Method and apparatus for use in wireless communication nodes

Abstract

The present application discloses a method and apparatus for use in wireless communication nodes. The method comprises: a first node receiving a first higher-layer message set, the first higher-layer message set being used for configuring a first resource set; evaluating first radio link quality on the basis of the first resource set; each time the evaluated first radio link quality is lower than a reference threshold, a physical layer of the first node sending a beam failure event indication to a higher layer of the first node; and when the value of a target counter is equal to or greater than a target threshold, triggering beam failure recovery, the target counter being used for counting the beam failure event indication, and the reference threshold depending on whether an evaluation manner for the first radio link quality is based on AI, wherein when the evaluation manner for the first radio link quality is not based on AI, the reference threshold is a first threshold, and when the evaluation manner for the first radio link quality is based on AI, the reference threshold is a second threshold. The method improves system performance.

Description

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

[0001] This application claims priority to Chinese Patent Application No. 202410923909.8, filed on July 10, 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] The 3rd Generation Partnership Project (3GPP) introduced Beam Failure Recovery (BFR) for Special Cells (SpCells) in Release 15, and BFR for Secondary Cells (SCells) in Release 16. BFR mechanisms prevent the triggering of Radio Link Failure (RLF) at higher layers.

[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 existing measurement mechanisms and beam failure recovery schemes may be unable to meet the needs of AI / ML when AI / ML functionality is introduced. To address this issue, this application discloses a solution. It should be noted that while many embodiments of this application are specifically designed for AI / ML, this application is also applicable to other solutions, such as traditional beam failure recovery schemes. Furthermore, adopting a unified solution for different scenarios (including but not limited to AI / ML-based solutions and traditional beam failure recovery 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; evaluate the quality of a first radio link based on the first resource set; whenever the evaluated quality of the first radio link is worse than a reference threshold, the physical layer of the first node sends a beam failure event indication to its higher layers.

[0010] 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.

[0011] The reference threshold is either a first threshold or a second threshold, and the reference threshold depends on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the reference threshold is the first threshold; when the evaluation method of the first wireless link quality is based on AI, the reference threshold is the second threshold.

[0012] As an example, the problem this application aims to solve includes: how to support AI-based wireless link quality assessment methods in the face of beam failure.

[0013] As an example, the problem this application aims to solve includes: how to adjust the reference threshold used to evaluate the quality of a wireless link.

[0014] As an example, in the above method, the triggering conditions for beam failure event indication are adjusted according to whether the wireless link quality assessment method is based on AI.

[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 evaluation method of the first wireless link quality is not based on AI and includes: the first resource set includes at least one RS resource, and the evaluated first wireless link quality is obtained based on measurements of all RS resources in the first resource set.

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

[0020] According to one aspect of this application, the evaluation method of the first wireless link quality is based on AI, comprising: the evaluated first wireless link quality is predicted or inferred.

[0021] As an example, the advantages of the above method include: reducing the measurements used for wireless link quality assessment and reducing the overhead of RS resources.

[0022] As an example, the advantages of the above method include: obtaining more and more accurate channel quality information through AI-based wireless link quality assessment, thereby improving system performance.

[0023] According to one aspect of this application, the evaluation method of the first wireless link quality is based on AI and includes: the evaluation method of the first wireless link quality is associated with a first type of identifier.

[0024] As an example, the advantages of the above method include: the evaluation method for determining the quality of the first wireless link through the first type of identifier simplifies the design.

[0025] According to one aspect of this application, the evaluation method of the first wireless link quality is based on AI and includes: the evaluation method of the first wireless link quality includes the first node performing a first operation, the first operation being based on training or AI, and the evaluated first wireless link quality depending on the output of the first operation.

[0026] As an example, the AI ​​(Artificial Intelligence) includes ML (Machine Learning).

[0027] As an example, the advantages of the above method include: better adaptability to various application scenarios and terminals, and improved flexibility and adaptability.

[0028] According to one aspect of this application, the first operation is associated with the first type of identifier.

[0029] As an example, the advantages of the above method include: determining the first operation through the first type of identifier simplifies the design.

[0030] According to one aspect of this application, the first radio link quality is for the radio link quality of a first serving cell, the beam failure event indication is for the first serving cell, the target counter is used for counting the beam failure event indication for the first serving cell, and the beam failure recovery is for the first serving cell.

[0031] According to one aspect of this application, the first wireless link quality is a wireless link quality for the first resource set, the beam failure event indication is a beam failure event indication for the first resource set, the target counter is used for counting the beam failure event indication for the first resource set, and the beam failure recovery is for the first resource set.

[0032] As an example, the advantages of the above method include: supporting beam failure recovery for cells and beam failure recovery for a given set of resources, improving flexibility and adaptability.

[0033] According to one aspect of this application, when the evaluation method of the first wireless link quality is not based on AI, the first resource set includes at least one RS resource, and the evaluated first wireless link quality is obtained based on measurements of all RS resources in the first resource set; when the evaluation method of the first wireless link quality is based on AI, the evaluated first wireless link quality is a predicted or inferred wireless link quality for the first resource set.

[0034] As an example, the advantages of the above method include: supporting both AI-based and non-AI-based wireless link quality assessments, providing good flexibility, and improving the accuracy of beam failure monitoring.

[0035] As an example, the advantages of the above method include: supporting AI-based assessment of wireless link quality and reducing RS resource-based measurements.

[0036] According to one aspect of this application, it is characterized by comprising:

[0037] Deploy the first operation.

[0038] As an example, the advantages of the above method include: it provides sufficient freedom for the first node, adapting to various different scenarios and terminals, and has adaptability and flexibility.

[0039] As an example, the advantages of the above method include: training for the first operation can be performed outside the first node, reducing the processing power requirements and power consumption of the first node.

[0040] As one example, the first node is a terminal.

[0041] As one example, the user equipment is a terminal.

[0042] This application discloses a method used in a second node for wireless communication, characterized by comprising:

[0043] Send a first higher-level message set, which is used to configure the first resource set;

[0044] Wherein, the target receiver of the first higher-layer message set evaluates the quality of the first wireless link based on the first resource set; whenever the evaluated quality of the first wireless link is worse than a reference threshold, the physical layer of the target receiver of the first higher-layer message set sends a beam failure event indication to its higher layer; beam failure recovery is triggered when the value of the target counter is equal to or greater than the target threshold; the target counter is used to count the beam failure event indication; the reference threshold is one of a first threshold or a second threshold, and the reference threshold depends on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the reference threshold is the first threshold; when the evaluation method of the first wireless link quality is based on AI, the reference threshold is the second threshold.

[0045] According to one aspect of this application, the second node is a base station.

[0046] According to one aspect of this application, the second node is a user equipment.

[0047] According to one aspect of this application, the second node is a relay node.

[0048] According to one aspect of this application, the evaluation method of the first wireless link quality is not based on AI and includes: the first resource set includes at least one RS resource, and the evaluated first wireless link quality is obtained based on measurements of all RS resources in the first resource set.

[0049] According to one aspect of this application, the evaluation method of the first wireless link quality is based on AI, comprising: the evaluated first wireless link quality is predicted or inferred.

[0050] According to one aspect of this application, the evaluation method of the first wireless link quality is based on AI and includes: the evaluation method of the first wireless link quality is associated with a first type of identifier.

[0051] According to one aspect of this application, the evaluation method of the first wireless link quality is based on AI and includes: the evaluation method of the first wireless link quality includes 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 evaluated first wireless link quality depending on the output of the first operation.

[0052] According to one aspect of this application, the first operation is associated with the first type of identifier.

[0053] According to one aspect of this application, the first radio link quality is for the radio link quality of a first serving cell, the beam failure event indication is for the first serving cell, the target counter is used for counting the beam failure event indication for the first serving cell, and the beam failure recovery is for the first serving cell.

[0054] According to one aspect of this application, the first wireless link quality is a wireless link quality for the first resource set, the beam failure event indication is a beam failure event indication for the first resource set, the target counter is used for counting the beam failure event indication for the first resource set, and the beam failure recovery is for the first resource set.

[0055] According to one aspect of this application, when the evaluation method of the first wireless link quality is not based on AI, the first resource set includes at least one RS resource, and the evaluated first wireless link quality is obtained based on measurements of all RS resources in the first resource set; when the evaluation method of the first wireless link quality is based on AI, the evaluated first wireless link quality is a predicted or inferred wireless link quality for the first resource set.

[0056] This application discloses a terminal, characterized in that the terminal includes: one or more processors and a memory;

[0057] 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.

[0058] This application discloses a base station, characterized in that the base station includes: one or more processors and a memory;

[0059] 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.

[0060] This application discloses a first node used for wireless communication, characterized in that it comprises:

[0061] A first processor receives a first higher-level message set, which is used to configure a first resource set; evaluates the quality of a first wireless link based on the first resource set; and whenever the evaluated quality of the first wireless link is worse than a reference threshold, the physical layer of the first node sends a beam failure event indication to its higher layer.

[0062] The first processor 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.

[0063] The reference threshold is either a first threshold or a second threshold, and the reference threshold depends on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the reference threshold is the first threshold; when the evaluation method of the first wireless link quality is based on AI, the reference threshold is the second threshold.

[0064] This application discloses a second node used for wireless communication, characterized in that it comprises:

[0065] The second processor sends a first higher-level message set, which is used to configure the first resource set;

[0066] Wherein, the target receiver of the first higher-layer message set evaluates the quality of the first wireless link based on the first resource set; whenever the evaluated quality of the first wireless link is worse than a reference threshold, the physical layer of the target receiver of the first higher-layer message set sends a beam failure event indication to its higher layer; beam failure recovery is triggered when the value of the target counter is equal to or greater than the target threshold; the target counter is used to count the beam failure event indication; the reference threshold is one of a first threshold or a second threshold, and the reference threshold depends on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the reference threshold is the first threshold; when the evaluation method of the first wireless link quality is based on AI, the reference threshold is the second threshold.

