-GW (Packet Data Network Gateway) / UPF 213. The MME / AMF / SMF 211 is the control node that handles signaling between the UE 201 and the core network 210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocol) 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. Internet services 230 include operator-compliant Internet protocol services, which may specifically include Internet, intranet, IMS (IP Multimedia Subsystem), and packet switching services.

[0290] As an example, the first node includes the UE201.

[0291] As one embodiment, the second node includes the node 203.

[0292] As an example, the wireless link between the UE201 and the node203 includes a cellular link.

[0293] As an example, the sender of the first higher-level message set includes the node 203.

[0294] As an example, the recipient of the first higher-level message set includes the UE201.

[0295] As an example, the sender indicated by the beam failure event includes the UE201.

[0296] As an example, the trigger for beam failure recovery includes the UE201.

[0297] As an example, the executor of the first operation includes the UE201.

[0298] As an example, the deployer of the first operation includes the UE201.

[0299] As an example, the first candidate resource is indicated to the UE201.

[0300] As an example, the channel quality of the first candidate resource is indicated to the UE201.

[0301] As an example, the first information is indicated to the UE201.

[0302] As an example, the sender of the beam failure recovery request includes the UE201.

[0303] As an example, the recipient of the beam failure recovery request includes node 203.

[0304] As an example, the recipient of the response to the beam failure recovery request includes the UE201.

[0305] As an example, the sender of the response to the beam failure recovery request includes the node 203.

[0306] Example 3

[0307] Example 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for the user plane and control plane according to an embodiment of this application, as shown in Figure 3.

[0308] Example 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for a user plane and control plane according to this application, as shown in Figure 3. 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 3 shows the radio protocol architecture for the control plane 300 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, using 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 herein as PHY 301. Layer 2 (L2 layer) 305 is above PHY 301 and is responsible for the link between the first communication node device and the second communication node device, or between two UEs. Layer L2 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, which terminate at the second communication node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. It also provides security through encrypted data packets and supports cross-cell mobility between the second communication node devices and the first communication node device. The RLC sublayer 303 provides upper-layer packet segmentation and reassembly, retransmission of lost packets, and packet reordering to compensate for out-of-order reception due to HARQ. The MAC sublayer 302 provides multiplexing between logical and transport channels. It is also responsible for allocating various radio resources (e.g., resource blocks) within a cell among the first communication node devices. Furthermore, the MAC sublayer 302 handles HARQ operations. In the control plane 300, the Radio Resource Control (RRC) sublayer 306 of Layer 3 (L3) is responsible for acquiring radio resources (i.e., radio bearers) and configuring the lower layers using RRC signaling between the second and first communication node devices. The user plane 350's radio protocol architecture includes Layer 1 (L1) and Layer 2 (L2). The radio protocol architecture for the first and second communication node devices in the user plane 350 is largely the same as the corresponding layers and sublayers in the control plane 300 for Physical Layer 351, PDCP sublayer 354 in L2 Layer 355, RLC sublayer 353 in L2 Layer 355, and MAC sublayer 352 in L2 Layer 355. However, 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 also includes an SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for mapping between QoS streams and data radio bearers (DRBs) to support service diversity. Although not illustrated, the first communication node device may have several upper layers above the L2 layer 355, including a network layer (e.g., IP layer) terminating at the P-GW on the network side and an application layer terminating at the other end of the connection (e.g., a remote UE, server, etc.).

[0309] As an example, the wireless protocol architecture in Figure 3 is applicable to the first node.

[0310] As an example, the wireless protocol architecture in Figure 3 is applicable to the second node.

[0311] As an example, the higher layer mentioned in this application refers to the layer above the physical layer.

[0312] As an example, the first higher-level message set is generated in the RRC sublayer 306.

[0313] As an example, the first higher-level message set is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0314] As an example, the first higher-level message set is generated in the RRC sublayer 306 and the MAC sublayer 302.

[0315] As an example, the beam failure event indication is generated in the PHY301 or the PHY351.

[0316] As an example, the target counter is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0317] As an example, the channel quality information of the first candidate resource is generated in the PHY301 or the PHY351.

[0318] As an example, the first information is generated in the PHY301 or the PHY351.

[0319] As an example, the beam failure recovery request is generated in the PHY301 or the PHY351.

[0320] As an example, the beam failure recovery request is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0321] As an example, the response to the beam failure recovery request is generated in the PHY301 or the PHY351.

[0322] As an example, the response to the beam failure recovery request is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0323] Example 4

[0324] Example 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of this application, as shown in Figure 4. Figure 4 is a block diagram of a first communication device 410 and a second communication device 450 communicating with each other in an access network.

[0325] The first communication device 410 includes a controller / processor 475, a memory 476, a receiver processor 470, a transmitter processor 416, a multi-antenna receiver processor 472, a multi-antenna transmitter processor 471, a transmitter / receiver 418, and an antenna 420.

[0326] 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.

[0327] 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 L2 layer functionality. In 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 operation, 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 L1 layer (i.e., physical layer). Transmit processor 416 performs 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), and M-quadrature amplitude modulation (M-QAM). 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, generating one or more parallel... The transmit processor 416 then maps each parallel stream to a subcarrier, multiplexes the modulated symbols with a reference signal (e.g., a pilot) in the time and / or frequency domains, and then uses an inverse fast Fourier transform (IFFT) to generate a physical channel carrying the time-domain multicarrier symbol stream. The multi-antenna transmit processor 471 then performs transmit analog precoding / beamforming operations on the time-domain multicarrier symbol stream. Each transmitter 418 converts the baseband multicarrier symbol stream provided by the multi-antenna transmit processor 471 into an RF stream, which is then provided to a different antenna 420.

[0328] 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 a signal through its corresponding antenna 452. Each receiver 454 recovers the information modulated onto the radio frequency carrier and converts the radio frequency stream into a baseband multicarrier symbol stream, which is then provided to the receiver processor 456. The receiver processor 456 and the multi-antenna receiver processor 458 implement various signal processing functions of the L1 layer. The multi-antenna receiver processor 458 performs receive analog precoding / beamforming operations on the baseband multicarrier symbol stream from the receiver 454. The receiver processor 456 uses a Fast Fourier Transform (FFT) to convert the baseband multicarrier 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 receiver processor 456, where the reference signal is used for channel estimation, and the data signal is recovered in the multi-antenna receiver processor 458 after multi-antenna detection to recover any parallel stream destined for the second communication device 450. Symbols on each parallel stream are demodulated and recovered in the receive processor 456, generating soft decisions. The receive processor 456 then decodes and deinterleaves the soft decisions to recover the upper-layer data and control signals transmitted over the physical channel by the first communication device 410. The upper-layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements the functions of Layer 2 (L2). The controller / processor 459 may be associated with a memory 460 storing program code and data. The memory 460 may be referred to as computer-readable media. In the DL (Layered Logic), the controller / processor 459 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels to recover upper-layer packets from the core network. The upper-layer packets are then provided to all protocol layers above Layer 2. Various control signals may also be provided to Layer 3 (L3) for L3 processing. The controller / processor 459 is also responsible for error detection using ACK and / or NACK protocols to support HARQ operation.

