Communication method and device, and storage medium

Abstract

The invention relates to a communication method, equipment and a storage medium. The method comprises: receiving first information sent by a network device, the first information comprising configuration of a reference signal resource, the reference signal resource being used by a terminal device for beam measurement; performing beam measurement according to the first information to obtain a beam group measurement result of at least one beam group; according to the beam group measurement result, second information is sent to the network device, the second information comprises information related to a first AI model, and the first AI model is a model used for executing beam prediction. That is, the terminal device can perform beam measurement according to the first information sent by the network device to obtain the beam group measurement result of the at least one beam group, and send the second information to the network device according to the beam group measurement result. And the network equipment can perform model reasoning, model performance monitoring or model training according to the second information, so that the system performance is improved.

Description

Communication method, device and storage medium Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a communication method, device, and storage medium. Background Art

[0002] In wireless communication systems, multiple transmission receive points (MTRPs) require beams from multiple transmission receive points (TRPs) to simultaneously serve terminal devices. During beam measurement, an artificial intelligence (AI) model can be used to predict the beam corresponding to each TRP. The accuracy of beam prediction depends on the performance of the AI ​​model.

[0003] Summary of the Invention

[0004] The embodiments of the present disclosure provide a communication method, a device, and a storage medium.

[0005] According to a first aspect of an embodiment of the present disclosure, a communication method is proposed, which is performed by a terminal device. The method includes:

[0006] receiving first information sent by a network device, where the first information includes configuration of a reference signal resource, where the reference signal resource is used by the terminal device to perform beam measurement;

[0007] performing beam measurement according to the first information to obtain a beam group measurement result of at least one beam group;

[0008] Based on the beam group measurement result, second information is sent to the network device, where the second information includes information related to a first AI model, where the first AI model is a model for performing beam prediction.

[0009] According to a second aspect of an embodiment of the present disclosure, a communication method is provided, which is performed by a network device. The method includes:

[0010] First information sent to a terminal device, the first information including configuration of a reference signal resource, where the reference signal resource is used by the terminal device to perform beam measurement;

[0011] Receive second information sent by the terminal device based on a beam group measurement result of at least one beam group, where the beam group measurement result is obtained by the terminal device performing beam measurement based on the first information, and the second information includes information related to a first AI model, where the first AI model is a model for performing beam prediction.

[0012] According to a third aspect of an embodiment of the present disclosure, a terminal device is provided, including:

[0013] a transceiver module configured to receive first information sent by a network device, where the first information includes a configuration of a reference signal resource, where the reference signal resource is used by the terminal device to perform beam measurement;

[0014] a processing module, configured to perform beam measurement according to the first information to obtain a beam group measurement result of at least one beam group;

[0015] The processing module is further configured to send second information to the network device based on the beam group measurement result, where the second information includes information related to the first AI model, and the first AI model is a model for performing beam prediction.

[0016] According to a fourth aspect of an embodiment of the present disclosure, a network device is provided, including:

[0017] a transceiver module configured to send first information to a terminal device, where the first information includes a configuration of a reference signal resource, where the reference signal resource is used by the terminal device to perform beam measurement;

[0018] The transceiver module is further configured to receive second information sent by the terminal device based on a beam group measurement result of at least one beam group, where the beam group measurement result is obtained by the terminal device performing beam measurement based on the first information, and the second information includes information related to the first AI model, which is a model for performing beam prediction.

[0019] According to a fifth aspect of an embodiment of the present disclosure, a communication device is proposed, comprising: one or more processors; wherein the communication device can be used to execute an optional implementation of the first aspect or the second aspect.

[0020] According to a sixth aspect of an embodiment of the present disclosure, a storage medium is proposed, which stores instructions. When the instructions are executed on a communication device, the communication device executes the method described in the optional implementation of the first aspect or the second aspect.

[0021] The technical solution provided by the embodiment of the present disclosure may include the following beneficial effects: receiving first information sent by a network device, the first information including the configuration of reference signal resources, and the reference signal resources being used by a terminal device to perform beam measurement; performing beam measurement according to the first information to obtain beam group measurement results of at least one beam group; and sending second information to the network device according to the beam group measurement results, the second information including information related to a first AI model, and the first AI model being a model for performing beam prediction. In other words, the terminal device may perform beam measurement according to the first information sent by the network device to obtain beam group measurement results of at least one beam group, and send second information to the network device according to the beam group measurement results. In this way, the network device may perform model inference, model performance monitoring, or model training according to the second information, thereby improving system performance.

[0022] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following drawings required for describing the embodiments are introduced. The following drawings are merely some embodiments of the present disclosure and do not impose specific limitations on the protection scope of the present disclosure.

[0024] FIG1 is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.

[0025] FIG2A is an interactive schematic diagram illustrating a communication method according to an embodiment of the present disclosure.

[0026] FIG2B is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure.

[0027] FIG2C is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure.

[0028] FIG2D is an interactive schematic diagram illustrating a communication method according to an embodiment of the present disclosure.

[0029] FIG3A is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0030] FIG3B is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0031] FIG3C is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0032] FIG3D is a flow chart illustrating a communication method according to an embodiment of the present disclosure.

[0033] FIG3E is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0034] FIG4A is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0035] FIG4B is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0036] FIG4C is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0037] FIG4D is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0038] FIG4E is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0039] FIG5 is an interactive schematic diagram illustrating a communication method according to an embodiment of the present disclosure.

[0040] FIG6A is a schematic structural diagram of a terminal device proposed in an embodiment of the present disclosure.

[0041] FIG6B is a schematic structural diagram of a network device proposed in an embodiment of the present disclosure.

[0042] FIG7A is a schematic structural diagram of a communication device proposed in an embodiment of the present disclosure.

[0043] FIG7B is a schematic diagram of the structure of the chip proposed in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0044] The embodiments of the present disclosure provide a communication method, a device, and a storage medium.

[0045] In a first aspect, an embodiment of the present disclosure provides a communication method, which is executed by a terminal device. The method includes:

[0046] receiving first information sent by a network device, where the first information includes configuration of a reference signal resource, where the reference signal resource is used by the terminal device to perform beam measurement;

[0047] performing beam measurement according to the first information to obtain a beam group measurement result of at least one beam group;

[0048] Based on the beam group measurement result, second information is sent to the network device, where the second information includes information related to a first AI model, where the first AI model is a model for performing beam prediction.

[0049] In the above embodiment, the terminal device can perform beam measurement based on the first information sent by the network device, obtain the beam group measurement results of at least one beam group, and send second information to the network device based on the beam group measurement results. In this way, the network device can perform model inference, model performance monitoring or model training based on the second information, thereby improving system performance.

[0050] In conjunction with some embodiments of the first aspect, in some embodiments, the beam group includes a first beam and a second beam, and performing beam measurement according to the first information to obtain a beam group measurement result of at least one beam group includes:

[0051] For each beam group, beam measurement is performed according to the first information to obtain a first beam measurement result of the first beam and a second beam measurement result of the second beam, and the beam group measurement result of the beam group is determined based on the first beam measurement result and the second beam measurement result.

[0052] In the above embodiment, the terminal device may determine the beam group measurement result of the beam group according to the beam measurement results of the two beams in the beam group.

[0053] In conjunction with some embodiments of the first aspect, in some embodiments, determining the beam group measurement result of the beam group based on the first beam measurement result and the second beam measurement result includes at least one of the following:

[0054] taking an average of the first beam measurement result and the second beam measurement result as the beam group measurement result;

[0055] taking a weighted average of the first beam measurement result and the second beam measurement result as the beam group measurement result;

[0056] The sum of the first measurement result, the second measurement result, and the product of the first measurement result and the second measurement result is used as the beam group measurement result.

[0057] In the above embodiment, the beam group measurement results can be calculated by various calculation methods, so that the determination method of the beam group measurement results is more flexible.

[0058] In conjunction with some embodiments of the first aspect, in some embodiments, the first information includes at least one of the following:

[0059] a first reference signal resource set, where the reference signal resources in the first reference signal resource set correspond to the beam to be measured;

[0060] a second reference signal resource set, where the reference signal resources in the second reference signal resource set correspond to the beam to be predicted;

[0061] a third reference signal resource set, wherein the third reference signal resource set includes resources used for interference measurement;

[0062] The relationship between the first reference signal resource set and the second reference signal resource set.

[0063] In the above embodiment, configuration of reference signal resources for beam measurement is provided so that the terminal device can perform beam measurement.

[0064] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing spatial beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

[0065] The first reference signal resource set is a subset of the second reference signal resource set;

[0066] The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

[0067] In the above embodiment, the relationship between the beam to be measured and the beam to be predicted corresponding to the spatial beam prediction model is provided so that the terminal device can accurately perform beam measurement.

[0068] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing time-domain beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

[0069] The first reference signal resource set is a subset of the second reference signal resource set;

[0070] The first reference signal resource set is the same as the second reference signal resource set;

[0071] The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

[0072] In the above embodiment, a relationship between the beam to be measured and the beam to be predicted corresponding to the time domain beam prediction model is provided so that the terminal device can accurately perform beam measurement.

[0073] In conjunction with some embodiments of the first aspect, in some embodiments, the first reference signal resource set includes multiple fourth reference signal resource sets, and different fourth reference signal resource sets correspond to different transceiver points TRP;

[0074] The second reference signal resource set includes multiple fifth reference signal resource sets, and different fifth reference signal resource sets correspond to different TRPs.

[0075] In the above embodiment, different TRPs may set different reference signal resource sets so that the terminal device can measure and report the measurement results in a group-based manner.

[0076] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing spatial beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

[0077] The fourth reference signal resource set is a subset of the fifth reference signal resource set;

[0078] The beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

[0079] In the above embodiment, the terminal device can perform beam measurement and reporting based on the reference signal resources of each TRP, thereby improving the communication performance of the terminal device based on multi-beam transmission.

[0080] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing time-domain beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

[0081] The fourth reference signal resource set is a subset of the fifth reference signal resource set;

[0082] The fourth reference signal resource set is the same as the fifth reference signal resource set;

[0083] The beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

[0084] In the above embodiment, the terminal device can perform beam measurement and reporting based on the reference signal resources of each TRP, thereby improving the communication performance of the terminal device based on multi-beam transmission.

[0085] In conjunction with some embodiments of the first aspect, in some embodiments, the second information includes at least one of the following:

[0086] Model training data;

[0087] Model performance monitoring data;

[0088] Model input data;

[0089] Model output data.

[0090] In the above embodiment, the terminal device can send various types of data to the network device so that the network device can monitor the performance of the first AI model, or perform inference through the first AI model, or train the first AI model.

[0091] In combination with some embodiments of the first aspect, in some embodiments, the model training data includes the model input data and the beam measurement results of the beam to be predicted.

[0092] In the above embodiment, the network device can train the first AI model through model training data.

[0093] In conjunction with some embodiments of the first aspect, in some embodiments, the model performance monitoring data includes at least one of the following:

[0094] Beam information of K beam groups;

[0095] the performance value of the first AI model;

[0096] first data, the first data including at least one of the following: model input data of the first AI model, model output data of the first AI model, and measurement data corresponding to the model output data, where the model output data is data output by the first AI model based on the model input data;

[0097] a specified event, where the specified event is triggered based on a comparison result between a performance value of the first AI model and a first threshold value or a first offset value;

[0098] First operation information, where the first operation information is used to indicate a management operation to be performed on the first AI model, where the management operation includes any one of the following: activating the first AI model, deactivating the first AI model, switching the first AI model, and not using the AI ​​model.

[0099] In the above embodiment, the network device can monitor the performance of the first AI model through model performance monitoring data, thereby improving the performance of the first AI model.

[0100] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0101] The K beam groups are determined from the at least one beam group according to the beam group measurement result.

[0102] In the above embodiment, the terminal device can select K beam groups according to the beam group measurement results obtained by measurement and send the beam information of the K beam groups to the network device, thereby reducing the amount of uplink transmission data.

[0103] In conjunction with some embodiments of the first aspect, in some embodiments, the K beam groups are optimal K beam groups, and determining the K beam groups from the at least one beam group according to the beam group measurement result includes:

[0104] The beam groups corresponding to the best K beam group measurement results are used as the best K beam groups, and the best K beam group measurement results include at least one of the first K beam group measurement results when the beam group measurement results are arranged from high to low.

[0105] In the above embodiment, the terminal device can select the beam information of the best K beam groups and send it to the network device, so that the performance of the first AI model determined by the network device is more accurate.

[0106] In conjunction with some embodiments of the first aspect, in some embodiments, the beam information includes at least one of the following:

[0107] An identifier of the reference signal resource corresponding to each beam in each optimal beam group;

[0108] The beam quality corresponding to the identifier of the reference signal resource.

[0109] In the above embodiment, the beam information may be an identifier of a reference signal resource, or may be a beam quality corresponding to the identifier, thereby improving the flexibility of the beam information.

[0110] In conjunction with some embodiments of the first aspect, in some embodiments, the performance value includes at least one of the following:

[0111] A beam group prediction accuracy rate, where the beam group prediction accuracy rate is an accuracy rate of including an actual optimal beam group in at least one predicted beam group;

[0112] a beam group quality difference, where the beam group quality difference is a difference between a measured beam quality of a first beam group and a measured beam quality of a second beam group, where the first beam group is the beam group with the strongest predicted beam quality and the second beam group is the beam group with the strongest measured beam quality;

[0113] A predicted beam group quality difference is a difference between a predicted beam quality of the first beam group and a measured beam quality of the first beam group.

[0114] In the above embodiment, the network device can monitor the performance of the first AI model through one or more of the performance values, thereby improving the flexibility and accuracy of performance monitoring.

[0115] In conjunction with some embodiments of the first aspect, in some embodiments, the designated event includes at least one of the following: a first event, a second event, a third event, a fourth event, a fifth event, and a sixth event;

[0116] The sending second information to the network device according to the beam group measurement result includes at least one of the following:

[0117] Determining, according to the beam group measurement result, that the beam group prediction accuracy is less than a first accuracy threshold, and sending the first event to the network device;

[0118] Determining, according to the beam group measurement result, that the beam group prediction accuracy is greater than a second accuracy threshold, and sending the second event to the network device;

[0119] determining, according to the beam group measurement result, that the beam group quality difference is less than a first difference threshold, and sending the third event to the network device;

[0120] determining, according to the beam group measurement result, that the beam group quality difference is greater than a second difference threshold, and sending the fourth event to the network device;

[0121] determining, according to the beam group measurement result, that the predicted beam group quality difference is less than a third difference threshold, and sending the fifth event to the network device;

[0122] Determine, according to the beam group measurement result, that the predicted beam group quality difference is greater than a fourth difference threshold, and send the sixth event to the network device.

[0123] In the above embodiment, the terminal device can judge the prediction result based on the beam group measurement result, and trigger different events based on the judgment result, so that the network device can determine the performance of the first AI model.

[0124] In conjunction with some embodiments of the first aspect, in some embodiments, sending second information to the network device according to the beam group measurement result includes:

[0125] determining the model output data and the measurement data corresponding to the model output data according to the beam group measurement result;

[0126] determining the first operation information according to the model output data and measurement data corresponding to the model output data;

[0127] The first operation information is sent to the network device.

[0128] In the above embodiment, the terminal device can determine the decision on the first AI model based on the model output data and the measurement data corresponding to the model output data, and inform the network device.

[0129] In conjunction with some embodiments of the first aspect, in some embodiments, determining the first operation information according to the model output data and the measurement data corresponding to the model output data includes:

[0130] The first AI model is in an inactive state, and it is determined based on the model output data and measurement data corresponding to the model output data that the performance of the first AI model meets the performance requirement, and the first operation information is determined to be to activate the first AI model; or

[0131] The first AI model is in an activated state. It is determined based on the model output data and measurement data corresponding to the model output data that performance of the first AI model does not meet performance requirements, and the first operation information is determined to be deactivating the first AI model.

[0132] In the above embodiment, the terminal device can determine the management operation of the AI ​​model according to the current state of the first AI model.

[0133] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing spatial beam prediction, and the model input data includes at least one of the following:

[0134] beam qualities of the N beams corresponding to the first reference signal resource set, the beam qualities comprising layer 1 reference signal received power L1-RSRP or layer 1 signal to interference plus noise ratio L1-SINR, where N is a positive integer;

[0135] identifiers of reference signal resources corresponding to the N beams corresponding to the first reference signal resource set;

[0136] The first indication information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be simultaneously received and / or simultaneously transmitted.

[0137] In the above embodiment, the model input data may include multiple different types, so as to monitor the performance of the first AI model through different data, or make more predictions through the first AI model.

[0138] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing spatial beam prediction, and the model output data includes at least one of the following:

[0139] At least one group;

[0140] identifiers of two reference signal resources corresponding to each group, wherein the reference signal resources are reference signal resources in the second reference signal resource set;

[0141] a beam quality corresponding to an identifier of each reference signal resource;

[0142] at least one third beam;

[0143] an identifier of a reference signal resource corresponding to each of the third beams, wherein the reference signal resource is a reference signal resource in the second reference signal resource set;

[0144] a beam quality corresponding to each of the third beams;

[0145] Second indication information, the second indication information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and / or sent simultaneously.

[0146] In the above embodiment, the model output data may include multiple different types, so as to monitor the performance of the first AI model or train the first AI model through different data.

