Communication method, communication device, communication system, storage medium and program product

Abstract

The embodiments of the present disclosure relate to a communication method, a communication device, a communication system, a storage medium and a program product. The communication method can be executed by a first node. The method comprises: acquiring first information, wherein the first information is used for indicating a first model; and determining a model pairing relationship on the basis of the first information, wherein the model pairing relationship is used for indicating at least one model pair, each model pair comprises the first model and a second model, and the first model is obtained by performing training on the basis of a third model and / or second information associated with the third model. In the present disclosure, a model pairing relationship is determined on the basis of first information, so as to determine a second model paired with a first model, thereby ensuring the compatibility of bilateral models and avoiding the system performance loss caused by model mismatches.

Description

Communication method, communication device, communication system, storage medium and program product TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of communication, and particularly relates to a communication method, a communication device, a communication system, a storage medium and a program product. BACKGROUND

[0002] With the progress of communication technology, an artificial intelligence (AI) model, a machine learning (ML) model or other models are introduced in a communication system. In a channel state information (CSI) feedback scenario, compression feedback of CSI can be implemented based on a CSI generation model at a terminal side, and recovery of CSI can be implemented based on a CSI recovery model at a network device side.

[0003] SUMMARY

[0004] There can be multiple CSI generation models at the terminal side, and there can also be multiple CSI recovery models at the network device side. On the one hand, how to implement model pairing between the terminal side and the network device side is a problem to be solved. On the other hand, even if the pairing is completed, if a new model is obtained by retraining a model at one end, how to implement pairing of the new model with a model at the other end is also a problem to be solved.

[0005] Embodiments of the present disclosure provide a communication method, a communication device, a communication system, a storage medium and a program product.

[0006] According to a first aspect of embodiments of the present disclosure, a communication method is provided, executed by a first node, and the method comprises: obtaining first information, the first information being used to indicate a first model; determining a model pairing relationship based on the first information, the model pairing relationship being used to indicate at least one model pair, each model pair comprising the first model and a second model, and the first model being obtained by training based on a third model and / or second information associated with the third model.

[0007] According to a second aspect of embodiments of the present disclosure, a communication method is provided, executed by a second node, and the method comprises: configuring first information for a first model, the first information being used to determine a model pairing relationship, the model pairing relationship being used to indicate at least one model pair, each model pair comprising the first model and a second model, and the first model being obtained by training based on a third model and / or second information associated with the third model.

[0008] According to a third aspect of embodiments of the present disclosure, a communication method is provided, performed by a third node, the method comprising: training a first model based on a third model and / or second information associated with the third model; obtaining first information indicating the first model; and sending the first information to a first node, the first information being used to trigger the first node to determine a model pairing relationship, the model pairing relationship being used to indicate at least one model pair, each model pair comprising the first model and a second model.

[0009] According to a fourth aspect of embodiments of the present disclosure, a communication method is provided, performed by a fifth node, the method comprising: receiving first information and first data processed by a first model; and processing the first data using a second model paired with the first model, the first information being used to indicate the first model, the second model being determined based on a model pairing relationship, the model pairing relationship being determined based on the first information, the model pairing relationship being used to indicate at least one model pair, each model pair comprising the first model and the second model.

[0010] According to a fifth aspect of embodiments of the present disclosure, a first node is provided, comprising: a first receiving module configured to obtain first information, the first information being used to indicate a first model; and a first processing module configured to determine a model pairing relationship based on the first information, the model pairing relationship being used to indicate at least one model pair, each model pair comprising the first model and a second model, the first model being trained based on a third model and / or second information associated with the third model.

[0011] According to a sixth aspect of embodiments of the present disclosure, a second node is provided, comprising: a second processing module configured to configure first information for a first model, the first information being used to determine a model pairing relationship, the model pairing relationship being used to indicate at least one model pair, each model pair comprising the first model and a second model, the first model being trained based on a third model and / or second information associated with the third model.

[0012] According to a seventh aspect of embodiments of the present disclosure, a third node is provided, comprising: a third processing module configured to train a first model based on a third model and / or second information associated with the third model; and a third receiving module configured to send the first information to a first node, the first information being used to trigger the first node to determine a model pairing relationship, the model pairing relationship being used to indicate at least one model pair, each model pair comprising the first model and a second model.

[0013] According to an eighth aspect of embodiments of the present disclosure, a fifth node is provided, comprising: a fourth transceiver configured to receive first information and first data processed by a first model; and a fourth processing module configured to process the first data using a second model paired with the first model, wherein the first information is used to indicate the first model, the second model is determined based on a model pairing relationship, the model pairing relationship is determined based on the first information, and the model pairing relationship is used to indicate at least one model pair, each model pair comprising the first model and the second model.

[0014] According to a ninth aspect of embodiments of the present disclosure, a communication device is provided, comprising: one or more processors; and wherein the communication device is configured to perform the communication method according to any one of the first aspect to the fourth aspect.

[0015] According to a tenth aspect of embodiments of the present disclosure, a communication system is provided, comprising: a first node, a second node, a third node, a fourth node, and a fifth node; wherein the first node is configured to implement the communication method according to the first aspect; the second node is configured to implement the communication method according to the second aspect; the third node is configured to implement the communication method according to the third aspect; the fourth node is configured to send the third model and / or the second information associated with the third model to the third node; and the fifth node is configured to implement the communication method according to the fourth aspect.

[0016] According to an eleventh aspect of embodiments of the present disclosure, a storage medium is provided, the storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the communication method according to any one of the first aspect to the fourth aspect.

[0017] According to a twelfth aspect of embodiments of the present disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the communication method according to any one of the first aspect to the fourth aspect.

[0018] According to a thirteenth aspect of embodiments of the present disclosure, a computer program is provided, comprising code that, when executed by a processor, implements the communication method according to any one of the first aspect to the fourth aspect.

[0019] According to a fourteenth aspect of embodiments of the present disclosure, a chip or chip system is provided, comprising processing circuitry configured to perform the communication method according to any one of the first aspect to the fourth aspect.

[0020] In the embodiment of the present disclosure, the first node obtains first information used for indicating a first model, the first model being trained based on a third model and / or second information associated with the third model, determines a model pairing relationship based on the first information, and determines a second model paired with the first model, so as to ensure the compatibility of the bilateral model and avoid the performance loss of the system caused by the mismatch of the model. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following describes the drawings required for the embodiments. The following drawings are only some embodiments of the present disclosure, and do not specifically limit the protection scope of the present disclosure.

[0022] FIG. 1A is a schematic diagram of an architecture of a communication system according to an embodiment of the present disclosure.

[0023] FIG. 1B is a schematic diagram of implementing CSI compression and recovery based on a bilateral model according to an embodiment of the present disclosure.

[0024] FIGS. 2A to 2D are schematic diagrams of interactions of a communication method according to an embodiment of the present disclosure.

[0025] FIGS. 3A to 3D are schematic diagrams of a flow of a communication method performed by a terminal according to an embodiment of the present disclosure.

[0026] FIGS. 3E to 3H are schematic diagrams of a flow of a communication method performed by a network device according to an embodiment of the present disclosure.

[0027] FIG. 4A is a schematic diagram of a flow of a communication method performed by a first node according to an embodiment of the present disclosure.

[0028] FIG. 4B is a schematic diagram of a flow of a communication method performed by a second node according to an embodiment of the present disclosure.

[0029] FIG. 4C is a schematic diagram of a flow of a communication method performed by a third node according to an embodiment of the present disclosure.

[0030] FIG. 4D is another schematic diagram of a flow of a communication method performed by a fifth node according to an embodiment of the present disclosure.

[0031] FIG. 4E is another schematic diagram of a flow of a communication method performed by a fourth node according to an embodiment of the present disclosure.

[0032] FIGS. 5A to 5D are schematic diagrams of structures of a first node, a second node, a third node and a fifth node according to an embodiment of the present disclosure.

[0033] FIG. 6 is another schematic diagram of a structure of a communication device according to an embodiment of the present disclosure.

[0034] FIG. 7 is a schematic diagram of a structure of a chip according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0035] The embodiments of the present disclosure provide a communication method, a communication device, a communication system, a storage medium and a program product.

[0036] In a first aspect, the embodiments of the present disclosure provide a communication method, performed by a first node, the method comprising: obtaining first information, the first information being used to indicate a first model; and determining a model pairing relationship based on the first information, the model pairing relationship being used to indicate at least one model pair, each model pair comprising the first model and a second model, the first model being trained based on a third model and / or second information associated with the third model.

[0037] In the embodiments of the present disclosure, the first node obtains the first information used to indicate the first model, the first model being trained based on the third model, and determines the model pairing relationship based on the first information, so as to determine the second model paired with the first model, thereby ensuring the compatibility of the bilateral model and avoiding the system performance loss caused by the model mismatch.

[0038] With reference to some embodiments of the first aspect, in some embodiments, the first model is used to perform a first processing process, and the second model is used to perform a second processing process, the first processing process and the second processing process being inverse processes of each other.

[0039] With reference to some embodiments of the first aspect, in some embodiments, the first model and the third model are used to perform the first processing process, the third model being indicated by third information configured by a second node, and the first information being the same as the second information.

[0040] In the embodiments of the present disclosure, by using the second information indicating the third model to indicate the first model, the number of model identifications can be reduced, the complexity of system maintenance of the model identification can be reduced, and the pairing efficiency can be improved.

[0041] With reference to some embodiments of the first aspect, in some embodiments, the second model and the third model are used to perform the second processing process, the second model being the third model; or the second model being trained based on the third model and / or the second information associated with the third model.

[0042] With reference to some embodiments of the first aspect, in some embodiments, the first information comprises at least one of: first indication information used to indicate the first model; second indication information used to indicate the first model and the second model; third indication information used to indicate training data of the first model; fourth indication information used to indicate a training session of the first model; fifth indication information used to indicate a type associated configuration of the training data of the first model; and sixth indication information used to indicate a condition of collecting the training data of the first model.

[0043] In some embodiments of the first aspect, in some embodiments, the second information comprises at least one of: third information indicating the third model; fourth information indicating a model structure of the third model; fifth information indicating a model parameter of the third model; and sixth information indicating training data of the third model.

[0044] In some embodiments of the first aspect, in some embodiments, the first information is determined based on the third information indicating the third model and the first value.

[0045] In the embodiments of the present disclosure, the first information can be determined based on the third information and the first data, so that the signaling interaction between the nodes of the first model trained and the nodes configured with the first information can be reduced, and the efficiency of configuring the first information and the model pairing can be improved.

[0046] In some embodiments of the first aspect, in some embodiments, the first information is further used to trigger the pairing of the first model.

[0047] In the second aspect, the embodiments of the present disclosure provide a communication method, executed by a second node, the method comprising: configuring a first information for a first model, the first information being used to determine a model pairing relationship, the model pairing relationship being used to indicate at least one model pair, each model pair comprising the first model and a second model, the first model being trained based on a third model and / or second information associated with the third model.

[0048] In some embodiments of the second aspect, in some embodiments, the first model is used to perform a first processing process, and the second model is used to perform a second processing process, the first processing process and the second processing process being inverse processes of each other.

[0049] In some embodiments of the second aspect, in some embodiments, the first model and the third model are used to perform the first processing process, the third model being indicated by third information configured by the second node, and the first information being the same as the third information.

[0050] In some embodiments of the second aspect, in some embodiments, the second model and the third model are used to perform the second processing process, the second model being the third model; or, the second model being trained based on the third model and / or the second information associated with the third model.

[0051] In some embodiments of the second aspect, in some embodiments, the first information comprises at least one of: first indication information for indicating the first model; second indication information for indicating the first model and the second model; third indication information for indicating training data of the first model; fourth indication information for indicating a training session of the first model; fifth indication information for indicating a type association configuration of the training data of the first model; and sixth indication information for indicating a condition for collecting the training data of the first model.

[0052] In some embodiments of the second aspect, in some embodiments, the second information comprises at least one of: third information for indicating the third model; fourth information for indicating a model structure of the third model; fifth information for indicating a model parameter of the third model; and sixth information for indicating training data of the third model.

[0053] In some embodiments of the second aspect, in some embodiments, the first information is determined based on the third information for indicating the third model and the first numerical value.

[0054] In some embodiments of the second aspect, in some embodiments, the method further comprises: sending, to a third node, the first information, the third node being configured to train the first model based on the third model.

[0055] In some embodiments of the second aspect, in some embodiments, the method further comprises: receiving a first message sent by a third node, the first message being configured to request the first information.

[0056] In a third aspect, the embodiments of the present disclosure provide a communication method, executed by a third node, the method comprising: training a first model based on third model associated seventh information; obtaining first information for indicating the first model; and sending the first information to a first node, the first information being configured to trigger the first node to determine a model pairing relationship, the model pairing relationship being configured to indicate at least one model pair, each model pair comprising the first model and a second model.

[0057] In some embodiments of the third aspect, in some embodiments, the first model is configured to perform a first processing process, and the second model is configured to perform a second processing process, the first processing process and the second processing process being inverse processes of each other.

[0058] In some embodiments of the third aspect, in some embodiments, the second model and the third model are configured to perform the second processing process, the second model being the third model; or the second model is trained based on the third model and / or second information associated with the third model.

[0059] In some embodiments of the third aspect, in some embodiments, the first model and the third model are used to perform the first processing procedure, the third model is indicated by third information configured by the second node, and the first information is the same as the third information.

[0060] In some embodiments of the third aspect, in some embodiments, the first information associated with the first model is obtained by receiving the first information sent by the second node, and the second node is configured to configure the first information for the first model.

[0061] In some embodiments of the third aspect, in some embodiments, the first information and the seventh information are received simultaneously, the second node and the fourth node are the same device, and the fourth node is configured to send the seventh information to the third node.

[0062] In some embodiments of the third aspect, in some embodiments, the method further includes: sending a first message to the second node, the first message being used to request the first information.

[0063] In some embodiments of the third aspect, in some embodiments, the first information includes at least one of the following: first indication information used to indicate the first model; second indication information used to indicate the first model and the second model; third indication information used to indicate training data of the first model; fourth indication information used to indicate a training session of the first model; fifth indication information used to indicate a type associated configuration of the training data of the first model; and sixth indication information used to indicate a condition for collecting the training data of the first model.

[0064] In some embodiments of the third aspect, in some embodiments, the second information includes at least one of the following: third information used to indicate the third model; fourth information used to indicate a model structure of the third model; fifth information used to indicate a model parameter of the third model; and sixth information used to indicate training data of the third model.

[0065] In some embodiments of the third aspect, in some embodiments, the first information is determined based on the third information indicating the third model and a first numerical value.

[0066] In a fourth aspect, the embodiments of the present disclosure provide a communication method, executed by a fifth node, the method comprising: receiving first information and first data processed by a first model; processing the first data using a second model paired with the first model; wherein the first information is used to indicate the first model, the second model is determined based on a model pairing relationship, the model pairing relationship is determined based on the first information, and the model pairing relationship is used to indicate at least one model pair, each model pair including the first model and the second model.

[0067] In some embodiments of the fourth aspect, in some embodiments, the first model is configured to perform a first processing procedure, and the second model is configured to perform a second processing procedure, the first processing procedure and the second processing procedure are inverse procedures of each other, and the first model is trained based on the third model and / or second information associated with the third model.

[0068] In some embodiments of the fourth aspect, in some embodiments, the first model and the third model are configured to perform the first processing procedure, and the third model is indicated by third information configured by the second node, and the first information is the same as the third information.

[0069] In some embodiments of the fourth aspect, in some embodiments, the second model and the third model are configured to perform the second processing procedure, and the second model is the third model, or the second model is trained based on the third model and / or second information associated with the third model.