[0067] As an example, compared with conventional solutions, this application has the following advantages:

[0068] Flexible methods for evaluating wireless link quality;

[0069] Enhanced overall system performance;

[0070] Lower air interface overhead;

[0071] More flexible and diverse input information;

[0072] Better flexibility and adaptability;

[0073] Enhanced reliability and robustness. Attached Figure Description

[0074] 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:

[0075] Figure 1 illustrates a flowchart of a first higher-level message set and beam failure event indication according to an embodiment of this application;

[0076] Figure 2 shows a schematic diagram of a network architecture according to an embodiment of this application;

[0077] 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;

[0078] Figure 4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of this application;

[0079] Figure 5 illustrates a flowchart of the transmission between a first node and a second node according to an embodiment of this application;

[0080] Figure 6 illustrates a schematic diagram showing that the evaluation of the first wireless link quality according to an embodiment of this application is obtained based on measurements of all RS resources in a first resource set;

[0081] Figure 7 illustrates a schematic diagram showing that the quality of a first wireless link, as evaluated according to an embodiment of this application, is predicted or inferred.

[0082] Figure 8 illustrates a schematic diagram of how a first wireless link quality assessment method is associated with a first type of identifier according to an embodiment of this application;

[0083] Figure 9 illustrates a schematic diagram of the evaluation of the quality of a first wireless link depending on the output of a first operation according to an embodiment of this application;

[0084] Figure 10 illustrates a schematic diagram of a first operation being associated with a first type of identifier according to an embodiment of this application;

[0085] Figure 11 illustrates a schematic diagram of a first operation based on training or AI according to an embodiment of this application;

[0086] Figures 12A-12B illustrate a schematic diagram of the relationship between a first wireless link quality and a beam failure event indication according to an embodiment of this application;

[0087] Figure 13 illustrates a schematic diagram of the relationship between a first wireless link quality and a first resource set according to an embodiment of this application;

[0088] Figure 14 shows a schematic diagram of a first operation according to an embodiment of this application;

[0089] Figure 15 shows a schematic diagram of the deployment of a first operation on a first node according to an embodiment of this application;

[0090] 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;

[0091] Figure 17 shows a schematic diagram of the AI / ML function deployment of a UE according to an embodiment of this application;

[0092] Figure 18 shows a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application;

[0093] Figure 19 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of this application;

[0094] Figure 20 shows a structural block diagram of a processing apparatus for a first node according to an embodiment of the present application;

[0095] Figure 21 shows a structural block diagram of a processing apparatus for a second node according to an embodiment of the present application. Detailed Implementation

[0096] 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.

[0097] Example 1

[0098] Example 1 illustrates a flowchart of a first higher-level message set and beam failure event indication 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 relationship between the steps.

[0099] In Embodiment 1, the first node receives a first higher-layer message set in step 101; evaluates the quality of the first wireless link according to a first resource set in step 102; sends a beam failure event indication in step 103; and triggers beam failure recovery in step 104. The first higher-layer message set is used to configure the first resource set. Whenever the evaluated quality of the first wireless link is worse than a reference threshold, 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 to count the beam failure event indication. The reference threshold is one of a first threshold or a second threshold, and the reference threshold depends on whether the evaluation method of the first wireless link quality is based on AI. When the evaluation method of the first wireless link quality is not based on AI, the reference threshold is the first threshold; when the evaluation method of the first wireless link quality is based on AI, the reference threshold is the second threshold.

[0100] As one embodiment, the first higher-level message set includes at least one higher-level message.

[0101] As one example, the first higher-level message set includes RRC messages.

[0102] As an example, the first higher-level message set includes at least one RRC message from either RRC messages or MAC CE messages.

[0103] As an example, the first higher-level message set includes RRC messages and MAC CE messages.

[0104] As one embodiment, the first higher-level message set includes some or all of the fields in one or more RRC IEs.

[0105] As one embodiment, the first higher-level message set includes some or all of the fields in an RRC IE.

[0106] As one embodiment, the first higher-level message set includes a portion of the fields in RRC IE RadioLinkMonitoringConfig.

[0107] As an example, the first higher-level message set includes a field in the RRC IE whose name includes failureDetectionResourcesToAddModList.

[0108] As an example, the first higher-level message set includes the failureDetectionResourcesToAddModList field in RRC IE RadioLinkMonitoringConfig.

[0109] As an example, the first higher-level message set includes the failureDetectionSet1 field and the failureDetectionSet2 field in RRC IE RadioLinkMonitoringConfig.

[0110] 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.

[0111] As an example, the first higher-level message set includes at least one field in the RRC IE whose name includes failureDetectionSet.

[0112] As an example, the name of the RRC message in the first higher-level message set includes failureDetectionResources.

[0113] As an example, the name of the RRC message in the first higher-level message set includes failureDetectionSet.

[0114] 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.

[0115] As an example, the MAC CE message in the first higher-level message set is a BFD-RS Indication MAC CE.

[0116] As an example, the name of the MAC CE message in the first higher-level message set includes BFD-RS Indication MAC CE.

[0117] As an example, the name of the MAC CE message in the first higher-level message set includes BFD.

[0118] 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.

[0119] 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.

[0120] As an example, RRC IE RadioLinkMonitoringConfig,

[0121] For the specific definitions of the failureDetectionResourcesToAddModList field, the failureDetectionSet1 field, and the failureDetectionSet2 field, please refer to section 6.3.2 of 3GPP TS38.331.

[0122] As an example, the specific definition of BFD-RS Indication MAC CE can be found in section 5.18.25 of 3GPP TS38.321.

[0123] As an example, the specific definition of IE RadioLinkMonitoringConfig can be found in section 6.3.2 of 3GPP TS38.331.

[0124] As an example, the first resource set includes at least one RS resource.

[0125] As an example, the first resource set consists of at least one RS resource.

[0126] As one embodiment, the first resource set includes at least one RS resource, or at least one of at least one beam.

[0127] As an example, the first resource set includes at least one of at least one RS resource, at least one training dataset, at least one air interface resource, or at least one beam.

[0128] 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.

[0129] As an example, when the evaluation method of the first wireless link quality is not based on AI, the first resource set consists of at least one RS resource; when the evaluation method of the first wireless link quality is based on AI, the first resource set includes at least one of at least one RS resource, at least one training dataset, at least one air interface resource, or at least one beam.

[0130] As an example, the first resource set is used for beam failure detection (BFD).

[0131] As an example, the first resource set is used for failure monitoring.

[0132] As one embodiment, the first resource set is

[0133] As one embodiment, the first resource set is

[0134] As one embodiment, the first resource set is

[0135] As one embodiment, the first resource set is or At least one of them.

[0136] As an example, For a specific definition, please refer to Chapter 6 of 3GPP TS38.213.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] As an example, the first higher-level message set is used to configure the index of each RS resource in the first resource set.

[0142] As an example, the index of an RS resource is used to identify the RS resource.

[0143] As an example, the index of an RS resource is the configuration index of the RS resource.

[0144] As an example, the index of an RS resource includes the configuration index of the RS resource.

[0145] As an example, an index of an SS / PBCH block resource is used to identify the SS / PBCH block resource.

[0146] As an example, an index of an SS / PBCH block resource is used to identify the configuration of the SS / PBCH block resource.

[0147] As an example, the index of a periodic CSI-RS resource is the configuration index of the periodic CSI-RS resource.

[0148] As an example, an index of a periodic CSI-RS resource includes a configuration index of the periodic CSI-RS resource.

[0149] As an example, the index of a CSI-RS resource is NZP-CSI-RS-ResourceId.

[0150] As an example, an index for a CSI-RS resource is a csi-RS-Index.

[0151] As an example, the index of an SS / PBCH block resource is the SSB-Index.

[0152] As an example, the index of an SS / PBCH block resource is the ssb-Index.

[0153] As an example, each RS resource in the first resource set depends on the configuration of the first higher-level message set.

[0154] As one embodiment, the first higher-level message set includes an index of each RS resource included in the first resource set.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] As a sub-implementation of the above embodiments, the first higher-level message set includes a portion of the domains in IE PDCCH-Config.

[0160] As a sub-implementation of the above embodiments, the first higher-level message set includes the controlResourceSetToAddModList field in IE PDCCH-Config.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] As an example, the evaluation of the first wireless link quality based on the first resource set is used for beam failure detection.

[0168] As an example, the specific procedures for beam failure monitoring can be found in Chapter 6 of 3GPP TS38.213.

[0169] As an example, the specific procedures for beam failure monitoring can be found in section 5.17 of 3GPP TS38.321.

[0170] 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 reference threshold.

[0171] As an example, the quality of the first wireless link is RSRP.

[0172] As an example, the quality of the first wireless link is L1-RSRP.

[0173] As an example, the quality of the first wireless link is SINR.

[0174] As an example, the quality of the first wireless link is L1-SINR.

[0175] As an example, the quality of the first wireless link is BLER.

[0176] As an example, the quality of the first wireless link is hypothetical BLER.

[0177] As an example, the evaluation method for the first wireless link quality is based on AI; the evaluated first wireless link quality is a predicted or inferred wireless link quality for the first resource set.

[0178] As an example, the evaluation method for the quality of the first wireless link is based on AI; the evaluated quality of the first wireless link is not obtained based on measurements of all RS resources in the first resource set.

[0179] As an example, the evaluation method for the quality of the first wireless link is based on AI; the evaluated quality of the first wireless link is not obtained based on measurements of RS resources in the first resource set.

[0180] As an example, the evaluation method for the first wireless link quality is based on AI; the first operation is used to predict or infer the wireless link quality for the first resource set.

[0181] As an example, the evaluation method of the first wireless link quality is based on AI; the first operation in this application is used to predict or infer the wireless link quality for the first resource set.

[0182] As one embodiment, the evaluation of the first radio link quality as a predicted or inferred radio link quality for the first resource set includes: the evaluation of the first radio link quality is not based on measurements of all RS resources in the first resource set.