[0147] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing time-domain beam prediction, and the model input data includes at least one of the following:

[0148] at least one historical time;

[0149] beam qualities of N beams corresponding to the first reference signal resource set corresponding to each historical time, where the beam qualities include L1-RSRP or L1-SINR, where N is a positive integer;

[0150] identifiers of reference signal resources corresponding to the N beams corresponding to the first reference signal resource set corresponding to each historical time;

[0151] The third indication information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be simultaneously received and / or simultaneously transmitted.

[0152] In the above embodiment, the model input data may include multiple different types, so as to monitor the performance of the first AI model through different data, or make more predictions through the first AI model.

[0153] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing time-domain beam prediction, and the model output data includes at least one of the following:

[0154] At least one future time, where the future time is a time corresponding to a beam predicted by the first AI model;

[0155] At least one group corresponding to each of the future times;

[0156] identifiers of two reference signal resources corresponding to each group corresponding to each of the future times, wherein the reference signal resources are reference signal resources in the second reference signal resource set;

[0157] a beam quality corresponding to an identifier of each reference signal resource corresponding to each future time;

[0158] at least one fourth beam corresponding to each of the future times;

[0159] an identifier of a reference signal resource corresponding to each of the fourth beams corresponding to each of the future time periods, wherein the reference signal resource is a reference signal resource in the second reference signal resource set;

[0160] The beam quality corresponding to each fourth beam at each future time;

[0161] The fourth indication information is used to indicate that the beams contained in at least one group corresponding to at least one future time in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and / or sent simultaneously.

[0162] In the above embodiment, the model output data may include multiple different types, so as to monitor the performance of the first AI model or train the first AI model through different data.

[0163] In a second aspect, an embodiment of the present disclosure provides a communication method, which is performed by a network device. The method includes:

[0164] First information sent to a terminal device, the first information including configuration of a reference signal resource, where the reference signal resource is used by the terminal device to perform beam measurement;

[0165] Receive second information sent by the terminal device based on a beam group measurement result of at least one beam group, where the beam group measurement result is obtained by the terminal device performing beam measurement based on the first information, and the second information includes information related to a first AI model, where the first AI model is a model for performing beam prediction.

[0166] In conjunction with some embodiments of the second aspect, in some embodiments, the beam group includes a first beam and a second beam, and the beam group measurement result is determined by:

[0167] For each beam group, beam measurement is performed according to the first information to obtain a first beam measurement result of the first beam and a second beam measurement result of the second beam, and the beam group measurement result of the beam group is determined based on the first beam measurement result and the second beam measurement result.

[0168] In conjunction with some embodiments of the second aspect, in some embodiments, determining the beam group measurement result of the beam group based on the first beam measurement result and the second beam measurement result includes at least one of the following:

[0169] taking an average of the first beam measurement result and the second beam measurement result as the beam group measurement result;

[0170] taking a weighted average of the first beam measurement result and the second beam measurement result as the beam group measurement result;

[0171] The sum of the first measurement result, the second measurement result, and the product of the first measurement result and the second measurement result is used as the beam group measurement result.

[0172] In conjunction with some embodiments of the first aspect, in some embodiments, the first information includes at least one of the following:

[0173] a first reference signal resource set, where the reference signal resources in the first reference signal resource set correspond to the beam to be measured;

[0174] a second reference signal resource set, where the reference signal resources in the second reference signal resource set correspond to the beam to be predicted;

[0175] a third reference signal resource set, wherein the third reference signal resource set includes resources used for interference measurement;

[0176] The relationship between the first reference signal resource set and the second reference signal resource set.

[0177] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is a model for performing spatial beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

[0178] The first reference signal resource set is a subset of the second reference signal resource set;

[0179] The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

[0180] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is a model for performing time-domain beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

[0181] The first reference signal resource set is a subset of the second reference signal resource set;

[0182] The first reference signal resource set is the same as the second reference signal resource set;

[0183] The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

[0184] In conjunction with some embodiments of the second aspect, in some embodiments, the first reference signal resource set includes multiple fourth reference signal resource sets, and different fourth reference signal resource sets correspond to different transceiver points TRP;

[0185] The second reference signal resource set includes multiple fifth reference signal resource sets, and different fifth reference signal resource sets correspond to different TRPs.

[0186] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is a model for performing spatial beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

[0187] The fourth reference signal resource set is a subset of the fifth reference signal resource set;

[0188] The beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

[0189] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is a model for performing time-domain beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

[0190] The fourth reference signal resource set is a subset of the fifth reference signal resource set;

[0191] The fourth reference signal resource set is the same as the fifth reference signal resource set;

[0192] The beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

[0193] In conjunction with some embodiments of the second aspect, in some embodiments, the second information includes at least one of the following:

[0194] Model training data;

[0195] Model performance monitoring data;

[0196] Model input data;

[0197] Model output data.

[0198] In combination with some embodiments of the second aspect, in some embodiments, the model training data includes the model input data and the beam measurement results of the beam to be predicted.

[0199] In conjunction with some embodiments of the second aspect, in some embodiments, the model performance monitoring data includes at least one of the following:

[0200] Beam information of K beam groups;

[0201] the performance value of the first AI model;

[0202] first data, the first data including at least one of the following: model input data of the first AI model, model output data of the first AI model, and measurement data corresponding to the model output data, where the model output data is data output by the first AI model based on the model input data;

[0203] a specified event, where the specified event is triggered based on a comparison result between a performance value of the first AI model and a first threshold value or a first offset value;

[0204] First operation information, where the first operation information is used to indicate a management operation to be performed on the first AI model, where the management operation includes any one of the following: activating the first AI model, deactivating the first AI model, switching the first AI model, and not using the AI ​​model.

[0205] In combination with some embodiments of the second aspect, in some embodiments, the K beam groups are the best K beam groups, and the best K beam groups are the beam groups corresponding to the best K beam group measurement results, and the best K beam group measurement results include at least one of the top K beam group measurement results when the beam group measurement results are arranged from high to low.

[0206] In conjunction with some embodiments of the second aspect, in some embodiments, the beam information includes at least one of the following:

[0207] An identifier of the reference signal resource corresponding to each beam in each optimal beam group;

[0208] The beam quality corresponding to the identifier of the reference signal resource.

[0209] In conjunction with some embodiments of the second aspect, in some embodiments, the performance value includes at least one of the following:

[0210] A beam group prediction accuracy rate, where the beam group prediction accuracy rate is an accuracy rate of including an actual optimal beam group in at least one predicted beam group;

[0211] a beam group quality difference, where the beam group quality difference is a difference between a measured beam quality of a first beam group and a measured beam quality of a second beam group, where the first beam group is the beam group with the strongest predicted beam quality and the second beam group is the beam group with the strongest measured beam quality;

[0212] A predicted beam group quality difference is a difference between a predicted beam quality of the first beam group and a measured beam quality of the first beam group.

[0213] In conjunction with some embodiments of the second aspect, in some embodiments, the designated event includes at least one of the following: a first event, a second event, a third event, a fourth event, a fifth event, and a sixth event; and the receiving of the second information sent by the terminal device according to the beam group measurement result of at least one beam group includes at least one of the following:

[0214] receiving the first event sent by the terminal device, where the first event is triggered when the terminal device determines, based on the beam group measurement result, that the beam group prediction accuracy is less than a first accuracy threshold;

[0215] receiving a second event sent by the terminal device, where the second event is triggered when the terminal device determines, based on the beam group measurement result, that the beam group prediction accuracy is greater than a second accuracy threshold;

[0216] receiving the third event sent by the terminal device, where the third event is triggered when the terminal device determines, based on the beam group measurement result, that the beam group quality difference is less than a first difference threshold;

[0217] receiving the fourth event sent by the terminal device, where the fourth event is triggered when the terminal device determines, based on the beam group measurement result, that the beam group quality difference is greater than a second difference threshold;

[0218] receiving the fifth event sent by the terminal device, where the fifth event is triggered when the terminal device determines, based on the beam group measurement result, that the predicted beam group quality difference is less than a third difference threshold;

[0219] Receive the sixth event sent by the terminal device, where the sixth event is triggered when the terminal device determines, based on the beam group measurement result, that the predicted beam group quality difference is greater than a fourth difference threshold.

[0220] In combination with some embodiments of the second aspect, in some embodiments, the first operation information is determined by the terminal device based on the model output data and measurement data corresponding to the model output data.

[0221] In combination with some embodiments of the second aspect, in some embodiments, the first operation information includes activating the first AI model, or deactivating the first AI model.

[0222] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is a model for performing spatial beam prediction, and the model input data includes at least one of the following:

[0223] beam qualities of the N beams corresponding to the first reference signal resource set, the beam qualities comprising layer 1 reference signal received power L1-RSRP or layer 1 signal to interference plus noise ratio L1-SINR, where N is a positive integer;

[0224] identifiers of reference signal resources corresponding to the N beams corresponding to the first reference signal resource set;

[0225] The first indication information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be simultaneously received and / or simultaneously transmitted.

[0226] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is a model for performing spatial beam prediction, and the model output data includes at least one of the following:

[0227] At least one group;

[0228] identifiers of two reference signal resources corresponding to each group, wherein the reference signal resources are reference signal resources in the second reference signal resource set;

[0229] a beam quality corresponding to an identifier of each reference signal resource;

[0230] at least one third beam;

[0231] an identifier of a reference signal resource corresponding to each of the third beams, wherein the reference signal resource is a reference signal resource in the second reference signal resource set;

[0232] a beam quality corresponding to each of the third beams;

[0233] Second indication information, the second indication information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and / or sent simultaneously.

[0234] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is a model for performing time-domain beamforming prediction, and the model input data includes at least one of the following:

[0235] at least one historical time;

[0236] beam qualities of N beams corresponding to the first reference signal resource set corresponding to each historical time, where the beam qualities include L1-RSRP or L1-SINR, where N is a positive integer;

[0237] identifiers of reference signal resources corresponding to the N beams corresponding to the first reference signal resource set corresponding to each historical time;

[0238] The third indication information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be simultaneously received and / or simultaneously transmitted.

[0239] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is a model for performing time-domain beam prediction, and the model output data includes at least one of the following:

[0240] At least one future time, where the future time is a time corresponding to a beam predicted by the first AI model;

[0241] At least one group corresponding to each of the future times;

[0242] identifiers of two reference signal resources corresponding to each group corresponding to each of the future times, wherein the reference signal resources are reference signal resources in the second reference signal resource set;

[0243] a beam quality corresponding to an identifier of each reference signal resource corresponding to each future time;

[0244] at least one fourth beam corresponding to each of the future times;

[0245] an identifier of a reference signal resource corresponding to each of the fourth beams corresponding to each of the future time periods, wherein the reference signal resource is a reference signal resource in the second reference signal resource set;

[0246] The beam quality corresponding to each fourth beam at each future time;

[0247] The fourth indication information is used to indicate that the beams contained in at least one group corresponding to at least one future time in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and / or sent simultaneously.

[0248] In a third aspect, an embodiment of the present disclosure provides a communication method, the method comprising:

[0249] The network device sends first information to the terminal device, where the first information includes configuration of a reference signal resource, where the reference signal resource is used by the terminal device to perform beam measurement;

[0250] The terminal device performs beam measurement according to the first information to obtain a beam group measurement result of at least one beam group;

[0251] The terminal device sends second information to the network device based on the beam group measurement result, where the second information includes information related to the first AI model, and the first AI model is a model for performing beam prediction.

[0252] In a fourth aspect, an embodiment of the present disclosure proposes a terminal device, which may include at least one of a transceiver module and a processing module; wherein the terminal device can be used to execute the optional implementation method of the first aspect.

[0253] In a fifth aspect, an embodiment of the present disclosure proposes a network device, which may include at least one of a transceiver module and a processing module; wherein the network device can be used to execute the optional implementation method of the second aspect.

[0254] In a sixth aspect, an embodiment of the present disclosure proposes a terminal device, which may include: one or more processors; wherein the terminal device can be used to execute the optional implementation method of the first aspect.

[0255] In a seventh aspect, an embodiment of the present disclosure proposes a network device, which may include: one or more processors; wherein, the network device can be used to execute the optional implementation method of the second aspect.

[0256] In an eighth aspect, an embodiment of the present disclosure proposes a communication device, which may include: one or more processors; wherein the communication device can be used to execute an optional implementation of the first aspect or the second aspect.

[0257] In the ninth aspect, an embodiment of the present disclosure proposes a communication system, which may include: a terminal device and a network device; wherein, the terminal device is configured to execute the method described in the optional implementation manner of the first aspect, and the network device is configured to execute the method described in the optional implementation manner of the second aspect.

[0258] In a tenth aspect, an embodiment of the present disclosure proposes a storage medium storing instructions, which, when executed on a communication device, enables the communication device to execute the method described in the optional implementation of the first aspect or the second aspect.

[0259] In an eleventh aspect, an embodiment of the present disclosure proposes a program product, which, when executed by a communication device, enables the communication device to execute the method described in the optional implementation manner of the first aspect or the second aspect.

[0260] In a twelfth aspect, an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the method described in the optional implementation of the first or second aspect.

[0261] In a thirteenth aspect, an embodiment of the present disclosure provides a chip or a chip system, wherein the chip or chip system includes a processing circuit configured to execute the method described in the optional implementation of the first aspect or the second aspect.

[0262] It is understandable that the above-mentioned terminal devices, network devices, communication devices, communication systems, storage media, program products, computer programs, chips, or chip systems can all be used to perform the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here.

[0263] The present disclosure provides a communication method, device, and storage medium. In some embodiments, the terms "communication method" and "information transmission method" are interchangeable; "communication device" and "information processing device" are interchangeable; and "communication system" and "information processing system" are interchangeable.

[0264] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0265] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0266] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0267] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0268] In some embodiments, "plurality" may refer to two or more.

[0269] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0270] In some embodiments, descriptions such as "at least one of A and B," "A and / or B," "A in one case, B in another case," or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.

[0271] In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

[0272] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0273] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0274] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0275] In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not less than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

[0276] In some embodiments, devices and the like can be interpreted as physical or virtual, and their names are not limited to those described in the embodiments. Terms such as "device," "equipment," "device," "circuit," "network element," "node," "function," "unit," "section," "system," "network," "chip," "chip system," "entity," and "subject" can be used interchangeably.

[0277] In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

[0278] In some embodiments, the terms "Access Network Device (AN Device)", "Radio Access Network Device (RAN Device)", "Base Station (BS)", "Radio Base Station (Radio Base Station)", "Fixed Station (Fixed Station)", "Node (Node)", "Access Point (Access Point)", "Transmission Point (TP)", "Reception Point (RP)", "Transmission and / or Reception Point (TRP))", "Panel (Panel)", "Antenna Panel (Antenna Panel)", "Antenna Array (Antenna Array)" "Cell (Cell)", "Macro Cell (Macro Cell)", "Small Cell (Small Cell)", "Femto Cell (Femto Cell)", "Pico Cell (Pico Cell)" "Sector (Sector)", "Cell Group (Cell Group)", "Serving Cell", "Carrier (Carrier)", "Component Carrier (Component Carrier)", "Bandwidth Part (BWP)" and the like can be used interchangeably.

[0279] In some embodiments, the terms "terminal", "terminal device", "user equipment (UE)", "user terminal" "mobile station (MS)", "mobile terminal (MT)", subscriber station (Subscriber Station), mobile unit (Mobile Unit), subscriber unit (Subscriber Unit), wireless unit (Wireless Unit), remote unit (Remote Unit), mobile device (Mobile Device), wireless device (Wireless Device), wireless communication device (Wireless Communication Device), remote device (Remote Device), mobile subscriber station (Mobile Subscriber Station), access terminal (Access Terminal), mobile terminal (Mobile Terminal), wireless terminal (Wireless Terminal), remote terminal (Remote Terminal), handset (Handset), user agent (User Agent), mobile client (Mobile Client), client (Client) and the like can be used interchangeably.

[0280] In some embodiments, the access network device, the core network device, or the network device can be replaced by a terminal. For example, the various embodiments of the present disclosure can also be applied to a structure in which the communication between the access network device, the core network device, or the network device and the terminal is replaced by communication between multiple terminals (for example, device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, it is also possible to set the structure in which the terminal has all or part of the functions of the access network device. In addition, terms such as "uplink" and "downlink" can also be replaced by terms corresponding to communication between terminals (for example, "side"). For example, uplink channels, downlink channels, etc. can be replaced by side channels or direct channels, and uplinks, downlinks, etc. can be replaced by side links or direct links.

[0281] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, the core network device, or the network device may have a structure that has all or part of the functions of the terminal.

[0282] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0283] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0284] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0285] FIG1 is a schematic diagram illustrating an architecture of a communication system according to an embodiment of the present disclosure. As shown in FIG1 , the communication system 100 may include a terminal device 101 and a network device 102 .

[0286] In some embodiments, the terminal device 101 may include at least one of a mobile phone, a wearable device, an Internet of Things device, a car with communication capabilities, a smart car, a tablet computer, a computer with wireless transceiver capabilities, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in smart grid, a wireless terminal device in transportation safety, a wireless terminal device in smart city, and a wireless terminal device in smart home, but is not limited thereto.

[0287] In some embodiments, the network device 102 may include at least one of an access network device and a core network device.

[0288] In some embodiments, the access network device may be a node or device that accesses the terminal device to the wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a Wi-Fi system, but is not limited thereto.