[0070] In some embodiments of the fourth aspect, in some embodiments, the first information includes at least one of: first indication information for indicating the first model; second indication information for indicating the first model and the second model; third indication information for indicating training data of the first model; fourth indication information for indicating a training session of the first model; fifth indication information for indicating a type of the training data of the first model; and sixth indication information for indicating a condition for collecting the training data of the first model.

[0071] In some embodiments of the fourth aspect, in some embodiments, the second information includes at least one of: third information for indicating the third model; fourth information for indicating a model structure of the third model; fifth information for indicating a model parameter of the third model; and sixth information for indicating training data of the third model.

[0072] In some embodiments of the fourth aspect, in some embodiments, the method further includes: receiving seventh information, the seventh information being used to indicate the second model.

[0073] In some embodiments of the fourth aspect, in some embodiments, the method further includes: receiving eighth information, the eighth information being used to indicate a model pairing relationship; and determining the second model paired with the first model based on the model pairing relationship.

[0074] In the fifth aspect, the embodiments of the present disclosure provide a first node, including: a first receiving module configured to obtain first information, the first information being used to indicate a first model; and a first processing module configured to determine a model pairing relationship based on the first information, the model pairing relationship being used to indicate at least one model pair, each model pair including the first model and a second model, and the first model being trained based on a third model and / or second information associated with the third model.

[0075] In some embodiments combined with the fifth aspect, in some embodiments, the first model is configured to perform a first processing procedure, and the second model is configured to perform a second processing procedure, and the first processing procedure and the second processing procedure are inverse procedures of each other.

[0076] In some embodiments combined with the fifth aspect, in some embodiments, the first model and the third model are configured to perform the first processing procedure, and the third model is indicated by the third information configured by the second node, and the first information is the same as the third information.

[0077] In some embodiments combined with the fifth aspect, in some embodiments, the second model and the third model are configured to perform the second processing procedure, and the second model is the third model; or the second model is trained based on the third model and / or second information associated with the third model.

[0078] In some embodiments combined with the fifth aspect, in some embodiments, the first information comprises at least one of: first indication information for indicating the first model; second indication information for indicating the first model and the second model; third indication information for indicating training data of the first model; fourth indication information for indicating a training session of the first model; fifth indication information for indicating a type associated configuration of the training data of the first model; and sixth indication information for indicating a condition of collecting the training data of the first model.

[0079] In some embodiments combined with the fifth aspect, in some embodiments, the second information comprises at least one of: third information for indicating the third model; fourth information for indicating a model structure of the third model; fifth information for indicating a model parameter of the third model; and sixth information for indicating training data of the third model.

[0080] In some embodiments combined with the fifth aspect, in some embodiments, the first information is determined based on the third information for indicating the third model and a first numerical value.

[0081] In some embodiments combined with the fifth aspect, in some embodiments, the first information is further used to trigger pairing of the first model.

[0082] In a sixth aspect, the embodiments of the present disclosure provide a second node, comprising: a second processing module configured to configure first information for a first model, the first information being used to determine a model pairing relationship, the model pairing relationship being used to indicate at least one model pair, each model pair comprising the first model and a second model, and the first model being trained based on a third model and / or second information associated with the third model.

[0083] With reference to some embodiments of the sixth aspect, in some embodiments, the first model is configured to perform a first processing procedure, and the second model is configured to perform a second processing procedure, and the first processing procedure and the second processing procedure are inverse procedures of each other.

[0084] With reference to some embodiments of the sixth aspect, in some embodiments, the first model and the third model are configured to perform a first processing procedure, and the third model is indicated by third information configured by the second node, and the first information is the same as the third information.

[0085] With reference to some embodiments of the sixth aspect, in some embodiments, the second model and the third model are configured to perform a second processing procedure, and the second model is the third model; or, the second model is trained based on the third model and / or second information associated with the third model.

[0086] With reference to some embodiments of the sixth aspect, in some embodiments, the first information comprises at least one of: first indication information for indicating the first model; second indication information for indicating the first model and the second model; third indication information for indicating training data of the first model; fourth indication information for indicating a training session of the first model; fifth indication information for indicating a type association configuration of the training data of the first model; and sixth indication information for indicating a condition for collecting the training data of the first model.

[0087] With reference to some embodiments of the sixth aspect, in some embodiments, the second information comprises at least one of: third information for indicating the third model; fourth information for indicating a model structure of the third model; fifth information for indicating a model parameter of the third model; and sixth information for indicating training data of the third model.

[0088] With reference to some embodiments of the sixth aspect, in some embodiments, the first information is determined based on the third information for indicating the third model and a first numerical value.

[0089] With reference to some embodiments of the sixth aspect, in some embodiments, the communication device further comprises a second transceiving model configured to send the first information to a third node, and the third node is configured to train the first model based on the third model.

[0090] With reference to some embodiments of the second aspect, in some embodiments, the second transceiving model is further configured to receive a first message sent by the third node, and the first message is used to request the first information.

[0091] In a seventh aspect, the embodiments of the present disclosure provide a third node, comprising: a third processing module configured to train a first model based on seventh information associated with a third model; and a third transceiver module configured to send first information to a first node, wherein the first information is used to trigger the first node to determine a model pairing relationship, and the model pairing relationship is used to indicate at least one model pair, and each model pair comprises the first model and a second model.

[0092] In some embodiments of the seventh aspect, the first model is used to perform a first processing process, and the second model is used to perform a second processing process, and the first processing process and the second processing process are inverse processes.

[0093] In some embodiments of the seventh aspect, the second model and the third model are used to perform the second processing process, and the second model is the third model; or the second model is trained based on the third model and / or second information associated with the third model.

[0094] In some embodiments of the seventh aspect, the first model and the third model are used to perform the first processing process, and the third model is indicated by third information configured by a second node, and the first information is the same as the third information.

[0095] In some embodiments of the seventh aspect, the third transceiver module is further configured to receive the first information sent by the second node, and the second node is used to configure the first information for the first model.

[0096] In some embodiments of the seventh aspect, the first information and the seventh information are received simultaneously, the second node and the fourth node are the same device, and the fourth node is used to send the seventh information to the third node.

[0097] In some embodiments of the seventh aspect, the third transceiver module is further configured to send a first message to the second node, and the first message is used to request the first information.

[0098] In some embodiments of the seventh aspect, the first information comprises at least one of: first indication information used to indicate the first model; second indication information used to indicate the first model and the second model; third indication information used to indicate training data of the first model; fourth indication information used to indicate a training session of the first model; fifth indication information used to indicate a type association configuration of the training data of the first model; and sixth indication information used to indicate a condition for collecting the training data of the first model.

[0099] In conjunction with some embodiments of the seventh aspect, in some embodiments, the second information includes at least one of the following: third information for indicating a third model; fourth information for indicating the model structure of the third model; fifth information for indicating the model parameters of the third model; and sixth information for indicating the training data of the third model.

[0100] In conjunction with some embodiments of the seventh aspect, in some embodiments, the first information is determined based on the third information indicating the third model and the first numerical calculation.

[0101] Eighthly, embodiments of this disclosure provide a network device, comprising: a fourth transceiver module configured to receive first information and first data processed by a first model; and a fourth processing module configured to process the first data using a second model paired with the first model; wherein the first information is used to indicate the first model, the second model is determined based on a model pairing relationship, the model pairing relationship is determined based on the first information, and the model pairing relationship is used to indicate at least one model pair, each model pair including the first model and the second model.

[0102] In conjunction with some embodiments of the eighth aspect, in some embodiments, a first model is used to perform a first processing procedure, a second model is used to perform a second processing procedure, the first processing procedure and the second processing procedure are inverse processes of each other, and the first model is trained based on a third model and / or second information associated with the third model.

[0103] In conjunction with some embodiments of the eighth aspect, in some embodiments, the first model and the third model are used to perform the first processing procedure, the third model being indicated by third information configured by the second node, and the first information being the same as the third information.

[0104] In conjunction with some embodiments of the eighth aspect, in some embodiments, the second model and the third model are used to perform the second processing procedure, wherein the second model is the third model; or, the second model is trained based on the third model and / or second information associated with the third model.

[0105] In conjunction with some embodiments of the eighth aspect, in some embodiments, the first information includes at least one of the following: first indication information for indicating a first model; second indication information for indicating a first model and a second model; third indication information for indicating training data of the first model; fourth indication information for indicating a training session of the first model; fifth indication information for indicating the configuration of type association of training data of the first model; and sixth indication information for indicating the conditions for collecting training data of the first model.

[0106] In some embodiments combined with the eighth aspect, in some embodiments, the second information comprises at least one of: third information used to indicate the third model; fourth information used to indicate a model structure of the third model; fifth information used to indicate a model parameter of the third model; and sixth information used to indicate training data of the third model.

[0107] In some embodiments combined with the eighth aspect, in some embodiments, the fourth transceiver is further configured to receive seventh information, the seventh information being used to indicate the second model.

[0108] In some embodiments combined with the eighth aspect, in some embodiments, the fourth transceiver is further configured to receive eighth information, the eighth information being used to indicate a model pairing relationship; and determine the second model paired with the first model based on the model pairing relationship.

[0109] In a ninth aspect, the embodiments of the present disclosure provide a communication device, comprising: one or more processors; wherein the communication device is configured to perform the communication method according to any one of the first aspect to the fourth aspect.

[0110] In a tenth aspect, the embodiments of the present disclosure provide a communication system, comprising: a first node, a second node, a third node, a fourth node and a fifth node; the first node is configured to implement the communication method according to the first aspect; the second node is configured to implement the communication method according to the second aspect; the third node is configured to implement the communication method according to the third aspect; the fourth node is configured to send the third model and / or the second information associated with the third model to the third node; and the fifth node is configured to implement the communication method according to the fourth aspect.

[0111] In an eleventh aspect, the embodiments of the present disclosure provide a storage medium, the storage medium stores instructions, when the instructions are executed on a communication device, the communication device performs the communication method according to any one of the first aspect to the fourth aspect.

[0112] In a twelfth aspect, the embodiments of the present disclosure provide a program product, when the program product is executed by a communication device, the communication device performs the communication method according to any one of the first aspect to the fourth aspect.

[0113] In a thirteenth aspect, the embodiments of the present disclosure provide a computer program, when the computer program is executed on a computer, the computer performs the method described in the optional implementation manner of any one of the first aspect to the fourth aspect.

[0114] In a fourteenth aspect, the embodiments of the present disclosure provide a chip or chip system. The chip or chip system comprises processing circuitry configured to perform the method described in the optional implementation manner of any one of the first aspect to the fourth aspect.

[0115] It is understood that the aforementioned communication devices, communication systems, storage media, program products, computer programs, chips, or chip systems are all used to execute the methods proposed in the embodiments of this disclosure. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0116] This disclosure provides a communication method, communication device, communication system, storage medium, and program product. In some embodiments, the terms "communication method" and "model pairing method," "method for determining pairing relationships," etc., can be used interchangeably, as can the terms "information processing system," "communication system," and "pairing system."

[0117] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular 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 particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0118] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0119] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0120] In this disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the," "the," "the," "the," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular or a plural expression.

[0121] In the embodiments disclosed herein, "multiple" refers to two or more.

[0122] In some embodiments, the terms "at least one of," "one or more of," "a plurality of," "multiple," and the like can be used interchangeably.

[0123] In some embodiments, the recitations "at least one of A, B," "A and / or B," "in one case A, in another case B," "in response to a case A, in response to a case B," and the like can include the following technical solutions according to the case: in some embodiments A (A is executed regardless of B); in some embodiments B (B is executed regardless of A); in some embodiments A and B are selected from A and B (A and B are selectively executed); in some embodiments A and B (A and B are both executed). When there are more branches such as A, B, C, and the like, the above is similar.

[0124] In some embodiments, the recitations "A or B" and the like can include the following technical solutions according to the case: in some embodiments A (A is executed regardless of B); in some embodiments B (B is executed regardless of A); in some embodiments A and B are selected from A and B (A and B are selectively executed). When there are more branches such as A, B, C, and the like, the above is similar.

[0125] The prefix words "first", "second", and the like in the embodiments of the present disclosure are merely used to distinguish different description objects, and do not constitute a limitation on the position, order, priority, quantity, or content of the description objects. The description of the description objects should refer to the description in the context of the claims or embodiments, and should not constitute an additional limitation because of the use of the prefix words. For example, the description objects are "fields", and the ordinal words before "fields" in "first field" and "second field" do not limit the position or order between "fields", and "first" and "second" do not limit whether the "fields" modified thereby are in the same message or not, nor limit the order of "first field" and "second field". For another example, the description objects are "levels", and the ordinal words before "levels" in "first level" and "second level" do not limit the priority between "levels". For another example, the quantity of the description objects is not limited by the ordinal words, and can be one or more. For example, "first device", wherein the quantity of "devices" can be one or more. In addition, the objects modified by different prefix words can be the same or different, for example, the description objects are "devices", and "first device" and "second device" can be the same device or different devices, and the types thereof can be the same or different; for another example, the description objects are "information", and "first information" and "second information" can be the same information or different information, and the content thereof can be the same or different.

[0126] In some embodiments, "comprising", "including", "to indicate", "carrying", can be interpreted as directly carrying A, or indirectly indicating A.

[0127] In some embodiments, the terms "in response to", "in response to determining", "in the case of", "when", "when", "if", "if" and the like can be replaced with each other.

[0128] In some embodiments, the terms "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", "above" and the like can be replaced with each other, and the terms "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", "below" and the like can be replaced with each other.

[0129] In some embodiments, the device and the like can be interpreted as physical or virtual, and the name is not limited to the name described in the embodiments. The terms "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject" and the like can be replaced with each other.

[0130] In some embodiments, "network" can be interpreted as a device (for example, access network device, core network device, etc.) contained in the network.

[0131] In some embodiments, the terms “network devices,” “access network devices (AN devices),” “radio access network devices (RAN devices),” “base stations (BSs),” “radio base stations,” “fixed stations,” “nodes,” “access network nodes,” “access points,” “transmission points (TPs),” “reception points (RPs),” “transmission / reception points (TRPs),” “panels,” “antenna panels,” “antenna arrays,” “cells,” “macro cells,” “small cells,” “femtocells,” “pico cells,” “sectors,” “cell groups,” “serving cells,” “carriers,” “component carriers,” “bandwidth parts (BWPs),” and the like can be used interchangeably.

[0132] In some embodiments, the terms "terminal," "terminal device," "user equipment (UE)," "user terminal," "mobile station (MS)," "mobile terminal (MT)," "subscriber station," "mobile unit," "subscriber unit," "wireless unit," "remote unit," "mobile device," "wireless device," "wireless communication device," "remote device," "mobile subscriber station," "access terminal," "mobile terminal," "wireless terminal," "remote terminal," "handset," "user agent," "mobile client," "client," and so on can be replaced with each other.

[0133] In some embodiments, the access network device, the core network device, or the network device can be replaced with a terminal. For example, the embodiments of the present disclosure can also be applied to a structure in which communication between the access network device, the core network device, or the network device and the terminal is replaced with communication between a plurality of terminals (e.g., device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, the terminal can also be configured to have all or part of the functions of the access network device. In addition, the terms "uplink," "downlink," and the like can also be replaced with terms corresponding to the inter-terminal communication (e.g., "side"). For example, the uplink channel, the downlink channel, and the like can be replaced with the side channel, and the uplink, the downlink, and the like can be replaced with the sidelink.

[0134] In some embodiments, the terminal can be replaced with the access network device, the core network device, or the network device. In this case, the access network device, the core network device, or the network device can also be configured to have all or part of the functions of the terminal.

[0135] In some embodiments, obtaining data, information, etc. can comply with laws and regulations of the country where the location is.