[0183] As one embodiment, the evaluation of the first wireless link quality as a predicted or inferred wireless link quality for the first resource set includes: the evaluation of the first wireless link quality is not based on measurements of RS resources in the first resource set.

[0184] As one embodiment, the condition that the evaluated first wireless link quality is not obtained based on measurements of all RS resources in the first resource set includes: the evaluated first wireless link quality is not expected to be obtained based on measurements of all RS resources in the first resource set.

[0185] As one embodiment, the condition that the evaluated first wireless link quality is not obtained based on measurements of RS resources in the first resource set includes: the evaluated first wireless link quality is not expected to be obtained based on measurements of RS resources in the first resource set.

[0186] As an example, the reference threshold is a real number.

[0187] As an example, the reference threshold is a non-negative real number.

[0188] As an example, the reference threshold is a non-negative real number that is no greater than 1.

[0189] As an example, the reference threshold is Qout_LR.

[0190] As an example, the reference threshold is one of Qout_LR, Qout_LR_SSB, or Qout_LR_CSI-RS.

[0191] As an example, the definitions of Qout_LR, Qout_LR_SSB and Qout_LR_CSI-RS can be found in 3GPP TS38.133.

[0192] As an example, the first threshold is a real number.

[0193] As an example, the first threshold is a non-negative real number.

[0194] As an example, the first threshold is a non-negative real number that is no greater than 1.

[0195] As an example, the first threshold is configurable.

[0196] As an example, the first threshold is a default value.

[0197] As an example, the first threshold is configured by the RRC parameter rlmInSyncOutOfSyncThreshold.

[0198] As an example, the first threshold is the default value of rlmInSyncOutOfSyncThreshold.

[0199] As an example, the specific definition of rlmInSyncOutOfSyncThreshold can be found in Chapter 6 of 3GPP TS38.213.

[0200] As an example, the definition of rlmInSyncOutOfSyncThreshold can be found in 3GPP TS38.133.

[0201] As an example, the second threshold is a real number.

[0202] As an example, the second threshold is a non-negative real number.

[0203] As an example, the second threshold is a non-negative real number not greater than 1.

[0204] As one example, the second threshold is configurable.

[0205] As an example, the second threshold is a default value.

[0206] As one example, the second threshold and the first threshold are linearly related.

[0207] As an example, the first threshold and the second threshold are indicated by different higher-level parameters.

[0208] As an example, the first threshold and the second threshold are configured separately.

[0209] As an example, the second threshold is equal to the sum of the first threshold and the first offset.

[0210] As a sub-implementation of the above embodiments, the first offset is configured.

[0211] As a sub-implementation of the above embodiment, the first offset is reported by the first node.

[0212] As a sub-implementation of the above embodiments, the first offset is predefined.

[0213] As a sub-implementation of the above embodiment, the first offset is a real number.

[0214] As a sub-implementation of the above embodiment, the first offset is a real number greater than -1 and less than 1.

[0215] As one example, the first threshold and the second threshold are different.

[0216] As an example, the second threshold is less than the first threshold.

[0217] As an example, in the above method, the threshold used by the AI-based evaluation method is lower than the threshold used by the non-AI-based evaluation method.

[0218] As an example, the benefits of the above method include: reducing beam failure recovery caused by errors in AI prediction or inference.

[0219] As an example, the advantages of the above method include: it is applicable to situations where the link quality evaluated by AI is lower than the actual link quality.

[0220] As one example, the second threshold is greater than the first threshold.

[0221] In the above methods, the threshold used by the AI-based evaluation method is greater than the threshold used by the non-AI-based evaluation method.

[0222] As an example, the advantages of the above method include: faster beam failure recovery and reduced link failure probability.

[0223] As an example, the advantages of the above method include: it is more suitable for situations where the link quality evaluated by AI is higher than the actual link quality.

[0224] As an example, higher-level parameters are used to indicate whether the evaluation method for the quality of the first wireless link is based on AI.

[0225] As an example, higher-level parameters are used to indicate whether the evaluation method for the quality of the first wireless link is allowed to be AI-based.

[0226] As an example, higher-level parameters are used to indicate whether AI-based evaluation methods are supported.

[0227] As an example, the first higher-layer message set is used to indicate whether the evaluation method of the first wireless link quality is based on AI.

[0228] As an example, the first higher-layer message set is used to indicate whether the evaluation method of the first wireless link quality is allowed to be based on AI.

[0229] As an example, the first higher-level message set is used to indicate whether an AI-based evaluation method is supported.

[0230] As an example, whether the evaluation method of the first wireless link quality is based on AI depends on whether the first node receives the first higher-layer parameter; the evaluation method of the first wireless link quality is based on AI only when the first node receives the first higher-layer parameter.

[0231] As an example, whether the first node supports the AI-based evaluation method depends on whether the first node receives the first higher-level parameter; the first node supports the AI-based evaluation method only when it receives the first higher-level parameter.

[0232] As an example, the first node indicates whether it supports AI-based evaluation methods through capability reporting.

[0233] As one embodiment, the quality of the first wireless link is evaluated based on a first resource set; whether the evaluation method of the first wireless link quality is based on AI depends on whether the first resource set includes resources that are not measured; when the first resource set includes resources that are not measured, the evaluation method of the first wireless link quality is based on AI; when all resources in the first resource set are measured, the evaluation method of the first wireless link quality is not based on AI.

[0234] As an example, the unmeasured resources include: resources that are not expected to be measured.

[0235] As one embodiment, the quality of the first wireless link is evaluated based on a first resource set; whether the evaluation method of the first wireless link quality is based on AI depends on whether the first resource set includes unmeasured RS resources; when the first resource set includes unmeasured RS resources, the evaluation method of the first wireless link quality is based on AI; when all RS resources in the first resource set are measured, the evaluation method of the first wireless link quality is not based on AI.

[0236] As an example, the RS resources that are not to be measured include: RS resources that are not expected to be measured.

[0237] As an example, the quality of the first wireless link is evaluated based on a first resource set; whether the evaluation method of the first wireless link quality is based on AI depends on whether the first resource set includes resources other than RS resources; when the first resource set includes resources other than RS resources, the evaluation method of the first wireless link quality is based on AI; when the first resource set consists of at least one RS resource, the evaluation method of the first wireless link quality is not based on AI.

[0238] As one example, resources other than RS resources include beams.

[0239] As an example, the resources other than the RS resources include at least one of training datasets, air interface resources, or beams.

[0240] As an example, the first wireless link quality is one of RSRP, L1-RSRP, SINR, or L1-SINR; the phrase "the evaluated first wireless link quality is worse than a reference threshold" means that the evaluated first wireless link quality is less than the reference threshold.

[0241] As a sub-example of the above embodiments, the unit of the reference threshold is dBm or dB.

[0242] As an example, the first wireless link quality is BLER; the phrase "the evaluated first wireless link quality is worse than a reference threshold" means that the evaluated first wireless link quality is greater than the reference threshold.

[0243] As a sub-example of the above embodiments, the reference threshold is the BLER threshold.

[0244] As an example, the first wireless link quality is a hypothetical BLER; the phrase "the evaluated first wireless link quality is worse than a reference threshold" means that the evaluated first wireless link quality is greater than the reference threshold.

[0245] 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.

[0246] 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.

[0247] Typically, the first node maintains the target counter at the MAC layer.

[0248] Typically, the MAC entity of the first node maintains the target counter.

[0249] 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.

[0250] Typically, the target counter is BFI_COUNTER.

[0251] Typically, the target counter is set to 0 when the target timer expires.

[0252] Typically, the target timer is beamFailureDetectionTimer.

[0253] As an example, the target counter is BFI_COUNTER.

[0254] As an example, the initial value of the target counter is 0.

[0255] As an example, the target threshold is a positive integer.

[0256] As an example, the target threshold is beamFailureInstanceMaxCount.

[0257] As an example, the target threshold is configured by the RRC parameter.

[0258] As an example, the RRC parameters for configuring the target threshold include all or part of the information in the beamFailureInstanceMaxCount field of RadioLinkMonitoringConfigIE.

[0259] As an example, the target timer is beamFailureDetectionTimer.

[0260] As an example, the initial value of the target timer is a positive integer.

[0261] As an example, the initial value of the target timer is a positive real number.

[0262] 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.

[0263] As an example, the initial value of the target timer is configured by the higher-level parameter beamFailureDetectionTimer.

[0264] As an example, the initial value of the target timer is configured by an IE.

[0265] As an example, the name of the IE that configures the initial value of the target timer includes RadioLinkMonitoring.

[0266] As an example, whether the beam failure recovery is a random access procedure depends on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the beam failure recovery is a random access procedure; when the evaluation method of the first wireless link quality is based on AI, the beam failure recovery is not a random access procedure.

[0267] As an example, whether the beam failure recovery is a random access procedure depends on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the beam failure recovery is a random access procedure; when the evaluation method of the first wireless link quality is based on AI, the beam failure recovery is based on scheduling requests.

[0268] As an example, whether the beam failure recovery is a random access procedure depends on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the beam failure recovery is a random access procedure; when the evaluation method of the first wireless link quality is based on AI, the beam failure recovery includes the first node triggering a scheduling request for beam failure recovery.

[0269] As one embodiment, whether the beam failure recovery includes a random access procedure depends on whether the evaluation method of the first wireless link quality is based on AI; the beam failure recovery includes a random access procedure only when the evaluation method of the first wireless link quality is not based on AI.

[0270] Example 2

[0271] Example 2 illustrates a schematic diagram of a network architecture according to an embodiment of this application, as shown in Figure 2.

[0272] 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.

[0273] As an example, the first node includes the UE201.

[0274] As one embodiment, the second node includes the node 203.

[0275] As an example, the wireless link between the UE201 and the node203 includes a cellular link.

[0276] As an example, the sender of the first higher-level message set includes the node 203.

[0277] As an example, the recipient of the first higher-level message set includes the UE201.

[0278] As an example, the sender indicated by the beam failure event includes the UE201.

[0279] As an example, the trigger for beam failure recovery includes the UE201.