[0289] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0290] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit (Control Unit). The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0291] In some embodiments, the core network device may be a single device, or may be multiple devices or a group of devices. The core network may include at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

[0292] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0293] The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1 , or a portion thereof, but are not limited thereto. The entities shown in FIG1 are examples. The communication system may include all or part of the entities shown in FIG1 , or may include other entities outside of FIG1 . The number and form of the entities are arbitrary, and the entities may be physical or virtual. The connection relationship between the entities is an example. The entities may be connected or disconnected, and the connection may be in any manner, including direct or indirect, wired or wireless.

[0294] The embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G New Radio (NR), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New Radio Access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X), systems utilizing other communication methods, and next-generation systems based on and extending these methods. Furthermore, multiple systems may be combined (for example, a combination of LTE or LTE-A with 5G).

[0295] In some embodiments of the present disclosure, the communication system may introduce a first artificial intelligence (AI) model, or other models for prediction. The first AI model may be one or more models, and the first AI model may include one or more functions. The first AI model may be deployed on the terminal device side or on the network device side.

[0296] In NR communications, for the FR2 (frequency range 2) communication band, since high-frequency channels attenuate quickly, beam-based transmission and reception can be used to ensure coverage.

[0297] In some embodiments, the network device may configure a reference signal resource set for beam measurement, and the terminal device may measure the reference signal resources in the reference signal resource set and report the IDs of X reference signal resources with relatively strong signal quality in the measurement results, as well as the Layer 1 Reference Signal Receiving Power (L1-RSRP) and / or Layer 1 Signal to Interference plus Noise Ratio (L1-SINR) of each of the X reference signal resources. The reference signal resource set configured by the network device includes X reference signal resources, each of which corresponds to a different transmit beam of the network device. For each reference signal resource, the terminal device needs to measure the reference signal resource through all receive beams, determine the beam measurement quality corresponding to each receive beam, and determine the strongest beam measurement quality from multiple beam measurement qualities. In the above measurement process, if the number of transmit beams of the network device is M and the number of receive beams of the terminal device is N, the number of beam pairs that the terminal device needs to measure is M*N.

[0298] In some embodiments, beam prediction is performed through an AI model. For example, for spatial beam prediction, the terminal device may measure only a portion of the beam pairs. For example, the beam pairs measured by the terminal device may be 1 / 8, 1 / 4, etc. of the M*N beam pairs. The beam measurement quality of the measured portion of the beam pairs is input into the AI ​​model, and the beam quality of the M*N beam pairs is predicted by the AI ​​model. For example, for spatial beam prediction, the terminal device may measure only a portion of the beams. For example, the beams measured by the terminal device may be 1 / 8, 1 / 4, etc. of the M transmit beams. The beam measurement quality of the measured portion of the beams is input into the AI ​​model, and the beam quality of the M transmit beams is predicted by the AI ​​model. For time-domain beam prediction, the terminal device may measure the beam quality of the beam pairs at historical times to obtain the beam historical measurement quality. Based on the beam historical measurement quality, the AI ​​model is used to predict the beam quality of the beam pairs at future times. Similarly, for time-domain beam prediction, the beam pairs can also be replaced with transmit beams.

[0299] In some embodiments, for spatial domain beam prediction, the measurement results of the beams in beam set setA can be predicted based on the measurement results of the beams in beam set setB; for time domain beam prediction, the measurement results of the beams in setA at future times can be predicted based on the measurement results of the beams in setB at historical times.

[0300] In some embodiments, for spatial beam prediction, the terminal device can measure the L1-RSRP of each beam in setB, input the measured multiple L1-RSRPs into the AI ​​model, and obtain the L1-RSRP of each beam in setA.

[0301] The relationship between setB and setA may include at least one of the following:

[0302] setB may be a subset of setA. For example, setA includes 32 reference signals (each reference signal corresponds to a beam direction), and setB includes N reference signals, where N<32, for example, N=8.

[0303] The beam corresponding to setB is a wide beam, and the beam corresponding to setA is a narrow beam. For example, setA includes 32 reference signals, each reference signal corresponds to a beam direction, and the range covered by the 32 reference signals is 120 degrees. setB includes N reference signals, for example, N=8, and the range covered by N reference signals is also 120 degrees. That is to say, the beam directions of multiple reference signals in setB cover the beam directions of multiple reference signals in setA. It can also be understood that the 32 / N reference signals in setA and the same reference signal in setB are in a quasi co-location (QCL) Type D relationship.

[0304] In some embodiments, for time domain beam prediction, the terminal device can measure the L1-RSRP of each beam in setB at historical time, input the measured multiple L1-RSRPs into the AI ​​model, and predict the L1-RSRP of each beam in setA at future time.

[0305] The relationship between setB and setA may include at least one of the following:

[0306] setB can be a subset of setA;

[0307] setB is the same as setA;

[0308] The beam corresponding to setB is a wide beam, and the beam corresponding to setA is a narrow beam.

[0309] In some embodiments, the output data of the AI ​​model mainly includes L1-RSRP and / or beam (pair) ID, but in the MTRP scenario, multiple TRP beams may be required to serve the terminal device at the same time. In this case, the terminal device is required to perform group-based beam reporting (group based beam report), and the terminal device needs to measure all beams. Its reference signal resource overhead is relatively large, and the complexity of the terminal device measurement is also relatively high. In this case, the AI ​​model can be used to predict the beam corresponding to each TRP, and the accuracy of the beam prediction depends on the performance of the AI ​​model. In the performance monitoring and training process of the AI ​​model, it is necessary to determine the optimal beam group (group). Therefore, how to determine the optimal beam group becomes an urgent problem to be solved.

[0310] FIG2A is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure. The method may be executed by the above-mentioned communication system. As shown in FIG2A , the method may include:

[0311] Step S2101: The network device sends first information to the terminal device.

[0312] In some embodiments, the terminal device may receive the first information. For example, the terminal device may receive the first information sent by the network device. For another example, the terminal device may also receive the first information sent by another entity.

[0313] In some embodiments, the first information may include a configuration of a reference signal resource, which may be used by the terminal device to perform beam measurement.

[0314] In some embodiments, the name of the first information is not limited, and may be, for example, "measurement request information", "measurement configuration information", "measurement indication information", etc.

[0315] In some embodiments, the first AI model is a model for performing beam prediction, and the first information may include at least one of the following:

[0316] A first reference signal resource set, where reference signal resources in the first reference signal resource set correspond to a beam to be measured;

[0317] a second reference signal resource set, where the reference signal resources in the second reference signal resource set correspond to the beam to be predicted;

[0318] a third reference signal resource set, the third reference signal resource set including resources for interference measurement;

[0319] A relationship between the first reference signal resource set and the second reference signal resource set.

[0320] Among them, the "beam to be measured" is the definition when the model is actually used. The "beam to be measured" can be understood as the beam that needs to be actually measured as the input of the AI ​​model. The beam here can be understood as a transmitting beam, or a transmitting and receiving beam pair.

[0321] In some embodiments, a beam may correspond to a reference signal resource, which may correspond to a reference signal, and each reference signal may correspond to a beam direction. For each beam, a network device may configure a reference signal resource corresponding to a reference signal, and a terminal device may measure the beam based on the reference signal. The reference signal may be a Channel State Information-Reference Signal (CSI-RS) or a Synchronization Signal Block (SSB).

[0322] In some embodiments, if the beam measurement result is L1-SINR, the first information may include the third reference signal resource set.

[0323] In some embodiments, the third reference signal resource set may include two resource sets used for interference measurement, one of which corresponds to the first reference signal resource set, and the other corresponds to the second reference signal resource set.

[0324] In some embodiments, the third reference signal resource may include a beam corresponding to each beam corresponding to the first reference signal resource set and the second reference signal resource set and used for interference measurement.

[0325] In some embodiments, if the first AI model is a model for performing spatial beamforming, the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

[0326] The first reference signal resource set is a subset of the second reference signal resource set;

[0327] The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

[0328] In some embodiments, if the first reference signal resource set is a subset of the second reference signal resource set, the first information may only include the second reference signal resource set.

[0329] The first reference signal resource set is a subset of the second reference signal resource set, indicating that the beam to be measured corresponding to the first reference signal resource set is a subset of the beam to be predicted corresponding to the second reference signal resource set. For example, if the beam to be predicted corresponding to the second reference signal resource set includes 32 beams, and the beam to be measured corresponding to the first reference signal resource set may include 4 beams among the 32 beams to be predicted corresponding to the second reference signal resource set, then the first reference signal resource set is a subset of the second reference signal resource set.

[0330] The first reference signal resource set is a subset of the second reference signal resource set, and can be represented by which reference signal resources in the second reference signal resource set the first reference signal resource set corresponds to. For example, the beam set corresponding to the second reference signal resource set is setA, and setA includes 32 beams (for convenience of explanation, the 32 beams can be recorded as beam 1, beam 2, beam 3, beam 4, beam 5, ..., beam 30, beam 31, beam 32), and the beam set corresponding to the first reference signal resource set is setB, and setB includes 4 beams (for convenience of explanation, the 4 beams can be recorded as beam 1, beam 2, beam 3, beam 4), beam 1 in setB corresponds to beam 8 in setA, beam 2 in setB corresponds to beam 16 in setA, beam 3 in setB corresponds to beam 24 in setA, and beam 4 in setB corresponds to beam 32 in setA. From the correspondence between the beams in setB and the beams in setA, it can be seen that the 4 beams in setB are a subset of the 32 beams in setA.

[0331] It should be noted that the number of beams corresponding to the first reference signal resource set and the number of beams corresponding to the second reference signal resource set are for exemplary purposes only, and the correspondence between the beams corresponding to the first reference signal resource set and the beams corresponding to the second reference signal resource set is also for exemplary purposes only. The embodiments of the present disclosure do not limit this.

[0332] The beam coverage range corresponding to the first reference signal resource set is the same as that corresponding to the second reference signal resource set, which can be interpreted as each wide beam corresponding to the first reference signal resource set can cover multiple narrow beams corresponding to the second reference signal resource set. For example, if the beam set corresponding to the first reference signal resource set is setB, setB includes 8 wide beams (for the convenience of explanation, the 8 beams can be recorded as beam 1, beam 2, beam 3, beam 4, ..., beam 8), the beam set corresponding to the second reference signal resource set is setA, setA includes 32 narrow beams (for the convenience of explanation, the 32 beams can be recorded as beam 1, beam 2, beam 3, beam 4, beam 5, ..., beam 30, beam 31, beam 32 ), the correspondence between each wide beam corresponding to the first reference signal resource set and the narrow beam corresponding to the second reference signal resource set covered by the wide beam can be expressed as beam 1 in setB covers beam 1, beam 2, beam 3 to beam 4 in setA, beam 2 in setB covers beam 5, beam 6, beam 7 to beam 8 in setA, and so on, beam 8 in setB covers beam 29, beam 30, beam 31 to beam 32 in setA.

[0333] In some embodiments, if the first reference signal resource set is a subset of the second reference signal resource set, the network device may send the second reference signal resource set to the terminal device.

[0334] It should be noted that “the network device may send the second reference signal resource set to the terminal device” may be interpreted as the network device sending the second reference signal resource set to the terminal device but not sending the first reference signal resource set to the terminal device.

[0335] In some embodiments, if the first AI model is a model for performing time-domain beamforming, the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

[0336] The first reference signal resource set is a subset of the second reference signal resource set;

[0337] The first reference signal resource set is the same as the second reference signal resource set;

[0338] The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

[0339] In some embodiments, if the first reference signal resource set is a subset of the second reference signal resource set, the first information may only include the second reference signal resource set.

[0340] The first reference signal resource set and the second reference signal resource set are identical, indicating that the beam to be measured corresponding to the first reference signal resource set is identical to the beam to be predicted corresponding to the second reference signal resource set. For the time-domain beam prediction model, the beam measurement results for each beam corresponding to the first reference signal resource set obtained from historical measurements can be used to predict the beam measurement results for each beam corresponding to the second reference signal resource set in the future. This allows the terminal device to avoid performing any measurements in the future.

[0341] In some embodiments, the first reference signal resource set may include multiple fourth reference signal resource sets, and different fourth reference signal resource sets correspond to different transceiver points TRP; the second reference signal resource set may include multiple fifth reference signal resource sets, and different fifth reference signal resource sets correspond to different TRPs.

[0342] For example, if the TRP includes a first TRP and a second TRP, the first reference signal resource may include a fourth reference signal resource set A and a fourth reference signal resource set B, and the second reference signal resource set includes a fifth reference signal resource set A and a fifth reference signal resource set B, then the fourth reference signal resource set A corresponds to the fifth reference signal resource set A, the fourth reference signal resource set B corresponds to the fifth reference signal resource set B, the fourth reference signal resource set A and the fifth reference signal resource set A correspond to the first TRP, and the fourth reference signal resource set B and the fifth reference signal resource set B correspond to the second TRP. That is, the fourth reference signal resource set A is the beam to be measured corresponding to the first TRP, and the fifth reference signal resource set A is the beam to be predicted corresponding to the first TRP; the fourth reference signal resource set B is the beam to be measured corresponding to the second TRP, and the fifth reference signal resource set B is the beam to be predicted corresponding to the second TRP.

[0343] In some embodiments, "different fourth reference signal resource sets correspond to different TRPs" and "different fifth reference signal resource sets correspond to different TRPs" can be understood as different fifth reference signal resource sets corresponding to different fourth reference signal resource sets, that is, the fifth reference signal resource set corresponds one-to-one to the fourth reference signal resource set.

[0344] In some embodiments, if the first AI model is a model for performing spatial beamforming, the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

[0345] The fourth reference signal resource set is a subset of the fifth reference signal resource set;

[0346] The beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

[0347] In some embodiments, if the first AI model is a model for performing time-domain beamforming, the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

[0348] The fourth reference signal resource set is a subset of the fifth reference signal resource set;

[0349] The fourth reference signal resource set is the same as the fifth reference signal resource set;

[0350] The beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

[0351] It should be noted that, for the description of the relationship between the fourth reference signal resource set and the fifth reference signal resource set, reference may be made to the description of the relationship between the first reference signal resource set and the second reference signal resource set, which will not be repeated here.

[0352] It should also be noted that when the fourth reference signal resource and the fifth reference signal resource correspond to the same TRP, the fourth reference signal resource and the fifth reference signal resource have the above-mentioned relationship.

[0353] Step S2102: The terminal device performs beam measurement for each beam group according to the first information, obtains a first beam measurement result of the first beam and a second beam measurement result of the second beam, and determines a beam group measurement result of the beam group based on the first beam measurement result and the second beam measurement result.

[0354] In some embodiments, the first beam measurement result may include a first beam quality, and the second beam measurement result may include a second beam quality.

[0355] In some embodiments, the first beam quality and the second beam quality may include L1-RSRP or L1-SINR.

[0356] In some embodiments, the first beam measurement result and the second beam measurement result may be L1-RSRP. The terminal device may perform beam measurement based on the reference signal resource corresponding to the first beam to obtain the first beam measurement result; and the terminal device may perform beam measurement based on the reference signal resource corresponding to the second beam to obtain the second beam measurement result.

[0357] In some embodiments, the first beam measurement result and the second beam measurement result may be L1-SINR. The terminal device may perform beam measurement based on the reference signal resources corresponding to the first beam and the reference signal resources in the third reference signal resource set to obtain the first beam measurement result, and perform beam measurement based on the reference signal resources corresponding to the second beam and the reference signal resources in the third reference signal resource set to obtain the second beam measurement result.

[0358] In some embodiments, the first beam measurement result may be L1-RSRP, and the second beam measurement result may be L1-SINR. The terminal device may perform beam measurement based on the reference signal resources corresponding to the first beam to obtain the first beam measurement result, and perform beam measurement based on the reference signal resources corresponding to the second beam and the reference signal resources in the third reference signal resource set to obtain the second beam measurement result.

[0359] In some embodiments, the beam group measurement result may be determined by at least one of the following methods:

[0360] Taking the average value of the first beam measurement result and the second beam measurement result as the beam group measurement result;

[0361] Taking the weighted average value of the first beam measurement result and the second beam measurement result as the beam group measurement result;

[0362] Taking the sum of the first measurement result, the second measurement result, and the product of the first measurement result and the second measurement result as the beam group measurement result.

[0363] For example, if the first beam measurement result is SINR#1 and the second beam measurement result is SINR#2, then the beam group measurement result SINR-group = (SINR#1 + SINR#2) / 2.

[0364] For another example, if the first beam measurement result is SINR#1 and the second beam measurement result is SINR#2, then the beam group measurement result SINR-group = W1*SINR#1 + W2*SINR#2, where W1 is the weight corresponding to the first beam, W2 is the weight corresponding to the second beam, W1 and W2 may be predefined, or may be indicated by the network device, or may also be determined according to the first beam measurement result and the second beam measurement result. For example, if SINR#1 > SINR#2, then W1 > W2, the maximum value of W1 may be 1, and the minimum value of W2 may be 0; or, if SINR#1 > SINR#2, then W1 < W2, the maximum value of W2 may be 1, and the minimum value of W1 may be 0.

[0365] For another example, the beam group measurement result may be obtained by calculating the Shannon formula, and the first beam measurement result, the second beam measurement result, and the beam group measurement result may satisfy the following equation:

[0366] log2(1+SINR-group)=log2(1+SINR#1)+log2(1+SINR#2)

[0367] From the above formula, we can get: SINR-group = SINR#1 + SINR#2 + SINR#1 * SINR#2

[0368] Step S2103: The terminal device determines model performance monitoring data based on the beam group measurement results.

[0369] In some embodiments, the name of the model performance monitoring data is not limited, and may be, for example, "performance report", "performance monitoring report", "model monitoring report", etc.