[0136] In some embodiments, data, information, etc. can be obtained after obtaining the consent of the user.

[0137] In addition, each element, each row, or each column in the table of the embodiments of the present disclosure can be implemented as an independent embodiment, and any combination of elements, rows, or columns can also be implemented as an independent embodiment.

[0138] FIG. 1A is a schematic diagram of an architecture of a communication system according to an embodiment of the present disclosure. As shown in FIG. 1A, the communication system 100 includes a first node 101, a second node 102, a third node 103, a fourth node 104, and a fifth node 105.

[0139] In some embodiments, the first node is configured to perform model pairing.

[0140] In some embodiments, the first node is configured to determine a model pairing relationship.

[0141] In some embodiments, the name of the first node is not limited, which is, for example, a “pairing node”, a “matching node”, an “association node”, etc.

[0142] In some embodiments, the second node is configured to configure indication information.

[0143] In some embodiments, the indication information can be used to indicate a model, in which case the indication information is indication information of the model. Alternatively, the indication information can be used to indicate training data of a model, in which case the indication information is indication information of the training data. Alternatively, the indication information can be used to indicate a model pair, in which case the indication information is indication information of the model pair. Alternatively, the indication information can be used to indicate a training session of a model, in which case the indication information is indication information of the training session. Alternatively, the indication information can be used to indicate a configuration of a type association of training data, in which case the indication information is indication information of the configuration. Alternatively, the indication information can be used to indicate a condition for collecting training data, in which case the indication information is indication information of the condition.

[0144] In some embodiments, the second node can be configured to configure indication information for at least one of a model, a model pair, training data, a training session, a configuration of a type association of training data, or a condition for collecting training data.

[0145] In some embodiments, the indication information can be an identification (ID).

[0146] In some embodiments, the name of the second node is not limited, which is, for example, a “configuration node”, an “assignment node”, an “ID assignment node”, an “ID configuration node”, etc.

[0147] In some embodiments, the third node can be configured to train the first model based on the third model.

[0148] In some embodiments, the third node can be configured to train the first model based on the seventh information associated with the third model.

[0149] In some embodiments, the third node can be configured to trigger model pairing.

[0150] In some embodiments, the third node is not limited in name, for example, it is a “trigger node”, “initiation node”, “request node”, “pairing trigger node”, “pairing initiation node”, “pairing request node”, “training node”, etc.

[0151] In some embodiments, the fourth node can be configured to send the third model;

[0152] In some embodiments, the fourth node can be configured to send the seventh information associated with the third model.

[0153] In some embodiments, the fourth node is not limited in name, for example, it is a “sending node”, “transferring node”, “model sending node”, “model transferring node”, etc.

[0154] In some embodiments, the fifth node can be configured to find the used model from the model pairing relationship.

[0155] In some embodiments, the fifth node is not limited in name, for example, it is a “model finding node”, “model application node”, “model use node”.

[0156] In some embodiments, the above-mentioned first node, second node, third node, fourth node and fifth node can be a terminal or a network device.

[0157] In some embodiments, the above-mentioned first node, second node, third node, fourth node and fifth node can be deployed in one device, or can be respectively deployed in multiple devices, each device having the function of one or more of the above-mentioned nodes.

[0158] In an example, terminal A is deployed with a third node, and access network device B is deployed with a first node, a second node, a fourth node and a fifth node.

[0159] In an example, terminal A is deployed with a second node, a fourth node and a fifth node, and access network device B is deployed with a first node and a third node.

[0160] In an example, terminal A is deployed with a first node, a fourth node and a fifth node, and access network device B is deployed with a second node and a third node.

[0161] In an example, the terminal A is deployed with the fourth node and the fifth node, the access network device B is deployed with the third node, and the core network device C is deployed with the first node and the second node.

[0162] In an example, the terminal A is deployed with the third node, the access network device B is deployed with the fourth node, the core network device C is deployed with the first node, the core network device D is deployed with the second node, and the core network device E is deployed with the fifth node.

[0163] In some embodiments, the terminal includes at least one of a mobile phone, a wearable device, an Internet of Things device, a communication-capable car, a smart car, a Pad, a computer with wireless transceiver function, 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, a wireless terminal device in smart home, and the like, but is not limited thereto.

[0164] In some embodiments, the network device can include an access network device and / or a core network device. The access network device is, for example, a node or device that accesses a terminal to a wireless network, and can include at least one of an evolved NodeB (eNB), a next generation eNB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved node B (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, an access node in a Wi-Fi system, but is not limited thereto.

[0165] In some embodiments, the technical solutions of the present disclosure can be applicable to an Open RAN architecture, at which time the interfaces between or within the network devices involved in the embodiments of the present disclosure can become internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be realized by software or programs.

[0166] In some embodiments, the access network device can be composed of a central unit (CU) and a distributed unit (DU), wherein the CU can also be referred to as a control unit. The CU-DU structure can split the protocol layers of the network device, and some of the protocol layers are controlled by the CU, and the rest or all of the protocol layers are distributed in the DU and controlled by the CU, but is not limited thereto.

[0167] In some embodiments, the core network device can be one device including a first network element, or a plurality of devices or device groups each including a first network element. The network element can be virtual or physical. The core network can include at least one of an evolved packet core (EPC), a 5G core network (5GCN), and a next generation core (NGC).

[0168] It can be understood that the communication system described in the embodiments of the present disclosure is for more clearly illustrating the technical solutions of the embodiments of the present disclosure, and does not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. Those skilled in the art can know that, with the evolution of system architecture and the appearance of new business scenarios, the technical solutions provided by the embodiments of the present disclosure are also applicable to similar technical problems.

[0169] The following embodiments of the present disclosure can be applied to the communication system 100 shown in FIG. 1A or part of the subject, but are not limited thereto. The subjects shown in FIG. 1A are exemplary, and the communication system can include all or part of the subjects in FIG. 1A, or other subjects other than FIG. 1A. The number and form of each subject is arbitrary, and the connection relationship between the subjects is exemplary. The subjects can not be connected or can be connected, and the connection can be in any way, can be direct connection or indirect connection, can be wired connection or wireless connection.

[0170] 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 (Bluetooth (registered trademark)), Public Land Mobile Network (PLMN) network, Device-to-Device (D2D) system, Machine to Machine (M2M) system, Internet of Things (IoT) system, Vehicle-to-Everything (V2X), system using other communication methods, next-generation system expanded based on them, and the like. Further, a plurality of systems can be combined (for example, combination of LTE or LTE-A and 5G, and the like).

[0171] Hereinafter, terms related to the present disclosure are explained and interpreted.

[0172] FIG. IB is a schematic diagram of a CSI compression and recovery based on a bilateral model according to an embodiment of the present disclosure. As shown in FIG. IB, the UE side can compress the downlink channel information H through a CSI generation model and send the quantized binary bit stream s to the gNB. The gNB side can recover the approximate H' of the original downlink information through a CSI recovery model.

[0173] In some embodiments, the method of training the CSI generation model and the CSI recovery model includes at least one of the following:

[0174] (1) Training the model on one side (e.g., the terminal side or the network device side) and then sending the trained model to the other side.

[0175] (2) Training the CSI generation model and the CSI recovery model on the terminal side and the network device side, respectively, through joint training. Alternatively, after training part of the model on one of the terminal side or the network side, training the other part of the bilateral model on the other side, wherein the model parameters of the first part are not updated.

[0176] (3) First training the model on one side, and then sending the training data or other auxiliary information to the other side to complete the training of the other part of the model.

[0177] In some embodiments, in order to reduce or alleviate the complexity of bilateral model training, the following options have been proposed:

[0178] Method 1: Standardize the model structure and parameters.

[0179] Method 2: Standardize the data set.

[0180] Method 3: Standardize the model structure, and the model parameters are transmitted between the network device side and the terminal side.

[0181] Method 4: Standardize the data format, and the data is transmitted between the network device side and the terminal side.

[0182] Method 5: Standardize the model format, and the reference model is transmitted between the network device side and the terminal side.

[0183] In some embodiments, according to the model parameters or the behavior of the terminal after receiving the model parameters, method 3 and method 5 can be further divided into:

[0184] For method 3:

[0185] Method 3a: Receive the model parameters and retrain the model to develop a different model.

[0186] Method 3b: The received model parameters are directly used for model inference.

[0187] For method 5:

[0188] Method 5a: training the received model, and developing a different model.

[0189] In some embodiments, method 4 can be further divided into:

[0190] For method 4, the data set is transmitted from the network device side to the terminal side, then

[0191] Method 4a: the data set is target CSI (target CSI) and feedback CSI (CSI feedback).

[0192] Method 4b: the data set is feedback CSI and reconstructed target CSI (reconstructed target CSI).

[0193] Method 4c: the data set is target CSI, feedback CSI and reconstructed target CSI.

[0194] There can be multiple CSI generation models on the terminal side, and there can also be multiple CSI recovery models on the network device side. On the one hand, how to realize the model pairing of the terminal side and the network device side is a problem to be solved. On the other hand, even if the pairing is completed, if a new model is obtained by retraining the model on one end, how to realize the pairing of the new model and the model on the other end is also a problem to be solved.

[0195] Embodiments of the present disclosure provide a communication method, a communication device, a communication system, a storage medium and a program product. First information used to indicate a first model is obtained, the first model is trained based on a third model and / or second information associated with the third model. A model pairing relationship is determined based on the first information, so as to determine a second model paired with the first model, thereby ensuring the compatibility of the bilateral models and avoiding the loss of system performance caused by the mismatch of the models.

[0196] In some embodiments, the first model is used to perform a first processing process, and the second model is used to perform a second processing process. The first processing process and the second processing process are inverse processes of each other.

[0197] In some embodiments, the first processing process can be an encoding process or a decoding process. In the case where the first processing process is an encoding process, the second processing process is a decoding process. In the case where the first processing process is a decoding process, the second processing process is an encoding process.

[0198] In some embodiments, the first processing procedure can be a compression procedure or a recovery procedure. In the case that the first processing procedure is a compression procedure, the second processing procedure is a recovery procedure. In the case that the first processing procedure is a recovery procedure, the second processing procedure is a compression procedure.

[0199] In some embodiments, the first processing procedure is a modulation procedure or a demodulation procedure. In the case that the first processing procedure is a modulation procedure, the second processing procedure is a demodulation procedure. In the case that the first processing procedure is a demodulation procedure, the second processing procedure is a modulation procedure.

[0200] In some embodiments, the first model and the second model can constitute a model pair, or in other words, the first model and the second model can constitute a bilateral model.

[0201] In some embodiments, the names of the first model and the second model are not limited, which are, for example, “generative model”, “recovery model”, “encoder”, “decoder”, “compression model”, “decompression model”, “modulation model”, “demodulation model”, and the like.

[0202] In some embodiments, the first model can be trained based on a third model.

[0203] In some embodiments, the second model can be trained based on a third model.

[0204] In some embodiments, the third model is a trained model, or a deployed model, or a standardized model.

[0205] In some embodiments, the name of the third model is not limited, which is, for example, “reference model”, “standard model”, “original model”, “old model”, and the like.

[0206] In some embodiments, the third model can be used to perform the first processing procedure. Alternatively, the third model can be used to perform the second processing procedure. Alternatively, the third model can be used to perform the first processing procedure and the second processing procedure, in which case the third model includes at least one model pair.

[0207] The following describes a model pairing procedure by taking a bilateral model as an example of an encoder model and a decoder model.

[0208] In the embodiments of the present disclosure, the model pairing procedure includes: (1) the fourth node sends a third model and / or second information associated with the third model. (2) The third node trains a first model based on the third model and / or the second information associated with the third model. (3) The second node configures first information for the first model. (4) The first node determines a model pairing relationship based on the first information.

[0209] In some embodiments, when the fourth node and the third node are deployed on the same device, the device can directly train the first model based on the third model and / or the second information associated with the third model.

[0210] In some embodiments, when the fourth node and the third node are deployed on different devices, the fourth node sends the third model and / or the second information associated with the third model to the third node.

[0211] In an example, the fourth node is deployed on a network device, the third node is deployed on a terminal and the network device, the network device sends the third model and / or the second information associated with the third model to the terminal, and the terminal trains the first model based on the third model and / or the second information associated with the third model, where the first model is an encoding model. The network device trains the first model based on the third model and / or the second information associated with the third model, where the first model is a decoding model. It can be understood that the terminal and the network device both retrain the first model based on the third model.

[0212] In some embodiments, when the third node and the second node are deployed on the same device, the device can directly configure the first information for the first model.

[0213] In some embodiments, when the third node and the second node are deployed on different devices, the third node can send a first message to the second node to request the second node to configure the first information. The second node can send the configured first information to the third node.

[0214] In some embodiments, when the third node and the first node are deployed on the same device, the device can directly determine the model pairing relationship based on the first information.

[0215] In some embodiments, when the third node and the first node are deployed on different devices, the third node can send a second message to the first node to trigger the first node to determine the model pairing relationship.

[0216] FIG. 2A is a first kind of interaction schematic diagram of a communication method according to embodiments of the present disclosure. As shown in FIG. 2A, embodiments of the present disclosure relate to a communication method. The communication method is performed by the communication system 100 and includes steps S2101 to S2110.

[0217] In embodiments of the present disclosure, the first node, the second node, the fourth node and the fifth node are deployed on a network device, and the third node is deployed on a terminal, for example, to illustrate the model pairing process and the model application process.

[0218] In some embodiments, the first model retrained by the terminal is an encoding model, and the second model paired with the first model is a decoding model.

[0219] In step S2101, the network device sends the third model and / or the second information.

[0220] In some embodiments, the terminal receives the third model.

[0221] In some embodiments, the third model is used by the terminal to train the first model.

[0222] In some embodiments, the third model comprises the encoding model A and / or the decoding model A. The encoding model A and the decoding model A are paired.

[0223] In some embodiments, the first model is the encoding model B. In the case that the third model is the encoding model A, the encoding model A can be used to train the encoding model B. In the case that the third model is the decoding model A, the decoding model A can be used to train the encoding model B. In the case that the third model comprises the encoding model A and the decoding model A, the encoding model A and the decoding model A can be used to generate the encoding model B.

[0224] In some embodiments, the second information is used by the terminal to train the first model.

[0225] In some embodiments, the second information comprises at least one of the following: third information (denoted as model identifier A), fourth information (denoted as model structure information), fifth information (denoted as model parameter information), and sixth information (denoted as training data information).

[0226] In some embodiments, the model identifier A is used to determine the third model, and the model identifier A is configured by the network device for the third model.

[0227] In some embodiments, the model structure information is used to determine the model structure A of the third model.

[0228] In some embodiments, the model structure A is used to train the first model. In other words, the model structure B of the first model is the same as the model structure A of the third model.

[0229] In some embodiments, the model parameter information is used to determine the model parameter A of the third model.

[0230] In some embodiments, the model parameter A is used to train the first model. In other words, the model parameter B of the first model is the same as the model parameter A of the third model.

[0231] In some embodiments, the training data information is used to determine the training data A of the third model. In some embodiments, the training data A comprises input data of the third model and / or output data of the third model.

[0232] In some embodiments, the training data A is used to train the first model. In other words, the training data B of the first model is the same as the training data A of the third model.

[0233] In some embodiments, in the case that the second information does not include the training data information, the network device can send the terminal a configuration B associated with the type of the training data, and the terminal can collect the training data B based on the configuration B, where the training data B is used to train the first model.

[0234] In some embodiments, the configuration B includes at least one of the following: the number of transmit antennas, the transmission bandwidth, the type of model input data, and the quantization manner of the training data. In this way, the terminal or the network device can determine the type of the collected training data based on the configuration B.