[0280] As an example, the executor of the first operation includes the UE201.

[0281] As an example, the deployer of the first operation includes the UE201.

[0282] Example 3

[0283] 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.

[0284] 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.).

[0285] As an example, the wireless protocol architecture in Figure 3 is applicable to the first node.

[0286] As an example, the wireless protocol architecture in Figure 3 is applicable to the second node.

[0287] As an example, the higher layer mentioned in this application refers to the layer above the physical layer.

[0288] As an example, the first higher-level message set is generated in the RRC sublayer 306.

[0289] As an example, the first higher-level message set is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0290] As an example, the first higher-level message set is generated in the RRC sublayer 306 and the MAC sublayer 302.

[0291] As an example, the beam failure event indication is generated in the PHY301 or the PHY351.

[0292] As an example, the target counter is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0293] Example 4

[0294] 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.

[0295] 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.

[0296] 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.

[0297] 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.

[0298] 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.

[0299] 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.

[0300] 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.

[0301] 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 includes at least: receiving a first higher-layer message set, the first higher-layer message set being used to configure a first resource set; evaluating a first wireless link quality based on the first resource set; whenever the evaluated first wireless link quality is worse than a reference threshold, the physical layer of the first node sends a beam failure event indication to its higher layers; triggering beam failure recovery when the value of a target counter is equal to or greater than a target threshold; the target counter being used for counting the beam failure event indication; the reference threshold is one of a first threshold or a second threshold, the reference threshold depending on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the reference threshold is the first threshold; when the evaluation method of the first wireless link quality is based on AI, the reference threshold is the second threshold.

[0302] 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; evaluating a first wireless link quality based on the first resource set; whenever the evaluated first wireless link quality is worse than the reference threshold, the physical layer of the first node sends a beam failure event indication to its higher layers; triggering beam failure recovery when the value of a target counter is equal to or greater than the target threshold; the target counter being used for counting the beam failure event indication; the reference threshold being one of a first threshold or a second threshold, the reference threshold depending on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the reference threshold is the first threshold; when the evaluation method of the first wireless link quality is based on AI, the reference threshold is the second threshold.

[0303] 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 includes at least: transmitting a first higher-layer message set, the first higher-layer message set being used to configure a first resource set; a target receiver of the first higher-layer message set evaluating a first wireless link quality based on the first resource set; whenever the evaluated first wireless link quality is worse than a reference threshold, the physical layer of the target receiver of the first higher-layer message set sending a beam failure event indication to its higher layer; beam failure recovery being triggered when the value of a target counter is equal to or greater than a target threshold; the target counter being used for counting the beam failure event indication; the reference threshold being one of a first threshold or a second threshold, the reference threshold depending on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the reference threshold is the first threshold; when the evaluation method of the first wireless link quality is based on AI, the reference threshold is the second threshold.

[0304] 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; a target receiver of the first higher-level message set evaluating a first wireless link quality based on the first resource set; whenever the evaluated first wireless link quality is worse than the reference threshold, the physical layer of the target receiver of the first higher-level message set sending a beam failure event indication to its higher layer; beam failure recovery being triggered when the value of a target counter is equal to or greater than the target threshold; the target counter being used for counting the beam failure event indication; the reference threshold being one of a first threshold or a second threshold, the reference threshold depending on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the reference threshold is the first threshold; when the evaluation method of the first wireless link quality is based on AI, the reference threshold is the second threshold.

[0305] As an example, the first node in this application includes the second communication device 450.

[0306] As an example, the second node in this application includes the first communication device 410.

[0307] 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.

[0308] 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.

[0309] 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}.

[0310] 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}.

[0311] 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}.

[0312] Example 5

[0313] 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 F52 are optional.

[0314] For the second node U1, the first higher-level message set is sent in step S511.

[0315] For the first node U2, in step S5201, a first operation is deployed; in step S521, a first higher-layer message set is received; in step S522, the quality of the first radio link is evaluated according to the first resource set; in step S5202, the first operation is executed; in step S523, the physical layer of the first node sends a beam failure event indication to its higher layer; and in step S524, beam failure recovery is triggered.

[0316] In Embodiment 5, the first higher-layer message set is used to configure the first resource set; whenever the evaluated quality of the first wireless link is worse than a reference threshold, the physical layer of the first node sends a beam failure event indication to its higher layer; when the value of the target counter is equal to or greater than the target threshold, beam failure recovery is triggered; the target counter is used to count the beam failure event indication; the reference threshold is one of a first threshold or a second threshold, and the reference threshold depends on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the reference threshold is the first threshold; when the evaluation method of the first wireless link quality is based on AI, the reference threshold is the second threshold.

[0317] As an example, the first node U2 is the first node in this application.

[0318] As an example, the second node U1 is the second node in this application.

[0319] 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.

[0320] 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.

[0321] 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.

[0322] In one embodiment, the second node U1 is the serving cell sustaining base station of the first node U2.

[0323] 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.

[0324] As an example, RAN domain-specific management functions provide AI training function management capabilities and AI inference function management capabilities.

[0325] 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.

[0326] 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.

[0327] As an example, MnF refers to Management Function.

[0328] 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.

[0329] As an example, RAN domain-specific management functions provide management capabilities for AI training and AI inference functions.

[0330] As an example, both the AI ​​training function and the AI ​​inference function are located in the gNB.

[0331] As an example, the management capabilities for both AI training and AI inference are provided locally by gNB.

[0332] As one embodiment, the deployment of the first operation includes: obtaining the first operation.

[0333] As an example, the deployment first operation includes: loading the first operation.

[0334] As one embodiment, the deployment of the first operation includes: submitting a request to load the first operation.

[0335] As an example, the steps in block F51 of Figure 5 are present.

[0336] As an example, when the evaluation method for the quality of the first wireless link is based on AI, the steps in block F51 of Figure 5 are present.

[0337] As an example, when the evaluation method of the first wireless link quality is based on AI, the step in block F52 of Figure 5 is present; when the evaluation method of the first wireless link quality is not based on AI, the step in block F52 of Figure 5 is absent.

[0338] As an example, the steps in block F51 of Figure 5 are present; the method used in the first node for wireless communication includes: deploying a first operation.

[0339] As an example, the deployment of the first operation occurs before the reception of the first higher-level message set.

[0340] As an example, the deployment of the first operation is later than the reception of the first higher-level message set.

[0341] As an embodiment, the steps in block F52 of Figure 5 are present; step S522 includes the steps in block F52, evaluating the quality of a first wireless link based on a first resource set, wherein the evaluation method of the first wireless link quality is based on AI; the evaluation method of the first wireless link quality includes the first node performing a first operation.

[0342] As an example, the steps in block F52 of Figure 5 are present; the method used in the first node for wireless communication includes: performing a first operation.

[0343] As an example, the first higher-layer message set is transmitted on PDSCH (Physical Downlink Shared Channel).

[0344] As an example, the Beam Failure Recovery (BFR) includes sending a random access preamble, sending a BFR MAC CE, sending a Truncated BFR MAC CE, sending an Enhanced BFR MAC CE, or sending at least one of the following:

[0345] As an example, the random access preamble is a contention-based random access preamble.

[0346] As an example, the random access preamble is a contention-free random access preamble.

[0347] As an example, the beam failure recovery (BFR) includes transmitting one of the following: BFR MAC CE, Truncated BFR MAC CE, Enhanced BFR MAC CE, or Truncated Enhanced BFR MAC CE.

[0348] As an example, the Beam Failure Recovery (BFR) includes a random access procedure.

[0349] As an example, the Beam Failure Recovery (BFR) includes a contention-based random access procedure.

[0350] As an example, the Beam Failure Recovery (BFR) includes a contention-free random access procedure.

[0351] As an example, the beam failure recovery is based on a scheduling request.

[0352] As one embodiment, the beam failure recovery includes the first node triggering a scheduling request (SR) for beam failure recovery.

[0353] As an example, the Beam Failure Recovery (BFR) includes the first node sending a PUSCH carrying a MAC CE with the name including BFR.

[0354] As an example, the Beam Failure Recovery (BFR) includes the first node transmitting a MAC CE with the name including BFR.

[0355] As an example, the Beam Failure Recovery (BFR) includes the first node transmitting an SR on the PUCCH and transmitting a MAC CE with the name including BFR on the PUSCH.

[0356] As an example, the beam failure recovery process is described in section 5.17 of 3GPP TS38.321.

[0357] As an example, the beam failure recovery process is described in Section 6 of 3GPP TS38.213.

[0358] 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.

[0359] As an example, the first candidate resource set is configured by higher-level parameters, which include one of candidateBeamRSList, candidateBeamRSListExt, or candidateBeamRSSCellList.

[0360] As an example, the first candidate resource set includes at least one RS resource.

[0361] As an example, the first candidate resource set includes at least one periodic CSI-RS resource.

[0362] As an example, the first candidate resource set includes one or both of periodic CSI-RS resources and SS / PBCH blocks.

[0363] As one embodiment, the first candidate resource set is

[0364] As one embodiment, the first candidate resource set is

[0365] As one embodiment, the first candidate resource set is

[0366] As one embodiment, the first candidate resource set is or At least one of them.

[0367] As an example, For a specific definition, please refer to Chapter 6 of 3GPP TS38.213.

[0368] As an example, the specific definitions of candidate beam monitoring and beam failure recovery can be found in Chapter 6 of 3GPP TS38.213.

[0369] As an example, the specific definitions of candidate beam monitoring and beam failure recovery can be found in section 5.17 of 3GPP TS38.321.

[0370] As an example, the specific definitions of candidateBeamRSList, candidateBeamRSListExt, and candidateBeamRSSCellList can be found in Chapter 6 of 3GPP TS38.213.

[0371] 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 wireless link quality measured based on at least one transmission opportunity of each candidate resource is better than a first reference threshold, or whether the first node evaluates whether the wireless link quality measured based on at least one transmission opportunity of each candidate resource is equal to or better than the first reference threshold.