[0370] In some embodiments, the model performance monitoring data may be used to determine the performance of the first AI model.

[0371] In some embodiments, the model performance monitoring data may include at least one of the following:

[0372] Beam information of K beam groups;

[0373] The performance value of the first AI model;

[0374] first data, where the first data may include at least one of the following: model input data of the first AI model, model output data of the first AI model, and measurement data corresponding to the model output data, where the model output data is data output by the first AI model based on the model input data;

[0375] a specified event, where the specified event is triggered based on a comparison result between the performance value of the first AI model and a first threshold value or a first offset value;

[0376] First operation information, where the first operation information is used to instruct a management operation to be performed on the first AI model. The management operation includes any one of the following: activating the first AI model, deactivating the first AI model, switching the first AI model, and not using the AI ​​model.

[0377] In some embodiments, K beam groups may be determined from the at least one beam group according to the beam group measurement result.

[0378] In some embodiments, the K beam groups may be the best K beam groups, and the terminal device may use the beam groups corresponding to the best K beam group measurement results as the best K beam groups, and the best K beam group measurement results include at least one beam group measurement result and the top K beam group measurement results when arranged from high to low.

[0379] For example, the terminal device can arrange at least one beam group measurement result corresponding to at least one beam group in descending order, determine the best K beam group measurement results at the front, and use the K beam groups corresponding to the best K beam group measurement results as the best K beam groups.

[0380] In some embodiments, the beam information may include at least one of the following:

[0381] An identifier of the reference signal resource corresponding to each beam in each optimal beam group;

[0382] The beam quality corresponding to the identifier of the reference signal resource.

[0383] The beam quality may be L1-RSRP or L1-SINR.

[0384] In some embodiments, the performance value of the first AI model may be used to indicate a performance indicator of the first AI model.

[0385] In some embodiments, if the first AI model is deployed on the network device side, the first data may include the model input data of the first AI model and the measurement data corresponding to the model output data; if the first AI model is deployed on the terminal device side, the first data may include the model output data and the measurement data corresponding to the model output data.

[0386] In some embodiments, the performance value may include at least one of the following:

[0387] A beam group prediction accuracy rate, where the beam group prediction accuracy rate is an accuracy rate of including an actual optimal beam group in at least one predicted beam group;

[0388] a beam group quality difference, where the beam group quality difference is a difference between a measured beam quality of a first beam group and a measured beam quality of a second beam group, where the first beam group is the beam group with the strongest predicted beam quality and the second beam group is the beam group with the strongest measured beam quality;

[0389] A predicted beam group quality difference is a difference between the predicted beam quality of the first beam group and the measured beam quality of the first beam group.

[0390] In some embodiments, the beam group prediction accuracy may be whether the at least one predicted beam group includes an actual optimal beam group. The actual optimal beam group may be a beam group with the best beam group measurement result.

[0391] For example, if the predicted at least one beam group includes the first beam group, the second beam group and the third beam group, and the actual optimal beam group is the second beam group, then it is determined that the beam group prediction is accurate; if the predicted at least one beam group includes the first beam group, the second beam group and the third beam group, and the actual optimal beam group is the fourth beam group, then it is determined that the beam group prediction is inaccurate.

[0392] In some embodiments, the beam group prediction accuracy rate may be N / M, where M is the number of model outputs and N is the number of model outputs where the beam group prediction is accurate.

[0393] In some embodiments, the specified event may be used to determine the performance of the first AI model.

[0394] In some embodiments, the designated event may be triggered based on a comparison result between the performance value of the first AI model and a first threshold value or a first offset value.

[0395] In some embodiments, the first threshold value or the first offset value may be specified by a protocol, configured by a network device, or an empirical value, which is not limited in the embodiments of the present disclosure.

[0396] In some embodiments, the designated event may include at least one of the following: a first event, a second event, a third event, a fourth event, a fifth event, and a sixth event.

[0397] It should be noted that the above-mentioned designated events are for illustration only, and the embodiments of the present disclosure do not limit the specific events included in the designated events.

[0398] In some embodiments, the terminal device triggers a first event when it determines, based on the beam group measurement result, that the beam group prediction accuracy is less than a first accuracy threshold.

[0399] In some embodiments, the terminal device triggers a second event when it determines, based on the beam group measurement result, that the beam group prediction accuracy is greater than a second accuracy threshold.

[0400] In some embodiments, the terminal device triggers a third event when determining, based on the beam group measurement result, that the beam group quality difference is less than a first difference threshold.

[0401] In some embodiments, the terminal device triggers a fourth event when determining, based on the beam group measurement result, that the beam group quality difference is greater than a second difference threshold.

[0402] In some embodiments, the terminal device triggers a fifth event when it determines, based on the beam group measurement result, that the predicted beam group quality difference is less than a third difference threshold.

[0403] In some embodiments, the terminal device triggers a sixth event when it determines, based on the beam group measurement result, that the predicted beam group quality difference is greater than a fourth difference threshold.

[0404] For example, the first accuracy threshold can be 80%, the second accuracy threshold can be 90%, the first difference threshold can be 1dB, and the second difference threshold can be 3dB. The first event is triggered when the beam group prediction accuracy is less than 80%, the second event is triggered when the beam prediction accuracy is greater than 90%, the third event is triggered when the beam group quality difference is less than 1dB, and the seventh and fourth events are triggered when the beam group quality difference is greater than 3dB.

[0405] In some embodiments, the first operation information may be used to indicate a management operation to be performed on the first AI model.

[0406] In some embodiments, the management operation includes any one of the following: activating the first AI model, deactivating the first AI model, switching the first AI model, and not using the AI ​​model.

[0407] In some embodiments, “switching the first AI model” can be interpreted as deactivating the first AI model and activating the second AI model, where the second AI model can be any model other than the first AI model, which is not limited in the embodiments of the present disclosure.

[0408] In some embodiments, “not using the AI ​​model” can be interpreted as not using any AI model, and can also be called fallback, that is, falling back to the traditional mode (a mode that does not use the AI ​​model).

[0409] In some embodiments, model output data and measurement data corresponding to the model output data may be determined based on the beam group measurement results, and first operation information may be determined based on the model output data and the measurement data corresponding to the model output data.

[0410] In some embodiments, if the difference between the model output data and the measurement data corresponding to the model output data is less than or equal to a first difference threshold, it can be determined that the performance of the first AI model meets the performance requirements; if the difference between the model output data and the measurement data corresponding to the model output data is greater than the first difference threshold, it can be determined that the performance of the first AI model does not meet the performance requirements.

[0411] It should be noted that the above method of determining whether the performance of the first AI model meets the performance requirements is for illustration only and is not limited to this in the embodiments of the present disclosure.

[0412] In some embodiments, the model output data and the measurement data corresponding to the model output data can be used to determine the performance of the first AI model.

[0413] In some embodiments, the terminal device can determine the model input data of the first AI model, input the model input data into the first AI model, and obtain the model output data output by the first AI model.

[0414] In some embodiments, the terminal device may determine the model input data of the first AI model based on the beam group measurement results.

[0415] In some embodiments, if the first AI model is a model for performing spatial beamforming, the model input data of the first AI model may include at least one of the following:

[0416] L1-RSRPs of N beams corresponding to the first reference signal resource set, where N is a positive integer;

[0417] identifiers of reference signal resources corresponding to the N beams corresponding to the first reference signal resource set;

[0418] The first indication information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and / or sent simultaneously.

[0419] In some embodiments, the identifier of the reference signal resource may be a synchronization signal block (SSB) ID or a channel state information reference signal (CSI-RS) ID.

[0420] In some embodiments, the first indication information may be indicated by the network device to the terminal device, or may be determined autonomously by the terminal device.

[0421] In some embodiments, if the first AI model can predict that the terminal device supports two beams that are simultaneously received and / or simultaneously transmitted as a group, the model input data may include first indication information, indicating that the first AI model is expected to output only two beams that the terminal device supports being simultaneously received as a group; or indicating that the first AI model is expected to output only two beams that the terminal device supports being simultaneously transmitted as a group; or instructing the first AI model to output two beams that the terminal device supports being simultaneously received and transmitted as a group. If the first AI model can only predict that the terminal device supports two beams that are simultaneously received as a group, or the first AI model can only predict that the terminal device supports two beams that are simultaneously transmitted as a group, or the first AI model can only predict that the terminal device supports two beams that are simultaneously received and transmitted as a group, the model input data may not include the first indication information.

[0422] In some embodiments, if the first AI model is a model for performing time-domain beamforming, the model input data of the first AI model may include at least one of the following:

[0423] at least one historical time;

[0424] L1-RSRP or L1-SINR of N beams corresponding to the first reference signal resource set corresponding to each historical time, where N is a positive integer;

[0425] identifiers of the reference signal resources corresponding to the N beams corresponding to the first reference signal resource set corresponding to each historical time;

[0426] The third indication information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and / or sent simultaneously.

[0427] In some embodiments, the historical time may be a time when measurement is performed on the first reference signal resource set.

[0428] In some embodiments, the historical time may include multiple times, and each historical time may be a time when a measurement is performed on the first reference signal resource set.

[0429] In some embodiments, the first reference signal resources corresponding to different historical times may be the same or different, which is not limited in the embodiments of the present disclosure.

[0430] The third indication information may be included in the model input data or not included in the model input data, and the details are the same as the description of the first indication information.

[0431] In some embodiments, the third indication information may be indicated by the network device to the terminal device, or may be determined autonomously by the terminal device.

[0432] In some embodiments, if the first AI model is a model for performing spatial beamforming, the model output data of the first AI model may include at least one of the following:

[0433] At least one group;

[0434] identifiers of two reference signal resources corresponding to each group, wherein the reference signal resources are reference signal resources in the second reference signal resource set;

[0435] The beam quality corresponding to the identifier of each reference signal resource;

[0436] at least one third beam;

[0437] an identifier of a reference signal resource corresponding to each third beam, wherein the reference signal resource is a reference signal resource in the second reference signal resource set;

[0438] The beam quality corresponding to each third beam;

[0439] The second indication information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and / or sent simultaneously.

[0440] In some embodiments, the groups may be beam groups.

[0441] In some embodiments, the two reference signal resources corresponding to each group may be two beams in the second reference signal resource set.

[0442] In some embodiments, the two reference signal resources corresponding to each group may be two beams in two fifth reference signal resource sets respectively.

[0443] For example, if the beam group includes reference signal resource A and reference signal resource B, reference signal resource A can be a beam in the fifth reference signal resource set A, and reference signal resource B can be a beam in the fifth reference signal resource set B.

[0444] In some embodiments, if the model output data includes a third beam, the model output data does not include any beam reported in a group with the third beam.

[0445] In some embodiments, the terminal device may separately indicate that the beams contained in each group are two beams supported by the terminal device that can be received and / or transmitted simultaneously.

[0446] In some embodiments, the terminal device may simultaneously indicate that the beams contained in each group are two beams supported by the terminal device that can be received and / or transmitted simultaneously.

[0447] In some embodiments, if the first AI model can predict that the terminal device supports two beams that are simultaneously received and / or simultaneously transmitted as a group, and the model input data does not include the first indication information, the output data may include the second indication information. If the first AI model can predict that the terminal device supports two beams that are simultaneously received and / or simultaneously transmitted as a group, and the model input data includes the first indication information, the model output data does not need to include the second indication information. If the first AI model can only predict that the terminal device supports two beams that are simultaneously received as a group, or the first AI model can only predict that the terminal device supports two beams that are simultaneously transmitted as a group, or the first AI model can only predict that the terminal device supports two beams that are simultaneously received and transmitted as a group, then the model output data may not include the second indication information.

[0448] It should be noted that the model output data includes all the information predicted by the first AI model.

[0449] In some embodiments, if the first AI model is a model for performing time-domain beamforming, the model output data of the first AI model may include at least one of the following:

[0450] At least one future time, where the future time is a time corresponding to a beam predicted by the first AI model;

[0451] At least one group corresponding to each future time;

[0452] identifiers of two reference signal resources corresponding to each group corresponding to each future time, wherein the reference signal resources are reference signal resources in the second reference signal resource set;

[0453] The beam quality corresponding to the identifier of each reference signal resource corresponding to each future time;

[0454] at least one fourth beam corresponding to each future time;

[0455] an identifier of a reference signal resource corresponding to each fourth beam corresponding to each future time, wherein the reference signal resource is a reference signal resource in the second reference signal resource set;

[0456] Each future time corresponds to the beam quality corresponding to each fourth beam;

[0457] The fourth indication information is used to indicate that the beams contained in at least one group corresponding to at least one future time in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and / or sent simultaneously.

[0458] In some embodiments, the future time may be the time when beam prediction is performed by the first AI model.

[0459] In some embodiments, the future time may include multiple times, and the first AI model may be used to perform beam prediction once at each future time.

[0460] In some embodiments, the terminal device may report the model output data corresponding to each future time separately.

[0461] In some embodiments, at each future time, the terminal device may respectively indicate that the beams contained in each group are two beams supported by the terminal device that can be received and / or transmitted simultaneously.

[0462] In some embodiments, at each future time, the terminal device may simultaneously indicate that the beams included in each group are two beams supported by the terminal device that can be received and / or transmitted simultaneously.

[0463] In some embodiments, the terminal device may simultaneously indicate multiple future times, and the beams contained in multiple groups are two beams supported by the terminal device that can be received and / or transmitted simultaneously.

[0464] In some embodiments, the fourth indication information may be included in the model output data or not included in the model output data, and the details are the same as those described for the second indication information.

[0465] In some embodiments, the measurement data corresponding to the model output data may be measurement data of the beam corresponding to the model output data in the second beam measurement result.

[0466] In some embodiments, the first AI model is in an inactive state. The performance of the first AI model is determined to meet performance requirements based on the model output data and measurement data corresponding to the model output data, and the first operation information is determined to activate the first AI model.

[0467] In some embodiments, the first AI model is in an activated state. Based on the model output data and the measurement data corresponding to the model output data, it is determined that the performance of the first AI model does not meet the performance requirements, and the first operation information is determined to be deactivating the first AI model.

[0468] Step S2104: The terminal device sends the model performance monitoring data to the network device.

[0469] In some embodiments, the network device may receive model performance monitoring data. For example, the network device may receive model performance monitoring data sent by a terminal device. For another example, the network device may also receive model performance monitoring data sent by other entities.

[0470] In some embodiments, the terminal device determines that the beam group prediction accuracy is less than a first accuracy threshold based on the beam group measurement result, and sends a first event to the network device.

[0471] In some embodiments, the terminal device determines that the beam group prediction accuracy is greater than a second accuracy threshold based on the beam group measurement result, and sends a second event to the network device.

[0472] In some embodiments, the terminal device determines, based on the beam group measurement result, that the beam group quality difference is less than a first difference threshold, and sends a third event to the network device.

[0473] In some embodiments, the terminal device determines, based on the beam group measurement result, that the beam group quality difference is greater than a second difference threshold, and sends a fourth event to the network device.

[0474] In some embodiments, the terminal device determines, based on the beam group measurement result, that the predicted beam group quality difference is less than a third difference threshold, and sends a fifth event to the network device.

[0475] In some embodiments, it is determined based on the beam group measurement result that the predicted beam group quality difference is greater than a fourth difference threshold, and a sixth event is sent to the network device.

[0476] In some embodiments, when the beam measurement result is capable of triggering multiple designated events, multiple designated events may be sent to the network device.

[0477] For example, if the beam group quality difference is determined to be less than the first difference threshold according to the beam group measurement result, and the predicted beam group quality difference is less than the third difference threshold, the third event and the fifth event can be sent to the network device.

[0478] In some embodiments, after the terminal device determines that the designated event is triggered, it can report the ID of the designated event to the network device.

[0479] In some embodiments, the terminal device may also report the performance value corresponding to the specified event to the network device.

[0480] In some embodiments, the terminal device may send model performance monitoring data including the performance value to the network device.

[0481] In some embodiments, the terminal device may determine a specified event based on the performance value, and send model performance monitoring data including the specified event to the network device.

[0482] It should be noted that the terminal device can send one or more items of model performance monitoring data to the network device, and the embodiments of the present disclosure are not limited to this.

[0483] Using the above method, the terminal device can perform beam measurement based on the first information sent by the network device, obtain the beam group measurement results of at least one beam group, and send model performance monitoring data to the network device based on the beam group measurement results. In this way, the network device can perform model performance monitoring based on the model performance monitoring data, thereby improving system performance.

[0484] The method involved in the embodiments of the present disclosure may include at least one of the above steps S2101 to S2104. For example, step S2101 can be implemented as an independent embodiment, step S2104 can be implemented as an independent embodiment, and steps S2102+S2103+S2104 can be implemented as independent embodiments, but are not limited thereto.

[0485] In some embodiments, steps S2101 to S2104 are all optional. For example, step S2101 is optional, and one or more of these steps may be omitted or replaced in different embodiments. For another example, step S2104 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0486] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2A .

[0487] FIG2B is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure. The method may be executed by the above-mentioned communication system. As shown in FIG2B , the method may include:

[0488] Step S2201: The network device sends first information to the terminal device.

[0489] The optional implementation of step S2201 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0490] Step S2202: The terminal device performs beam measurement for each beam group according to the first information, obtains a first beam measurement result of the first beam and a second beam measurement result of the second beam, and determines a beam group measurement result of the beam group based on the first beam measurement result and the second beam measurement result.

[0491] The optional implementation of step S2202 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0492] Step S2203: The terminal device determines the model training data according to the beam group measurement results.