[0235] In some embodiments, in the case that the second information does not include the training data information, the network device can send the terminal a condition B for collecting the training data, and the terminal can collect the training data B based on the condition B, where the training data B is used to train the first model.

[0236] In some embodiments, the condition B includes at least one of the following: the channel scenario, the maximum rank supported by the terminal or the network device, and the movement state information of the terminal.

[0237] In some embodiments, the model identifier A includes at least one of the following: seventh indication information, eighth indication information, ninth indication information, tenth indication information, eleventh indication information, and twelfth indication information.

[0238] In some embodiments, the seventh indication information is used to indicate the third model. In other words, the seventh indication information is the identifier (ID) of the third model.

[0239] In an example, in the case that the third model is an encoding model A, the seventh indication information is the identifier of the encoding model A (encoderA ID). In the case that the third model is a decoding model A, the seventh indication information is the identifier of the decoding model A (decoderA ID). In the case that the third model includes the encoding model A and the decoding model A, the seventh indication information is the identifier of the model pair A (pairedA ID).

[0240] In some embodiments, the eighth indication information is used to indicate the encoding model A and the decoding model A. In other words, the eighth indication information is the identifier of the model pair A. It can be understood that the identifier of the model pair A can be used to indicate the encoding model A, can be used to indicate the decoding model A, and can be used to indicate both the encoding model A and the decoding model A.

[0241] In some embodiments, the ninth indication information is used to indicate the training data A of the third model. In other words, the ninth indication information is an identification of the training data A (datasetA ID). It can be understood that the third model is indicated by the identification of the training data of the third model.

[0242] In some embodiments, the tenth indication information is used to indicate the training session A of the third model. In other words, the tenth indication information is an identification of the training session A (training sessionA ID). It can be understood that the third model is indicated by the identification of the training session of the third model.

[0243] In some embodiments, the eleventh indication information is used to indicate the type association configuration A of the training data A of the third model. In other words, the eleventh indication information is an identification of the configuration A. It can be understood that the third model is indicated by the identification of the type association configuration of the training data of the third model.

[0244] In some embodiments, the configuration A includes at least one of the following: the number of transmit antenna ports, the transmission bandwidth, the model input data type, the quantization manner of the training data. In this way, the terminal or the network device can determine the type of the collected training data based on the configuration A. In some embodiments, the configuration A is configured by the network device.

[0245] In some embodiments, the twelfth indication information is used to indicate the condition A for collecting the training data A of the third model. In other words, the twelfth indication information is an identification of the condition A. It can be understood that the third model is indicated by the identification of the collection condition of the training data of the third model.

[0246] In some embodiments, the condition A includes at least one of the following: the channel scenario, the maximum rank supported by the terminal or the network device, the movement state information of the terminal.

[0247] In some embodiments, the identification of the configuration A and the condition A can be the same (for example, the identification of the configuration A and the condition A are both associatedA ID).

[0248] In some embodiments, the network device sends the third model can be implemented by radio resource control (RRC) signaling, medium access control control element (MAC-CE) signaling, downlink control information (DCI), etc.

[0249] In step S2102, the terminal trains the first model based on the third model and / or the second information.

[0250] In some embodiments, the terminal can train the first model based on the model structure A and the model parameters A of the third model in the case that the network device sends the third model.

[0251] In some embodiments, the terminal can obtain the third model based on the model identifier A indicating the third model, and train the first model based on the third model in the case that the second information sent by the network device includes the model identifier A.

[0252] In some embodiments, the terminal can train the first model based on the model structure A of the third model in the case that the second information sent by the network device includes the model structure information.

[0253] In some embodiments, the terminal can train the first model based on the model parameters A of the third model in the case that the second information sent by the network device includes the model parameter information.

[0254] In some embodiments, the terminal can train the first model based on the training data A of the third model in the case that the second information sent by the network device includes the training data information.

[0255] In some embodiments, the terminal can collect the training data of the first model based on the configuration B associated with the training data sent by the network device, and / or the condition B for collecting the training data in the case that the second information sent by the network device does not include the training data information.

[0256] In some embodiments, the above methods for training the first model can be used in combination without conflict, which will not be repeated here.

[0257] In step S2103, the terminal sends the first message.

[0258] In some embodiments, the network device receives the first message.

[0259] In some embodiments, the first message is used to request the first information (denoted as model identifier B) for the first model.

[0260] In some embodiments, the model identifier B includes one of the following: first indication information, second indication information, third indication information, fourth indication information, fifth indication information, and sixth indication information.

[0261] In some embodiments, the first indication information is used to indicate the first model. In an example, the first indication information is an identifier of an encoding model B (encoderB ID).

[0262] In some embodiments, the second indication information is used to indicate a model pair B. The model pair B includes a first model (i.e., an encoding model B) and a second model (i.e., a decoding model B). It can be understood that the first model is indicated by the identification of the model pair B. In an example, the second indication information is an identification (pairdeB ID) of the model pair B.

[0263] In some embodiments, the third indication information is used to indicate training data B of the first model. It can be understood that the first model is indicated by the identification of the training data of the first model. In an example, the third indication information is an identification (datasetB ID) of the training data B.

[0264] In some embodiments, the fourth indication information is used to indicate a training session B of the first model. It can be understood that the first model is indicated by the identification of the training session of the first model. In an example, the fourth indication information is an identification (training sessionB ID) of the training session B.

[0265] In some embodiments, the fifth indication information is used to indicate a type-association configuration B of the training data B of the first model. It can be understood that the first model is indicated by the identification of the type-association configuration of the training data of the first model. In an example, the fifth indication information is an identification (AssociatedB ID) of the configuration B.

[0266] In some embodiments, the sixth indication information is used to indicate a condition A for collecting the training data A of the first model. It can be understood that the first model is indicated by the identification of the collection condition of the training data of the first model. In an example, the sixth indication information is an identification (AssociatedB ID) of the condition B.

[0267] In some embodiments, the first message is further used to notify the network device that the terminal has completed the training of the first model.

[0268] In some embodiments, the name of the first message is not limited, which is, for example, a “training completion message”, an “ID request message”, etc.

[0269] In some embodiments, step S2103 can be omitted, in which case the network device can send the third model and the model identification B together to the terminal in the process of performing step S2101, or send the second information and the model identification B together to the terminal, or send the third model, the second information and the model identification B together to the terminal. In this way, the signaling interaction between the terminal and the network device can be saved, and the efficiency of model pairing can be improved, but at the same time, the network device can temporarily not know whether the terminal has retrained a new model.

[0270] In step S2104, the network device sends the model identifier B.

[0271] In some embodiments, the terminal receives the model identifier B.

[0272] In some embodiments, step S2104 and step S2101 can be performed simultaneously. In some embodiments, step S2104 can also be performed before step S2102. That is, the network device can configure the model identifier B in advance before the terminal trains the first model, so as to save the signaling interaction between the terminal and the network device and improve the efficiency of configuration identifier and model pairing.

[0273] In some embodiments, both step S2103 and step S2104 can be omitted. In this case, the terminal can determine the model identifier A indicating the third model as the model identifier B, that is, the model identifier B is the same as the model identifier A. In other words, the model identifier A indicates both the third model and the first model. In this way, the number of model identifiers can be reduced, the complexity of maintaining model identifiers by the system can be reduced, and the pairing efficiency can be improved.

[0274] In some embodiments, the terminal or the network device can determine the model identifier B based on the identifier and the first value specified by the protocol or configured by the network. In an example, the type of the identifier specified by the protocol or configured by the network can be one or more of encoder ID, paired ID, dataset ID, training Session ID, and associated ID, and the first value is X. Taking the encoder ID as an example of the identifier specified by the protocol or configured by the network, the model identifier B is encoder ID+X.

[0275] In some embodiments, the identifier specified by the protocol or configured by the network can be the model identifier A.

[0276] In step S2105, the terminal sends a second message.

[0277] In some embodiments, the network device receives the second message.

[0278] In some embodiments, the second message is used to trigger the model pairing process.

[0279] In some embodiments, the second message carries the model identifier B, and in this case, the second message is used to trigger pairing of the first model indicated by the model identifier B.

[0280] In some embodiments, the name of the second message is not limited, which is, for example, “pairing request message”, “pairing trigger message”, “pairing start message”, etc.

[0281] In some embodiments, steps S2102 to S2105 can be omitted, in which case the CWN determines the first frequency point based on the network indication.

[0282] In step S2106, the network device determines the model pairing relationship based on the model identifier B.

[0283] In some embodiments, the encoding model B indicated by the model identifier B is trained based on a third model, and the third model can include the encoding model A and / or the decoding model A. Therefore, the network device determines that the decoding model B paired with the encoding model B is the decoding model A, and establishes the pairing relationship between the model identifier B of the encoding model B and the model identifier A of the decoding model A. In an example, the model pairing relationship includes: encoderB ID-decoderA ID. In an example, the model pairing relationship further includes: encoderA ID-decoderA ID.

[0284] In some embodiments, in the case that the encoderA ID, pairedA ID, datasetA ID, trainingA session ID of the encoding model A and the encoderB ID, pairedB ID, datasetB ID, trainingB session ID of the encoding model B are the same, since the associated IDs of the encoding model A and the encoding model B are different, the network device can determine the model pairing relationship by using the associatedB ID.

[0285] In step S2107, the terminal performs data processing using the first model to obtain first data.

[0286] In some embodiments, in the case that the terminal is deployed with multiple models, the terminal can select one model from among them for data processing, which can be a standard model or a reference model deployed by the network device, or a new model obtained by retraining based on the standard model or the reference model.

[0287] In step S2108, the terminal sends the model identifier B and the first data.

[0288] In some embodiments, the network device receives the model identifier B and the first data.

[0289] In some embodiments, the model identifier B is used by the network device to determine a second model paired with the first model based on the model pairing relationship.

[0290] In step S2109, the network device determines a second model paired with the first model based on the model identifier B and the model pairing relationship.

[0291] In some embodiments, the model pairing relationship is used to indicate at least one model pair, each model pair including an encoding model and a decoding model. The network device determines the model pairing relationship in advance, and finds the second model from the model pairing relationship based on the model identifier B.

[0292] In step S2110, the network device performs data processing on the first data using the second model.

[0293] In the embodiments of the present disclosure, the above steps S2101 to S2106 are a model pairing process, and steps S2107 to S2110 are a model application process, which can be executed separately.

[0294] In some embodiments, the first node, the second node, the fourth node and the fifth node can also be deployed in the terminal, and the third node can also be deployed in the network device.

[0295] In some embodiments, the first model obtained by the network device through retraining is a decoding model, and the second model paired with the first model is an encoding model.

[0296] In some embodiments, the network device in steps S2101 to S2106 can be replaced by the terminal, and the terminal in steps S2101 to S2106 can be replaced by the network device.

[0297] In some embodiments, the terminal can send the third model, the second information, the model identifier B, the first message or the second message to the network device through RRC signaling, uplink control information (DCI), a physical uplink control channel (PUCCH) and a physical uplink shared channel (PUSCH).

[0298] In some embodiments, in the case that the first node is deployed in the terminal (i.e., the terminal determines the model pairing relationship), the network device cannot determine the second model paired with the first model, therefore, the terminal can send the first data and seventh information used to indicate the second model to the network device in the process of step S2108, so that the network device performs processing on the first data using the second model. Alternatively, the model identifier B, the first data and eighth information used to indicate the model pairing relationship are sent to the network device, so that the network device determines the second model used based on the model pairing relationship.

[0299] In some embodiments, the terminal can send the model identifier B, the first data and the eighth information to the network device in the process of performing step S2108. Alternatively, the terminal sends the eighth information to the network device after step S2106 and before step S2107.

[0300] In some embodiments, the seventh information indicating the second model is determined by the terminal from a model pairing relationship determined by itself.

[0301] The communication method related to the embodiments of the present disclosure can include at least one of steps S2101 to S2110. For example, step S2101 can be implemented as an independent embodiment. For example, step S2102 can be implemented as an independent embodiment. For example, step S2103 can be implemented as an independent embodiment. For example, step S2104 can be implemented as an independent embodiment. For example, step S2105 can be implemented as an independent embodiment. For example, step S2106 can be implemented as an independent embodiment. For example, step S2107 can be implemented as an independent embodiment. For example, step S2108 can be implemented as an independent embodiment. For example, step S2109 can be implemented as an independent embodiment. For example, step S2110 can be implemented as an independent embodiment. For example, step S2101 and step S2104 can be combined to be implemented as an independent embodiment. For example, step S2104, step S2105 and step S2106 can be combined to be implemented as an independent embodiment. For example, step S2108, step S2109 and step S2110 can be combined to be implemented as an independent embodiment.

[0302] FIG. 2B is a second kind of interaction schematic diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG. 2B, the embodiment of the present disclosure relates to a communication method. The communication method is performed by the communication system 100 and includes steps S2201 to S2209.

[0303] In the embodiments of the present disclosure, the first node, the fourth node and the fifth node are deployed in the network device, and the second node and the third node are deployed in the terminal. The model pairing process and the model application process are taken as examples.

[0304] In some embodiments, the first model obtained by the terminal retraining is an encoding model, and the second model paired with the first model is a decoding model.

[0305] In step S2201, the network device sends the third model and / or the second information.

[0306] Other optional implementation manners of step S2201 can refer to the optional implementation manners of step S2102 of FIG. 2A and other associated parts in the embodiments related to FIG. 2A, which are not described herein again.

[0307] In step S2202, the terminal trains the first model based on the third model and / or the second information.

[0308] Other optional implementation manners of step S2202 can refer to the optional implementation manners of step S2102 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which are not described herein again.

[0309] In step S2203, the terminal configures a model identifier B for the first model.

[0310] In some embodiments, the terminal can determine the model identifier B as the model identifier A, i.e., the model identifier B is the same as the model identifier A. In other words, the model identifier A indicates both the third model and the first model. In this way, the number of model identifiers can be reduced, the complexity of maintaining the model identifiers by the system can be reduced, and the pairing efficiency can be improved.

[0311] In some embodiments, the terminal can also determine the model identifier B based on an identifier and a first value specified by a protocol or configured by a network. In an example, the type of the identifier specified by the protocol or configured by the network can be one or more of an encoder ID, a paired ID, a dataset ID, a training session ID, and an associated ID, and the first value is X. Taking the encoder ID as an example of the identifier specified by the protocol or configured by the network, the model identifier B is encoder ID+X.

[0312] In some embodiments, the identifier specified by the protocol or configured by the network can be the model identifier A.

[0313] In step S2204, the terminal sends the second message.

[0314] Other optional implementation manners of step S2204 can refer to the optional implementation manners of step S2105 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which are not described herein again.

[0315] In step S2205, the network device determines the model pairing relationship based on the model identifier B.

[0316] Other optional implementation manners of step S2205 can refer to the optional implementation manners of step S2106 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which are not described herein again.

[0317] In step S2206, the terminal performs data processing using the first model to obtain first data.

[0318] Other optional implementation manners of step S2206 can refer to the optional implementation manners of step S2107 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which are not described here again.

[0319] In step S2207, the terminal sends the model identifier B and the first data.

[0320] Other optional implementation manners of step S2207 can refer to the optional implementation manners of step S2108 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which are not described here again.

[0321] In step S2208, the network device determines the second model paired with the first model based on the model identifier B and the model pairing relationship.

[0322] Other optional implementation manners of step S2208 can refer to the optional implementation manners of step S2109 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which are not described here again.

[0323] In step S2209, the network device performs data processing on the first data using the second model.

[0324] Other optional implementation manners of step S2209 can refer to the optional implementation manners of step S2110 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which are not described here again.

[0325] In the embodiments of the present disclosure, the above steps S2201 to S2205 are the model pairing process, and steps S2206 to S2209 are the model application process, which can be executed separately.