[0329] 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 the controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmission functions at the first communication device 410 described in the DL, the controller / processor 459 implements 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, implementing L2 layer functions for the user plane and control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. Transmit processor 468 performs modulation mapping and channel coding processing, while multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based and non-codebook-based precoding, and beamforming processing. Subsequently, transmit processor 468 modulates the generated parallel stream into a multi-carrier / single-carrier symbol stream. After analog precoding / beamforming operations in multi-antenna transmit processor 457, the stream is provided to different antennas 452 via transmitter 454. Each transmitter 454 first converts the baseband symbol stream provided by multi-antenna transmit processor 457 into a radio frequency symbol stream before providing it to antenna 452.

[0330] In the transmission from the second communication device 450 to the first communication device 410, the function at the first communication device 410 is similar to the receiving function 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 the multi-antenna receiving processor 472 and the receiving processor 470. The receiving processor 470 and the multi-antenna receiving processor 472 jointly implement the L1 layer functions. The controller / processor 475 implements the 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 computer-readable media. The controller / processor 475 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels 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 ACK and / or NACK protocols to support HARQ operation.

[0331] As one embodiment, 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 with the at least one processor. The second communication device 450 means at least: receiving a first higher-level message set, the first higher-level message set being used to configure a first resource set and a first candidate resource set; evaluating a first radio link quality based on the first resource set; the physical layer of the first node indicating a first candidate resource in the first candidate resource set to its higher layers; wherein 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 the 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 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.

[0332] As one embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program that, when executed by at least one processor, produces actions including: receiving a first higher-level message set, the first higher-level message set being used to configure a first resource set and a first candidate resource set; evaluating a first wireless link quality based on the first resource set; the physical layer of the first node indicating a first candidate resource in the first candidate resource set to its higher layers; wherein 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 wireless link quality is worse than the 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 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 one embodiment, 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 means at least: transmitting a first higher-level message set, the first higher-level message set being used to configure a first resource set and a first candidate resource set; wherein a target receiver of the first higher-level message set evaluates a first wireless link quality based on the first resource set; the physical layer of the target receiver of the first higher-level 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, the first candidate resource being one of the plurality of candidate resources; the evaluated first wireless link quality is worse than the 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 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 one embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program that, when executed by at least one processor, produces actions including: sending a first higher-level message set, the first higher-level message set being used to configure a first resource set and a first candidate resource set; wherein a target receiver of the first higher-level message set evaluates a first wireless link quality based on the first resource set; the physical layer of the target receiver of the first higher-level 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 wireless link quality is worse than the 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 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.

[0335] As an example, the first node in this application includes the second communication device 450.

[0336] As an example, the second node in this application includes the first communication device 410.

[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, and the data source 467} is used to receive the first higher-level message 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, and the memory 476} is used to transmit the first higher-level message 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, and 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, and the memory 476} is used to transmit the reference signal in the first resource set.

[0339] 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, and 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, and the memory 476} is used to transmit the reference signal in the first candidate resource set.

[0340] As an example, at least one of the following is used to transmit the beam failure event indication: {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, and the data source 467}.

[0341] As an example, at least one of the following is used to trigger the beam failure recovery: {the antenna 452, the receiver / transmitter 454, the receiver processor 456, the transmitter processor 468, the multi-antenna receiver processor 458, the multi-antenna transmitter processor 457, the controller / processor 459, the memory 460, and the data source 467}.

[0342] As an example, at least one of the following is used to perform the first operation in this application: {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, and the data source 467}.

[0343] 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, and the data source 467} is used to indicate the first candidate resource in the first candidate resource set.

[0344] 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, and the data source 467} is used to indicate the channel quality of the first candidate resource.

[0345] As an example, at least one of the following is used to indicate the first information: {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, and the data source 467}.

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

[0347] As an example, at least one of {the antenna 452, the receiver 454, the receiver processor 456, the multi-antenna receiver processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to send a beam failure recovery request; at least one of {the antenna 420, the transmitter 418, the transmitter processor 416, the multi-antenna transmitter processor 471, the controller / processor 475, and the memory 476} is used to receive a beam failure recovery request.

[0348] Example 5

[0349] Example 5 illustrates a flowchart of a transmission between a first node and a second node according to an embodiment of this application, as shown in Figure 5. In Figure 5, the second node U1 and the first node U2 are communication nodes transmitting via an air interface. In Figure 5, the steps in blocks F51 to F57 are optional.

[0350] For the second node U1, in step S511, a first higher-level message set is sent; in step S5101, a beam failure recovery request is received; and in step S5102, a response to the beam failure recovery request is sent.

[0351] For the first node U2, in step S521, a first higher-layer message set is received; in step S522, the quality of a first radio link is evaluated based on 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, beam failure recovery is triggered; in step S5203, a first operation is performed; 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 further indicates the channel quality of the first candidate resource to its higher layer; in step S5205, the physical layer of the first node further indicates first information to its higher layer; in step S5206, a beam failure recovery request is sent; and in step S5207, a response to the beam failure recovery request is received.

[0352] In Embodiment 5, the first higher-layer message set is used to configure a first resource set and a first candidate resource set; the quality of a first radio link is evaluated based on 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 layers; the first candidate resource set includes multiple candidate resources, and the first candidate resource is one of the multiple candidate resources; the evaluated quality of the first radio link 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 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.

[0353] As an example, the first node U2 is the first node in this application.

[0354] As an example, the second node U1 is the second node in this application.

[0355] As one embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between the base station equipment and the user equipment.

[0356] As one embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between the relay node device and the user equipment.

[0357] As one embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between user equipment and user equipment.

[0358] In one embodiment, the second node U1 is the serving cell sustaining base station of the first node U2.

[0359] As an example, the AI ​​training function in the RAN (Radio Access Network) domain is located in the RAN domain-specific management function, while the AI ​​inference function is located in the UE.

[0360] As an example, RAN domain-specific management functions provide AI training function management capabilities and AI inference function management capabilities.

[0361] As an example, the AI ​​training function is located in the RAN domain-specific management function, while the AI ​​inference function is located locally in the gNB.

[0362] As an example, the management capability of AI training is provided by RAN domain-specific management functions, while the management capability of AI inference is provided locally by the gNB.

[0363] As an example, MnF refers to Management Function.

[0364] As an example, both the AI ​​training function and the AI ​​inference function are located in the UE, wherein the UE provides the ability to train and infer.

[0365] As an example, RAN domain-specific management functions provide management capabilities for AI training and AI inference functions.

[0366] As an example, both the AI ​​training function and the AI ​​inference function are located in the gNB.

[0367] As an example, the management capabilities for both AI training and AI inference are provided locally by gNB.

[0368] As an example, the steps in block F51 of Figure 5 are present; the method used in the first node for wireless communication includes: the physical layer of the first node sending a beam failure event indication to its higher layers.

[0369] As an example, the steps in block F52 of Figure 5 are present; the method used 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 a target threshold; the target counter is used for counting indicated by the beam failure event.

[0370] As an example, the steps in block F53 of Figure 5 are present.

[0371] As an example, when the channel quality of the first candidate resource is obtained based on AI, the step in block F53 of Figure 5 exists; when the channel quality of the first candidate resource is not obtained based on AI, the step in block F53 of Figure 5 does not exist.

[0372] As an example, the steps in block F53 of Figure 5 are present; the method used in the first node for wireless communication includes: performing a first operation, the first operation being training-based or AI-based, the channel quality of the first candidate resource depending on the output of the first operation.

[0373] As an example, the steps in block F54 of Figure 5 are present; the method used in the first node for wireless communication includes: the physical layer of the first node further instructs its higher layers on the channel quality of the first candidate resource.