[0493] In some embodiments, the model training data may include model input data and beam measurement results of the beam to be predicted.

[0494] It should be noted that the model input data can refer to the description in step S2103 and will not be repeated here. The beam measurement result of the beam to be predicted can be the beam measurement result obtained by the terminal device performing beam measurement based on the reference signal resources in the second reference signal resource set.

[0495] In some embodiments, the beam to be predicted can also be interpreted as the beam output by the first AI model.

[0496] Step S2204: The terminal device sends model training data to the network device.

[0497] In some embodiments, the network device may receive model training data. For example, the network device may receive model training data sent by a terminal device. For another example, the network device may also receive model training data sent by other entities.

[0498] In some embodiments, on the network device side where the first AI model is deployed, the model training data can be used for model training on the network device to obtain the first AI model.

[0499] Using the above method, the terminal device can perform beam measurement based on the first information sent by the network device, obtain the beam group measurement results of at least one beam group, and send model training data to the network device based on the beam group measurement results. In this way, the network device can perform model training based on the model training data to obtain the first AI model.

[0500] The method involved in the embodiments of the present disclosure may include at least one of the above steps S2201 to S2204. For example, step S2201 can be implemented as an independent embodiment, step S2204 can be implemented as an independent embodiment, and steps S2202+S2203+S2204 can be implemented as independent embodiments, but are not limited thereto.

[0501] In some embodiments, steps S2201 to S2204 are all optional. For example, step S2201 is optional, and one or more of these steps may be omitted or replaced in different embodiments. For another example, step S2204 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0502] FIG2C is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure. The method may be executed by the above-mentioned communication system. As shown in FIG2C , the method may include:

[0503] Step S2301: The network device sends first information to the terminal device.

[0504] The optional implementation of step S2301 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0505] Step S2302: The terminal device performs beam measurement for each beam group according to the first information, obtains a first beam measurement result of the first beam and a second beam measurement result of the second beam, and determines a beam group measurement result of the beam group based on the first beam measurement result and the second beam measurement result.

[0506] The optional implementation of step S2302 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0507] Step S2303: The terminal device determines the model input data according to the beam group measurement results.

[0508] It should be noted that the model input data can refer to the description in step S2103 and will not be repeated here.

[0509] Step S2304: The terminal device sends model input data to the network device.

[0510] In some embodiments, the network device may receive model input data. For example, the network device may receive model input data sent by a terminal device. For another example, the network device may also receive model input data sent by other entities.

[0511] In some embodiments, on the network device side where the first AI model is deployed, the model input data can be used by the network device to perform model inference to obtain a beam prediction result for the beam to be predicted.

[0512] Using the above method, the terminal device can perform beam measurement based on the first information sent by the network device, obtain the beam group measurement results of at least one beam group, and send model input data to the network device based on the beam group measurement results. In this way, the network device can perform model inference based on the model input data, thereby obtaining the beam prediction result output by the first AI model.

[0513] The method involved in the embodiments of the present disclosure may include at least one of the above steps S2301 to S2304. For example, step S2301 can be implemented as an independent embodiment, step S2304 can be implemented as an independent embodiment, and steps S2302+S2303+S2304 can be implemented as independent embodiments, but are not limited thereto.

[0514] In some embodiments, steps S2301 to S2304 are all optional. For example, step S2301 is optional, and one or more of these steps may be omitted or replaced in different embodiments. For another example, step S2304 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0515] FIG2D is an interactive diagram illustrating a communication method according to an embodiment of the present disclosure. The method may be executed by the above-mentioned communication system. As shown in FIG2D , the method may include:

[0516] Step S2401: The network device sends first information to the terminal device.

[0517] The optional implementation of step S2401 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0518] Step S2402: The terminal device performs beam measurement for each beam group according to the first information, obtains a first beam measurement result of the first beam and a second beam measurement result of the second beam, and determines a beam group measurement result of the beam group based on the first beam measurement result and the second beam measurement result.

[0519] The optional implementation of step S2402 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0520] Step S2403: The terminal device determines the model output data according to the beam group measurement results.

[0521] It should be noted that the model output data can refer to the description in step S2103 and will not be repeated here.

[0522] Step S2404: The terminal device sends the model output data to the network device.

[0523] In some embodiments, the network device may receive model output data. For example, the network device may receive model output data sent by a terminal device. For another example, the network device may also receive model output data sent by other entities.

[0524] In some embodiments, the first AI model is deployed on the terminal device side, and the model output data can be the data output by the first AI model based on the model input data, and the model output data can be used for beam management of the network device.

[0525] Using the above method, the terminal device can perform beam measurement based on the first information sent by the network device, obtain the beam group measurement results of at least one beam group, and send model output data to the network device based on the beam group measurement results. In this way, the network device can perform beam management based on the model output data.

[0526] The method involved in the embodiments of the present disclosure may include at least one of the above steps S2401 to S2404. For example, step S2401 can be implemented as an independent embodiment, step S2404 can be implemented as an independent embodiment, and steps S2402+S2403+S2404 can be implemented as independent embodiments, but are not limited thereto.

[0527] In some embodiments, steps S2401 to S2404 are all optional. For example, step S2401 is optional, and one or more of these steps may be omitted or replaced in different embodiments. For another example, step S2404 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0528] In some embodiments, the names of information, etc. are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codeword", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0529] In some embodiments, "obtain", "get", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, autonomous implementation, etc.

[0530] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0531] In some embodiments, terms such as "certain", "preset", "preset", "setting", "indicated", "a certain", "any", and "first" can be interchangeable. "Specific A", "preset A", "preset A", "setting A", "indicated A", "a certain A", "any A", and "first A" can be interpreted as A pre-specified in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., or as specific A, a certain A, any A, or first A, etc., but not limited to this.

[0532] FIG3A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3A , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:

[0533] Step S3101: Obtain first information.

[0534] The optional implementation of step S3101 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0535] In some embodiments, the terminal device may receive the first information sent by the network device, but is not limited thereto. The terminal device may also receive the first information sent by other entities.

[0536] In some embodiments, the terminal device may obtain first information specified by the protocol.

[0537] In some embodiments, the terminal device may obtain the first information from an upper layer(s).

[0538] In some embodiments, step S3101 may be omitted, and the terminal device may autonomously implement the reference signal resources indicated by the first information, or the above function may be default or by default.

[0539] Step S3102: For each beam group, perform beam measurement according to the first information to obtain a first beam measurement result of the first beam and a second beam measurement result of the second beam, and determine a beam group measurement result of the beam group according to the first beam measurement result and the second beam measurement result.

[0540] The optional implementation of step S3102 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0541] Step S3103: Determine model performance monitoring data based on the beam group measurement results.

[0542] The optional implementation of step S3103 can refer to the optional implementation of step S2103 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0543] Step S3104: Send model performance monitoring data.

[0544] The optional implementation of step S3104 can refer to the optional implementation of step S2104 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0545] In some embodiments, the terminal device may send the model performance monitoring data to the network device, but is not limited thereto. The terminal device may also send the model performance monitoring data to other entities.

[0546] The method involved in the embodiments of the present disclosure may include at least one of the above steps S3101 to S3104. For example, step S3101 can be implemented as an independent embodiment, step S3104 can be implemented as an independent embodiment, and steps S3102 + S3103 can be implemented as independent embodiments, but are not limited thereto.

[0547] In some embodiments, the above steps S3101 to S3104 are all optional steps. For example, steps S3101 and S3104 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0548] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 3A .

[0549] FIG3B is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3B , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:

[0550] Step S3201: Obtain first information.

[0551] The optional implementation of step S3201 can be found in step S2101 of FIG. 2A , the optional implementation of step S3101 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.

[0552] Step S3202: For each beam group, perform beam measurement according to the first information to obtain a first beam measurement result of the first beam and a second beam measurement result of the second beam, and determine a beam group measurement result of the beam group according to the first beam measurement result and the second beam measurement result.

[0553] The optional implementation of step S3202 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0554] Step S3203: Determine model training data based on the beam group measurement results.

[0555] The optional implementation of step S3203 can refer to the optional implementation of step S2203 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0556] Step S3204: Send model training data.

[0557] The optional implementation of step S3204 can refer to the optional implementation of step S2204 in Figure 2B and other related parts in the embodiment involved in Figure 2B, which will not be repeated here.

[0558] In some embodiments, the terminal device may send model training data to the network device, but is not limited thereto. The terminal device may also send model training data to other entities.

[0559] The method involved in the embodiments of the present disclosure may include at least one of the above steps S3201 to S3204. For example, step S3201 can be implemented as an independent embodiment, step S3204 can be implemented as an independent embodiment, and steps S3202 + S3203 can be implemented as independent embodiments, but are not limited thereto.

[0560] In some embodiments, the above steps S3201 to S3204 are all optional steps. For example, steps S3201 and S3204 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0561] FIG3C is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3C , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:

[0562] Step S3301: Obtain first information.

[0563] The optional implementation of step S3301 can be found in step S2101 of FIG. 2A , the optional implementation of step S3101 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be repeated here.

[0564] Step S3302: For each beam group, perform beam measurement according to the first information to obtain a first beam measurement result of the first beam and a second beam measurement result of the second beam, and determine a beam group measurement result of the beam group according to the first beam measurement result and the second beam measurement result.

[0565] The optional implementation of step S3302 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0566] Step S3303: Determine model input data based on the beam group measurement results.

[0567] The optional implementation of step S3303 can refer to the optional implementation of step S2303 in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.

[0568] Step S3304: Send model input data.

[0569] The optional implementation of step S3304 can refer to the optional implementation of step S2304 in Figure 2C and other related parts in the embodiment involved in Figure 2C, which will not be repeated here.

[0570] In some embodiments, the terminal device may send the model input data to the network device, but is not limited thereto. The terminal device may also send the model input data to other entities.

[0571] The method involved in the embodiments of the present disclosure may include at least one of the above steps S3301 to S3304. For example, step S3301 can be implemented as an independent embodiment, step S3304 can be implemented as an independent embodiment, and steps S3302 + S3303 can be implemented as independent embodiments, but are not limited thereto.

[0572] In some embodiments, the above steps S3301 to S3304 are all optional steps. For example, steps S3301 and S3304 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0573] FIG3D is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3D , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:

[0574] Step S3401: Obtain first information.

[0575] The optional implementation of step S3401 can refer to the optional implementation of step S2101 in Figure 2A, step S3101 in Figure 3A, and other related parts in the embodiments involved in Figures 2A and 3A, which will not be repeated here.

[0576] Step S3402: For each beam group, perform beam measurement according to the first information to obtain a first beam measurement result of the first beam and a second beam measurement result of the second beam, and determine a beam group measurement result of the beam group according to the first beam measurement result and the second beam measurement result.

[0577] The optional implementation of step S3402 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0578] Step S3403: Determine model output data based on the beam group measurement results.

[0579] The optional implementation of step S3403 can refer to the optional implementation of step S2403 in Figure 2D and other related parts in the embodiment involved in Figure 2D, which will not be repeated here.

[0580] Step S3404: Send model output data.

[0581] The optional implementation of step S3404 can refer to the optional implementation of step S2403 in Figure 2D and other related parts in the embodiment involved in Figure 2D, which will not be repeated here.

[0582] The method involved in the embodiments of the present disclosure may include at least one of the above steps S3401 to S3404. For example, step S3401 can be implemented as an independent embodiment, step S3404 can be implemented as an independent embodiment, and steps S3402 + S3403 can be implemented as independent embodiments, but are not limited thereto.

[0583] In some embodiments, steps S3401 to S3404 are all optional steps. For example, steps S3401 and S3404 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0584] FIG3E is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3E , the embodiment of the present disclosure relates to a communication method, which can be executed by a terminal device. The method may include:

[0585] Step S3501: Obtain first information.

[0586] The optional implementation of step S3501 can be found in step S2101 of FIG. 2A , the optional implementation of step S3101 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be repeated here.

[0587] Step S3502: Perform beam measurement according to the first information to obtain a beam group measurement result of at least one beam group.

[0588] The optional implementation of step S3502 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0589] Step S3503: Send second information according to the beam group measurement result.

[0590] For optional implementations of step S3503, reference may be made to step S2104 in FIG. 2A , step S2204 in FIG. 2B , step S2304 in FIG. 2C , step S2404 in FIG. 2D , step S3104 in FIG. 3A , step S3204 in FIG. 3B , step S3304 in FIG. 3C , and step S3404 in FIG. 3D , as well as other related parts in the embodiments involved in FIG. 2A , FIG. 2B , FIG. 2C , FIG. 2D , FIG. 3A , FIG. 3B , FIG. 3C , and FIG. 3D , which will not be repeated here.

[0591] In some embodiments, the beam group includes a first beam and a second beam, and performing beam measurement according to the first information to obtain a beam group measurement result of at least one beam group includes:

[0592] For each beam group, beam measurement is performed according to the first information to obtain a first beam measurement result of the first beam and a second beam measurement result of the second beam, and the beam group measurement result of the beam group is determined based on the first beam measurement result and the second beam measurement result.

[0593] In some embodiments, determining the beam group measurement result of the beam group according to the first beam measurement result and the second beam measurement result includes at least one of the following:

[0594] taking an average of the first beam measurement result and the second beam measurement result as the beam group measurement result;

[0595] taking a weighted average of the first beam measurement result and the second beam measurement result as the beam group measurement result;

[0596] The sum of the first measurement result, the second measurement result, and the product of the first measurement result and the second measurement result is used as the beam group measurement result.

[0597] In some embodiments, the first information includes at least one of the following:

[0598] a first reference signal resource set, where the reference signal resources in the first reference signal resource set correspond to the beam to be measured;

[0599] a second reference signal resource set, where the reference signal resources in the second reference signal resource set correspond to the beam to be predicted;

[0600] a third reference signal resource set, wherein the third reference signal resource set includes resources used for interference measurement;

[0601] The relationship between the first reference signal resource set and the second reference signal resource set.

[0602] In some embodiments, the first AI model is a model for performing spatial beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

[0603] The first reference signal resource set is a subset of the second reference signal resource set;

[0604] The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

[0605] In some embodiments, the first AI model is a model for performing time-domain beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

[0606] The first reference signal resource set is a subset of the second reference signal resource set;

[0607] The first reference signal resource set is the same as the second reference signal resource set;

[0608] The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

[0609] In some embodiments, the first reference signal resource set includes multiple fourth reference signal resource sets, and different fourth reference signal resource sets correspond to different transceiver points TRP;

[0610] The second reference signal resource set includes multiple fifth reference signal resource sets, and different fifth reference signal resource sets correspond to different TRPs.

[0611] In some embodiments, the first AI model is a model for performing spatial beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

[0612] The fourth reference signal resource set is a subset of the fifth reference signal resource set;

[0613] The beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

[0614] In some embodiments, the first AI model is a model for performing time-domain beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

[0615] The fourth reference signal resource set is a subset of the fifth reference signal resource set;

[0616] The fourth reference signal resource set is the same as the fifth reference signal resource set;

[0617] The beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

[0618] In some embodiments, the second information includes at least one of the following:

[0619] Model training data;

[0620] Model performance monitoring data;

[0621] Model input data;

[0622] Model output data.

[0623] In some embodiments, the model training data includes the model input data and beam measurement results of the beam to be predicted.

[0624] In some embodiments, the model performance monitoring data includes at least one of the following:

[0625] Beam information of K beam groups;

[0626] the performance value of the first AI model;

[0627] first data, the first data including at least one of the following: model input data of the first AI model, model output data of the first AI model, and measurement data corresponding to the model output data, where the model output data is data output by the first AI model based on the model input data;

[0628] a specified event, where the specified event is triggered based on a comparison result between a performance value of the first AI model and a first threshold value or a first offset value;

[0629] First operation information, where the first operation information is used to indicate a management operation to be performed on the first AI model, where the management operation includes any one of the following: activating the first AI model, deactivating the first AI model, switching the first AI model, and not using the AI ​​model.

[0630] In some embodiments, the method further comprises:

[0631] The K beam groups are determined from the at least one beam group according to the beam group measurement result.

[0632] In some embodiments, the K beam groups are optimal K beam groups, and determining the K beam groups from the at least one beam group according to the beam group measurement results includes:

[0633] The beam groups corresponding to the best K beam group measurement results are used as the best K beam groups, and the best K beam group measurement results include at least one of the first K beam group measurement results when the beam group measurement results are arranged from high to low.

[0634] In some embodiments, the beam information includes at least one of the following:

[0635] An identifier of the reference signal resource corresponding to each beam in each optimal beam group;

[0636] The beam quality corresponding to the identifier of the reference signal resource.

[0637] In some embodiments, the performance value includes at least one of the following:

[0638] A beam group prediction accuracy rate, where the beam group prediction accuracy rate is an accuracy rate of including an actual optimal beam group in at least one predicted beam group;

[0639] a beam group quality difference, where the beam group quality difference is a difference between a measured beam quality of a first beam group and a measured beam quality of a second beam group, where the first beam group is the beam group with the strongest predicted beam quality and the second beam group is the beam group with the strongest measured beam quality;

[0640] A predicted beam group quality difference is a difference between a predicted beam quality of the first beam group and a measured beam quality of the first beam group.