[0326] In some embodiments, the first node, the fourth node and the fifth node can also be deployed in the terminal, and the second node and the third node can also be deployed in the network device.

[0327] In some embodiments, the first model obtained by the network device retraining is a decoding model, and the second model paired with the first model is an encoding model.

[0328] In some embodiments, the network device in steps S2201 to S2205 can be replaced by the terminal, and the terminal in steps S2201 to S2205 can be replaced by the network device.

[0329] In some embodiments, in the case where the first node is deployed at the terminal (i.e., the terminal determines the model pairing relationship), the network device cannot determine the second model paired with the first model, and therefore, the terminal can send the first data and the seventh information indicating the second model to the network device in the process of performing step S2207, so that the network device processes the first data using the second model. Alternatively, the model identifier B, the first data, and the eighth information indicating the model pairing relationship are sent to the network device, so that the network device determines the second model used based on the model pairing relationship.

[0330] In some embodiments, the terminal can send the model identifier B, the first data, and the eighth information to the network device in the process of performing step S2207. Alternatively, the terminal sends the eighth information to the network device after step S2205 and before step S2206.

[0331] In some embodiments, the seventh information indicating the second model is determined by the terminal based on the model pairing relationship determined by the terminal itself.

[0332] The communication method related to the embodiments of the present disclosure can include at least one of steps S2201 to S2209. For example, step S2201 can be implemented as an independent embodiment. For example, step S2202 can be implemented as an independent embodiment. For example, step S2203 can be implemented as an independent embodiment. For example, step S2204 can be implemented as an independent embodiment. For example, step S2205 can be implemented as an independent embodiment. For example, step S2206 can be implemented as an independent embodiment. For example, step S2207 can be implemented as an independent embodiment. For example, step S2208 can be implemented as an independent embodiment. For example, step S2209 can be implemented as an independent embodiment. For example, steps S2203, S2204, and S2205 can be combined and implemented as an independent embodiment. For example, steps S2207, S2208, and S2209 can be combined and implemented as an independent embodiment. But not limited to this.

[0333] FIG. 2C is a third kind of interaction schematic diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG. 2C, the embodiments of the present disclosure relate to a communication method. The communication method is performed by the communication system 100 and includes steps S2301 to S2308.

[0334] In the embodiments of the present disclosure, the second node, the fourth node are deployed at the network device, and the first node, the third node, and the fifth node are deployed at the terminal. The model pairing process and the model application process are taken as an example.

[0335] In some embodiments, the first model retrained by the terminal is an encoding model, and the second model paired with the first model is a decoding model.

[0336] In step S2301, the network device sends the third model and / or the second information.

[0337] Other optional implementation manners of step S2301 can refer to the optional implementation manners of step S2101 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0338] In step S2302, the terminal trains the first model based on the third model and / or the second information.

[0339] Other optional implementation manners of step S2302 can refer to the optional implementation manners of step S2102 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0340] In step S2303, the terminal sends the first message.

[0341] Other optional implementation manners of step S2303 can refer to the optional implementation manners of step S2103 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0342] In step S2304, the network device sends the model identifier B.

[0343] Other optional implementation manners of step S2304 can refer to the optional implementation manners of step S2104 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0344] In step S2305, the terminal determines the model pairing relationship based on the model identifier B.

[0345] In some embodiments, the encoding model B indicated by the model identifier B is trained based on the third model, and the third model can include the encoding model A and / or the decoding model A. Therefore, the terminal determines that the decoding model B paired with the encoding model B is the decoding model A, and establishes the pairing relationship between the model identifier B of the encoding model B and the model identifier A of the decoding model A. In an example, the model pairing relationship includes: encoderB ID-decoderA ID. In an example, the model pairing relationship further includes: encoderA ID-decoderA ID.

[0346] In some embodiments, in the case that the encoderA ID, pairedA ID, datasetA ID, trainingA session ID of the encoding model A are the same as the encoderB ID, pairedB ID, datasetB ID, trainingB session ID of the encoding model B, since the associated IDs of the encoding model A and the encoding model B are different, the network device can determine the model pairing relationship by using the associatedB ID.

[0347] In step S2306, the terminal performs data processing on the first data by using the first model.

[0348] Other optional implementation manners of step S2306 can refer to the optional implementation manners of step S2107 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0349] In step S2307, the terminal sends the first data and the seventh information.

[0350] In some embodiments, the seventh information is used to indicate the second model paired with the first model.

[0351] In some embodiments, the seventh information indicating the second model is determined by the terminal based on the model pairing relationship determined by the terminal itself.

[0352] In some embodiments, the terminal can send the model identifier B, the first data and the eighth information. The eighth information is used to indicate the model pairing relationship.

[0353] Other optional implementation manners of step S2307 can refer to the optional implementation manners of step S2108 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0354] In step S2308, the network device performs data processing on the first data by using the second model.

[0355] Other optional implementation manners of step S2308 can refer to the optional implementation manners of step S2109 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0356] In the embodiments of the present disclosure, the above steps S2301 to S2305 are the model pairing process, and steps S2306 to S2308 are the model application process, which can be executed separately.

[0357] In some embodiments, the second node and the fourth node can also be deployed in the terminal, and the first node, the third node and the fifth node can also be deployed in the network device. In this case, the network device re-trains the obtained first model as a decoding model, and the second model paired with the first model is an encoding model.

[0358] In some embodiments, the network device in steps S2301, S2302 and S2305 can be replaced by a terminal, and the terminal in steps S2301, S2302 and S2305 can be replaced by a network device.

[0359] In some embodiments, in the case where the function of the first node is deployed in the network device (i.e., the network device determines the model pairing relationship), the terminal only needs to send the model identifier B and the first data in the process of performing step S2307, and the network device can determine the used second model based on the previously determined model pairing relationship.

[0360] The communication method related to the embodiments of the present disclosure can include at least one of steps S2301 to S2308. For example, step S2301 can be implemented as an independent embodiment. For example, step S2302 can be implemented as an independent embodiment. For example, step S2303 can be implemented as an independent embodiment. For example, step S2304 can be implemented as an independent embodiment. For example, step S2305 can be implemented as an independent embodiment. For example, step S2306 can be implemented as an independent embodiment. For example, step S2307 can be implemented as an independent embodiment. For example, step S2308 can be implemented as an independent embodiment. For example, steps S2303, S2304 and S2305 can be combined to be implemented as an independent embodiment. For example, steps S2307 and S2308 can be combined to be implemented as an independent embodiment. But not limited to this.

[0361] In some embodiments, steps S2301 and S2304 can be performed simultaneously.

[0362] In some embodiments, steps S2303 and S2304 can be omitted.

[0363] FIG. 2D is a fourth kind of interaction schematic diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG. 2D, the embodiment of the present disclosure relates to a communication method. The communication method is performed by the communication system 100 and includes steps S2401 to S2408.

[0364] In the embodiments of the present disclosure, the fourth node is deployed in the network device, and the first node, the second node, the third node and the fifth node are deployed in the terminal, which is taken as an example to illustrate the model pairing process and the model application process.

[0365] In some embodiments, the first model retrained by the terminal is an encoding model, and the second model paired with the first model is a decoding model.

[0366] In step S2401, the network device sends the third model and / or the second information.

[0367] Other optional implementation manners of step S2401 can refer to the optional implementation manners of step S2101 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be described here.

[0368] In step S2402, the terminal trains the first model based on the third model and / or the second information.

[0369] Other optional implementation manners of step S2402 can refer to the optional implementation manners of step S2102 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be described here.

[0370] In step S2403, the terminal configures the model identifier B for the first model.

[0371] In some embodiments, the terminal can determine the model identifier B as the model identifier A, that is, the model identifier B is the same as the model identifier A. In other words, the model identifier A indicates both the third model and the first model. In this way, the number of model identifiers can be reduced, the complexity of maintaining the model identifiers by the system can be reduced, and the pairing efficiency can be improved.

[0372] In some embodiments, the terminal can also determine the model identifier B based on the identifier and the first value specified by the protocol or configured by the network. In an example, the type of the identifier specified by the protocol or configured by the network can be one or more of encoder ID, paired ID, dataset ID, training Session ID, and associated ID, and the first value is X. Taking the encoder ID as an example of the identifier specified by the protocol or configured by the network, the model identifier B is encoder ID+X.

[0373] In some embodiments, the identifier specified by the protocol or configured by the network can be the model identifier A.

[0374] In step S2404, the terminal determines the model pairing relationship based on the model identifier B.

[0375] In some embodiments, the model identifier B indicates that the encoding model B is trained based on a third model, and the third model can include the encoding model A and / or the decoding model A. Therefore, the terminal determines the decoding model B paired with the encoding model B as the decoding model A, and establishes a pairing relationship between the model identifier B of the encoding model B and the model identifier A of the decoding model A. In an example, the model pairing relationship includes: encoderB ID-decoderA ID. In an example, the model pairing relationship further includes: encoderA ID-decoderA ID.

[0376] In some embodiments, in the case that the encoderA ID, pairedA ID, datasetA ID, trainingA session ID of the encoding model A are the same as the encoderB ID, pairedB ID, datasetB ID, trainingB session ID of the encoding model B, since the associated IDs of the encoding model A and the encoding model B are different, the network device can determine the model pairing relationship by using the associatedB ID.

[0377] In step S2405, the terminal performs data processing on the first data by using the first model.

[0378] Other optional implementation manners of step S2405 can refer to the optional implementation manners of step S2107 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be described here.

[0379] In step S2406, the terminal sends the model identifier B, the first data and the eighth information.

[0380] In some embodiments, the terminal sends the first data and the seventh information.

[0381] Other optional implementation manners of step S2406 can refer to the optional implementation manners of step S2108 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be described here.

[0382] In step S2407, the network device determines a second model paired with the first model based on the model identifier B and the model pairing relationship.

[0383] Other optional implementation manners of step S2407 can refer to the optional implementation manners of step S2109 in FIG. 2A and other associated parts in the embodiments involved in FIG. 2A, which will not be described here.

[0384] In step S2408, the network device performs data processing on the first data by using the second model.

[0385] Other optional implementation of step S2408 can refer to the optional implementation of step S2109 in FIG. 2A, and other associated parts in the embodiments involved in FIG. 2A, which are not repeated here.

[0386] In the embodiments of the present disclosure, the above steps S2401 to S2404 are model pairing processes, and steps S2405 to S2408 are model application processes, which can be executed separately.

[0387] In some embodiments, the fourth node can also be deployed in the terminal, and the first node, the second node, the third node and the fifth node can also be deployed in the network device. In this case, the first model obtained by the network device retraining is a decoding model, and the second model paired with the first model is an encoding model.

[0388] In some embodiments, the network device in steps S2401 to S2404 can be replaced by the terminal, and the terminal in steps S2401 to S2404 can be replaced by the network device.

[0389] In some embodiments, in the case where the function of the first node is deployed in the network device (i.e., the network device determines the model pairing relationship), the terminal only needs to send the model identifier B and the first data in the process of executing step S2406, and the network device can determine the used second model based on the previously determined model pairing relationship.

[0390] The communication method involved in the embodiments of the present disclosure can include at least one of steps S2401 to S2408. For example, step S2401 can be implemented as an independent embodiment. For example, step S2402 can be implemented as an independent embodiment. For example, step S2403 can be implemented as an independent embodiment. For example, step S2404 can be implemented as an independent embodiment. For example, step S2405 can be implemented as an independent embodiment. For example, step S2406 can be implemented as an independent embodiment. For example, step S2407 can be implemented as an independent embodiment. For example, step S2408 can be implemented as an independent embodiment.

[0391] For example, step S2403 and step S2404 can be combined as an independent embodiment. For example, step S2406, step S2407 and step S2408 can be combined as an independent embodiment.

[0392] In some embodiments, the terms such as "bilateral model", "model pair", "model group" and the like can be replaced with each other.

[0393] In some embodiments, the terms "pairing", "matching", "associating", and the like can be replaced with each other.

[0394] In some embodiments, the terms "model application", "model inference", "model usage", and the like can be replaced with each other.

[0395] In some embodiments, the names of information and the like are not limited to the names described in the embodiments, and the terms "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codebook", "codeword", "codepoint", "bit", "data", "program", "chip", and the like can be replaced with each other.

[0396] In some embodiments, the terms "carrying", "including", "containing", "packaging", and the like can be replaced with each other.

[0397] In some embodiments, the terms "radio", "wireless", "radio access network (RAN)", "access network (AN)", "RAN-based", and the like can be replaced with each other.

[0398] In some embodiments, the terms "acquiring", "obtaining", "getting", "receiving", "transmitting", "bidirectional transmission", "sending and / or receiving" can be replaced with each other, which can be interpreted as receiving from other subjects, acquiring from protocols, acquiring from higher layers, obtaining by processing oneself, implementing autonomously, and the like.

[0399] In some embodiments, the terms "sending", "transmitting", "reporting", "transmitting", "requesting", "bidirectional transmission", "sending and / or receiving", and the like can be replaced with each other.

[0400] In some embodiments, the terms "issuing", "returning", "feedback", "response", "reply", and the like can be replaced with each other.

[0401] In some embodiments, the terms "certain", "preset", "pre-set", "set", "indicated", "a certain", "any", "first", and the like can be replaced with each other, "certain A", "preset A", "pre-set A", "set A", "indicated A", "a certain A", "any A", "first A" can be interpreted as A predetermined in a protocol or the like, can be interpreted as A obtained by setting, configuring, or indicating, or the like, can be interpreted as certain A, a certain A, any A, or first A, and the like, but are not limited thereto.

[0402] In some embodiments, the determination or judgment can be made by a value represented by 1 bit (0 or 1), can be made by a true or false value (Boolean value) represented by true or false, can be made by comparison of numerical values (for example, comparison with a predetermined value), but is not limited thereto.

[0403] FIG. 3A is a first flow diagram illustrating a communication method performed by a terminal according to an embodiment of the present disclosure. As shown in FIG. 3A, the present embodiment relates to a communication method performed by a terminal. The above communication method includes steps S3101 to S3107.

[0404] In step S3101, a third model and / or second information is received.

[0405] The optional implementation of step S3101 can refer to the optional implementation of step S2101 of FIG. 2A, other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0406] In step S3102, a first model is trained based on the third model and / or the second information.

[0407] The optional implementation of step S3102 can refer to the optional implementation of step S2102 of FIG. 2A, other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0408] In step S3103, a first message is sent.

[0409] The optional implementation of step S3103 can refer to the optional implementation of step S2103 of FIG. 2A, other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0410] In step S3104, a model identifier B is received.

[0411] The optional implementation of step S3104 can refer to the optional implementation of step S2104 of FIG. 2A, other associated parts in the embodiments involved in FIG. 2A, which will not be repeated here.

[0412] In step S3105, the second message is sent.

[0413] Optional implementation of step S3105 can refer to optional implementation of step S2105 in FIG. 2A, other associated parts in embodiments involved in FIG. 2A, and the like, which will not be repeated here.

[0414] In step S3106, data processing is performed using the first model to obtain first data.

[0415] Optional implementation of step S3106 can refer to optional implementation of step S2107 in FIG. 2A, other associated parts in embodiments involved in FIG. 2A, and the like, which will not be repeated here.

[0416] In step S3107, the model identifier B and the first data are sent.

[0417] Optional implementation of step S3107 can refer to optional implementation of step S2108 in FIG. 2A, other associated parts in embodiments involved in FIG. 2A, and the like, which will not be repeated here.

[0418] FIG. 3B is a second terminal flowchart of a communication method performed by a terminal according to an embodiment of the present disclosure. As shown in FIG. 3B, the embodiment of the present disclosure relates to a communication method performed by a terminal. The above-mentioned communication method comprises steps S3201 to S3206.