[0374] As an example, the steps in block F55 of Figure 5 are present; the method used in the first node for wireless communication includes: the physical layer of the first node further instructs its higher layers to indicate first information.

[0375] As an example, the indication of the channel quality of the first candidate resource precedes the indication of the first information.

[0376] As an example, the indication of the channel quality of the first candidate resource is no earlier than the indication of the first information.

[0377] As an example, the step in block F56 of Figure 5 is present; the method used in the first node for wireless communication includes: sending a beam failure recovery request.

[0378] As an example, the step in block F56 of Figure 5 is present; the method used in the second node for wireless communication includes: receiving a beam failure recovery request.

[0379] As an example, the step in block F57 of Figure 5 is present; the method used in the first node for wireless communication includes: receiving a response to the beam failure recovery request.

[0380] As an example, the step in block F57 of Figure 5 is present; the method in the second node used for wireless communication includes: sending a response to the beam failure recovery request.

[0381] As an example, the first higher-layer message set is transmitted on PDSCH (Physical Downlink Shared Channel).

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

[0383] As an example, the beam failure recovery request is transmitted on the PUCCH (Physical Uplink Control Channel).

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

[0385] As an example, the response to the beam failure recovery request is transmitted on the PDCCH (Physical Downlink Control Channel).

[0386] Example 6

[0387] Example 6 illustrates a schematic diagram of the channel quality of a first candidate resource according to an embodiment of this application, obtained by measuring the RSRP of the first candidate resource; as shown in Figure 6. In Example 6, 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 obtained by measuring the RSRP of the first candidate resource.

[0388] As an example, 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 obtained by measuring the RSRP of the first candidate resource.

[0389] As an example, 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 obtained by measuring the L1-RSRP of the first candidate resource.

[0390] As an example, 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 obtained by measuring the SINR of the first candidate resource.

[0391] As an example, 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 obtained by measuring the L1-SINR of the first candidate resource.

[0392] As an example, 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 obtained by measuring the BLER of the first candidate resource.

[0393] As an example, 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 obtained by measuring the first candidate resource using a hypothetical BLER.

[0394] As an example, 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 obtained by measuring the RSRP, SINR, BLER, or hypothetical BLER of the first candidate resource.

[0395] Example 7

[0396] Example 7 illustrates a schematic diagram of a first candidate resource according to an embodiment of this application, where the channel quality is predicted or inferred; as shown in Figure 7. In Example 7, the channel quality of the first candidate resource is obtained based on AI, including: the channel quality of the first candidate resource is obtained through prediction or inference.

[0397] As one example, the prediction includes AI prediction.

[0398] As an example, the reasoning includes AI reasoning.

[0399] As one embodiment, 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.

[0400] As an example, the fact that the channel quality of the first candidate resource is not obtained based on AI includes: the channel quality of the first candidate resource is obtained without using an AI model.

[0401] As an example, the channel quality of the first candidate resource is obtained based on AI, including information based on artificial intelligence or machine learning.

[0402] As one embodiment, the channel quality of the first candidate resource is obtained based on AI, including information generated based on a neural network.

[0403] As an example, the channel quality of the first candidate resource is obtained based on AI, including information generated based on CNN (Conventional Neural Networks).

[0404] As an example, the channel quality of the first candidate resource is not obtained based on AI, meaning that the channel quality of the first candidate resource does not include information based on artificial intelligence or machine learning.

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

[0406] As an example, the channel quality of the first candidate resource is not obtained based on AI, including: the channel quality of the first candidate resource does not include information generated based on CNN.

[0407] As one embodiment, the channel quality of the first candidate resource is obtained by prediction or inference, including: the first node obtains the channel quality of the first candidate resource by prediction or inference.

[0408] As an example, the channel quality of the first candidate resource is obtained by prediction or inference, including: the channel quality of the first candidate resource is not obtained based on the measurement of the RS resource.

[0409] As one embodiment, the fact that the channel quality of the first candidate resource is not obtained based on the measurement of the RS resource includes: the channel quality of the first candidate resource is not expected to be obtained based on the measurement of the RS resource.

[0410] 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, or is related to implementation.

[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 RSRP, L1-RSRP, SINR, or L1-SINR predicted or inferred from 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 average value of RSRP, L1-RSRP, SINR, or L1-SINR obtained by predicting or inferring at least one transmission timing of the first candidate resource.

[0413] 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 RSRP, L1-RSRP, SINR, or L1-SINR obtained by prediction or inference of at least one transmission timing of the first candidate resource.

[0414] As an example, the channel quality of the first candidate resource is BLER; obtaining the channel quality of the first candidate resource based on AI is the BLER predicted or inferred from the first candidate resource.

[0415] 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 BLER obtained from at least one transmission timing prediction or inference of the first candidate resource.

[0416] 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 maximum value of BLER obtained by predicting or inferring at least one transmission timing of 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 based on AI evaluation is a hypothetical BLER predicted or inferred from the first candidate resource.

[0418] As an example, the channel quality of the first candidate resource is a hypothetical BLER; the first wireless link quality based on AI evaluation is the average of the hypothetical BLERs obtained by predicting or inferring at least one transmission opportunity of the first candidate resource.

[0419] As an example, the channel quality of the first candidate resource is a hypothetical BLER; the first wireless link quality based on AI evaluation is the maximum value of the hypothetical BLER obtained by predicting or inferring at least one transmission opportunity of the first candidate resource.

[0420] Example 8

[0421] Example 8 illustrates a schematic diagram of the channel quality of a first candidate resource depending on the output of a first operation according to an embodiment of this application; as shown in Figure 8. In Example 8, the channel quality of the first candidate resource is obtained based on AI, including: the first node performing a first operation, the first operation being based on training or AI, and the channel quality of the first candidate resource depending on the output of the first operation.

[0422] As one embodiment, the channel quality of the first candidate resource being obtained based on AI includes: the first node performing a first operation, the first operation being based on training or AI, and the channel quality of the first candidate resource depending on the output of the first operation; the channel quality of the first candidate resource not being obtained based on AI includes: the acquisition of the channel quality of the first candidate resource not including the first node performing the first operation.

[0423] As one embodiment, the channel quality of the first candidate resource is obtained based on AI, which includes: the first higher-level message set indicating that the channel quality of the first candidate resource is obtained based on AI by indicating a first type of identifier.

[0424] As one embodiment, 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.

[0425] As one embodiment, 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 AI functions identified by a first type of identifier.

[0426] As one embodiment, the channel quality of the first candidate resource is obtained based on AI, which includes: the acquisition of the channel quality of the first candidate resource is performed in an AI entity identified by a first type of identifier.

[0427] As an example, the output of the first operation is used to generate the channel quality of the first candidate resource.

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

[0429] As an example, the channel quality of the first candidate resource includes the post-processed output of the first operation.

[0430] As an example, the channel quality of the first candidate resource includes the truncated and / or quantized output of the first operation.

[0431] As an example, the output of the first operation, after post-processing, is used to generate the channel quality of the first candidate resource.

[0432] As an example, the output of the first operation, after being truncated and / or quantized, is used to generate the channel quality of the first candidate resource.

[0433] As an example, some or all of the output of the first operation is post-processed and used to generate the channel quality of the first candidate resource.

[0434] As an example, some 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.

[0435] As one 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-dependent. These are some typical but non-limiting implementations.