[0641] In some embodiments, the designated event includes at least one of the following: a first event, a second event, a third event, a fourth event, a fifth event, and a sixth event;

[0642] The sending second information to the network device according to the beam group measurement result includes at least one of the following:

[0643] Determining, according to the beam group measurement result, that the beam group prediction accuracy is less than a first accuracy threshold, and sending the first event to the network device;

[0644] Determining, according to the beam group measurement result, that the beam group prediction accuracy is greater than a second accuracy threshold, and sending the second event to the network device;

[0645] determining, according to the beam group measurement result, that the beam group quality difference is less than a first difference threshold, and sending the third event to the network device;

[0646] determining, according to the beam group measurement result, that the beam group quality difference is greater than a second difference threshold, and sending the fourth event to the network device;

[0647] determining, according to the beam group measurement result, that the predicted beam group quality difference is less than a third difference threshold, and sending the fifth event to the network device;

[0648] Determine, according to the beam group measurement result, that the predicted beam group quality difference is greater than a fourth difference threshold, and send the sixth event to the network device.

[0649] In some embodiments, the sending second information to the network device according to the beam group measurement result includes:

[0650] determining the model output data and the measurement data corresponding to the model output data according to the beam group measurement result;

[0651] determining the first operation information according to the model output data and measurement data corresponding to the model output data;

[0652] The first operation information is sent to the network device.

[0653] In some embodiments, determining the first operation information according to the model output data and the measurement data corresponding to the model output data includes:

[0654] The first AI model is in an inactive state, and it is determined based on the model output data and measurement data corresponding to the model output data that the performance of the first AI model meets the performance requirement, and the first operation information is determined to be to activate the first AI model; or

[0655] The first AI model is in an activated state. It is determined based on the model output data and measurement data corresponding to the model output data that performance of the first AI model does not meet performance requirements, and the first operation information is determined to be deactivating the first AI model.

[0656] In some embodiments, the first AI model is a model for performing spatial beam prediction, and the model input data includes at least one of the following:

[0657] beam qualities of the N beams corresponding to the first reference signal resource set, the beam qualities comprising layer 1 reference signal received power L1-RSRP or layer 1 signal to interference plus noise ratio L1-SINR, where N is a positive integer;

[0658] identifiers of reference signal resources corresponding to the N beams corresponding to the first reference signal resource set;

[0659] The first indication information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be simultaneously received and / or simultaneously transmitted.

[0660] In some embodiments, the first AI model is a model for performing spatial beam prediction, and the model output data includes at least one of the following:

[0661] At least one group;

[0662] identifiers of two reference signal resources corresponding to each group, wherein the reference signal resources are reference signal resources in the second reference signal resource set;

[0663] a beam quality corresponding to an identifier of each reference signal resource;

[0664] at least one third beam;

[0665] an identifier of a reference signal resource corresponding to each of the third beams, wherein the reference signal resource is a reference signal resource in the second reference signal resource set;

[0666] a beam quality corresponding to each of the third beams;

[0667] Second indication information, the second indication information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and / or sent simultaneously.

[0668] In some embodiments, the first AI model is a model for performing time-domain beam prediction, and the model input data includes at least one of the following:

[0669] at least one historical time;

[0670] beam qualities of N beams corresponding to the first reference signal resource set corresponding to each historical time, where the beam qualities include L1-RSRP or L1-SINR, where N is a positive integer;

[0671] identifiers of reference signal resources corresponding to the N beams corresponding to the first reference signal resource set corresponding to each historical time;

[0672] The third indication information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be simultaneously received and / or simultaneously transmitted.

[0673] In some embodiments, the first AI model is a model for performing time-domain beam prediction, and the model output data includes at least one of the following:

[0674] At least one future time, where the future time is a time corresponding to a beam predicted by the first AI model;

[0675] At least one group corresponding to each of the future times;

[0676] identifiers of two reference signal resources corresponding to each group corresponding to each of the future times, wherein the reference signal resources are reference signal resources in the second reference signal resource set;

[0677] a beam quality corresponding to an identifier of each reference signal resource corresponding to each future time;

[0678] at least one fourth beam corresponding to each of the future times;

[0679] an identifier of a reference signal resource corresponding to each of the fourth beams corresponding to each of the future time periods, wherein the reference signal resource is a reference signal resource in the second reference signal resource set;

[0680] The beam quality corresponding to each fourth beam at each future time;

[0681] The fourth indication information is used to indicate that the beams contained in at least one group corresponding to at least one future time in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and / or sent simultaneously.

[0682] FIG4A is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4A , the embodiment of the present disclosure relates to a communication method, which can be performed by a network device. The method may include:

[0683] Step S4101: Send the first information.

[0684] The optional implementation of step S4101 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0685] In some embodiments, the network device may send the first information to the terminal device, but is not limited thereto. The network device may also send the first information to other entities.

[0686] Step S4102: Obtain model performance monitoring data.

[0687] The optional implementation of step S4102 can refer to the optional implementation of step S2104 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0688] In some embodiments, the network device may receive the type performance monitoring data sent by the terminal device, but is not limited thereto, and the network device may also receive the type performance monitoring data sent by other entities.

[0689] In some embodiments, the above steps are all optional steps.

[0690] FIG4B is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4B , the embodiment of the present disclosure relates to a communication method, which can be performed by a network device. The method may include:

[0691] Step S4201: Send the first message.

[0692] The optional implementation of step S4201 can refer to the optional implementation of step S2101 in Figure 2A, the optional implementation of step S4101 in Figure 4A, and other related parts in the embodiments involved in Figures 2A and 4A, which will not be repeated here.

[0693] Step S4202: Obtain model training data.

[0694] The optional implementation of step S4202 can refer to the optional implementation of step S2204 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0695] In some embodiments, the above steps are all optional steps.

[0696] FIG4C is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4C , an embodiment of the present disclosure relates to a communication method, which can be performed by a network device. The method may include:

[0697] Step S4301: Send the first information.

[0698] The optional implementation of step S4301 can be found in step S2101 of FIG. 2A , the optional implementation of step S4101 of FIG. 4A , and other related parts in the embodiments involved in FIG. 2A and FIG. 4A , which will not be repeated here.

[0699] Step S4302: Obtain model input data.

[0700] The optional implementation of step S4302 can refer to the optional implementation of step S2304 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0701] In some embodiments, the above steps are all optional steps.

[0702] FIG4D is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4D , the embodiment of the present disclosure relates to a communication method, which can be performed by a network device. The method may include:

[0703] Step S4401: Send the first information.

[0704] The optional implementation of step S4401 can refer to the optional implementation of step S2101 in Figure 2A, the optional implementation of step S4101 in Figure 4A, and other related parts in the embodiments involved in Figures 2A and 4A, which will not be repeated here.

[0705] Step S4402: Obtain model output data.

[0706] The optional implementation of step S4402 can refer to the optional implementation of step S2404 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0707] In some embodiments, the above steps are all optional steps.

[0708] FIG4E is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4E , the embodiment of the present disclosure relates to a communication method, which can be performed by a network device. The method may include:

[0709] Step S4501: Send the first information.

[0710] The optional implementation of step S4501 can refer to the optional implementation of step S2101 in Figure 2A, the optional implementation of step S4101 in Figure 4A, and other related parts in the embodiments involved in Figures 2A and 4A, which will not be repeated here.

[0711] Step S4502: Obtain second information.

[0712] For optional implementations of step S4502, reference may be made to step S2104 in FIG. 2A , step S2204 in FIG. 2B , step S2304 in FIG. 2C , step S2404 in FIG. 2D , step S4104 in FIG. 4A , step S4204 in FIG. 4B , step S4304 in FIG. 4C , and step S4404 in FIG. 4D , as well as other related parts in the embodiments involved in FIG. 2A , FIG. 2B , FIG. 2C , FIG. 2D , FIG. 4A , FIG. 4B , FIG. 4C , and FIG. 4D , which will not be repeated here.

[0713] In some embodiments, the beam group includes a first beam and a second beam, and the beam group measurement result is determined by:

[0714] For each beam group, beam measurement is performed according to the first information to obtain a first beam measurement result of the first beam and a second beam measurement result of the second beam, and the beam group measurement result of the beam group is determined based on the first beam measurement result and the second beam measurement result.

[0715] In some embodiments, determining the beam group measurement result of the beam group according to the first beam measurement result and the second beam measurement result includes at least one of the following:

[0716] taking an average of the first beam measurement result and the second beam measurement result as the beam group measurement result;

[0717] taking a weighted average of the first beam measurement result and the second beam measurement result as the beam group measurement result;

[0718] The sum of the first measurement result, the second measurement result, and the product of the first measurement result and the second measurement result is used as the beam group measurement result.

[0719] In some embodiments, the first information includes at least one of the following:

[0720] a first reference signal resource set, where the reference signal resources in the first reference signal resource set correspond to the beam to be measured;

[0721] a second reference signal resource set, where the reference signal resources in the second reference signal resource set correspond to the beam to be predicted;

[0722] a third reference signal resource set, wherein the third reference signal resource set includes resources used for interference measurement;

[0723] The relationship between the first reference signal resource set and the second reference signal resource set.

[0724] In some embodiments, the first AI model is a model for performing spatial beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

[0725] The first reference signal resource set is a subset of the second reference signal resource set;

[0726] The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

[0727] In some embodiments, the first AI model is a model for performing time-domain beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following:

[0728] The first reference signal resource set is a subset of the second reference signal resource set;

[0729] The first reference signal resource set is the same as the second reference signal resource set;

[0730] The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

[0731] In some embodiments, the first reference signal resource set includes multiple fourth reference signal resource sets, and different fourth reference signal resource sets correspond to different transceiver points TRP;

[0732] The second reference signal resource set includes multiple fifth reference signal resource sets, and different fifth reference signal resource sets correspond to different TRPs.

[0733] In some embodiments, the first AI model is a model for performing spatial beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

[0734] The fourth reference signal resource set is a subset of the fifth reference signal resource set;

[0735] The beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

[0736] In some embodiments, the first AI model is a model for performing time-domain beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following:

[0737] The fourth reference signal resource set is a subset of the fifth reference signal resource set;

[0738] The fourth reference signal resource set is the same as the fifth reference signal resource set;

[0739] The beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

[0740] In some embodiments, the second information includes at least one of the following:

[0741] Model training data;

[0742] Model performance monitoring data;

[0743] Model input data;

[0744] Model output data.

[0745] In some embodiments, the model training data includes the model input data and beam measurement results of the beam to be predicted.

[0746] In some embodiments, the model performance monitoring data includes at least one of the following:

[0747] Beam information of K beam groups;

[0748] the performance value of the first AI model;

[0749] first data, the first data including at least one of the following: model input data of the first AI model, model output data of the first AI model, and measurement data corresponding to the model output data, where the model output data is data output by the first AI model based on the model input data;

[0750] a specified event, where the specified event is triggered based on a comparison result between a performance value of the first AI model and a first threshold value or a first offset value;

[0751] First operation information, where the first operation information is used to indicate a management operation to be performed on the first AI model, where the management operation includes any one of the following: activating the first AI model, deactivating the first AI model, switching the first AI model, and not using the AI ​​model.

[0752] In some embodiments, the K beam groups are the best K beam groups, and the best K beam groups are the beam groups corresponding to the best K beam group measurement results, and the best K beam group measurement results include at least one of the top K beam group measurement results when the beam group measurement results are arranged from high to low.

[0753] In some embodiments, the beam information includes at least one of the following:

[0754] An identifier of the reference signal resource corresponding to each beam in each optimal beam group;

[0755] The beam quality corresponding to the identifier of the reference signal resource.

[0756] In some embodiments, the performance value includes at least one of the following:

[0757] A beam group prediction accuracy rate, where the beam group prediction accuracy rate is an accuracy rate of including an actual optimal beam group in at least one predicted beam group;

[0758] a beam group quality difference, where the beam group quality difference is a difference between a measured beam quality of a first beam group and a measured beam quality of a second beam group, where the first beam group is the beam group with the strongest predicted beam quality and the second beam group is the beam group with the strongest measured beam quality;

[0759] A predicted beam group quality difference is a difference between a predicted beam quality of the first beam group and a measured beam quality of the first beam group.

[0760] In some embodiments, the designated event includes at least one of the following: a first event, a second event, a third event, a fourth event, a fifth event, and a sixth event; and the receiving of the second information sent by the terminal device according to the beam group measurement result of at least one beam group includes at least one of the following:

[0761] receiving the first event sent by the terminal device, where the first event is triggered when the terminal device determines, based on the beam group measurement result, that the beam group prediction accuracy is less than a first accuracy threshold;

[0762] receiving a second event sent by the terminal device, where the second event is triggered when the terminal device determines, based on the beam group measurement result, that the beam group prediction accuracy is greater than a second accuracy threshold;

[0763] receiving the third event sent by the terminal device, where the third event is triggered when the terminal device determines, based on the beam group measurement result, that the beam group quality difference is less than a first difference threshold;

[0764] receiving the fourth event sent by the terminal device, where the fourth event is triggered when the terminal device determines, based on the beam group measurement result, that the beam group quality difference is greater than a second difference threshold;

[0765] receiving the fifth event sent by the terminal device, where the fifth event is triggered when the terminal device determines, based on the beam group measurement result, that the predicted beam group quality difference is less than a third difference threshold;

[0766] Receive the sixth event sent by the terminal device, where the sixth event is triggered when the terminal device determines, based on the beam group measurement result, that the predicted beam group quality difference is greater than a fourth difference threshold.

[0767] In some embodiments, the first operation information is determined by the terminal device according to the model output data and measurement data corresponding to the model output data.

[0768] In some embodiments, the first operation information includes activating the first AI model, or deactivating the first AI model.

[0769] In some embodiments, the first AI model is a model for performing spatial beam prediction, and the model input data includes at least one of the following:

[0770] beam qualities of the N beams corresponding to the first reference signal resource set, the beam qualities comprising layer 1 reference signal received power L1-RSRP or layer 1 signal to interference plus noise ratio L1-SINR, where N is a positive integer;

[0771] identifiers of reference signal resources corresponding to the N beams corresponding to the first reference signal resource set;

[0772] The first indication information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be simultaneously received and / or simultaneously transmitted.

[0773] In some embodiments, the first AI model is a model for performing spatial beam prediction, and the model output data includes at least one of the following:

[0774] At least one group;

[0775] identifiers of two reference signal resources corresponding to each group, wherein the reference signal resources are reference signal resources in the second reference signal resource set;

[0776] a beam quality corresponding to an identifier of each reference signal resource;

[0777] at least one third beam;

[0778] an identifier of a reference signal resource corresponding to each of the third beams, wherein the reference signal resource is a reference signal resource in the second reference signal resource set;

[0779] a beam quality corresponding to each of the third beams;

[0780] Second indication information, the second indication information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and / or sent simultaneously.

[0781] In some embodiments, the first AI model is a model for performing time-domain beam prediction, and the model input data includes at least one of the following:

[0782] at least one historical time;

[0783] beam qualities of N beams corresponding to the first reference signal resource set corresponding to each historical time, where the beam qualities include L1-RSRP or L1-SINR, where N is a positive integer;

[0784] identifiers of reference signal resources corresponding to the N beams corresponding to the first reference signal resource set corresponding to each historical time;

[0785] The third indication information is used to indicate that the beams contained in at least one group in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be simultaneously received and / or simultaneously transmitted.

[0786] In some embodiments, the first AI model is a model for performing time-domain beam prediction, and the model output data includes at least one of the following:

[0787] At least one future time, where the future time is a time corresponding to a beam predicted by the first AI model;

[0788] At least one group corresponding to each of the future times;

[0789] identifiers of two reference signal resources corresponding to each group corresponding to each of the future times, wherein the reference signal resources are reference signal resources in the second reference signal resource set;

[0790] a beam quality corresponding to an identifier of each reference signal resource corresponding to each future time;

[0791] at least one fourth beam corresponding to each of the future times;

[0792] an identifier of a reference signal resource corresponding to each of the fourth beams corresponding to each of the future time periods, wherein the reference signal resource is a reference signal resource in the second reference signal resource set;

[0793] The beam quality corresponding to each fourth beam at each future time;

[0794] The fourth indication information is used to indicate that the beams contained in at least one group corresponding to at least one future time in the group-based beam information output by the first AI model are two beams supported by the terminal device that can be received and / or sent simultaneously.

[0795] FIG5 is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG5 , the embodiment of the present disclosure relates to a communication method, which can be executed by a communication system. The method may include:

[0796] Step S5101: The network device sends first information to the terminal device.

[0797] The optional implementation of step S5101 can be found in the optional implementation of step S2101 in Figure 2A, step S3101 in Figure 3A, step S4101 in Figure 4A, and other related parts in the embodiments involved in Figures 2A, 3A, and 4A, which will not be repeated here.

[0798] Step S5102: The terminal device performs beam measurement according to the first information to obtain a beam group measurement result of at least one beam group.

[0799] The optional implementation of step S5102 can be found in step S2102 of FIG. 2A , the optional implementation of step S3102 of FIG. 3A , and other related parts in the embodiments involved in FIG. 2A and FIG. 3A , which will not be described in detail here.

[0800] Step S5103: The terminal device sends second information to the network device according to the beam group measurement result.

[0801] The optional implementation of step S5103 can be found in the optional implementation of step S2104 in Figure 2A, step S2204 in Figure 2B, step S2304 in Figure 2C, step S2404 in Figure 2D, and other related parts in the embodiments involved in Figures 2A, 2B, 2C, and 2D, which will not be repeated here.

[0802] In some embodiments, the above method may include the method described in the embodiments of the above communication system, terminal equipment, network equipment, etc., which will not be repeated here.