[0372] As an example, the evaluation period is TEvaluate_CBD_SSB or TEvaluate_CBD_CSI-RS.

[0373] As an example, one of the candidate resources in the first candidate resource set is an SS / PBCH block resource, and the radio link quality is based on the L1-RSRP measured from the candidate resource.

[0374] As an example, one of the candidate resources in the first candidate resource set is a CSI-RS resource. The radio link quality is obtained by subtracting a first power value from the L1-RSRP measured for 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.

[0375] As an example, the wireless link quality is L1-RSRP (Layer 1 Reference Signal Received Power).

[0376] As an example, the wireless link quality is L1-RSRP; when the wireless link quality is greater than the first reference threshold, the wireless link quality is better than the first reference threshold; when the wireless link quality is less than the first reference threshold, the wireless link quality is worse than the first reference threshold.

[0377] As an example, the wireless link quality is L1-RSRP; when the wireless link quality evaluated based on a candidate resource in the first candidate resource set is better than a first reference threshold, the physical layer of the first node sends the configuration index of the candidate resource and the measured L1-RSRP to its higher layers.

[0378] As an example, the wireless link quality is L1-RSRP; when the wireless link 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 of the candidate resource and the measured L1-RSRP to its higher layers.

[0379] As an example, a first candidate resource set is used for candidate beam detection; 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 channel quality of the first candidate resource is equal to or greater than a first reference threshold.

[0380] As an example, the physical layer of the first node sends the configuration index of the first candidate resource and the measured L1-RSRP to its higher layers.

[0381] As an example, the first reference threshold is Qin_LR.

[0382] As an example, the first reference threshold is Q. in,LR .

[0383] As an example, the first reference threshold is indicated by a higher-level parameter rsrp-ThresholdSSB or rsrp-ThresholdBFR.

[0384] As an example, the first power value is configured by the higher-level parameter powerControlOffsetSS.

[0385] As an example, Q in,LR For the definition, please refer to Chapter 6 of 3GPP TS38.213.

[0386] As an example, the specific definitions of rsrp-ThresholdSSB and rsrp-ThresholdBFR can be found in Chapter 6 of 3GPP TS38.213.

[0387] Example 6

[0388] Example 6 illustrates a schematic diagram showing that the evaluation of the first wireless link quality according to an embodiment of this application is obtained based on measurements of all RS resources in a first resource set; as shown in Figure 6, the resources in the first resource set are respectively represented as RS resource #1, ..., RS resource #N1. In Example 6, the evaluation method of the first wireless link quality is not based on AI and includes: the first resource set includes at least one RS resource, and the evaluated first wireless link quality is obtained based on measurements of all RS resources in the first resource set.

[0389] As one example, how to evaluate the quality of the first wireless link based on measurements of all RS resources in the first resource set is determined by the manufacturer of the first node, or is implementation-dependent. Some typical but non-limiting implementations are described below:

[0390] As an example, the first wireless link quality is one of RSRP, L1-RSRP, SINR, or L1-SINR; the evaluation of the first wireless link quality based on measurements of all RS resources in the first resource set includes: the evaluation of the first wireless link quality is based on the maximum value of RSRP, L1-RSRP, SINR, or L1-SINR obtained from measurements of all RS resources in the first resource set.

[0391] As an example, the first wireless link quality is BLER; the evaluation of the first wireless link quality is obtained based on measurements of all RS resources in the first resource set, including: the evaluation of the first wireless link quality is based on the minimum BLER measured from all RS resources in the first resource set.

[0392] As an example, the first wireless link quality is a hypothetical BLER; the evaluated first wireless link quality is obtained based on measurements of all RS resources in the first resource set, including: the evaluated first wireless link quality is the minimum of the hypothetical BLER obtained based on measurements of all RS resources in the first resource set.

[0393] Example 7

[0394] Example 7 illustrates a schematic diagram of the evaluation of a first wireless link quality according to an embodiment of this application, where the quality is predicted or inferred; as shown in Figure 7. In Example 7, the evaluation method of the first wireless link quality based on AI includes: the evaluated first wireless link quality is predicted or inferred.

[0395] As one example, the prediction includes AI prediction.

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

[0397] As one embodiment, the evaluation method of the first wireless link quality based on AI includes: the evaluation of the first wireless link quality uses an AI model.

[0398] As an example, the method of evaluating the quality of the first wireless link not based on AI includes: the evaluation of the quality of the first wireless link does not use an AI model.

[0399] As an example, the evaluation method of the first wireless link quality based on AI includes: the evaluation of the first wireless link quality includes information based on artificial intelligence or machine learning.

[0400] As one embodiment, the evaluation method of the first wireless link quality based on AI includes: the evaluated first wireless link quality includes information generated based on a neural network.

[0401] As an example, the evaluation method of the first wireless link quality based on AI includes: the evaluated first wireless link quality includes information generated based on CNN (Conventional Neural Networks).

[0402] As an example, the method of evaluating the quality of the first wireless link is not based on AI, including: the evaluated quality of the first wireless link does not include information based on artificial intelligence or machine learning.

[0403] As an example, the evaluation method of the first wireless link quality is not based on AI, including: the evaluated first wireless link quality does not include information generated based on neural networks.

[0404] As an example, the evaluation method of the first wireless link quality is not based on AI, including: the evaluated first wireless link quality does not include information generated based on CNN.

[0405] As one embodiment, the evaluation of the first wireless link quality being predicted or inferred includes: the first node obtaining the evaluation of the first wireless link quality through prediction or inference.

[0406] As an example, the assessment of the first wireless link quality being predicted or inferred includes: the assessment of the first wireless link quality is not obtained based on measurements of at least one RS resource.

[0407] As one embodiment, the evaluation of the first wireless link quality not obtained based on measurements of at least one RS resource includes: the evaluation of the first wireless link quality is not expected to be obtained based on measurements of at least one RS resource.

[0408] As one example, how to evaluate the quality of the first wireless link based on AI is determined by the manufacturer of the first node, or is implementation-related. Some typical but non-limiting implementations are described below:

[0409] As an example, the first wireless link quality is one of RSRP, L1-RSRP, SINR, or L1-SINR; the first wireless link quality based on AI evaluation is the maximum value of RSRP, L1-RSRP, SINR, or L1-SINR predicted or inferred for each RS resource in the first resource set.

[0410] As an example, the first wireless link quality is BLER; the first wireless link quality based on AI evaluation is the minimum BLER predicted or inferred for each RS resource in the first resource set.

[0411] As an example, the first wireless link quality is a hypothetical BLER; the first wireless link quality based on AI evaluation is the minimum of the hypothetical BLER predicted or inferred for each RS resource in the first resource set.

[0412] Example 8

[0413] Example 8 illustrates a schematic diagram of an evaluation method for the first wireless link quality according to an embodiment of this application, associated with a first type of identifier; as shown in Figure 8. In Example 8, the evaluation method for the first wireless link quality is AI-based and includes: the evaluation method for the first wireless link quality is associated with a first type of identifier.

[0414] As one embodiment, the evaluation method of the first wireless link quality based on AI includes: the evaluation method of the first wireless link quality is associated with a first type of identifier; the evaluation method of the first wireless link quality not based on AI includes: the evaluation method of the first wireless link quality is not associated with a first type of identifier.

[0415] As an example, the first type of identifier is a non-negative integer.

[0416] As an example, the first type of identifier is a string.

[0417] As an example, the first type of identifier is used to identify AI models.

[0418] As an example, the first type of identifier is used to identify AI entities.

[0419] As an example, the first type of identifier is used to identify AI functions.

[0420] 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.

[0421] As an example, the first type of identifier is a model identifier.

[0422] As an example, the first type of identifier is used to identify an AI model.

[0423] As an example, the first type of identifier is used by the first node to identify an AI model.

[0424] 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.

[0425] 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.

[0426] As an example, the first type of identifier is used to identify or indicate a set of resources.

[0427] 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.

[0428] As an example, the first type of identifier is used to identify or indicate a set of resources.

[0429] As an example, the first type of identifier is used to identify or indicate the training dataset.

[0430] 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.

[0431] As one embodiment, the evaluation method of the first wireless link quality associated with the first type of identifier includes: the evaluation method of the first wireless link quality includes the first node performing a first operation, the first operation being training-based or AI-based, and the evaluated first wireless link quality depending on the output of the first operation; the first operation is associated with the first type of identifier.

[0432] As one embodiment, the evaluation method of the first wireless link quality associated with the first type of identifier includes: the first higher-layer message set indicating the first type of identifier.

[0433] As an example, the first higher-level message set explicitly indicates the first type of identifier.

[0434] As an example, the first higher-level message set implicitly indicates the first type of identifier.

[0435] As one embodiment, the first higher-level message set includes a reference field, and the reference field included in the first higher-level message set indicates the first type of identifier.

[0436] As one embodiment, the method of evaluating the quality of the first wireless link being associated with a first type of identifier includes: the first higher-layer message set indicating that the method of evaluating the quality of the first wireless link is associated with a first type of identifier by indicating the first type of identifier.

[0437] As one embodiment, the evaluation method of the first wireless link quality associated with the first type of identifier includes: evaluating the first wireless link quality using an AI model identified by the first type of identifier.

[0438] As one embodiment, the evaluation method of the first wireless link quality associated with the first type of identifier includes: evaluating the first wireless link quality for an AI function identified by the first type of identifier.

[0439] As one embodiment, the evaluation method of the first wireless link quality associated with the first type of identifier includes: the first wireless link quality is evaluated by an AI entity identified by the first type of identifier.

[0440] Example 9

[0441] Example 9 illustrates a schematic diagram of the evaluation of a first wireless link quality according to an embodiment of this application, where the evaluation depends on the output of a first operation; as shown in Figure 9. In Example 9, the evaluation method of the first wireless link quality based on AI includes: the evaluation method of the first wireless link quality includes the first node performing a first operation, the first operation being based on training or AI, and the evaluated first wireless link quality depending on the output of the first operation.

[0442] As an example, the first operation is used to predict or infer the quality of the first wireless link.