[0436] Example 9

[0437] Example 9 illustrates a schematic diagram of a first operation being associated with a first type of identifier according to an embodiment of this application; as shown in Figure 9. In Example 9, the first operation is associated with the first type of identifier.

[0438] As an example, the first operation is identified by the first type of identifier.

[0439] As an example, the AI ​​model used in the first operation is identified by the first type of identifier.

[0440] As an example, the AI ​​entity to which the first operation belongs is identified by the first type of identifier.

[0441] As an example, the AI ​​entity performing the first operation is identified by the first type of identifier.

[0442] As one example, the first operation is used for an AI function identified by the first type of identifier.

[0443] As an example, the advantages of the above method include that identifying an AI entity or function through the first type of identifier simplifies the design and unifies the understanding of different AI entities or functions across multiple nodes.

[0444] As an example, the first type of identifier is a model identifier.

[0445] As an example, the first type of identifier is used to identify an AI model.

[0446] As an example, the first type of identifier is used by the first node to identify an AI model.

[0447] As an example, the first type of identifier is used by the first node to determine the AI ​​model adopted by the first operation.

[0448] As an example, the advantages of the above method include that identifying an AI model / entity / function through the first type of identifier simplifies the design and unifies the understanding of different AI entities / functions across multiple nodes.

[0449] As one embodiment, the first type of identifier is used to identify or indicate a set of reference resources, and the measurement of the set of reference resources is used to obtain a training dataset for the first operation.

[0450] As an example, the first type of identifier is used to identify the configuration information of the reference resource set, and the measurement of the reference resource set is used to obtain the training dataset for the first operation.

[0451] As one embodiment, the training for obtaining the first operation is identified by the first type of identifier.

[0452] As an example, the dataset used for training the first operation is identified by the first type of identifier.

[0453] As an example, the benefits of the above method include establishing consensus among different AI functions by identifying an AI training or AI training dataset to recognize the inferences generated by that AI training or AI training dataset, further simplifying the design.

[0454] As an example, the first type of identifier is a non-negative integer.

[0455] As an example, the first type of identifier is a string.

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

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

[0458] As an example, the first type of identifier is used to identify AI functions.

[0459] As an example, the advantages of the above method include that identifying an AI entity or function through the first type of identifier simplifies the design and unifies the understanding of different AI entities or functions across multiple nodes.

[0460] As an example, the first type of identifier is a model identifier.

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

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

[0463] 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.

[0464] As an example, the advantages of the above method include that identifying an AI model / entity / function through the first type of identifier simplifies the design and unifies the understanding of different AI entities / functions across multiple nodes.

[0465] As an example, the first type of identifier is used to identify or indicate a set of resources.

[0466] As one embodiment, the first type of identifier is used to identify or indicate a set of resources, the measurement of which is used to obtain a training dataset.

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

[0468] As an example, the first type of identifier is used to identify or indicate the training dataset.

[0469] As an example, the benefits of the above method include establishing consensus among different AI functions by identifying an AI training or AI training dataset to recognize the inferences generated by that AI training or AI training dataset, further simplifying the design.

[0470] Example 10

[0471] Example 10 illustrates a schematic diagram of a first operation based on training or AI according to an embodiment of this application; as shown in Figure 10. In Example 10, the first operation is based on training or AI.

[0472] As an example, the first operation is based on training or AI.

[0473] As an example, the first operation includes inference.

[0474] As an example, the reasoning includes AI reasoning.

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

[0476] As an example, the first operation includes an AI entity for inference.

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

[0478] As an example, the first operation includes a portion of an AI entity used for inference.

[0479] As one embodiment, the first operation includes reasoning for obtaining the first information report.

[0480] As an example, the reasoning includes AI (Artificial Intelligence) inference.

[0481] As an example, the first operation is used for an AI function.

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

[0483] As an example, the first operation is performed at a higher level than the first node.

[0484] As an example, the model for the first operation is obtained through training.

[0485] As an example, the training for the first operation is performed by the first node.

[0486] As an example, the training of the first operation is performed by the sender of the first information set.

[0487] As an example, the training for the first operation is performed by the core network.

[0488] As an example, the training of the first operation is performed by an AI training producer.

[0489] As an example, the training of the first operation is performed by the MDA (Management Data Analytics Function).

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

[0491] As an example, the training of the first operation is performed by the MDA function of the sender located in the first information set.

[0492] As an example, the training of the first operation is performed by NWDAF (Network Data Analytics Function).

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

[0494] As an example, the training of the first operation is performed by the MnS (Management Service) producer.

[0495] As an example, the first operation requires deployment.

[0496] As an example, the first operation is obtained by loading.

[0497] As an example, the first operation is obtained from the serving cell of the first node.

[0498] As an example, the first operation is obtained from the sustaining base station of the serving cell of the first node.

[0499] As an example, the first node deploys the first operation.

[0500] As an example, the first operation does not require deployment.

[0501] As an example, the first operation is obtained from the core network.

[0502] As an example, the first operation is based on artificial intelligence or machine learning.

[0503] As an example, the first operation is based on a neural network.

[0504] As an example, the first operation is based on CNN (Conventional Neural Networks).

[0505] As one example, the first operation includes preprocessing.

[0506] As one example, the first operation includes post-processing.

[0507] As one example, the post-processing includes DFT.

[0508] As one example, the post-processing includes quantization.

[0509] As an example, the post-processing includes one or more of the following: angular domain to spatial domain transformation, spatial domain to angular domain transformation, time domain to frequency domain transformation, and frequency domain to time domain transformation.

[0510] As one example, the post-processing includes truncation and / or padding.

[0511] As an example, the first operation includes one or more of convolution, pooling, cascading, and activation.

[0512] As one embodiment, the first operation includes a fully connected layer.

[0513] As an example, the first operation includes a pooling layer.

[0514] As one embodiment, the first operation includes at least one convolutional layer.

[0515] As an example, the first operation includes at least one encoding layer.

[0516] As an example, an encoding layer includes at least one convolutional layer and one pooling layer.

[0517] As an example, in a 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 a fully connected layer; the fully connected layer transforms the vector into an output.

[0518] As an example, some or all of the following parameters in the first operation—convolution kernel size, number of convolutional layers, convolution stride, pooling kernel size, pooling kernel stride, pooling function, activation function, and number of feature maps—are obtained through training.

[0519] As an example, some or all of the convolution kernel, pooling kernel, pooling function, activation function, parameters of the pooling function, and parameters of the activation function in the first operation are obtained through training.

[0520] Without loss of generality, the parameters or AI model used in the first operation are determined by the manufacturer of the first node.

[0521] As an example, the first operation includes localization based on artificial intelligence or machine learning.

[0522] As one example, the first operation includes artificial intelligence or machine learning-assisted positioning.

[0523] As an example, the first node is a user (consumer).

[0524] As an example, the first node is the user of the AI ​​function.

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

[0526] As an example, the first node is the user who trained the AI.

[0527] As an example, the first node is an MnS (Management Service) user.

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

[0529] As an example, the first node is the AI ​​training producer.

[0530] As one example, the first operation includes preprocessing.

[0531] As an example, the preprocessing includes DFT (Discrete Fourier Transform).

[0532] As an example, the preprocessing includes one or more of matrix decomposition, matrix transformation, and projection.

[0533] As an example, the preprocessing includes one or more of quantization, spatial-to-angular-domain transformation, angular-to-spatial-domain transformation, frequency-to-time-domain transformation, and time-to-frequency-domain transformation.