[0419] In step S3201, a third model is received.

[0420] Optional implementation of step S3201 can refer to optional implementation of step S2201 in FIG. 2B, other associated parts in embodiments involved in FIG. 2B, and the like, which will not be repeated here.

[0421] In step S3202, a first model is trained based on the third model.

[0422] Optional implementation of step S3202 can refer to optional implementation of step S2202 in FIG. 2B, other associated parts in embodiments involved in FIG. 2B, and the like, which will not be repeated here.

[0423] In step S3203, a model identifier B is configured for the first model.

[0424] Optional implementation of step S3203 can refer to optional implementation of step S2203 in FIG. 2B, other associated parts in embodiments involved in FIG. 2B, and the like, which will not be repeated here.

[0425] In step S3204, a second message is sent.

[0426] The optional implementation of step S3204 can refer to the optional implementation of step S2204 in FIG. 2B, other associated parts in the embodiments related to FIG. 2B, and so on, details are not repeated here.

[0427] In step S3205, data processing is performed using the first model to obtain first data.

[0428] The optional implementation of step S3205 can refer to the optional implementation of step S2206 in FIG. 2B, other associated parts in the embodiments related to FIG. 2B, and so on, details are not repeated here.

[0429] In step S3206, the model identifier B and the first data are sent.

[0430] The optional implementation of step S3206 can refer to the optional implementation of step S2207 in FIG. 2B, other associated parts in the embodiments related to FIG. 2B, and so on, details are not repeated here.

[0431] FIG. 3C is a third flow diagram of a communication method performed by a terminal according to an embodiment of the present disclosure. As shown in FIG. 3C, the embodiment of the present disclosure relates to a communication method performed by a terminal. The above-mentioned communication method includes steps S3301 to S3307.

[0432] In step S3301, a third model and / or second information are received.

[0433] The optional implementation of step S3301 can refer to the optional implementation of step S2301 in FIG. 2C, other associated parts in the embodiments related to FIG. 2C, and so on, details are not repeated here.

[0434] In step S3302, a first model is trained based on the third model and / or the second information.

[0435] The optional implementation of step S3302 can refer to the optional implementation of step S2302 in FIG. 2C, other associated parts in the embodiments related to FIG. 2C, and so on, details are not repeated here.

[0436] In step S3303, a first message is sent.

[0437] The optional implementation of step S3303 can refer to the optional implementation of step S2303 in FIG. 2C, other associated parts in the embodiments related to FIG. 2C.

[0438] In step S3304, a model identifier B is received.

[0439] The optional implementation of step S3304 can refer to the optional implementation of step S2304 in FIG. 2C, other associated parts in the embodiments related to FIG. 2C, and so on, details are not repeated here.

[0440] In step S3305, a model pairing relationship is determined based on the model identifier B.

[0441] Optional implementation of step S3305 can refer to optional implementation of step S2305 in FIG. 2C, other associated parts in the embodiments involved in FIG. 2C, and the like, which will not be repeated here.

[0442] In step S3306, data processing is performed using the first model to obtain first data.

[0443] Optional implementation of step S3306 can refer to optional implementation of step S2306 in FIG. 2C, other associated parts in the embodiments involved in FIG. 2C, and the like, which will not be repeated here.

[0444] In step S3307, the first data and seventh information are sent.

[0445] Optional implementation of step S3307 can refer to optional implementation of step S2307 in FIG. 2C, other associated parts in the embodiments involved in FIG. 2C, and the like, which will not be repeated here.

[0446] FIG. 3D is a fourth flow diagram of a communication method performed by a terminal according to an embodiment of the present disclosure. As shown in FIG. 3D, the embodiment of the present disclosure relates to a communication method performed by a terminal. The above-mentioned communication method comprises steps S3401 to S3406.

[0447] In step S3401, a third model and / or second information are received.

[0448] Optional implementation of step S3401 can refer to optional implementation of step S2401 in FIG. 2D, other associated parts in the embodiments involved in FIG. 2D, and the like, which will not be repeated here.

[0449] In step S3402, a first model is trained based on the third model and / or the second information.

[0450] Optional implementation of step S3402 can refer to optional implementation of step S2402 in FIG. 2D, other associated parts in the embodiments involved in FIG. 2D, and the like, which will not be repeated here.

[0451] In step S3403, a model identifier B is configured for the first model.

[0452] Optional implementation of step S3403 can refer to optional implementation of step S2403 in FIG. 2D, other associated parts in the embodiments involved in FIG. 2D, and the like, which will not be repeated here.

[0453] In step S3404, a model pairing relationship is determined based on the model identifier B.

[0454] Optional implementation of step S3404 can refer to optional implementation of step S2404 in FIG. 2D, other associated parts in the embodiments involved in FIG. 2D, and the like, which will not be repeated here.

[0455] In step S3405, data processing is performed using the first model to obtain first data.

[0456] Optional implementation of step S3405 can refer to optional implementation of step S2405 in FIG. 2D, other associated parts in the embodiments involved in FIG. 2D, and the like, which will not be repeated here.

[0457] In step S3406, the model identifier B, the first data, and the eighth information are sent.

[0458] Optional implementation of step S3406 can refer to optional implementation of step S2406 in FIG. 2D, other associated parts in the embodiments involved in FIG. 2D, and the like, which will not be repeated here.

[0459] FIG. 3E is a first flow diagram of a communication method performed by a network device according to an embodiment of the present disclosure. As shown in FIG. 3E, the embodiment of the present disclosure relates to a communication method performed by a network device. The above-mentioned communication method includes steps S3501 to S3508.

[0460] In step S3501, a third model and / or second information are sent.

[0461] Optional implementation of step S3501 can refer to optional implementation of step S2101 in FIG. 2A, other associated parts in the embodiments involved in FIG. 2A, and the like, which will not be repeated here.

[0462] In step S3502, a first message is received.

[0463] Optional implementation of step S3502 can refer to optional implementation of step S2103 in FIG. 2A, other associated parts in the embodiments involved in FIG. 2A, and the like, which will not be repeated here.

[0464] In step S3503, a model identifier B is sent.

[0465] Optional implementation of step S3503 can refer to optional implementation of step S2104 in FIG. 2A, other associated parts in the embodiments involved in FIG. 2A, and the like, which will not be repeated here.

[0466] In step S3504, a second message is received.

[0467] The optional implementation of step S3504 can refer to the optional implementation of step S2105 in FIG. 2A, other associated parts in the embodiments related to FIG. 2A, and the like, details are not repeated here.

[0468] In step S3505, the model pairing relationship is determined based on the model identifier B.

[0469] The optional implementation of step S3505 can refer to the optional implementation of step S2106 in FIG. 2A, other associated parts in the embodiments related to FIG. 2A, and the like, details are not repeated here.

[0470] In step S3506, the model identifier B and the first data are received.

[0471] The optional implementation of step S3506 can refer to the optional implementation of step S2108 in FIG. 2A, other associated parts in the embodiments related to FIG. 2A, and the like, details are not repeated here.

[0472] In step S3507, the second model paired with the first model is determined based on the model identifier B and the model pairing relationship.

[0473] The optional implementation of step S3507 can refer to the optional implementation of step S2109 in FIG. 2A, other associated parts in the embodiments related to FIG. 2A, and the like, details are not repeated here.

[0474] In step S3508, the first data is processed using the second model.

[0475] The optional implementation of step S3508 can refer to the optional implementation of step S2110 in FIG. 2A, other associated parts in the embodiments related to FIG. 2A, and the like, details are not repeated here.

[0476] FIG. 3F is a second flow diagram of a communication method performed by a network device according to an embodiment of the present disclosure. As shown in FIG. 3F, the embodiment of the present disclosure relates to a communication method, which is performed by a network device. The above-mentioned communication method includes steps S3601 to S3606.

[0477] In step S3601, a third model and / or second information are sent.

[0478] The optional implementation of step S3601 can refer to the optional implementation of step S2201 in FIG. 2B, other associated parts in the embodiments related to FIG. 2B, and the like, details are not repeated here.

[0479] In step S3602, a second message is received.

[0480] The optional implementation of step S3602 can refer to the optional implementation of step S2204 in FIG. 2B, other associated parts in the embodiments related to FIG. 2B, and the like, details are not described herein.

[0481] In step S3603, the model pairing relationship is determined based on the model identifier B.

[0482] The optional implementation of step S3603 can refer to the optional implementation of step S2205 in FIG. 2B, other associated parts in the embodiments related to FIG. 2B, and the like, details are not described herein.

[0483] In step S3604, the model identifier B and the first data are received.

[0484] The optional implementation of step S3604 can refer to the optional implementation of step S2207 in FIG. 2B, other associated parts in the embodiments related to FIG. 2B, and the like, details are not described herein.

[0485] In step S3605, the second model paired with the first model is determined based on the model identifier B and the model pairing relationship.

[0486] The optional implementation of step S3605 can refer to the optional implementation of step S2208 in FIG. 2B, other associated parts in the embodiments related to FIG. 2B, and the like, details are not described herein.

[0487] In step S3606, the first data is processed using the second model.

[0488] The optional implementation of step S3606 can refer to the optional implementation of step S2209 in FIG. 2B, other associated parts in the embodiments related to FIG. 2B, and the like, details are not described herein.

[0489] FIG. 3G is a third flow diagram of a communication method performed by a network device according to an embodiment of the present disclosure. As shown in FIG. 3G, the embodiment of the present disclosure relates to a communication method, which is performed by a network device. The above communication method includes steps S3701 to S3705.

[0490] In step S3701, the third model and / or the second information are transmitted.

[0491] The optional implementation of step S3701 can refer to the optional implementation of step S2301 in FIG. 2C, other associated parts in the embodiments related to FIG. 2C, and the like, details are not described herein.

[0492] In step S3702, the first message is received.

[0493] The optional implementation of step S3702 can refer to the optional implementation of step S2303 in FIG. 2C, other associated parts in the embodiments involved in FIG. 2C, and the like.

[0494] In step S3703, a model identifier B is sent.

[0495] The optional implementation of step S3703 can refer to the optional implementation of step S2304 in FIG. 2C, other associated parts in the embodiments involved in FIG. 2C, and the like.

[0496] In step S3704, first data and seventh information are received.

[0497] The optional implementation of step S3704 can refer to the optional implementation of step S2307 in FIG. 2C, other associated parts in the embodiments involved in FIG. 2C, and the like.

[0498] In step S3705, the first data is processed using a second model.

[0499] The optional implementation of step S3705 can refer to the optional implementation of step S2308 in FIG. 2C, other associated parts in the embodiments involved in FIG. 2C, and the like.

[0500] FIG. 3H is a fourth flow diagram of a communication method performed by a network device according to an embodiment of the present disclosure. As shown in FIG. 3H, the embodiment of the present disclosure relates to a communication method, which is performed by a network device. The above communication method includes steps S3801 to S3804.

[0501] In step S3801, a third model and / or second information are sent.

[0502] The optional implementation of step S3801 can refer to the optional implementation of step S2401 in FIG. 2D, other associated parts in the embodiments involved in FIG. 2D, and the like.

[0503] In step S3802, a model identifier B, first data and eighth information are received.

[0504] The optional implementation of step S3802 can refer to the optional implementation of step S2406 in FIG. 2D, other associated parts in the embodiments involved in FIG. 2D, and the like.

[0505] In step S3803, based on the model identifier B and a model pairing relationship, a second model paired with the first model is determined.

[0506] Optional implementation of step S3803 can refer to optional implementation of step S2407 in FIG. 2D, other associated parts in the embodiments involved in FIG. 2D, details are not repeated here.

[0507] In step S3804, the first data is processed using the second model.

[0508] Optional implementation of step S3804 can refer to optional implementation of step S2408 in FIG. 2D, other associated parts in the embodiments involved in FIG. 2D, details are not repeated here.

[0509] FIG. 4A is a flow diagram of a communication method performed by a first node according to an embodiment of the present disclosure. As shown in FIG. 4A, the embodiment of the present disclosure relates to a communication method, which is performed by a first node. The above-mentioned communication method comprises steps S4101-S4102.

[0510] In step S4101, first information is obtained.

[0511] Optional implementation of step S4101 can refer to optional implementation of step S2105 in FIG. 2A, optional implementation of step S2204 in FIG. 2B, optional implementation of step S2304 in FIG. 2C, optional implementation of step S2403 in FIG. 2D, other associated parts in the embodiments involved in FIG. 2A, other associated parts in the embodiments involved in FIG. 2B, other associated parts in the embodiments involved in FIG. 2C, other associated parts in the embodiments involved in FIG. 2D, details are not repeated here.

[0512] In step S4102, based on the first information, a model pairing relationship is determined.

[0513] Optional implementation of step S4102 can refer to optional implementation of step S2106 in FIG. 2A, optional implementation of step S2205 in FIG. 2B, optional implementation of step S2305 in FIG. 2C, optional implementation of step S2404 in FIG. 2D, other associated parts in the embodiments involved in FIG. 2A, other associated parts in the embodiments involved in FIG. 2B, other associated parts in the embodiments involved in FIG. 2C, other associated parts in the embodiments involved in FIG. 2D, details are not repeated here.

[0514] FIG. 4B is a flow diagram of a communication method performed by a second node according to an embodiment of the present disclosure. As shown in FIG. 4B, the embodiment of the present disclosure relates to a communication method, which is performed by a second node. The above-mentioned communication method comprises step S4201.

[0515] In step S4201, a first model is configured with first information.

[0516] The optional implementation of step S4201 can refer to the optional implementation of step S2104 in FIG. 2A, the optional implementation of step S2203 in FIG. 2B, the optional implementation of step S2304 in FIG. 2C, the optional implementation of step S2403 in FIG. 2D, other associated parts in the embodiments related to FIG. 2A, other associated parts in the embodiments related to FIG. 2B, other associated parts in the embodiments related to FIG. 2C, other associated parts in the embodiments related to FIG. 2D, and details are not described herein.

[0517] FIG. 4C is a flow diagram of a method for performing communication on a third node side, according to an embodiment of the present disclosure. As shown in FIG. 4C, the embodiments of the present disclosure relate to a communication method, which is performed by a third node. The above-mentioned communication method comprises steps S4301 to S4303.

[0518] In step S4301, a first model is trained based on a third model and / or second information associated with the third model.

[0519] The optional implementation of step S4301 can refer to the optional implementation of step S2102 in FIG. 2A, the optional implementation of step S2202 in FIG. 2B, the optional implementation of step S2302 in FIG. 2C, the optional implementation of step S2402 in FIG. 2D, other associated parts in the embodiments related to FIG. 2A, other associated parts in the embodiments related to FIG. 2B, other associated parts in the embodiments related to FIG. 2C, other associated parts in the embodiments related to FIG. 2D, and details are not described herein.

[0520] In step S4302, first information indicating the first model is obtained.

[0521] The optional implementation of step S4302 can refer to the optional implementation of step S2104 in FIG. 2A, the optional implementation of step S2203 in FIG. 2B, the optional implementation of step S2304 in FIG. 2C, the optional implementation of step S2403 in FIG. 2D, other associated parts in the embodiments related to FIG. 2A, other associated parts in the embodiments related to FIG. 2B, other associated parts in the embodiments related to FIG. 2C, and details are not described herein.

[0522] In step S4303, the first information is sent to a first node.

[0523] The optional implementation of step S4303 can refer to the optional implementation of step S2105 in FIG. 2A, the optional implementation of step S2204 in FIG. 2B, other associated parts in the embodiments related to FIG. 2A, other associated parts in the embodiments related to FIG. 2B, other associated parts in the embodiments related to FIG. 2C, and details are not described herein.