[0443] As one embodiment, the evaluation method of the first wireless link quality based on AI includes: the evaluation method of the first wireless link quality includes the first node performing a first operation, the first operation being based on training or AI, and the evaluated first wireless link quality depending on the output of the first operation; the evaluation method of the first wireless link quality not based on AI includes: the evaluation method of the first wireless link quality does not include the first node performing the first operation.

[0444] As an example, the output of the first operation is used to generate the evaluated quality of the first wireless link.

[0445] As an example, the quality of the first wireless link being evaluated includes the output of the first operation.

[0446] As an example, the quality of the first wireless link being evaluated includes the post-processed output of the first operation.

[0447] As an example, the quality of the first wireless link being evaluated includes the truncated and / or quantized output of the first operation.

[0448] As an example, the output of the first operation, after post-processing, is used to generate the evaluated first wireless link quality.

[0449] As an example, the output of the first operation, after being truncated and / or quantized, is used to generate the evaluated first wireless link quality.

[0450] As an example, some or all of the output of the first operation is post-processed and used to generate the evaluated first wireless link quality.

[0451] As an example, some or all of the output of the first operation, after being truncated and / or quantized, is used to generate the evaluated first wireless link quality.

[0452] As one example, how the output of the first operation is used to generate the evaluated first wireless link quality is determined by the manufacturer of the first node, or is implementation-dependent. These are some typical but non-limiting implementations.

[0453] As an example, the output of the first operation includes channel quality.

[0454] As an example, the output of the first operation includes information beyond channel quality.

[0455] As an example, the output of the first operation includes wireless link quality.

[0456] As an example, the output of the first operation includes information beyond wireless link quality.

[0457] As an example, the output of the first operation includes compressed wireless link quality.

[0458] As one example, the first operation includes wireless link quality compression based on artificial intelligence or machine learning.

[0459] As an example, the first operation includes wireless link quality prediction or wireless link quality estimation based on artificial intelligence or machine learning.

[0460] As an example, the input to the first operation includes the reception quality of at least one physical channel or physical signal.

[0461] As an example, the input to the first operation includes the quality of the wireless link obtained from measurements based on at least one RS resource.

[0462] As an example, the input to the first operation includes measurements obtained based on the first resource set.

[0463] As an example, the input to the first operation includes the wireless link quality obtained based on the first resource set.

[0464] Example 10

[0465] Example 10 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 10. In Example 10, the first operation is associated with the first type of identifier.

[0466] As an example, the first operation is identified by the first type of identifier.

[0467] As an example, the AI ​​model used in the first operation is identified by the first type of identifier.

[0468] As an example, the AI ​​entity to which the first operation belongs is identified by the first type of identifier.

[0469] As an example, the AI ​​entity performing the first operation is identified by the first type of identifier.

[0470] As one example, the first operation is used for an AI function identified by the first type of identifier.

[0471] 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.

[0472] As an example, the first type of identifier is a model identifier.

[0473] As an example, the first type of identifier is used to identify an AI model.

[0474] As an example, the first type of identifier is used by the first node to identify an AI model.

[0475] As an example, the first type of identifier is used by the first node to determine the AI ​​model adopted by the first operation.

[0476] 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.

[0477] 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.

[0478] 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.

[0479] As one embodiment, the training for obtaining the first operation is identified by the first type of identifier.

[0480] As an example, the dataset used for training the first operation is identified by the first type of identifier.

[0481] 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.

[0482] Example 11

[0483] Example 11 illustrates a schematic diagram of a first operation based on training or AI according to an embodiment of this application; as shown in Figure 11. In Example 11, the first operation is based on training or AI.

[0484] As an example, the first operation is based on training or AI.

[0485] As an example, the first operation includes inference.

[0486] As an example, the reasoning includes AI reasoning.

[0487] As one example, the first operation includes an AI entity.

[0488] As an example, the first operation includes an AI entity for inference.

[0489] As an example, the first operation includes a portion of an AI entity.

[0490] As an example, the first operation includes a portion of an AI entity used for inference.

[0491] As one embodiment, the first operation includes reasoning for obtaining the first information report.

[0492] As an example, the reasoning includes AI (Artificial Intelligence) inference.

[0493] As an example, the first operation is used for an AI function.

[0494] As an example, the first operation is performed by the physical layer of the first node.

[0495] As an example, the first operation is performed at a higher level than the first node.

[0496] As an example, the model for the first operation is obtained through training.

[0497] As an example, the training for the first operation is performed by the first node.

[0498] As an example, the training of the first operation is performed by the sender of the first information set.

[0499] As an example, the training for the first operation is performed by the core network.

[0500] As an example, the training of the first operation is performed by an AI training producer.

[0501] As an example, the training of the first operation is performed by the MDA (Management Data Analytics Function).

[0502] As an example, the training of the first operation is performed by the MDA function located at the first node.

[0503] As an example, the training of the first operation is performed by the MDA function of the sender located in the first information set.

[0504] As an example, the training of the first operation is performed by NWDAF (Network Data Analytics Function).

[0505] As an example, the training of the first operation is performed by the MDAS (Management Data Analytics Service) producer.

[0506] As an example, the training of the first operation is performed by the MnS (Management Service) producer.

[0507] As an example, the first operation requires deployment.

[0508] As an example, the first operation is obtained by loading.

[0509] As an example, the first operation is obtained from the serving cell of the first node.

[0510] As an example, the first operation is obtained from the sustaining base station of the serving cell of the first node.

[0511] As an example, the first node deploys the first operation.

[0512] As an example, the first operation does not require deployment.

[0513] As an example, the first operation is obtained from the core network.

[0514] As an example, the first operation is based on artificial intelligence or machine learning.

[0515] As an example, the first operation is based on a neural network.

[0516] As an example, the first operation is based on CNN (Conventional Neural Networks).

[0517] As one example, the first operation includes preprocessing.

[0518] As one example, the first operation includes post-processing.

[0519] As one example, the post-processing includes DFT.

[0520] As one example, the post-processing includes quantization.

[0521] 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.

[0522] As one example, the post-processing includes truncation and / or padding.

[0523] As an example, the first operation includes one or more of convolution, pooling, cascading, and activation.

[0524] As one embodiment, the first operation includes a fully connected layer.

[0525] As an example, the first operation includes a pooling layer.

[0526] As one embodiment, the first operation includes at least one convolutional layer.

[0527] As an example, the first operation includes at least one encoding layer.

[0528] As an example, an encoding layer includes at least one convolutional layer and one pooling layer.

[0529] 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.

[0530] 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.

[0531] 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.

[0532] Without loss of generality, the parameters or AI model used in the first operation are determined by the manufacturer of the first node.

[0533] As an example, the first operation includes localization based on artificial intelligence or machine learning.

[0534] As one example, the first operation includes artificial intelligence or machine learning-assisted positioning.

[0535] As an example, the first node is a user (consumer).

[0536] As an example, the first node is the user of the AI ​​function.

[0537] As an example, the first node is the user of AI inference.

[0538] As an example, the first node is the user who trained the AI.

[0539] As an example, the first node is an MnS (Management Service) user.

[0540] As an example, the first node is the producer of AI inference.

[0541] As an example, the first node is the AI ​​training producer.

[0542] As one example, the first operation includes preprocessing.

[0543] As an example, the preprocessing includes DFT (Discrete Fourier Transform).

[0544] As an example, the preprocessing includes one or more of matrix decomposition, matrix transformation, and projection.

[0545] 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.

[0546] As one example, the preprocessing includes truncation and / or padding.

[0547] As one example, the preprocessing includes mapping.

[0548] As one example, the preprocessing includes mapping to vectors.

[0549] As one example, the preprocessing includes labeling.

[0550] As an example, the label refers to a mark made with a label.

[0551] Examples 12A-12B

[0552] Examples 12A-12B illustrate schematic diagrams illustrating the relationship between a first wireless link quality and a beam failure event indication according to an embodiment of this application, as shown in Figures 12A-12B.

[0553] In Example 12A, 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.

[0554] In embodiment 12B, 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.

[0555] 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.

[0556] As a sub-implementation of the above embodiments, the first resource set is

[0557] 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.

[0558] As a sub-implementation of the above embodiments, the first resource set is or

[0559] Example 13

[0560] Example 13 illustrates a schematic diagram of the relationship between a first wireless link quality and a first resource set according to an embodiment of this application; as shown in Figure 13. In Example 13, when the evaluation method of the first wireless link quality is not based on AI, the first resource set includes at least one RS resource, and the evaluated first wireless link quality is obtained based on measurements of all RS resources in the first resource set; when the evaluation method of the first wireless link quality is based on AI, the evaluated first wireless link quality is a predicted or inferred wireless link quality for the first resource set.

[0561] As one embodiment, the evaluated first wireless link quality is a predicted or inferred wireless link quality for the first resource set, including: the first node obtains the evaluated first wireless link quality through prediction or inference for the first resource set.

[0562] As one embodiment, evaluating the first wireless link quality as a predicted or inferred wireless link quality for the first resource set includes evaluating the first wireless link quality as not obtained from measurements of the first resource set.

[0563] As one embodiment, the evaluation of the first radio link quality not obtained based on measurements of the first resource set includes: the evaluation of the first radio link quality not obtained based on measurements of any RS resource in the first resource set.

[0564] As one embodiment, the evaluation of the first wireless link quality not obtained based on the measurement of the first resource set includes: the evaluation of the first wireless link quality is not expected to be obtained based on the measurement of the first resource set.

[0565] As one embodiment, the evaluation of the first radio link quality not based on measurements of the first resource set includes: the evaluation of the first radio link quality is not expected to be based on measurements of any RS resource in the first resource set.

[0566] Example 14

[0567] Example 14 illustrates a schematic diagram of a first operation according to an embodiment of this application; as shown in Figure 14. In Example 14, the first operation includes K1 sub-operations, where K1 is a positive integer not greater than 1. In Figure 14, the K1 sub-operations are respectively represented as sub-operation #0, ..., sub-operation #(K1-1).