[0534] As one example, the preprocessing includes truncation and / or padding.

[0535] As one example, the preprocessing includes mapping.

[0536] As one example, the preprocessing includes mapping to vectors.

[0537] As one example, the preprocessing includes labeling.

[0538] As an example, the label refers to a mark made with a label.

[0539] As an example, the first node deploys the first operation.

[0540] As one embodiment, the deployment includes obtaining the first operation.

[0541] As one example, the deployment includes obtaining an AI entity.

[0542] As one example, the deployment includes obtaining an AI entity that performs the first operation.

[0543] As one example, the deployment includes obtaining an AI entity that includes AI functions to perform the first operation.

[0544] As one example, the deployment includes loading the first operation.

[0545] As one example, the deployment includes submitting a request to load the first operation.

[0546] As an example, the first operation is obtained from the serving cell of the first node.

[0547] As an example, the first operation is obtained from the sustaining base station of the serving cell of the first node.

[0548] As an example, the first operation is obtained from the core network.

[0549] As an example, the deployment is accomplished by an AI function.

[0550] As an example, the deployment is accomplished by AI functionality deployed on the first node.

[0551] As an example, the deployment is accomplished by an AI deployment function.

[0552] As an example, the deployment is accomplished by the AI ​​deployment function deployed on the first node.

[0553] As an example, the deployment is accomplished using AI inference functionality.

[0554] As an example, the deployment is accomplished by an AI inference function deployed on the first node.

[0555] As an example, the deployment is performed by an AI entity.

[0556] As an example, the deployment is performed by an AI entity deployed on the first node.

[0557] As an example, the deployment is performed by an AI entity with a deployment function.

[0558] As an example, the deployment is performed by an AI entity with deployment capabilities deployed on the first node.

[0559] As an example, the deployment is accomplished by an AI entity with an inference function.

[0560] As an example, the deployment is performed by an AI entity with inference capabilities deployed on the first node.

[0561] Example 11

[0562] Example 11 illustrates a schematic diagram of the channel quality of a first candidate resource according to an embodiment of this application; as shown in Figure 11. 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; the channel quality of the first candidate resource is RSRP only when the channel quality of the first candidate resource is not obtained based on AI.

[0563] As an example, 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.

[0564] As an example, 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.

[0565] As an example, 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.

[0566] As an example, 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.

[0567] Example 12

[0568] Example 12 illustrates a schematic diagram in which the physical layer of a first node according to an embodiment of the present application further indicates the channel quality of a first candidate resource to its higher layers; as shown in Figure 12. In Example 12, the physical layer of the first node further indicates the channel quality of the first candidate resource to its higher layers.

[0569] As an example, the physical layer of the first node also indicates the channel quality of the first candidate resource to its higher layers, wherein the channel quality of the first candidate resource is RSRP, L1-RSRP, SINR, L1-SINR, BLER, or hypothetical BLER.

[0570] As an example, 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 layers.

[0571] As an example, the physical layer of the first node also indicates the channel quality of the first candidate resource to its higher layers, the channel quality of the first candidate resource being the RSRP obtained by measuring the first candidate resource.

[0572] As an example, the physical layer of the first node also indicates the channel quality of the first candidate resource to its higher layers, the channel quality of the first candidate resource being obtained through prediction or inference.

[0573] Example 13

[0574] Example 13 illustrates a schematic diagram of first information according to an embodiment of this application; as shown in Figure 13. In Example 13, the physical layer of the first node further indicates the first information to its higher layers; 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 obtained by measuring the RSRP of the first candidate resource.

[0575] As an example, the physical layer of the first node also indicates first information to its higher layers; the first information is used to indicate whether the channel quality of the first candidate resource is obtained based on AI.

[0576] As an example, the physical layer of the first node also indicates first information to its higher layers; 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.

[0577] As one embodiment, the first information includes a first field, which indicates whether the channel quality of the first candidate resource is 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 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.

[0579] As an example, the first field of the first information includes one bit. When the first field of the first information is 0, the channel quality of the first candidate resource is obtained based on AI; when the first field of the first information is 1, the channel quality of the first candidate resource is not obtained based on AI.

[0580] As one embodiment, the first information includes a second field, which indicates whether the channel quality of the first candidate resource is obtained by measuring the RSRP of the first candidate resource.

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

[0582] 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 obtained by measuring the RSRP of 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 obtained by measuring the RSRP of the first candidate resource.

[0583] Example 14

[0584] Example 14 illustrates a schematic diagram of beam failure event indication and beam failure recovery according to an embodiment of this application, as shown in Figure 14. In step 141, the physical layer of the first node sends a beam failure event indication to its higher layers; beam failure recovery is triggered in step 142. In Example 14, the physical layer of the first node sends a beam failure event indication to its higher layers; beam failure recovery is triggered when the value of a target counter is equal to or greater than a target threshold; the target counter is used for counting the beam failure event indication.

[0585] As an example, whenever the evaluated quality of the first wireless link is worse than a second reference threshold, the physical layer of the first node sends a beam failure event indication to its higher layers.

[0586] As an example, the beam failure event indication refers to: beam failure instance indication.

[0587] As one 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 to count the beam failure event indication for the first serving cell, and the beam failure recovery is for the first serving cell.

[0588] As one embodiment, the first wireless link quality is the wireless 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 to count the beam failure event indication for the first resource set, and the beam failure recovery is for the first resource set.

[0589] As an example, the first resource set is configured for a first BWP, which is a BWP of a first serving cell; the first radio link quality is for the radio link quality of the first serving cell; the beam failure event indication is for the first serving cell; the target counter is used to count the beam failure event indication for the first serving cell; and the beam failure recovery is for the first serving cell.

[0590] As a sub-implementation of the above embodiments, the first resource set is

[0591] As one embodiment, the first resource set is one of two resource sets configured for the first BWP, the first BWP is a BWP of the 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 to count the beam failure event indication for the first resource set; and the beam failure recovery is for the first resource set.

[0592] As a sub-implementation of the above embodiments, the first resource set is or

[0593] Typically, the sentence "when the value of the target counter is equal to or greater than the target threshold" means: if and only if the value of the target counter is equal to or greater than the target threshold.

[0594] Typically, the sentence "when the value of the target counter is equal to or greater than the target threshold" means: as a response where the value of the target counter is equal to or greater than the target threshold.

[0595] Typically, the first node maintains the target counter at the MAC layer.

[0596] Typically, the MAC entity of the first node maintains the target counter.

[0597] 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.

[0598] Typically, the target counter is BFI_COUNTER.

[0599] Typically, the target counter is set to 0 when the target timer expires.

[0600] Typically, the target timer is beamFailureDetectionTimer.

[0601] As an example, the target counter is BFI_COUNTER.

[0602] As an example, the initial value of the target counter is 0.

[0603] As an example, the target threshold is a positive integer.

[0604] As an example, the target threshold is beamFailureInstanceMaxCount.

[0605] As an example, the target threshold is configured by the RRC parameter.

[0606] As an example, the RRC parameters for configuring the target threshold include all or part of the information in the beamFailureInstanceMaxCount field of the RadioLinkMonitoringConfig IE.

[0607] As an example, the target timer is beamFailureDetectionTimer.

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

[0609] As an example, the initial value of the target timer is a positive real number.

[0610] As an example, the initial value of the target timer is in units of the Qout,LR reporting period of the beam failure detection RS.