[0524] FIG. 4D is a flow diagram illustrating a method for performing communication by a fourth node, according to an embodiment of the present disclosure. As shown in FIG. 4D, the present embodiment of the present disclosure relates to a method for communication, performed by a fourth node. The method for communication comprises step S4401.

[0525] In step S4401, a third model and / or second information associated with the third model is transmitted.

[0526] The optional implementation of step S4401 can refer to the optional implementation of step S2101 in FIG. 2A, the optional implementation of step S2201 in FIG. 2B, the optional implementation of step S2301 in FIG. 2C, the optional implementation of step S2401 in FIG. 2D, other associated parts in the embodiments related to FIG. 2A, other associated parts in the embodiments related to FIG. 2B, other associated parts in the embodiments related to FIG. 2C, other associated parts in the embodiments related to FIG. 2D, and will not be described here.

[0527] FIG. 4E is a flow diagram illustrating a method for performing communication by a fifth node, according to an embodiment of the present disclosure. As shown in FIG. 4E, the present embodiment of the present disclosure relates to a method for communication, performed by a fifth node. The method for communication comprises step S4501 to step S4502.

[0528] In step S4501, first information and first data processed by a first model are received.

[0529] The optional implementation of step S4501 can refer to the optional implementation of step S2108 in FIG. 2A, the optional implementation of step S2207 in FIG. 2B, the optional implementation of step S2307 in FIG. 2C, the optional implementation of step S2406 in FIG. 2D, other associated parts in the embodiments related to FIG. 2A, other associated parts in the embodiments related to FIG. 2B, other associated parts in the embodiments related to FIG. 2C, other associated parts in the embodiments related to FIG. 2D, and will not be described here.

[0530] In step S4502, the first data is processed using a second model paired with the first model.

[0531] The optional implementation of step S4501 can refer to the optional implementation of step S2110 in FIG. 2A, the optional implementation of step S2209 in FIG. 2B, the optional implementation of step S2308 in FIG. 2C, the optional implementation of step S2408 in FIG. 2D, other associated parts in the embodiments related to FIG. 2A, other associated parts in the embodiments related to FIG. 2B, other associated parts in the embodiments related to FIG. 2C, other associated parts in the embodiments related to FIG. 2D, and will not be described here.

[0532] In some embodiments, the embodiments of the first node involved in FIG. 4A, the embodiments of the second node involved in FIG. 4B, the embodiments of the third node involved in FIG. 4C, and the embodiments of the fourth node involved in FIG. 4D can be used in combination with each other, or can be independently implemented.

[0533] In the following, the technical solutions of the embodiments of the present disclosure are exemplarily described through specific embodiments.

[0534] In some embodiments, for case 1, the network device side delivers at least one of the model, the training data, and the model parameters to the terminal.

[0535] In some embodiments, the terminal side re-trains an encoder model based on at least one of the model, the training data, and the model parameters.

[0536] In some embodiments, the re-trained encoder is assigned at least one of an encoder ID, a paired ID, a dataset ID, and a training session ID, and model pairing is implemented according to the assigned encoder ID, paired ID, dataset ID, and training session ID.

[0537] In some embodiments, the ID assigned to the re-trained encoder ID is the same as the encoder ID, paired ID, dataset ID, and training session ID determined before training.

[0538] In some embodiments, the network device side delivers configuration information associated with the collection of the training data set or condition information of the network device side to the terminal, and model pairing is implemented according to the associated configuration information or condition information.

[0539] In some embodiments, the configuration information or the condition information is distinguished by defining an associated ID (associated ID).

[0540] In some embodiments, the above-mentioned configuration information can include the number of ports, the model input data type, the quantization method, etc.; and the condition information of the network device side can include the channel scene, etc.

[0541] In some embodiments, one or more of the above-mentioned encoder ID, paired ID, dataset ID, and training session ID, and one or more of the configuration information, the condition information of the network device side, and the associated ID are used together to implement model pairing.

[0542] In some embodiments, the determination of the encoder ID, paired ID, dataset ID, training session ID, associated ID comprises at least one of the following:

[0543] Manner 1: The network device allocates and indicates to the terminal.

[0544] Manner 1-1: One or more of the encoder ID, paired ID, dataset ID, training session ID, associated ID, and at least one of the model, dataset, model parameters are delivered together by the network device to the terminal.

[0545] Manner 1-2: After the model is updated, the terminal indicates to the network device that the model retraining is complete, and then the network device allocates one or more of the encoder ID, paired ID, dataset ID, training session ID, associated ID to the terminal.

[0546] Manner 2: The terminal determines and reports to the network device.

[0547] Manner 2-1: After receiving one or more of the model, model parameters, dataset, configuration information, and network device side condition information delivered by the network device, the terminal reports one or more of the encoder ID, paired ID, dataset ID, training session ID, associated ID to the network device.

[0548] Manner 2-2: After the model training is completed on the terminal side, one or more of the encoder ID, paired ID, dataset ID, training session ID, associated ID are reported to the network device.

[0549] Manner 3: Based on one or more of the predefined encoder ID, paired ID, dataset ID, training session ID, associated ID, such as Encoder ID+X, where the value of X is a predefined relevant numerical value, or indicated by the network device or determined by the terminal report.

[0550] In some embodiments, for case 2, the terminal side delivers the model, dataset or model parameters to the network device.

[0551] In some embodiments, the network device side re-trains a decoder model.

[0552] In some embodiments, the model pairing method is similar to the model pairing method in case 1 above.

[0553] In some embodiments, case 2 differs from case 1 in that a decoder ID is assigned to the re-trained decoder.

[0554] In some embodiments, case 2 differs from case 1 in that the terminal sends the terminal side condition information related to the training data to the network device. The terminal side condition information can include the supported maximum rank, terminal side mobile state related information, etc.

[0555] In some embodiments, the determination of the encoder ID, pair ID, dataset ID, and ID associated with the configuration information or condition information includes at least one of the following:

[0556] Method 4: Network device assignment.

[0557] Method 4-1: After the network device receives one or more of the information of the model, dataset, and model parameters passed by the terminal side, the network device assigns one or more of the decoder ID, paired ID, dataset ID, training session ID, and associated ID.

[0558] Method 4-2: After the network device side completes the model update, the network device assigns one or more of the encoder ID, paired ID, dataset ID, training session ID, and associated ID.

[0559] Method 5: Terminal determination and sending to network device.

[0560] Method 5-1: The terminal reports one or more of the information of the model, model parameters, dataset, and terminal side condition information passed to the network device to the network device together with one or more of the encoder ID, paired ID, dataset ID, training session ID, and associated ID.

[0561] Manner 5-2: After the network device side completes the training of the Decoder model, the network device notifies the terminal that the model training has been completed through indication information. After receiving the indication information, the terminal reports one or more IDs of the encoder ID, paired ID, dataset ID, training Session ID, and associated ID to the network device.

[0562] Manner 6: Determined based on a predefined method. The same as manner 3 in case 1.

[0563] In some embodiments, for case 1, the network device side has completed the training of the encoder and decoder models, or the network device side has completed the deployment of the encoder and decoder models. The encoder and decoder on the network device side can also be standardized models. The encoder and decoder trained or deployed on the network device side serve as reference models.

[0564] In some embodiments, the network device transmits the trained reference model dncoder to the terminal through model transfer, model parameter transfer, or sending of the encoder input and output data set when training the encoder.

[0565] In some embodiments, the terminal side can train a new encoder according to the received model (the model can include the encoder and / or decoder).

[0566] In some embodiments, if the terminal side has obtained the model structure information of the encoder and / or decoder, the terminal side can train a new encoder according to the received model parameters (the model can include the model parameters of the encoder and / or decoder).

[0567] In some embodiments, the terminal side can also train a new encoder according to the received input and output data set of the encoder.

[0568] In some embodiments, the terminal side can include multiple different Encoders. In order to ensure that the encoder adopted on the terminal side and the decoder adopted on the network device side are in a paired matching relationship when performing inference, the following provides related methods for ensuring that the Encoder and Decoder are in a matching relationship.

[0569] In some embodiments, the terminal is assigned at least one of an encoder ID, a paired ID, a dataset ID, and a training session ID. For example, after the terminal trains a new encoder, the network device assigns a new encoder ID to the terminal. The network device maintains a mapping relationship between the newly trained encoder and the decoder on the network device side. When the terminal informs the network device of the encoder used by the terminal, the network device selects a corresponding decoder for inference. When the terminal uses an encoder for inference, the terminal sends the ID of the selected encoder to the network device to indicate the encoder used by the terminal.

[0570] In some embodiments, if the newly trained encoder of the terminal still uses the original encoder ID, the network device no longer assigns a new ID to the terminal. Even if the encoder ID corresponds to multiple encoders, the network device still uses the decoder corresponding to the encoder for inference.

[0571] In some embodiments, if the network device only transmits a model or model parameters, the terminal side training encoder also needs corresponding training data sets. The terminal can collect training data sets according to the configuration information of the network device side, such as the number of transmitting antenna ports, transmission bandwidth, and the like, or the terminal collects training data sets according to the network device side condition information transmitted by the network device, such as UMa or UMi scenario information.

[0572] In some embodiments, the corresponding configuration information or condition information can be associated with an ID, i.e., an associated ID. The associated ID can be indicated by the network device side to the terminal. The terminal associates the associated ID with the trained encoder. If the terminal informs the network device of the associated ID, the network device can determine the model used by the terminal based on the associated ID.

[0573] In some embodiments, if the ID of the newly trained encoder of the terminal is the same as the encoder ID originally transmitted by the network device to the terminal, in order to distinguish the two encoders, the associated ID described above can be used. That is, the associated IDs corresponding to the two encoders are different, and the network device side can determine the encoder used by the terminal according to the associated ID corresponding to the encoder, and thus select a decoder matched with the encoder.

[0574] In some embodiments, the above is only illustratively indicated that the encoder ID or associated ID is assigned and indicated to the terminal by the network device side. The encoder ID or associated ID can also be determined based on the terminal side or determined in a protocol predefined manner.

[0575] In some embodiments, in addition to the encoder ID, associated ID, it is also possible to assign one or more of the paired ID, dataset ID, training session ID. These IDs can be sent to the terminal together with the model, model parameters, dataset, or indicated to the terminal after the terminal completes the model training. The above ID indication method can be realized through one or more of RRC, MAC-CE, DCI signaling.

[0576] In some embodiments, the assigned ID can be a global ID or a local ID. It is also possible to include multiple local IDs under one global ID. For example, the encoder associated by the network device side delivery includes a local ID 1 under a global ID. After the terminal re-trains an encoder, the network device side indicates the local ID 2 under the global ID to the terminal. It can avoid assigning more global IDs to a terminal.

[0577] In some embodiments, for case 2, the terminal side first completes the training of the encoder and decoder model. Similar to case 1, the difference is that the terminal side sends the corresponding model, model parameters or dataset to the network device, and the network device re-trains a decoder. The dataset sent by the terminal to the network device is the input and output dataset for training the decoder. The network device side can train a new decoder based on the received model, model parameters or dataset. In order to ensure that the decoder adopted by the network device side matches the encoder on the terminal side, and avoid performance loss caused by model mismatch. The terminal and the network device side also need to realize the matching of the bilateral model through one or more of the encoder ID, paired ID, dataset ID, training session ID or associated ID.

[0578] In some embodiments, the associated ID is associated with the terminal side condition information such as supported maximum rank, terminal side mobile state related information, etc. When the network device side deploys multiple Decoders, the corresponding Decoder can be selected through the associated ID indicated by the terminal.

[0579] In some embodiments, the encoder ID, paired ID, dataset ID, training session ID, or associated ID can be indicated to the terminal by the network device, or reported to the network device by the terminal, or determined by predefinition. The encoder ID, paired ID, dataset ID, training session ID, or associated ID can be delivered together with the model parameter, model, or dataset, or delivered or indicated to the opposite terminal after the model is updated.

[0580] The embodiments of the present disclosure also propose a device for implementing any of the above methods, for example, a terminal including units or modules for implementing the steps performed by the terminal in any of the above methods. For another example, another network device is also proposed, including units or modules for implementing the steps performed by the network device (such as an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0581] It should be understood that the division of each unit or module in the above apparatus is only a logical function division, and all or part of them can be integrated into a physical entity or physically separated in actual implementation. In addition, the units or modules in the apparatus can be implemented in the form of processor calling software: for example, the apparatus includes a processor connected with a memory, the memory stores instructions, and the processor calls the instructions stored in the memory to implement any of the above methods or realize the functions of each unit or module of the above apparatus, wherein the processor is, for example, a general processor such as a central processing unit (CPU) or a microprocessor, and the memory is a memory in the apparatus or a memory outside the apparatus. Alternatively, the units or modules in the apparatus can be implemented in the form of hardware circuit, and the functions of part or all of the units or modules can be realized by the design of hardware circuit. The above hardware circuit can be understood as one or more processors; for example, in one implementation, the above hardware circuit is an application-specific integrated circuit (ASIC), and the functions of part or all of the above units or modules are realized by the design of the logical relationship of elements in the circuit; for example, in another implementation, the above hardware circuit is a programmable logic device (PLD), and a field programmable gate array (FPGA) is taken as an example, which can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by a configuration file, so as to realize the functions of part or all of the above units or modules. All units or modules of the above apparatus can be implemented in the form of processor calling software, or all units or modules can be implemented in the form of hardware circuit, or part of the units or modules are implemented in the form of processor calling software, and the remaining part is implemented in the form of hardware circuit.

[0582] In the embodiments of the present disclosure, the processor is a circuit with signal processing capability. In one implementation, the processor can be a circuit with instruction reading and running capability, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), a digital signal processor (DSP), and the like. In another implementation, the processor can implement certain functions through a logical relationship of a hardware circuit, and the logical relationship of the 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 the reconfigurable hardware circuit, the processor loads a configuration document to implement the configuration of the hardware circuit. It can be understood that the processor loads instructions to implement the functions of the above part or all units or modules. In addition, the hardware circuit can also be designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), and the like.

[0583] FIG. 5A is a structural schematic diagram of a first node according to an embodiment of the present disclosure. As shown in FIG. 5A, the first node 5100 can include a first transceiver module 5101 and a first processing module 5102. In some embodiments, the first transceiver module 5101 is configured to obtain first information, the first information being used to indicate a first model. The first processing module 5102 is configured to determine a model pairing relationship based on the first information, the model pairing relationship being used to indicate at least one model pair, each model pair including the first model and a second model, and the first model being trained based on a third model and / or second information associated with the third model. In some embodiments, the first transceiver module 5101 is configured to perform at least one of the communication steps (for example, step S2105, but not limited thereto) performed by the first node in any of the above methods, and details are not described herein again.

[0584] In some embodiments, the first node described above can be deployed in a terminal or a network device.

[0585] FIG. 5B is a schematic structural diagram of a second node according to an embodiment of the present disclosure. As shown in FIG. 5B, the second node 5200 can include a second processing module 5201. In some embodiments, the second processing module 5201 can be configured to configure first information for a first model, the first information being used to determine a model pairing relationship, the model pairing relationship being used to indicate at least one model pair, each model pair including the first model and a second model, the first model being trained based on a third model and / or second information associated with the third model. In some embodiments, the second node 5200 can further include a second transceiver module 5202. In some embodiments, the second transceiver module can be configured to send the first information to a third node, the third node being used to train the first model based on the third model. In some embodiments, the second transceiver module 5202 can be configured to perform at least one of the communication steps (for example, step S2103, step S2104, but not limited thereto) performed by the second node in any of the above methods, and details are not described herein.

[0586] In some embodiments, the above-mentioned second node can be deployed in a terminal or a network device.