[0803] Example 1

[0804] In some embodiments, the first AI model is used to perform a spatial beam prediction model, and the input data of the first AI model may include at least one of the following:

[0805] L1-RSRP or L1-SINR of the beams in setB1 and setB2 (or the beam identifier ID, i.e., reference signal resource ID: SSB ID or CSI-RS resource ID)

[0806] L1-RSRP or L1-SINR of the beam in setB (or the beam identifier ID, i.e., reference signal resource ID: SSB ID or CSI-RS resource ID, can also be added). setB indicates that setB1 and setB2 are not distinguished and are mixed into one setB.

[0807] When the input data is L1-SINR, each reference signal resource in setBi is configured with a corresponding reference signal resource for measuring interference;

[0808] It is desired that the two beams included in the output beam group (or beam pair) are two beams that the terminal supports simultaneous reception and / or transmission.

[0809] Among them, setB1 and setB2 correspond to different reference signal resource sets, that is, correspond to different TRPs, setB1 and setA1 correspond to the same TRP, and setB2 and setA2 correspond to the same TRP.

[0810] The relationship between set Bi and set Ai may include at least one of the following: set Bi is a subset of set Ai, set Bi is a wide beam and set Ai is a narrow beam (one wide beam of set Bi covers multiple narrow beams of set Ai).

[0811] When setB does not distinguish between setB1 and setB2, setA does not distinguish between setA1 and setA2.

[0812] In some embodiments, the beam can be beam, QCL Type D, spatial setting, spatial filter, spatial relation info, and Transmission Configuration Indication (TCI) state.

[0813] In some embodiments, the first AI model is a model for performing airspace beam prediction. The output data of the first AI model may include at least one of the following:

[0814] N beam pairs, and two reference signal resource IDs corresponding to each beam pair. Among them, the two reference signals are two in set A, or one in set A1 and one in set A2 respectively;

[0815] N beam pairs, two reference signal resource IDs corresponding to each beam pair, and the L1 - SINR corresponding to each reference signal resource ID;

[0816] M beams, that is, there is no other beam that can be paired with it, so it is reported in a single form;

[0817] The L1 - SINR corresponding to the beam.

[0818] In some embodiments, when the first AI model is a model for performing time - domain beam prediction, compared with the input data of the first AI model for performing airspace beam prediction, the input data of the first AI model for performing time - domain beam prediction further includes multiple historical times, and each historical time includes an input data of the first AI model for performing airspace beam prediction. When set Bi < set Ai, that is, set Bi is a subset of set Ai, set Bi of multiple historical times remains unchanged, or the beams included in set Bi of multiple historical times are different. For example, multiple set Bis can be combined into a set Ai.

[0819] In some embodiments, when the first AI model is a model for performing time - domain beam prediction, the relationship between set Bi and set Ai may also include that set Bi is the same as set Ai.

[0820] In some embodiments, when the first AI model is a model for performing time - domain beam prediction, compared with the output data of the first AI model for performing airspace beam prediction, the output data of the first AI model for performing time - domain beam prediction further includes multiple future times, and each future time includes an output data of the first AI model for performing airspace beam prediction.

[0821] Embodiment 2

[0822] In some embodiments, the terminal device may receive the first information sent by the network device, determine the reference signal resource based on the first information, obtain the first report, and send the first report to the network device.

[0823] In some embodiments, the first report may be obtained based on measurements, and the first report includes information of K beam groups.

[0824] In some embodiments, the information of the K beam groups may be information of the best K beam groups.

[0825] In some embodiments, the optimal beam group may be determined based on the L1-SINR of the beam group, and the L1-SINR of the beam group may be determined based on the L1-SINRs corresponding to the two beams in the beam group. Specific determination methods may include the following:

[0826] (1) The average value of the L1-SINR corresponding to the two beams in the beam group can be used as the L1-SINR of the beam group;

[0827] (2) The weighted average of the L1-SINRs corresponding to the two beams in the beam group can be used as the L1-SINR of the beam group. For example, the beam with a larger L1-SINR can account for a larger proportion, and the maximum proportion can be 1, that is, the L1-SINR of the beam group is a larger L1-SINR value. Alternatively, the beam with a smaller L1-SINR can account for a larger proportion, and the maximum proportion can be 1, that is, the L1-SINR of the beam group is a smaller L1-SINR value.

[0828] (3) Determine the L1-SINR for the beam group based on the Shannon capacity formula: log2(1+SINR-group)=log2(1+SINR#1)+log2(1+SINR#2), where SINR#1 and SINR#2 are the L1-SINRs for the two beams in the beam group, respectively. Therefore, SINR-group=SINR#1+SINR#2+SINR#1*SINR#2, where SINR-group is the L1-SINR for the beam group.

[0829] In some embodiments, the information of the K beam groups may include at least one of the following:

[0830] Reference signal resource identifiers corresponding to the two beams in each group;

[0831] Each reference signal resource identifier corresponds to the L1-SINR.

[0832] Example 3 (based on Example 2):

[0833] In some embodiments, the first report may also include model performance monitoring data, which may include data used to calculate a performance metric, or a calculated performance metric, or an event triggered based on comparison of the performance metric with a threshold, or an operational decision made for model management (deactivating the model, activating the model, switching the model, or fallback to a non-AI mode).

[0834] Example 4 (based on Example 3):

[0835] In some embodiments, the performance metric used for model performance monitoring may include at least one of the following:

[0836] Beam group prediction accuracy;

[0837] Beam group L1-SINR difference;

[0838] The difference in predicted L1-SINR of the beam group.

[0839] In some embodiments, the beam group prediction accuracy may be the accuracy of the actual best beam group among the predicted K beam groups.

[0840] In some embodiments, the beam group L1-SINR difference may be a difference between the predicted actual L1-SINR of the best beam group and the actual L1-SINR of the best beam group.

[0841] In some embodiments, the difference of the predicted L1-SINR of the beam group may be a difference between an actual L1-SINR of the predicted best beam group and a predicted L1-SINR of the predicted best beam group.

[0842] In some embodiments, the L1-SINR of the beam group may be determined by at least one of the above methods (1) to (3).

[0843] Example 5 (based on Example 3):

[0844] In some embodiments, data used to calculate performance metrics for model performance monitoring may include at least one of the following:

[0845] When the model is on the terminal device side, the terminal device needs to report the predicted value output by the model and the corresponding measured value of each predicted value. The output predicted value can refer to the description of the model output data in the embodiment shown in Figure 2A above, and the measured value can be a measured value corresponding to each output value;

[0846] When the model resides on the network device, the model output values ​​are also on the network device, so the terminal device only needs to report the measured values ​​for each value corresponding to the predicted value output by the model. Furthermore, for the input of the network device-side model, the terminal device also needs to report the model input data. However, the model input data and the measured values ​​used for model performance monitoring can be in the same report (performance monitoring report) or in different reports.

[0847] Example 6 (based on Example 3 or 4):

[0848] In some embodiments, the terminal device may report an event triggered based on a performance metric.

[0849] Network devices configure events. For example, event 1 is triggered when the prediction accuracy of the top-1 beam group is less than 80%; event 2 is triggered when the prediction accuracy of the top-1 beam group is greater than 90%; event 3 is triggered when the difference in the L1-SINR value of the beam group is less than 1dB; event 4 is triggered when the difference in the L1-SINR value of the beam group is greater than 3dB... Therefore, the terminal device can determine whether to trigger and which event to trigger based on the predicted value output by the terminal device-side model and the actual measured value, and then report the corresponding event ID and further report the value of the performance metric corresponding to the triggering event.

[0850] Example 7 (based on Example 3):

[0851] In some embodiments, the terminal device can make a judgment based on the predicted value of the terminal device side model and the actual measured value to determine whether it is necessary to activate or deactivate or switch the AI ​​model or function (the above AI model performance monitoring can be based on the performance monitoring of the model or function), and inform the network device side of the terminal device of the decision.

[0852] In some embodiments, if the model is in an activated state and is found to have poor performance, it is deactivated.

[0853] In some embodiments, if the model is in an inactive state and is found to have good performance, it is activated.

[0854] Example 8 (based on Example 2):

[0855] In some embodiments, the reference signal resource configuration information may include reference signal resources within setB and setA. If different TRPs are distinguished, the reference signal resource configuration information may include reference signal resources within setB1, setB2, setA1, and setA2. If setB is a subset of setA, the reference signal resource configuration information may only include the reference signal resources of setA. If setBi is a subset of setAi, the reference signal resource configuration information may only include the reference signal resources of setAi. If the input data and output data of the first AI model are L1-SINR, the reference signal resource configuration information also includes the reference signal resources corresponding to set B and set A for interference measurement.

[0856] Example 9 (based on Example 2):

[0857] In some embodiments, the first report may be obtained based on measurements, the first report may be used for model training, and the first report may further include model inputs, which are also obtained based on measurements.

[0858] Example 10 (based on Example 2):

[0859] In some embodiments, the first report may be obtained based on a model output, that is, the first report is an output of a model on the terminal device side, and the input is obtained based on measurements.

[0860] In some embodiments of the present disclosure, a communication system is provided, which may include a terminal device and a network device, wherein the terminal device can execute the communication method executed by the terminal device in the aforementioned embodiment of the present disclosure; the network device can execute the communication method executed by the network device in the aforementioned embodiment of the present disclosure.

[0861] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0862] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), and the functions of some or all of the above units or modules are realized by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0863] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of a hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0864] Figure 6A is a structural diagram of a terminal device proposed in an embodiment of the present disclosure. As shown in Figure 6A, the terminal device 101 may include at least one of a transceiver module 6101, a processing module 6102, etc. In some embodiments, the transceiver module 6101 is configured to receive first information sent by a network device, wherein the first information includes the configuration of a reference signal resource, and the reference signal resource is used for the terminal device to perform beam measurement; the processing module 6102 is configured to perform beam measurement according to the first information to obtain a beam group measurement result of at least one beam group; the processing module 6102 is further configured to send second information to the network device according to the beam group measurement result, wherein the second information includes information related to the first AI model, and the first AI model is a model for performing beam prediction. Optionally, the transceiver module 6101 can be used to perform at least one of the communication steps such as sending and / or receiving performed by the terminal device 101 in any of the above methods (for example, step S2101, step S3101, but not limited to this), which will not be repeated here. Optionally, the processing module 6102 can be used to execute at least one of the other steps (such as step S2102, step S2103, but not limited to these) performed by the terminal device 101 in any of the above methods, which will not be repeated here.

[0865] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

[0866] Figure 6B is a structural diagram of a network device proposed in an embodiment of the present disclosure. As shown in Figure 6B, the network device 102 may include: at least one of a transceiver module 6201, a processing module 6202, etc. In some embodiments, the transceiver module 6201 is configured to send first information to the terminal device, wherein the first information includes the configuration of reference signal resources, and the reference signal resources are used by the terminal device to perform beam measurement; the transceiver module 6201 is also configured to receive the second information sent by the terminal device based on the beam group measurement result of at least one beam group, wherein the beam group measurement result is obtained by the terminal device performing beam measurement based on the first information, and the second information includes information related to the first AI model, and the first AI model is a model for performing beam prediction. Optionally, the transceiver module 6201 can be used to execute at least one of the communication steps such as sending and / or receiving (for example, step S4101, but not limited to this) performed by the network device 102 in any of the above methods, which will not be repeated here.

[0867] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

[0868] In some embodiments, the processing module can be a single module or can include multiple submodules. Optionally, the multiple submodules respectively execute all or part of the steps required to be executed by the processing module. Optionally, the processing module can be interchangeable with the processor.

[0869] Figure 7A is a schematic diagram of the structure of a communication device 7100 proposed in an embodiment of the present disclosure. Communication device 7100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user device, etc.), a chip, a chip system, or a processor that supports a first device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal to implement any of the above methods. Communication device 7100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0870] As shown in FIG7A , the communication device 7100 includes one or more processors 7101. The processor 7101 may be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processing unit may be used to control a communication device (e.g., a base station, a baseband chip, an IoT device, an IoT device chip, a DU or CU, etc.), execute programs, and process program data. The communication device 7100 is used to perform any of the above methods.

[0871] In some embodiments, the communication device 7100 further includes one or more memories 7102 for storing instructions. Optionally, all or part of the memories 7102 may be located outside the communication device 7100.

[0872] In some embodiments, the communication device 7100 further includes one or more transceivers 7103. When the communication device 7100 includes one or more transceivers 7103, the transceiver 7103 performs at least one of the communication steps such as sending and / or receiving in the above method (for example, step S2101 and step S4101, but not limited thereto), and the processor 7101 performs at least one of the other steps (for example, step S2102, but not limited thereto).

[0873] In some embodiments, a transceiver may include a receiver and / or a transmitter. The receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, and transceiver circuit may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0874] In some embodiments, the communication device 7100 may include one or more interface circuits. Optionally, the interface circuits are connected to the memory 7102 and may be used to receive signals from the memory 7102 or other devices, or to send signals to the memory 7102 or other devices. For example, the interface circuits may read instructions stored in the memory 7102 and send the instructions to the processor 7101.

[0875] The communication device 7100 described in the above embodiments may be a first device or an IoT device, but the scope of the communication device 7100 described in the present disclosure is not limited thereto, and the structure of the communication device 7100 may not be limited by FIG. 7A . The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, an IoT device, an intelligent IoT device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a first device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0876] 7B is a schematic diagram of the structure of a chip 7200 proposed in an embodiment of the present disclosure. If the communication device 7100 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 7200 shown in FIG7B , but the present disclosure is not limited thereto.

[0877] The chip 7200 includes one or more processors 7201 , and the chip 7200 is configured to execute any of the above methods.

[0878] In some embodiments, the chip 7200 further includes one or more interface circuits 7203. Optionally, the interface circuit 7203 is connected to the memory 7202. The interface circuit 7203 can be used to receive signals from the memory 7202 or other devices, and can be used to send signals to the memory 7202 or other devices. For example, the interface circuit 7203 can read instructions stored in the memory 7202 and send the instructions to the processor 7201.

[0879] In some embodiments, the interface circuit 7203 executes at least one of the communication steps such as sending and / or receiving in the above method (for example, step S2101, step S4101, but not limited to this), and the processor 7201 executes at least one of the other steps (for example, step S2102, but not limited to this).

[0880] In some embodiments, terms such as interface circuit, interface, transceiver pin, and transceiver may be used interchangeably.

[0881] In some embodiments, the chip 7200 further includes one or more memories 7202 for storing instructions. Alternatively, all or part of the memory 7202 may be external to the chip 7200.

[0882] The embodiments of the present disclosure further provide a storage medium having instructions stored thereon. When the instructions are executed on the communication device 7100, the communication device 7100 executes any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto, and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto, and may also be a temporary storage medium.

[0883] The present disclosure also provides a program product, which, when executed by the communication device 7100, enables the communication device 7100 to perform any of the above methods. Optionally, the program product may be a computer program product.

[0884] The embodiments of the present disclosure also provide a computer program, which, when executed on a computer, enables the computer to execute any one of the above methods.

Claims

1. A communication method, characterized in that, performed by a terminal device, the method comprising: receiving first information sent by a network device, the first information including a configuration of a reference signal resource for the terminal device to perform beam measurement; performing beam measurement according to the first information to obtain beam group measurement results of at least one beam group; sending second information to the network device according to the beam group measurement results, the second information including information related to a first artificial intelligence (AI) model, the first AI model being a model for performing beam prediction.

2. The method according to claim 1, characterized in that, the beam group includes a first beam and a second beam, and the performing beam measurement according to the first information to obtain beam group measurement results of at least one beam group includes: for each beam group, performing beam measurement according to the first information to obtain a first beam measurement result of the first beam and a second beam measurement result of the second beam, and determining the beam group measurement result of the beam group according to the first beam measurement result and the second beam measurement result.

3. The method according to claim 2, characterized in that, the determining the beam group measurement result of the beam group according to the first beam measurement result and the second beam measurement result includes at least one of the following: taking an average value of the first beam measurement result and the second beam measurement result as the beam group measurement result; taking a weighted average value of the first beam measurement result and the second beam measurement result as the beam group measurement result; taking a sum value of the first measurement result, the second measurement result, and a product of the first measurement result and the second measurement result as the beam group measurement result.

4. The method according to any one of claims 1-3, characterized in that, the first information includes at least one of the following: a first reference signal resource set, the reference signal resources in the first reference signal resource set corresponding to the beams to be measured; a second reference signal resource set, the reference signal resources in the second reference signal resource set corresponding to the beams to be predicted; a third reference signal resource set, the third reference signal resource set including resources for interference measurement; a relationship between the first reference signal resource set and the second reference signal resource set.

5. The method according to claim 4, characterized in that, the first AI model is a model for performing spatial domain beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following: the first reference signal resource set is a subset of the second reference signal resource set; the beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

6. The method according to claim 4, characterized in that, The first AI model is a model for performing time-domain beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following: The first reference signal resource set is a subset of the second reference signal resource set; The first reference signal resource set is the same as the second reference signal resource set; The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

7. The method according to any one of claims 4-6, wherein, The first reference signal resource set includes a plurality of fourth reference signal resource sets, and different fourth reference signal resource sets correspond to different transceiver points (TRPs); The second reference signal resource set includes a plurality of fifth reference signal resource sets, and different fifth reference signal resource sets correspond to different TRPs.