[0611] As an example, the initial value of the target timer is configured by the higher-level parameter beamFailureDetectionTimer.

[0612] As an example, the initial value of the target timer is configured by an IE.

[0613] As an example, the name of the IE that configures the initial value of the target timer includes RadioLinkMonitoring.

[0614] Example 15

[0615] Example 15 illustrates a schematic diagram of a beam failure recovery request according to an embodiment of this application; as shown in Figure 15. 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.

[0616] As one embodiment, the beam failure recovery includes the first node sending a beam failure recovery request and the sender of the first higher-level message set sending a response to the beam failure recovery request.

[0617] 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.

[0618] As an example, the beam failure recovery is based on a scheduling request, which includes a scheduling request (SR) for beam failure recovery.

[0619] As an example, the beam failure recovery request includes a random access preamble, which corresponds to a second candidate resource in the first candidate resource set.

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

[0621] As an example, the random access preamble is a contention-free random access preamble.

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

[0623] As an example, the Beam Failure Recovery (BFR) includes a contention-free random access procedure.

[0624] As an example, the beam failure recovery is based on a scheduling request.

[0625] As one embodiment, the beam failure recovery includes the first node triggering a scheduling request (SR) for beam failure recovery.

[0626] As one embodiment, the beam failure recovery request includes a first MAC CE, 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, the first PDCCH indicating an uplink grant for a new transmission for the first HARQ process.

[0627] As an example, the name of the first MAC CE includes BFR.

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

[0629] As an example, the first MAC CE is an Enhanced BFR MAC CE or a Truncated Enhanced BFR MAC CE.

[0630] As an example, the first MAC CE indicates a second candidate resource in the first candidate resource set.

[0631] As an example, whenever the evaluated quality of the first wireless link is worse than a second reference threshold, the physical layer of the first node sends a beam failure event indication to its higher layers, and the physical layer of the first node indicates a candidate resource in the first candidate resource set to its higher layers; the second candidate resource is one of all candidate resources indicated by the physical layer of the first node to its higher layers.

[0632] As an example, a higher layer of the first node selects a second candidate resource from the first candidate resource set and indicates the second candidate resource to its physical layer.

[0633] As an example, the second candidate resource is the first candidate resource.

[0634] As an example, the second candidate resource is not the first candidate resource.

[0635] As an example, the beam failure recovery request includes a MAC CE with the name including BFR.

[0636] As an example, the beam failure recovery process is described in section 5.17 of 3GPP TS38.321.

[0637] As an example, the beam failure recovery process is described in Section 6 of 3GPP TS38.213.

[0638] Example 16

[0639] Example 16 illustrates a schematic diagram of RAN (Radio Access Network) domain AI / ML function deployment according to one embodiment of this application, as shown in Figure 16. The gNB in ​​Example 16 can be replaced with, for example, an eNB, or a network device such as a 6G base station.

[0640] AI / ML related functions include ML training (also known as AI training, or AI / ML training), ML testing, and ML inference (also known as AI inference, or AI / ML inference), etc. ML training, ML testing, and ML inference functions can be deployed independently or co-located. Deployment of AI / ML related functions can be implemented through software, such as downloading and / or running executable files; or it can be implemented through a combination of software and hardware, such as accelerating specific computing units through hardware to improve computing speed or save power.

[0641] ML training functionality 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, ML training functionality for MDA (Management Data Analytics) can be deployed on MDAF (MDA Function); ML training for network data analytics can be deployed on NWDAF (Network Data Analytics Function), meaning the ML training functionality is an MTLF (Model Training Logical Function).

[0642] The ML inference function 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.

[0643] Similarly, ML testing capabilities can also be deployed in cross-domain management systems or domain-specific management systems.

[0644] In Example 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 gNB 1405, the AI / ML inference function 1406 is located in gNB 1407, and so on.

[0645] In Figure 16, the management of ML inference functions of multiple base stations is completed by RAN domain management function 1403, that is, data interaction with RAN domain MnS (Management Service) consumer / cross-domain management 1401 (as shown by the dashed arrow in Figure 14).

[0646] Optionally, the management of ML inference function can also be completed by the base station itself, that is, each base station can independently interact with the RAN domain MnS consumer / cross-domain management 1401.

[0647] It should be noted that Example 16 is merely a non-limiting implementation; optionally, the ML training function of the RAN domain may also be deployed at the base station; or optionally, some base stations may deploy both the ML inference function and the ML training function of the RAN domain, while some base stations may only deploy the ML inference function.

[0648] As an example, one of the gNBs (or base stations) in Example 16 is the second node of this application.

[0649] As an example, the second processor in this application includes an AL / ML inference function, namely 1404 or 1406, as shown in Figure 16.

[0650] Example 17

[0651] Example 17 illustrates a schematic diagram of the deployment of AI / ML functionality in a UE according to one embodiment of this application; as shown in Figure 17. The RAN domain ML training function 1505 in Figure 17 is optional.

[0652] UE function 1504 is deployed in the first node of this application, and the UE function 1504 includes AI / ML inference function 1506; the AI / ML inference function 1506 uses an ML model (also called an AI model) for inference; an ML model is typically trained before being used for AI / ML inference.

[0653] As an example, the first information report in this application is obtained through inference by the AI / ML inference function 1506.

[0654] As an example, the first processor in this application includes an AL / ML inference function 1506 in Figure 17.

[0655] As an example, the UE function 1504 includes a RAN domain ML training function 1505, which runs training data through an ML model to obtain a relevant loss and adjusts the parameters of the ML model based on the calculated loss; the ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[0656] The above embodiments can reduce the complexity of the base station, or save air interface resources caused by reporting training data; however, the above embodiments place high demands on the processing capabilities of the UE side.

[0657] Optionally, the UE function 1504 also includes a CN domain ML training function (not shown in Figure 17).

[0658] Optionally, the UE function 1504 also includes an AI / ML deployment function—not shown in Figure 17—for loading ML models and data.

[0659] As an example, the first node indicates whether it supports ML training function (RAN domain or CN domain) through capability reporting. The capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0660] As an example, the ML model and the associated metadata are loaded by the first node from a network device or a remote server.

[0661] Optionally, the UE function 1504 is an MnS (Management Service) producer that 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 double arrow 1507).

[0662] Optionally, the UE function 1504 is an MnS consumer that loads data from the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 for AI / ML-related management, such as managing data requests, ML model activation, and / or ML training (as shown by double arrow 1507).

[0663] As an example, the ML model is based on a neural network.

[0664] As an example, the ML model is based on CNN (Conventional Neural Networks).

[0665] As an example, the ML model is based on the Transformer architecture.

[0666] Example 18

[0667] Example 18 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application; as shown in Figure 18. Figure 18(a) includes a third processor, a fourth processor, and a fifth processor, and Figure 18(b) includes a third processor, a fourth processor, a fifth processor, and a sixth processor.

[0668] In Example 18(a), the third processor sends a first dataset to the fourth processor and a second dataset to the fifth processor; the fourth processor generates a target first-type parameter set based on the first dataset, and sends the generated target first-type parameter set to the fifth processor; the fifth processor processes the second dataset using the target first-type parameter set to obtain a first-type output. In Figure 18(a), the first-type feedback is optional.