[0568] As an example, each of the K1 sub-operations is based on training.

[0569] As an example, at least one of the K1 sub-operations is based on training.

[0570] As an example, each of the K1 training-based sub-operations is based on the same training executor.

[0571] As an example, two of the K1 sub-operations are based on different training executors.

[0572] As an example, at least one of the K1 sub-operations needs to be deployed.

[0573] As an example, at least one of the K1 sub-operations needs to be loaded.

[0574] As an example, all the sub-operations that need to be loaded in the K1 sub-operations are loaded from the same producer.

[0575] As an example, two of the K1 sub-operations that need to be loaded are loaded from different producers.

[0576] As an example, at least one of the K1 sub-operations is not based on training.

[0577] As an example, at least one of the K1 sub-operations is based on a codebook for precoding defined in 3GPP R18 or a version prior to 3GPP R18.

[0578] As an example, one or more of the K1 sub-operations are AI-based.

[0579] As an example, one or more of the K1 sub-operations include inference.

[0580] As an example, one or more of the K1 sub-operations include AI inference.

[0581] As an example, one or more of the K1 sub-operations include AI inference for CSI.

[0582] As an example, the AI ​​(Artificial Intelligence) includes ML (Machine Learning).

[0583] As an example, one or more of the K1 sub-operations include preprocessing.

[0584] As an example, one or more of the K1 sub-operations include post-processing.

[0585] As an example, among the K1 sub-operations, two sub-operations are sequential, such as all the sub-operations in Figure 14(a), sub-operations #2 to #(K1-1) in Figure 14(b), and sub-operations #0 to #(K1-4) in Figure 14(c).

[0586] As an example, the two sub-operations being serial means that the output of one of the two sub-operations is used as the input of the other of the two sub-operations.

[0587] As an example, among the K1 sub-operations, two sub-operations are parallel, such as sub-operation #0 and sub-operation #1 in Figure 14(b), and sub-operation #(K1-3) and sub-operation #(K1-2) in Figure 14(c).

[0588] As an example, two sub-operations being parallel means that the outputs of the two sub-operations are used together as the input of another sub-operation.

[0589] As an example, the K1 sub-operations include one or more of convolution, pooling, cascading, or activation.

[0590] As an example, one of the K1 sub-operations includes a fully connected layer.

[0591] As an example, one of the K1 sub-operations includes a pooling layer.

[0592] As an example, one of the K1 sub-operations includes at least one convolutional layer.

[0593] As an example, one of the K1 sub-operations includes at least one coding layer.

[0594] As an example, two of the K1 sub-operations include a fully connected layer and at least one coding layer.

[0595] As an example, an encoding layer includes at least one convolutional layer and one pooling layer.

[0596] Example 15

[0597] Example 15 illustrates a schematic diagram of a first node deploying a first operation according to an embodiment of this application; as shown in Figure 15. In Example 15, the first node deploys the first operation.

[0598] As one embodiment, the deployment includes obtaining the first operation.

[0599] As one example, the deployment includes obtaining an AI entity.

[0600] As one example, the deployment includes obtaining an AI entity that performs the first operation.

[0601] As one example, the deployment includes obtaining an AI entity that includes AI functions to perform the first operation.

[0602] As one example, the deployment includes loading the first operation.

[0603] As one example, the deployment includes submitting a request to load the first operation.

[0604] As an example, the request in Figure 15 is a request from the first node to load the first operation.

[0605] As an example, the response in Figure 15 is a response to the request made by the first node to load the first operation.

[0606] As an example, the first node obtains the first operation through the response shown in Figure 15.

[0607] As an example, the first operation is obtained from the serving cell of the first node.

[0608] As an example, the first operation is obtained from the sustaining base station of the serving cell of the first node.

[0609] As an example, the first operation is obtained from the core network.

[0610] As an example, the first operation is obtained from loading from the first producer.

[0611] As an example, the first producer provides the first operation to the first node via the response shown in Figure 15.

[0612] As an example, the deployment is accomplished by an AI function.

[0613] As an example, the deployment is accomplished by AI functionality deployed on the first node.

[0614] As an example, the deployment is accomplished by an AI deployment function.

[0615] As an example, the deployment is accomplished by the AI ​​deployment function deployed on the first node.

[0616] As an example, the deployment is accomplished using AI inference functionality.

[0617] As an example, the deployment is accomplished by an AI inference function deployed on the first node.

[0618] As an example, the deployment is performed by an AI entity.

[0619] As an example, the deployment is performed by an AI entity deployed on the first node.

[0620] As an example, the deployment is performed by an AI entity with a deployment function.

[0621] As an example, the deployment is performed by an AI entity with deployment capabilities deployed on the first node.

[0622] As an example, the deployment is accomplished by an AI entity with an inference function.

[0623] As an example, the deployment is performed by an AI entity with inference capabilities deployed on the first node.

[0624] As one embodiment, the deployment includes obtaining the first operation from a first producer.

[0625] As one embodiment, the deployment includes requesting a first producer to load the first operation.

[0626] As one embodiment, the deployment includes loading the first operation from the first producer.

[0627] As an example, the first producer generates and provides the AL entity.

[0628] As an example, the first producer generates and provides AL functionality.

[0629] As an example, the first producer is the producer of the first operation.

[0630] As an example, the first producer includes an AL entity producer.

[0631] As one example, the first producer includes an AL function producer.

[0632] As one example, the first producer includes an AL deployment producer.

[0633] As one example, the first producer includes an AL loading producer.

[0634] As one example, the first producer includes an AL-trained producer.

[0635] As an example, the first producer includes an AL inference producer.

[0636] As an example, the first producer includes the producer of the AL entity deployment.

[0637] As one example, the first producer includes the producer that loads the AL entity.

[0638] As an example, the first producer includes an MnS (Management Service) producer.

[0639] As an example, the sender of the first higher-level message set is the first producer.

[0640] As an example, the sender of the first higher-level message set is different from the first producer.

[0641] As an example, the training for obtaining the first operation is performed by the first producer.

[0642] As an example, the executor used to obtain the training for the first operation is different from the first producer.

[0643] As one example, the AI ​​includes ML (Machine Learning).

[0644] Example 16

[0645] 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.

[0646] 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.

[0647] 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).

[0648] 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.

[0649] Similarly, ML testing capabilities can also be deployed in cross-domain management systems or domain-specific management systems.

[0650] 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.

[0651] 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).

[0652] 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.

[0653] 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.

[0654] As an example, one of the gNBs (or base stations) in Example 16 is the second node of this application.

[0655] As an example, the second processor in this application includes an AL / ML inference function, namely 1404 or 1406, as shown in Figure 16.

[0656] Example 17

[0657] 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.

[0658] 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.

[0659] As an example, the first information report in this application is obtained through inference by the AI / ML inference function 1506.

[0660] As an example, the first processor in this application includes an AL / ML inference function 1506 in Figure 17.

[0661] 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.

[0662] 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.

[0663] Optionally, the UE function 1504 also includes a CN domain ML training function (not shown in Figure 17).

[0664] Optionally, the UE function 1504 also includes an AI / ML deployment function—not shown in Figure 17—for loading ML models and data.

[0665] 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.

[0666] As an example, the ML model and the associated metadata are loaded by the first node from a network device or a remote server.

[0667] 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).

[0668] 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).

[0669] As an example, the ML model is based on a neural network.

[0670] As an example, the ML model is based on CNN (Conventional Neural Networks).

[0671] As an example, the ML model is based on the Transformer architecture.

[0672] Example 18

[0673] 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.

[0674] 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.

[0675] 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.

[0676] As an example, in Figure 18(a), the fifth processor sends the first type of output to the second node in this application.

[0677] As an example, in Figure 18(a), a single-side AI model is used 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.

[0678] As an example, in Figure 18(a), a single-side AI model is used to evaluate the wireless link quality, and the fifth processor performs the first operation, which is used to evaluate the wireless link quality.

[0679] As an example, the AI ​​includes machine learning (ML) inference.

[0680] As an example, the fifth processor performs the first operation.

[0681] 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.

[0682] 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.

[0683] 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.

[0684] As one embodiment, the fifth processor belongs to the first node, and the sixth processor belongs to the second node.

[0685] As an example, the second dataset includes the input of the first operation.

[0686] As an example, the second dataset includes information obtained based on the first configuration and the M1 configurations.

[0687] As an example, the first dataset includes training data.

[0688] As an example, the fourth processor belongs to the producer of the first operation.

[0689] As one embodiment, the fourth processor includes an AI training producer.

[0690] As one embodiment, the fourth processor includes an AI training function.

[0691] 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.

[0692] As an example, the fourth processor belongs to the first node.

[0693] The above embodiments avoid passing the first dataset to the second node.

[0694] As one example, the fourth processor belongs to the second node.

[0695] The above embodiments support joint training and optimize system performance.

[0696] As an example, the fourth processor belongs to the core network.

[0697] The above embodiments support network-wide joint training, further optimizing system performance.

[0698] As an example, the second dataset includes inference data.

[0699] As one embodiment, the fifth processor includes an AI inference producer.

[0700] As one embodiment, the fifth processor includes an AI inference function.

[0701] As an example, the fifth processor belongs to the first node.

[0702] 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.

[0703] As an example, the first operation is described by the target first type of parameter group.

[0704] As an example, the target first type of parameter group is used to construct the first operation.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] Example 19

[0711] 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.

[0712] 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.

[0713] 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.

[0714] As an example, the first stage includes AI model training.

[0715] As an example, the first stage includes AI model training and AI testing.

[0716] As an example, the AI ​​includes machine learning (ML) inference.

[0717] As an example, the AI ​​model training includes initial training and re-training of one or a group of AI entities.

[0718] As an example, the training of the AI ​​model depends on training data.

[0719] As an example, the AI ​​model training includes AI entity validation.

[0720] As an example, the AI ​​entity verification is used to evaluate the performance of the AI ​​entity.

[0721] As an example, the AI ​​entity verification relies on verification data.