8. The method according to claim 7, wherein, The first AI model is a model for performing spatial-domain beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following: The fourth reference signal resource set is a subset of the fifth reference signal resource set; The beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

9. The method according to claim 7, wherein, The first AI model is a model for performing time-domain beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following: The fourth reference signal resource set is a subset of the fifth reference signal resource set; The fourth reference signal resource set is the same as the fifth reference signal resource set; The beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

10. The method according to any one of claims 4-9, wherein, The second information includes at least one of the following: Model training data; Model performance monitoring data; Model input data; Model output data.

11. The method according to claim 10, wherein, The model training data includes the model input data and the beam measurement results of the beam to be predicted.

12. The method according to claim 10 or 11, wherein, The model performance monitoring data includes at least one of the following: Beam information of K beam groups; The performance value of the first AI model; First data, where the first data includes at least one of the following: model input data of the first AI model, model output data of the first AI model, measurement data corresponding to the model output data, and the model output data is data output by the first AI model according to the model input data; A specified event, where the specified event is triggered based on a comparison result between a performance value of the first AI model and a first threshold value or a first offset value; First operation information, where the first operation information is used to indicate a management operation on the first AI model, and the management operation includes any one of the following: activating the first AI model, deactivating the first AI model, switching the first AI model, not using the AI model.

13. The method according to claim 12, wherein, the method further includes: determining the K beam groups from the at least one beam group according to the beam group measurement result.

14. The method according to claim 13, wherein, the K beam groups are the best K beam groups, and the determining the K beam groups from the at least one beam group according to the beam group measurement result includes: taking the beam groups corresponding to the best K beam group measurement results as the best K beam groups, and the best K beam group measurement results include the first K beam group measurement results when at least one of the beam group measurement results is arranged from high to low.

15. The method according to any one of claims 12 - 14, wherein, the beam information includes at least one of the following: identifiers of reference signal resources corresponding to each beam in each best beam group; beam quality corresponding to the identifier of the reference signal resource.

16. The method according to any one of claims 12 - 15, wherein, the performance value includes at least one of the following: Beam group prediction accuracy rate, where the beam group prediction accuracy rate is the accuracy rate of including the actual best beam group in at least one predicted beam group; Beam group quality difference degree, where the beam group quality difference degree is the difference between the measured beam quality of the first beam group and the measured beam quality of the second beam group, the first beam group is the beam group with the strongest predicted beam quality, and the second beam group is the beam group with the strongest measured beam quality; Predicted beam group quality difference degree, where the predicted beam group quality difference degree is the difference between the predicted beam quality and the measured beam quality of the first beam group.

17. The method according to claim 16, wherein, the specified event includes at least one of the following: a first event, a second event, a third event, a fourth event, a fifth event, a sixth event; and the sending the second information to the network device according to the beam group measurement result includes at least one of the following: determining that the beam group prediction accuracy rate is less than a first accuracy threshold according to the beam group measurement result, and sending the first event to the network device; determining that the beam group prediction accuracy rate is greater than a second accuracy threshold according to the beam group measurement result, and sending the second event to the network device; Determine that the beam group quality difference is less than the first difference threshold according to the beam group measurement result, and send the third event to the network device; Determine that the beam group quality difference is greater than the second difference threshold according to the beam group measurement result, and send the fourth event to the network device; Determine that the predicted beam group quality difference is less than the third difference threshold according to the beam group measurement result, and send the fifth event to the network device; Determine that the predicted beam group quality difference is greater than the fourth difference threshold according to the beam group measurement result, and send the sixth event to the network device.

18. The method according to any one of claims 12-17, wherein, Sending the second information to the network device according to the beam group measurement result includes: Determining the model output data and the measurement data corresponding to the model output data according to the beam group measurement result; Determining the first operation information according to the model output data and the measurement data corresponding to the model output data; Sending the first operation information to the network device.

19. The method according to claim 18, wherein, Determining the first operation information according to the model output data and the measurement data corresponding to the model output data includes: When the first AI model is in an inactive state, determining that the performance of the first AI model meets the performance requirements according to the model output data and the measurement data corresponding to the model output data, and determining that the first operation information is to activate the first AI model; or, When the first AI model is in an active state, determining that the performance of the first AI model does not meet the performance requirements according to the model output data and the measurement data corresponding to the model output data, and determining that the first operation information is to deactivate the first AI model.

20. The method according to any one of claims 10-19, wherein, The first AI model is a model for performing spatial domain beam prediction, and the model input data includes at least one of the following: The beam quality of N beams corresponding to the first reference signal resource set, where the beam quality includes layer 1 reference signal received power L1-RSRP or layer 1 signal-to-interference-plus-noise ratio L1-SINR, and N is a positive integer; The identifier of the reference signal resource corresponding to N beams corresponding to the first reference signal resource set; First indication information, which is used to indicate that at least one group in the group-based beam information output by the first AI model contains two beams that the terminal device supports to receive and / or transmit simultaneously.

21. The method according to any one of claims 10-20, wherein, The first AI model is a model for performing spatial domain beam prediction, and the model output data includes at least one of the following: At least one group; The identifiers of two reference signal resources corresponding to each group, where the reference signal resource is a reference signal resource within the second reference signal resource set; The beam quality corresponding to the identifier of each reference signal resource; At least one third beam; The identifier of the reference signal resource corresponding to each of the third beams, where the reference signal resource is a reference signal resource within the second reference signal resource set; The beam quality corresponding to each of the third beams; Second indication information, where the second indication information is used to indicate that at least one beam included in a group in the group-based beam information output by the first AI model is two beams that the terminal device supports to receive and / or transmit simultaneously.

22. The method according to any one of claims 10-19, characterized in that, The first AI model is a model for performing time-domain beam prediction, and the model input data includes at least one of the following: At least one historical time; The beam quality of the N beams corresponding to the first reference signal resource set corresponding to each of the historical times, where the beam quality includes L1-RSRP or L1-SINR, and N is a positive integer; The identifier of the reference signal resource corresponding to the N beams corresponding to the first reference signal resource set corresponding to each of the historical times; Third indication information, where the third indication information is used to indicate that at least one beam included in a group in the group-based beam information output by the first AI model is two beams that the terminal device supports to receive and / or transmit simultaneously.

23. The method according to any one of claims 10-19, characterized in that, The first AI model is a model for performing time-domain beam prediction, and the model output data includes at least one of the following: At least one future time, where the future time is the beam corresponding time predicted by the first AI model for the beam; At least one group corresponding to each of the future times; The identifiers of two reference signal resources corresponding to each group corresponding to each of the future times, where the reference signal resource is a reference signal resource within the second reference signal resource set; The beam quality corresponding to the identifier of each reference signal resource corresponding to each of the future times; At least one fourth beam corresponding to each of the future times; The identifier of the reference signal resource corresponding to each of the fourth beams corresponding to each of the future times, where the reference signal resource is a reference signal resource within the second reference signal resource set; The beam quality corresponding to each of the fourth beams corresponding to each of the future times; Fourth indication information, where the fourth indication information is used to indicate that at least one beam included in at least one group corresponding to at least one future time in the group-based beam information output by the first AI model is two beams that the terminal device supports to receive and / or transmit simultaneously.

24. A communication method, characterized in that, It is executed by a network device, and the method includes: Sending first information to a terminal device, where the first information includes the configuration of a reference signal resource, and the reference signal resource is used for the terminal device to perform beam measurement; Receive second information sent by the terminal device according to beam group measurement results of at least one beam group, where the beam group measurement results are obtained by the terminal device performing beam measurement according to the first information, and the second information includes information related to a first AI model, and the first AI model is a model for performing beam prediction.

25. The method according to claim 24, wherein, the beam group includes a first beam and a second beam, and the beam group measurement results are determined by the following method: For each beam group, perform beam measurement according to the first information to obtain a first beam measurement result of the first beam and a second beam measurement result of the second beam, and determine the beam group measurement result of the beam group according to the first beam measurement result and the second beam measurement result.

26. The method according to claim 25, wherein, determining the beam group measurement result of the beam group according to the first beam measurement result and the second beam measurement result includes at least one of the following: Taking the average value of the first beam measurement result and the second beam measurement result as the beam group measurement result; Taking the weighted average value of the first beam measurement result and the second beam measurement result as the beam group measurement result; Taking the sum value of the first measurement result, the second measurement result, and the product of the first measurement result and the second measurement result as the beam group measurement result.

27. The method according to any one of claims 24-26, wherein, the first information includes at least one of the following: A first reference signal resource set, where the reference signal resources in the first reference signal resource set correspond to the beams to be measured; A second reference signal resource set, where the reference signal resources in the second reference signal resource set correspond to the beams to be predicted; A third reference signal resource set, where the third reference signal resource set includes resources for interference measurement; The relationship between the first reference signal resource set and the second reference signal resource set.

28. The method according to claim 27, wherein, the first AI model is a model for performing spatial domain beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following: The first reference signal resource set is a subset of the second reference signal resource set; The beam corresponding to the first reference signal resource set is a wide beam, the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

29. The method according to claim 27, wherein, the first AI model is a model for performing time domain beam prediction, and the relationship between the first reference signal resource set and the second reference signal resource set includes at least one of the following: The first reference signal resource set is a subset of the second reference signal resource set; The first reference signal resource set is the same as the second reference signal resource set; The beam corresponding to the first reference signal resource set is a wide beam, and the beam corresponding to the second reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the first reference signal resource set and the second reference signal resource set are the same.

30. The method according to any one of claims 27-29, wherein, the first reference signal resource set includes a plurality of fourth reference signal resource sets, and different fourth reference signal resource sets correspond to different transceiver points (TRPs); the second reference signal resource set includes a plurality of fifth reference signal resource sets, and different fifth reference signal resource sets correspond to different TRPs.

31. The method according to claim 30, wherein, the first AI model is a model for performing spatial domain beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following: the fourth reference signal resource set is a subset of the fifth reference signal resource set; the beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

32. The method according to claim 30, wherein, the first AI model is a model for performing time domain beam prediction, and the relationship between the fourth reference signal resource set and the fifth reference signal resource set includes at least one of the following: the fourth reference signal resource set is a subset of the fifth reference signal resource set; the fourth reference signal resource set is the same as the fifth reference signal resource set; the beam corresponding to the fourth reference signal resource set is a wide beam, the beam corresponding to the fifth reference signal resource set is a narrow beam, and the beam coverage ranges corresponding to the fourth reference signal resource set and the fifth reference signal resource set are the same.

33. The method according to any one of claims 27-32, wherein, the second information includes at least one of the following: model training data; model performance monitoring data; model input data; model output data.

34. The method according to claim 33, wherein, the model training data includes the model input data and the beam measurement results of the beam to be predicted.

35. The method according to claim 33 or 34, wherein, the model performance monitoring data includes at least one of the following: beam information of K beam groups; the performance value of the first AI model; first data, the first data includes at least one of the following: the model input data of the first AI model, the model output data of the first AI model, the measurement data corresponding to the model output data, and the model output data is the data output by the first AI model according to the model input data; A specified event, which is triggered based on a comparison result between a performance value of the first AI model and a first threshold value or a first offset value; First operation information, which is used to indicate a management operation on the first AI model, and the management operation includes any one of the following: activating the first AI model, deactivating the first AI model, switching the first AI model, not using the AI model.

36. The method according to claim 35, wherein, The K beam groups are the best K beam groups, and the best K beam groups are the beam groups corresponding to the measurement results of the best K beam groups, and the measurement results of the best K beam groups include the first K measurement results of at least one of the beam group measurement results when arranged from high to low.

37. The method according to claim 35 or 36, wherein, The beam information includes at least one of the following: The identifier of the reference signal resource corresponding to each beam in each best beam group; The beam quality corresponding to the identifier of the reference signal resource.

38. The method according to any one of claims 35-37, wherein, The performance value includes at least one of the following: The beam group prediction accuracy rate, which is the accuracy rate of including the actual best beam group in at least one predicted beam group; The beam group quality difference degree, which is the difference between the measured beam quality of the first beam group and the measured beam quality of the second beam group, where the first beam group is the beam group with the strongest predicted beam quality, and the second beam group is the beam group with the strongest measured beam quality; The predicted beam group quality difference degree, which is the difference between the predicted beam quality of the first beam group and the measured beam quality of the first beam group.

39. The method according to claim 38, wherein, The specified event includes at least one of the following: a first event, a second event, a third event, a fourth event, a fifth event, a sixth event; the receiving of the second information sent by the terminal device according to the beam group measurement results of at least one beam group includes at least one of the following: Receiving the first event sent by the terminal device, where the first event is triggered when the terminal device determines that the beam group prediction accuracy rate is less than a first accuracy rate threshold according to the beam group measurement results; Receiving the second event sent by the terminal device, where the second event is triggered when the terminal device determines that the beam group prediction accuracy rate is greater than a second accuracy rate threshold according to the beam group measurement results; Receiving the third event sent by the terminal device, where the third event is triggered when the terminal device determines that the beam group quality difference degree is less than a first difference degree threshold according to the beam group measurement results; Receiving the fourth event sent by the terminal device, where the fourth event is triggered when the terminal device determines that the beam group quality difference degree is greater than a second difference degree threshold according to the beam group measurement results; Receive the fifth event sent by the terminal device, where the fifth event is triggered when the terminal device determines that the predicted beam group quality difference is less than a third difference threshold based on the beam group measurement result; Receive the sixth event sent by the terminal device, where the sixth event is triggered when the terminal device determines that the predicted beam group quality difference is greater than a fourth difference threshold based on the beam group measurement result.

40. The method according to any one of claims 35 - 39, characterized in that, the first operation information is determined by the terminal device based on the model output data and the measurement data corresponding to the model output data.

41. The method according to claim 40, characterized in that, the first operation information includes activating the first AI model or deactivating the first AI model.

42. The method according to any one of claims 33 - 41, characterized in that, the first AI model is a model for performing spatial domain beam prediction, and the model input data includes at least one of the following: the beam quality of N beams corresponding to the first reference signal resource set, where the beam quality includes layer 1 reference signal received power L1 - RSRP or layer 1 signal - to - interference - plus - noise ratio L1 - SINR, and N is a positive integer; the identifiers of the reference signal resources corresponding to the N beams of the first reference signal resource set; first indication information, which is used to indicate that at least one group of the beam information based on groups output by the first AI model contains two beams that the terminal device supports to receive and / or transmit simultaneously.

43. The method according to any one of claims 33 - 42, characterized in that, the first AI model is a model for performing spatial domain beam prediction, and the model output data includes at least one of the following: at least one group; the identifiers of two reference signal resources corresponding to each group, where the reference signal resource is a reference signal resource within the second reference signal resource set; the beam quality corresponding to the identifier of each reference signal resource; at least one third beam; the identifiers of the reference signal resources corresponding to each third beam, where the reference signal resource is a reference signal resource within the second reference signal resource set; the beam quality corresponding to each third beam; second indication information, which is used to indicate that at least one group of the beam information based on groups output by the first AI model contains two beams that the terminal device supports to receive and / or transmit simultaneously.

44. The method according to any one of claims 33 - 41, characterized in that, the first AI model is a model for performing time domain beam prediction, and the model input data includes at least one of the following: at least one historical time; the beam quality of N beams corresponding to the first reference signal resource set at each historical time, where the beam quality includes L1 - RSRP or L1 - SINR, and N is a positive integer; The identifiers of the reference signal resources corresponding to the N beams corresponding to the first reference signal resource set corresponding to each of the historical times; Third indication information, which is used to indicate that at least one group of the beam information based on groups output by the first AI model contains two beams that the terminal device supports to receive and / or transmit simultaneously.

45. The method according to any one of claims 33-41, characterized in that, The first AI model is a model for performing time-domain beam prediction, and the model output data includes at least one of the following: At least one future time, where the future time is the time corresponding to the beam predicted by the first AI model; At least one group corresponding to each of the future times; The identifiers of two reference signal resources corresponding to each group corresponding to each of the future times, where the reference signal resource is a reference signal resource within the second reference signal resource set; The beam quality corresponding to the identifier of each reference signal resource corresponding to each of the future times; At least one fourth beam corresponding to each of the future times; The identifiers of the reference signal resources corresponding to each of the fourth beams corresponding to each of the future times, where the reference signal resource is a reference signal resource within the second reference signal resource set; The beam quality corresponding to each of the fourth beams corresponding to each of the future times; Fourth indication information, which is used to indicate that at least one group of the beam information based on groups output by the first AI model corresponding to at least one future time contains two beams that the terminal device supports to receive and / or transmit simultaneously.

46. A terminal device, characterized in that, including: A transceiver module, configured to receive first information sent by a network device, where the first information includes a configuration of a reference signal resource for the terminal device to perform beam measurement; A processing module, configured to perform beam measurement according to the first information to obtain beam group measurement results of at least one beam group; The processing module is further configured to send second information to the network device according to the beam group measurement results, where the second information includes information related to a first AI model, and the first AI model is a model for performing beam prediction.

47. A network device, characterized in that, including: A transceiver module, configured to send first information to a terminal device, where the first information includes a configuration of a reference signal resource for the terminal device to perform beam measurement; The transceiver module is further configured to receive second information sent by the terminal device according to beam group measurement results of at least one beam group, where the beam group measurement results are obtained by the terminal device performing beam measurement according to the first information, and the second information includes information related to a first AI model, and the first AI model is a model for performing beam prediction.

48. A communication device, characterized in that, characterized in that it includes: One or more processors; Among them, the communication device is used to execute the communication method described in any one of claims 1 to 23 or claims 24 to 45.

49. A storage medium storing instructions, wherein, when the instructions run on a communication device, the communication device is caused to execute the communication method described in any one of claims 1 to 23 or claims 24 to 45.

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