[0669] In Example 18(b), the third processor sends a first dataset to the fourth processor and a second dataset to the fifth processor; the fourth processor generates a target first-type parameter set based on the first dataset, and sends the generated target first-type parameter set to the fifth processor; the fifth processor processes the second dataset using the target first-type parameter set to obtain a first-type output, and sends the first-type output to the sixth processor. In Figure 18(b), the first-type feedback and the second-type feedback are optional.

[0670] As an example, in Figure 18(a), the fifth processor sends the first type of output to the second node in this application.

[0671] As an example, Figure 18(a) uses a single-side AI model for beam prediction or channel information prediction, and the fifth processor executes the first operation, which is used for beam prediction or channel information prediction.

[0672] As an example, Figure 18(a) uses a single-side AI model to obtain the channel quality of the first candidate resource, the fifth processor performs the first operation, and the channel quality of the first candidate resource depends on the output of the first operation.

[0673] As an example, the AI ​​includes machine learning (ML) inference.

[0674] As an example, the fifth processor performs the first operation.

[0675] As an example, the fifth processor sends a first type of feedback to the fourth processor, and the first type of feedback is used to trigger a recalculation or update of the target first type of parameter group.

[0676] As one embodiment, the sixth processor sends a second type of feedback to the third processor, the second type of feedback being used to generate the first dataset or the second dataset, or the second type of feedback being used to trigger the sending of the first dataset or the second dataset.

[0677] As one embodiment, the third processor generates the first dataset and the second dataset based on measurements of a first type of wireless signal, the first type of wireless signal including downlink RS.

[0678] As one embodiment, the fifth processor belongs to the first node, and the sixth processor belongs to the second node.

[0679] As an example, the second dataset includes the input of the first operation.

[0680] As an example, the second dataset includes information obtained based on the first configuration and the M1 configurations.

[0681] As an example, the first dataset includes training data.

[0682] As an example, the fourth processor belongs to the producer of the first operation.

[0683] As one embodiment, the fourth processor includes an AI training producer.

[0684] As one embodiment, the fourth processor includes an AI training function.

[0685] As an example, the fourth processor is used for model training, and the trained model is described by the target first class of parameter sets.

[0686] As an example, the fourth processor belongs to the first node.

[0687] The above embodiments avoid passing the first dataset to the second node.

[0688] As one example, the fourth processor belongs to the second node.

[0689] The above embodiments support joint training and optimize system performance.

[0690] As an example, the fourth processor belongs to the core network.

[0691] The above embodiments support network-wide joint training, further optimizing system performance.

[0692] As an example, the second dataset includes inference data.

[0693] As one embodiment, the fifth processor includes an AI inference producer.

[0694] As one embodiment, the fifth processor includes an AI inference function.

[0695] As an example, the fifth processor belongs to the first node.

[0696] As an example, the fifth processor constructs a model based on the target first type of parameter group, and then inputs the second dataset into the constructed model to obtain the first type of output.

[0697] As an example, the first operation is described by the target first type of parameter group.

[0698] As an example, the target first type of parameter group is used to construct the first operation.

[0699] As an example, the fifth processor generates a recovery dataset based on the first type of output, and the error between the recovery dataset and the second dataset is used to generate the first type of feedback.

[0700] 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 fails to meet the requirements, the fourth processing opportunity recalculates the target first type of parameter set.

[0701] As an example, when the error is too large or the update has not been performed for too long, the performance of the trained model is considered to be unsatisfactory.

[0702] As an example, the target first type of parameter group includes one or more of the following: convolution kernel size, number of convolution layers, convolution stride, pooling kernel size, pooling kernel stride, pooling function, activation function, or number of feature maps.

[0703] As an example, the target first type of parameter group includes one or more of the following: convolution kernel, pooling kernel, pooling function, activation function, parameters of pooling function, or parameters of activation function.

[0704] Example 19

[0705] Example 19 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of this application; as shown in Figure 19. Figure 19 includes a third operation, a fourth operation, a fifth operation, a sixth operation, and a seventh operation. In Example 19, the third and fourth operations belong to a first stage, the fifth operation belongs to a second stage, the sixth operation belongs to a third stage, and the seventh operation belongs to a fourth stage. In Figure 19, the arrowed lines indicate the sequence of processes.

[0706] 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.

[0707] 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 emulation phase.

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

[0709] As an example, the first stage includes AI model training and AI testing.

[0710] As an example, the AI ​​includes machine learning (ML) inference.

[0711] As an example, the AI ​​model training includes initial training and re-training of one or a group of AI entities.

[0712] As an example, the training of the AI ​​model depends on training data.

[0713] As an example, the AI ​​model training includes AI entity validation.

[0714] As an example, the AI ​​entity verification is used to evaluate the performance of the AI ​​entity.

[0715] As an example, the AI ​​entity verification relies on verification data.

[0716] As an example, if the AI ​​entity verification results do not meet expectations, the AI ​​model will be retrained.

[0717] As an example, the AI ​​testing includes testing the validated AI entity to estimate the performance of the trained AI model.

[0718] As an example, if the AI ​​test results meet expectations, the AI ​​entity proceeds to the next stage; otherwise, the AI ​​model will be retrained.

[0719] As an example, the AI ​​test relies on test data.

[0720] As an example, the second stage includes AI simulation, which performs inference of AI entities in a simulation environment.

[0721] As an example, the AI ​​simulation estimates the performance of AI entity inference in a simulation environment before using the AI ​​entity.

[0722] As one embodiment, the second stage is optional.

[0723] As an example, the third stage includes AI entity loading, which is to obtain trained AI entities to obtain the desired AI inference capabilities.

[0724] As an example, the third stage is optional.

[0725] As an example, the third stage is no longer needed when the training and inference functions are co-located.

[0726] As an example, the fourth stage includes AI inference.

[0727] As an example, the seventh operation includes the first operation.

[0728] Example 20

[0729] Example 20 illustrates a structural block diagram of a processing apparatus in a first node according to an embodiment of the present application; as shown in Figure 20. In Figure 20, the processing apparatus 2000 in the first node includes a first processor 2001.

[0730] As one example, the first node is a user equipment.

[0731] As one example, the user equipment is a terminal.

[0732] As an example, the first node is a relay node device.

[0733] As an example, the first processor 2001 includes at least one of the following in embodiment 4: {antenna 452, receiver / transmitter 454, receiving processor 456, transmitting processor 468, multi-antenna receiving processor 458, multi-antenna transmitting processor 457, controller / processor 459, memory 460, data source 467}.

[0734] In embodiment 20, the first processor 2001 receives a first higher-level message set, which is used to configure a first resource set and a first candidate resource set; and evaluates the quality of a first wireless link based on the first resource set.

[0735] In embodiment 20, the first processor 2001, the physical layer of the first node indicates the first candidate resource in the first candidate resource set to its higher layers;

[0736] In Example 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 wireless 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.

[0737] As an example, 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 obtained by measuring the RSRP of the first candidate resource.

[0738] 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 through prediction or inference.

[0739] As one embodiment, the channel quality of the first candidate resource is obtained based on AI, including: the first node performing a first operation, the first operation being based on training or AI, and the channel quality of the first candidate resource depending on the output of the first operation.

[0740] As an example, the AI ​​(Artificial Intelligence) includes ML (Machine Learning).

[0741] As an example, the first operation is associated with the first type of identifier.

[0742] As an example, 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; the channel quality of the first candidate resource is RSRP only when the channel quality of the first candidate resource is not obtained based on AI.

[0743] As one embodiment, it includes:

[0744] The first processor 2001, the physical layer of the first node, also indicates the channel quality of the first candidate resource to its higher layers.