[0722] As an example, if the AI ​​entity verification results do not meet expectations, the AI ​​model will be retrained.

[0723] As an example, the AI ​​testing includes testing the validated AI entity to estimate the performance of the trained AI model.

[0724] 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.

[0725] As an example, the AI ​​test relies on test data.

[0726] As an example, the second stage includes AI simulation, which performs inference of AI entities in a simulation environment.

[0727] As an example, the AI ​​simulation estimates the performance of AI entity inference in a simulation environment before using the AI ​​entity.

[0728] As one embodiment, the second stage is optional.

[0729] As an example, the third stage includes AI entity loading, which is to obtain trained AI entities to obtain the desired AI inference capabilities.

[0730] As an example, the third stage is optional.

[0731] As an example, the third stage is no longer needed when the training and inference functions are co-located.

[0732] As an example, the fourth stage includes AI inference.

[0733] As an example, the seventh operation includes the first operation.

[0734] Example 20

[0735] 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.

[0736] As one example, the first node is a user equipment.

[0737] As one example, the user equipment is a terminal.

[0738] As an example, the first node is a relay node device.

[0739] 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}.

[0740] In embodiment 20, the first processor 2001 receives a first higher-level message set, which is used to configure a first resource set; evaluates the quality of a first wireless link based on the first resource set; and whenever the evaluated quality of the first wireless link is worse than a reference threshold, the physical layer of the first node sends a beam failure event indication to its higher layer.

[0741] In embodiment 20, 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.

[0742] In Example 20, the reference threshold is one of a first threshold or a second threshold, and the reference threshold depends on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the reference threshold is the first threshold; when the evaluation method of the first wireless link quality is based on AI, the reference threshold is the second threshold.

[0743] As an example, the evaluation method of the first wireless link quality is not based on AI and includes: the first resource set includes at least one RS resource, and the evaluated first wireless link quality is obtained based on measurements of all RS resources in the first resource set.

[0744] As an example, the evaluation method of the first wireless link quality based on AI includes: the evaluated first wireless link quality is predicted or inferred.

[0745] As one embodiment, the evaluation method of the first wireless link quality based on AI includes: the evaluation method of the first wireless link quality is associated with a first type of identifier.

[0746] As one embodiment, the evaluation method of the first wireless link quality based on AI includes: the evaluation method of the first wireless link quality includes the first node performing a first operation, the first operation being based on training or AI, and the evaluated first wireless link quality depending on the output of the first operation.

[0747] As an example, the first operation is associated with the first type of identifier.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] As an example, when the evaluation method of the first wireless link quality is not based on AI, the first resource set includes at least one RS resource, and the evaluated first wireless link quality is obtained based on measurements of all RS resources in the first resource set; when the evaluation method of the first wireless link quality is based on AI, the evaluated first wireless link quality is a predicted or inferred wireless link quality for the first resource set.

[0753] As one embodiment, it includes:

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

[0755] As an example, the first processor 2001 receives signals in the first resource set.

[0756] 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.

[0757] As an example, the first operation is based on training or AI.

[0758] As an example, the first operation requires deployment.

[0759] As an example, the first operation is obtained by loading.

[0760] Example 21

[0761] 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.

[0762] In one embodiment, the second node is a base station device.

[0763] In one embodiment, the second node is a user equipment.

[0764] As one embodiment, the second node is a relay node device.

[0765] 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}.

[0766] In embodiment 21, the second processor 2101 sends a first higher-level message set, which is used to configure a first resource set.

[0767] In embodiment 21, the target receiver of the first higher-layer message set evaluates the quality of the first wireless link based on the first resource set; whenever the evaluated quality of the first wireless link is worse than a reference threshold, the physical layer of the target receiver of the first higher-layer message set sends a beam failure event indication to its higher layer; beam failure recovery is triggered when the value of the target counter is equal to or greater than the target threshold; the target counter is used to count the beam failure event indication; the reference threshold is one of a first threshold or a second threshold, and the reference threshold depends on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the reference threshold is the first threshold; when the evaluation method of the first wireless link quality is based on AI, the reference threshold is the second threshold.

[0768] As an example, the evaluation method of the first wireless link quality is not based on AI and includes: the first resource set includes at least one RS resource, and the evaluated first wireless link quality is obtained based on measurements of all RS resources in the first resource set.

[0769] As an example, the evaluation method of the first wireless link quality based on AI includes: the evaluated first wireless link quality is predicted or inferred.

[0770] As one embodiment, the evaluation method of the first wireless link quality based on AI includes: the evaluation method of the first wireless link quality is associated with a first type of identifier.

[0771] As one embodiment, the evaluation method of the first wireless link quality based on AI includes: the evaluation method of the first wireless link quality includes 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 evaluated first wireless link quality depending on the output of the first operation.

[0772] As an example, the first operation is associated with the first type of identifier.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] As an example, when the evaluation method of the first wireless link quality is not based on AI, the first resource set includes at least one RS resource, and the evaluated first wireless link quality is obtained based on measurements of all RS resources in the first resource set; when the evaluation method of the first wireless link quality is based on AI, the evaluated first wireless link quality is a predicted or inferred wireless link quality for the first resource set.

[0778] As one embodiment, the second processor 2101 sends a signal in the first resource set.

[0779] As one embodiment, the second processor 2101 sends a reference signal in the first resource set, which includes one or more RS resources.

[0780] As an example, the first operation is based on training or AI.

[0781] As an example, the first operation requires deployment.

[0782] As an example, the first operation is obtained by loading.

[0783] 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.

[0784] 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; The quality of the first wireless link is evaluated based on the first resource set; whenever the evaluated quality of the first wireless link is worse than a reference threshold, 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. The reference threshold is either a first threshold or a second threshold, and the reference threshold depends on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the reference threshold is the first threshold; when the evaluation method of the first wireless link quality is based on AI, the reference threshold is the second threshold.

2. The method in the first node according to claim 1, characterized in that, The evaluation method for the first wireless link quality is not based on AI, including: the first resource set includes at least one RS resource, and the evaluated first wireless link quality is obtained based on measurements of all RS resources in the first resource set.

3. The method in the first node according to claim 1 or 2, characterized in that, The evaluation method for the quality of the first wireless link is based on AI, including: the evaluated quality of the first wireless link is predicted or inferred.

4. The method in the first node according to any one of claims 1 to 3, characterized in that, The evaluation method for the first wireless link quality is based on AI and includes: the evaluation method for the first wireless link quality is associated with a first type of identifier.

5. The method in the first node according to any one of claims 1 to 4, characterized in that, The evaluation method for the first wireless link quality based on AI includes: the evaluation method for the first wireless link quality includes the first node performing a first operation, the first operation being based on training or AI, and the evaluated first wireless link quality depending on the output of the first operation.

6. The method in the first node according to claim 5, characterized in that, The first operation is associated with the first type of identifier.

7. The method in the first node according to claim 5, characterized in that, include: Deploy the first operation.

8. The method in the first node according to any one of claims 1 to 7, characterized in that, The first radio link quality refers to the radio link quality of the first serving cell, the beam failure event indication refers to 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 refers to the first serving cell. or, The first wireless link quality refers to the wireless link quality for the first resource set, the beam failure event indication refers to 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 refers to the first resource set.

9. The method in the first node according to any one of claims 1 to 8, characterized in that, When the evaluation method for the first wireless link quality is not based on AI, the first resource set includes at least one RS resource, and the evaluated first wireless link quality is obtained based on measurements of all RS resources in the first resource set; when the evaluation method for the first wireless link quality is based on AI, the evaluated first wireless link quality is a predicted or inferred wireless link quality for the first resource set.

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 the first resource set; Wherein, the target receiver of the first higher-layer message set evaluates the quality of the first wireless link based on the first resource set; whenever the evaluated quality of the first wireless link is worse than a reference threshold, the physical layer of the target receiver of the first higher-layer message set sends a beam failure event indication to its higher layer; beam failure recovery is triggered when the value of the target counter is equal to or greater than the target threshold; the target counter is used to count the beam failure event indication; the reference threshold is one of a first threshold or a second threshold, and the reference threshold depends on whether the evaluation method of the first wireless link quality is based on AI; when the evaluation method of the first wireless link quality is not based on AI, the reference threshold is the first threshold; when the evaluation method of the first wireless link quality is based on AI, the reference threshold is the second threshold.

12. The method in the second node according to claim 11, characterized in that, The evaluation method for the first wireless link quality is not based on AI, including: the first resource set includes at least one RS resource, and the evaluated first wireless link quality is obtained based on measurements of all RS resources in the first resource set.

13. The method in the second node according to claim 11 or 12, characterized in that, The evaluation method for the quality of the first wireless link is based on AI, including: the evaluated quality of the first wireless link is predicted or inferred.

14. The method in the second node according to any one of claims 11 to 13, characterized in that, The evaluation method for the first wireless link quality is based on AI and includes: the evaluation method for the first wireless link quality is associated with a first type of identifier.

15. The method in the second node according to any one of claims 11 to 14, characterized in that, The evaluation method for the first wireless link quality based on AI includes: the evaluation method for the first wireless link quality includes 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 evaluated first wireless link quality depending on the output of the first operation.

16. The method in the second node according to claim 15, characterized in that, The first operation is associated with the first type of identifier.

17. The method in the second node according to any one of claims 11 to 16, characterized in that, The first radio link quality refers to the radio link quality of the first serving cell, the beam failure event indication refers to 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 refers to the first serving cell. or, The first wireless link quality refers to the wireless link quality for the first resource set, the beam failure event indication refers to 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 refers to the first resource set.

18. The method in the second node according to any one of claims 11 to 17, characterized in that, When the evaluation method for the first wireless link quality is not based on AI, the first resource set includes at least one RS resource, and the evaluated first wireless link quality is obtained based on measurements of all RS resources in the first resource set; when the evaluation method for the first wireless link quality is based on AI, the evaluated first wireless link quality is a predicted or inferred wireless link quality for the first resource set.

19. 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 18.

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