[0745] As one embodiment, it includes:

[0746] The first processor 2001, the physical layer of the first node also indicates first information to its higher layers;

[0747] 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 obtained by measuring the RSRP of the first candidate resource.

[0748] As one embodiment, it includes:

[0749] The first processor 2001, the physical layer of the first node sends a beam failure event indication to its higher layers;

[0750] 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.

[0751] As one embodiment, it includes:

[0752] The first processor 2001 sends a beam failure recovery request and receives a response to the beam failure recovery request.

[0753] Among them, the beam failure recovery is triggered.

[0754] As one embodiment, it includes:

[0755] The first processor 2001 deploys the first operation.

[0756] As an example, the first processor 2001 receives signals in the first resource set.

[0757] As one embodiment, the first processor 2001 receives a reference signal in the first resource set, the first resource set including one or more RS resources.

[0758] As an example, the first processor 2001 receives a signal in the first candidate resource set.

[0759] As one embodiment, the first processor 2001 receives a reference signal in the first candidate resource set, which includes one or more RS resources.

[0760] As an example, the first operation is based on training or AI.

[0761] As an example, the first operation requires deployment.

[0762] As an example, the first operation is obtained by loading.

[0763] Example 21

[0764] Example 21 illustrates a structural block diagram of a processing apparatus for a second node according to an embodiment of the present application; as shown in Figure 21. In Figure 21, the processing apparatus 2100 in the second node includes a second processor 2101.

[0765] In one embodiment, the second node is a base station device.

[0766] In one embodiment, the second node is a user equipment.

[0767] As one embodiment, the second node is a relay node device.

[0768] As one embodiment, the second processor 2101 includes at least one of the following in embodiment 4: {antenna 420, receiver / transmitter 418, receiving processor 470, transmitting processor 416, multi-antenna receiving processor 472, multi-antenna transmitting processor 471, controller / processor 475, memory 476}.

[0769] In embodiment 21, the second processor 2101 sends a first higher-level message set, which is used to configure a first resource set and a first candidate resource set.

[0770] In embodiment 21, the target receiver of the first higher-layer message set evaluates the quality of the first radio link based on the first resource set; the 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 multiple candidate resources, and the first candidate resource is one of the multiple candidate resources; the evaluated quality of the first radio link 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.

[0771] As an example, 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 obtained by measuring the RSRP of the first candidate resource.

[0772] 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 through prediction or inference.

[0773] As an example, the channel quality of the first candidate resource is obtained based on AI, including: the target receiver of the first higher-level message set performs a first operation, the first operation being based on training or AI, and the channel quality of the first candidate resource depending on the output of the first operation.

[0774] As an example, the first operation is associated with the first type of identifier.

[0775] As an example, 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; the channel quality of the first candidate resource is RSRP only when the channel quality of the first candidate resource is not obtained based on AI.

[0776] As one embodiment, it includes:

[0777] The first processor 2001, the physical layer of the target receiver of the first higher-level message set, further indicates the channel quality of the first candidate resource to its higher layer.

[0778] As one embodiment, it includes:

[0779] The first processor 2001, the physical layer of the target receiver of the first higher-level message set further indicates first information to its higher layer;

[0780] 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 obtained by measuring the RSRP of the first candidate resource.

[0781] As one embodiment, it includes:

[0782] The first processor 2001, the physical layer of the target receiver of the first higher-level message set sends a beam failure event indication to its higher layer;

[0783] 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.

[0784] As one embodiment, it includes:

[0785] The second processor 2101 receives a beam failure recovery request and sends a response to the beam failure recovery request.

[0786] Among them, the beam failure recovery is triggered.

[0787] As one embodiment, the second processor 2101 sends a signal in the first resource set.

[0788] As one embodiment, the second processor 2101 sends a reference signal in the first resource set, which includes one or more RS resources.

[0789] As one embodiment, the second processor 2101 sends a signal in the first candidate resource set.

[0790] As one embodiment, the second processor 2101 sends a reference signal in the first candidate resource set, which includes one or more RS resources.

[0791] As an example, the first operation is based on training or AI.

[0792] As an example, the first operation requires deployment.

[0793] As an example, the first operation is obtained by loading.

[0794] Those skilled in the art will understand that all or part of the steps in the above methods can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium, such as a read-only memory, hard disk, or optical disk. Optionally, all or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Accordingly, each module unit in the above embodiments can be implemented in hardware or in the form of software functional modules. This application is not limited to any specific combination of software and hardware. The user equipment, terminal, and UE in this application include, but are not limited to, drones, communication modules on drones, remote-controlled aircraft, aircraft, small aircraft, mobile phones, tablets, laptops, vehicle-mounted communication equipment, vehicles, RSUs, wireless sensors, internet access cards, IoT terminals, RFID terminals, NB-IoT terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, internet access cards, vehicle-mounted communication equipment, low-cost mobile phones, low-cost tablets, and other wireless communication devices. The base stations or system equipment 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), GNSS, relay satellites, satellite base stations, airborne base stations, RSUs (Road Side Units), drones, and testing equipment, such as transceivers or signaling testers that simulate some functions of a base station, and other wireless communication equipment.

[0795] Those skilled in the art will understand that the present invention can be practiced in other specified forms without departing from its core or essential characteristics. Therefore, the embodiments disclosed herein should in any way be considered descriptive rather than restrictive. The scope of the invention is defined by the appended claims rather than the foregoing description, and all modifications within their equivalent meaning and scope are considered to be included therein.

Claims

1. A method used in a first node of wireless communication, characterized in that, include: Receive a first higher-level message set, which is used to configure a first resource set and a first candidate resource set; The quality of the first wireless link is evaluated based on the first resource set; The physical layer of the first node indicates the first candidate resource in the first candidate resource set to its higher layers; Wherein, the first candidate resource set includes multiple candidate resources, and the first candidate resource is one of the multiple candidate resources; the evaluated quality of the first wireless link is worse than the 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 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.

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 obtained by measuring the RSRP of 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 through 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 performing a first operation, the first operation being based on training or AI, and the channel quality of the first candidate resource depending on the 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 of identifier.

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; the channel quality of the first candidate resource is RSRP only if the channel quality of the first candidate resource is not obtained based on AI.

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 layers.

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 the first information to its higher layers; 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 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 layers; 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 indicated by the beam failure event.

10. A terminal, characterized in that, The terminal includes: one or more processors and memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the terminal to perform the method as described in any one of claims 1 to 9.

11. A method used in a second node for wireless communication, characterized in that, include: Send a first higher-level message set, which is used to configure a first resource set and a first candidate resource set; Wherein, the target receiver of the first higher-layer message set evaluates the quality of the first radio link based on 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 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.

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 obtained by measuring the RSRP of 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 through 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, including: the target receiver of the first higher-level message set performs a first operation, the first operation being based on training or AI, and the channel quality of the first candidate resource depending 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 of identifier.

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; the channel quality of the first candidate resource is RSRP only if the channel quality of the first candidate resource is not obtained based on AI.

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 receiver of the first higher-level message set also indicates the channel quality of the first candidate resource to its higher layers.

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 receiver of the first higher-level message set also indicates the 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 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 receiver in the first higher-level 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 the target threshold, beam failure recovery is triggered; the target counter is used for counting indicated by the beam failure event.

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, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the base station to perform the method as described in any one of claims 11 to 19.

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