[0587] FIG. 5C is a schematic structural diagram of a third node according to an embodiment of the present disclosure. As shown in FIG. 5C, the third node 5300 can include a third processing module 5301 and a third transceiver module 5302. In some embodiments, the third processing module 5301 is configured to train a first model based on a third model and / or second information associated with the third model. The third transceiver module 5302 is configured to send first information to a first node, the first information being used to trigger the first node to determine a model pairing relationship, the model pairing relationship being used to indicate at least one model pair, each model pair including the first model and a second model. In some embodiments, the above-mentioned third transceiver module 5302 is configured to perform at least one of the communication steps (for example, S2103, but not limited thereto) performed by the third node in any of the above methods, and details are not described herein.

[0588] In some embodiments, the above-mentioned third node can be deployed in a terminal or a network device.

[0589] FIG. 5D is an example structural diagram of the fifth node, according to an embodiment of the present disclosure. As shown in FIG. 5D, the fifth node 5400 can include a fourth transceiver module 5401 and a fourth processing module 5402. In some embodiments, the fourth transceiver module 5401 can be configured to receive the first information and the first data processed by the first model. In some embodiments, the fourth processing module 5402 can be configured to process the first data using a second model paired with the first model, wherein the first information is used to indicate the first model, and the second model is determined based on a model pairing relationship, the model pairing relationship is determined based on the first information, and the model pairing relationship is used to indicate at least one model pair, each model pair including the first model and the second model. In some embodiments, the fourth transceiver module 5401 can be configured to perform at least one of the communication steps (for example, step S2108, but not limited thereto) of transmitting and / or receiving and the like performed by the fifth node in any one of the above methods, which will not be described here.

[0590] In some embodiments, the third node described above can be deployed in a terminal or a network device.

[0591] In some embodiments, the transceiver module described above can include a transmitting module and / or a receiving module. The transmitting module and the receiving module can be separate or integrated together. Alternatively, the transceiver module described above can be mutually replaced with a transceiver.

[0592] FIG. 6 is a structural schematic diagram of a communication device, according to an embodiment of the present disclosure. The communication device 6100 can be any one of the first node, the second node, the third node, the fourth node, and the fifth node, can be a chip, a chip system, or a processor supporting the first node to implement any one of the above methods, can be a chip, a chip system, or a processor supporting the second node to implement any one of the above methods, can be a chip, a chip system, or a processor supporting the third node to implement any one of the above methods, can be a chip, a chip system, or a processor supporting the fourth node to implement any one of the above methods, and can be a chip, a chip system, or a processor supporting the fifth node to implement any one of the above methods. The communication device 6100 can be used to implement the methods described in the above method embodiments, and specific reference can be made to the descriptions in the above method embodiments.

[0593] As shown in FIG. 6, the communication device 6100 includes one or more processors 6101. The processor 6101 can be a general processor or a special-purpose processor, etc., such as a baseband processor or a central processing unit. The baseband processor can be configured to process communication protocols and communication data, the central processing unit can be configured to control a communication apparatus (e.g., a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process data of the programs. Optionally, the communication device 6100 is configured to perform any of the above methods. Optionally, the one or more processors 6101 are configured to invoke instructions to cause the communication device 6100 to perform any of the above methods.

[0594] In some embodiments, the communication device 6100 further includes one or more transceivers 6102. When the communication device 6100 includes the one or more transceivers 6102, the transceiver 6102 performs at least one of the communication steps (e.g., steps S3101, steps S3201, steps S3301, steps S3401, but not limited to) in the above methods, and the processor 6101 performs at least one of the other steps (e.g., steps S3104, steps S3402, but not limited to). In optional embodiments, the transceiver 6102 can include a receiver and / or a transmitter, which can be separate or integrated together. Optionally, the terms transceiver, transceiving unit, transceiver, transceiving circuit, interface circuit, interface, etc. can be replaced with each other, and the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc. can be replaced with each other, and the terms receiver, receiving unit, receiver, receiving circuit, etc. can be replaced with each other.

[0595] In some embodiments, the communication device 6100 further includes one or more memories 6103 for storing data. Optionally, all or part of the memory 6103 can also be outside the communication device 6100. In optional embodiments, the communication device 6100 can include one or more interface circuits 6104. Optionally, the interface circuit 6104 is connected to the memory 6103, and the interface circuit 6104 can be configured to receive data from the memory 6103 or other devices, and can be configured to send data to the memory 6103 or other devices. For example, the interface circuit 6104 can read data stored in the memory 6103 and send the data to the processor 6101.

[0596] The communication device 6100 described in the above embodiments can be a network device or a terminal, but the scope of the communication device 6100 described in the present disclosure is not limited thereto, and the structure of the communication device 6100 can not be limited to that of FIG. 6. The communication device can be a standalone device or can be part of a larger device. For example, the communication device can be: 1) a standalone integrated circuit (IC), or a chip, or a chip system or subsystem; (2) a set of one or more ICs, which can optionally also include storage components for storing data, programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, a smart terminal device, a cellular phone, a wireless device, a handset, a mobile unit, a vehicle-mounted device, a network device, a cloud device, an artificial intelligence device, and the like; (6) others, and the like.

[0597] FIG. 7 is a structural schematic diagram of a chip according to an embodiment of the present disclosure. For the case where the communication device 6100 can be a chip or a chip system, the structural schematic diagram of the chip 7100 shown in FIG. 7 can be referred to, but is not limited thereto.

[0598] The chip 7100 includes one or more processors 7101. The chip 7100 is configured to perform any of the above methods.

[0599] In some embodiments, the chip 7100 further includes one or more interface circuits 7102. Optionally, the terms interface circuit, interface, transceiver pin, and the like can be replaced with each other. In some embodiments, the chip 7100 further includes one or more memories 7103 for storing data. Optionally, all or part of the memory 7103 can be outside the chip 7100. Optionally, the interface circuit 7102 is connected with the memory 7103, and the interface circuit 7102 can be configured to receive data from the memory 7103 or other devices, and the interface circuit 7102 can be configured to send data to the memory 7103 or other devices. For example, the interface circuit 7102 can read data stored in the memory 7103 and send the data to the processor 7101.

[0600] In some embodiments, the interface circuit 7102 performs at least one of the communication steps (for example, step S3101, step S3201, step S3301, step S3401, but not limited thereto) of sending and / or receiving in the above methods. The interface circuit 7102 performing the communication steps such as sending and / or receiving in the above methods means that the interface circuit 7102 performs data interaction between the processor 7101, the chip 7100, the memory 7103, or a transceiver device. In some embodiments, the processor 7101 performs at least one of the other steps (for example, step S3104, step S3402, but not limited thereto).

[0601] The modules and / or devices described in each embodiment of the virtual device, the physical device, the chip, etc. can be combined or separated as appropriate. Alternatively, some or all of the steps can be performed cooperatively by a number of modules and / or devices, which are not limited here.

[0602] The embodiment of the present disclosure further provides a storage medium, and instructions are stored on the storage medium. When the instructions are run on the communication device 6100, the communication device 6100 performs any one of the above methods. Alternatively, the storage medium is an electronic storage medium. Alternatively, the storage medium is a computer readable storage medium, but is not limited to this, and can also be a storage medium readable by other devices. Alternatively, the storage medium can be a non-transitory storage medium, but is not limited to this, and can also be a transitory storage medium.

[0603] The embodiment of the present disclosure further provides a program product, and the program product is executed by the communication device 6100, so that the communication device 6100 performs any one of the above methods. Alternatively, the program product is a computer program product.

[0604] The embodiment of the present disclosure further provides a computer program, and when the computer program is run on a computer, the computer executes any one of the above methods.

[0605] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. The present disclosure is intended to cover any and all variations of the application that fall within the scope of the present application. It is submitted that the true scope of the application should not be limited by the description of the preferred embodiments, but rather by the following claims.

[0606] It should be understood that the application is not limited to the precise construction that has been described above and shown in the accompanying drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application. The scope of the application should be limited only by the appended claims.

Claims

1. A communication method, performed by a first node, comprising: obtaining first information, the first information being used to indicate the first model; determining, based on the first information, a model pairing relationship, the model pairing relationship being used to indicate at least one model pair, each model pair comprising the first model and a second model, the first model being trained based on a third model and / or second information associated with the third model.

2. The method of claim 1, wherein, the first model is used to perform a first processing procedure, and the second model is used to perform a second processing procedure, the first processing procedure and the second processing procedure being inverse procedures of each other.

3. The method of claim 2, wherein, the first model and the third model are used to perform the first processing procedure, the third model being indicated by third information configured by a second node, the first information being the same as the third information.

4. The method of claim 2, wherein, the second model and the third model are used to perform the second processing procedure, the second model being the third model; or the second model being trained based on the third model and / or the second information.

5. The method according to any one of claims 1 to 4, wherein, the first information comprises at least one of the following: first indication information, used to indicate the first model; second indication information, used to indicate the first model and the second model; third indication information, used to indicate training data of the first model; fourth indication information, used to indicate a training session of the first model; fifth indication information, used to indicate a type association configuration of the training data of the first model; sixth indication information, used to indicate a condition of collecting the training data of the first model.

6. The method according to any one of claims 1 to 5, wherein, the second information comprises at least one of the following: third information, used to indicate the third model; fourth information, used to indicate a model structure of the third model; fifth information, used to indicate a model parameter of the third model; sixth information, used to indicate training data of the third model.

7. The method according to any one of claims 1 to 6, wherein, the first information is determined based on third information indicating the third model and a first numerical value.

8. The method according to any one of claims 1 to 7, wherein, the first information is further used to trigger pairing of the first model. 9.A communication method, performed by a second node, comprising: configuring, for a first model, first information, the first information being used to determine a model pairing relationship, the model pairing relationship being used to indicate at least one model pair, each model pair comprising the first model and a second model, the first model being trained based on a third model and / or second information associated with the third model.

10. The method of claim 9, wherein, the first model is used to perform a first processing procedure, and the second model is used to perform a second processing procedure, the first processing procedure and the second processing procedure being inverse procedures of each other.

11. The method of claim 10, wherein, the first model and the third model are used to perform the first processing procedure, the third model being indicated by third information configured by a second node, the first information being the same as the third information.

12. The method of claim 10, wherein, the second model and the third model are used to perform the second processing procedure, the second model being the third model; or the second model being trained based on the third model.

13. The method according to any one of claims 9 to 12, wherein, the first information comprises at least one of the following: first indication information, used to indicate the first model; The second indication information is used to indicate the first model and the second model. The third indication information is used to indicate training data of the first model. The fourth indication information is used to indicate a training session of the first model. The fifth indication information is used to indicate a type association configuration of the training data of the first model. The sixth indication information is used to indicate a condition of collecting the training data of the first model.

14. The method according to any one of claims 9 to 13, wherein, The second information comprises at least one of the following: The third information is used to indicate the third model. The fourth information is used to indicate a model structure of the third model. The fifth information is used to indicate a model parameter of the third model. The sixth information is used to indicate training data of the third model.

15. The method according to any one of claims 9 to 14, wherein, The first information is determined based on the third information indicating the third model and a first numerical value.

16. The method according to any one of claims 9 to 15, wherein, The method further comprises: sending the first information to a third node, the third node being configured to train the first model based on the third model.

17. The method of claim 16, wherein, The method further comprises: receiving a first message sent by a third node, the first message being used to request the first information.

18. A communication method, performed by a third node, the method comprising: training a first model based on a third model and / or second information associated with the third model; obtaining first information used to indicate the first model; sending the first information to a first node; the first information being used to trigger the first node to determine a model pairing relationship, the model pairing relationship being used to indicate at least one model pair, each model pair comprising the first model and a second model.

19. The method of claim 18, wherein, The first model is used to perform a first processing process, and the second model is used to perform a second processing process, the first processing process and the second processing process being inverse processes of each other.

20. The method of claim 19, wherein, The second model and the third model are used to perform the second processing process, and the second model is a third model; or the second model is trained based on the third model and / or the second information.

21. The method of claim 19, wherein, The first model and the third model are used to perform the first processing process, and the third model is indicated by third information configured by a second node, and the first information is the same as the third information.

22. The method of any one of claims 18 to 20, wherein, The obtaining of the first information associated with the first model comprises: receiving first information sent by a second node, the second node being configured to configure the first information for the first model.

23. The method of claim 22, wherein, The first information and the seventh information are received simultaneously, the second node and a fourth node are the same device, and the fourth node is configured to send the seventh information to the third node.

24. The method of claim 22, wherein, The method further comprises: sending a first message to the second node, the first message being used to request the first information.

25. The method of any one of claims 18 to 24, wherein, The first information comprises at least one of the following: first indication information is used to indicate the first model; second indication information is used to indicate the first model and the second model; third indication information is used to indicate training data of the first model; fourth indication information is used to indicate a training session of the first model; fifth indication information is used to indicate a type association configuration of the training data of the first model; Sixth indication information, used for indicating a condition of collecting training data of the first model.

26. The method of any one of claims 18 to 25, wherein, The second information comprises at least one of: Third information, used for indicating the third model; Fourth information, used for indicating a model structure of the third model; Fifth information, used for indicating a model parameter of the third model; Sixth information, used for indicating training data of the third model.

27. The method of any one of claims 18 to 26, wherein, The first information is determined based on the third information indicating the third model and a first numerical value.

28. A communication method, performed by a fifth node, the method comprising: receiving first information and first data processed by a first model; processing the first data using a second model paired with the first model; wherein the first information is used for indicating the first model, the second model is determined based on a model pairing relationship, the model pairing relationship is determined based on the first information, and the model pairing relationship is used for indicating at least one model pair, each model pair comprising the first model and the second model.

29. The method of claim 28, wherein, The first model is used for performing a first processing procedure, the second model is used for performing a second processing procedure, the first processing procedure and the second processing procedure are inverse procedures of each other, and the first model is trained based on a third model and / or second information associated with the third model.

30. The method of claim 29, wherein, The first model and the third model are used for performing the first processing procedure, and the third model is indicated by third information configured by a second node, and the first information is the same as the third information.

31. The method of claim 29, wherein, The second model and the third model are used for performing the second processing procedure, and the second model is the third model, or the second model is trained based on the third model and / or second information associated with the third model.

32. The method of any one of claims 28 to 31, wherein, The first information comprises at least one of: First indication information, used for indicating the first model; Second indication information, used for indicating the first model and the second model; Third indication information, used for indicating training data of the first model; Fourth indication information, used for indicating a training session of the first model; Fifth indication information, used for indicating a type association configuration of the training data of the first model; Sixth indication information, used for indicating a condition of collecting training data of the first model.

33. The method of any one of claims 29 to 32, wherein, The second information comprises at least one of: Third information, used for indicating the third model; Fourth information, used for indicating a model structure of the third model; Fifth information, used for indicating a model parameter of the third model; Sixth information, used for indicating training data of the third model.

34. The method of any one of claims 28 to 33, wherein, The method further comprises: receiving seventh information, the seventh information being used for indicating the second model.

35. The method of any one of claims 28 to 34, wherein, The method further comprises: receiving eighth information, the eighth information being used for indicating the model pairing relationship; determining the second model paired with the first model based on the model pairing relationship.

36. A first node, comprising: a first receiving and transmitting module configured to obtain first information, the first information being used for indicating the first model; The first processing module is configured to determine a model pairing relationship based on the first information, the model pairing relationship being used to indicate at least one model pair, each model pair including the first model and a second model, the first model being trained based on a third model and / or second information associated with the third model.

37. A second node, comprising: The second processing module is configured to configure first information for a first model, the first information being used to determine a model pairing relationship, the model pairing relationship being used to indicate at least one model pair, each model pair including the first model and a second model, the first model being trained based on a third model and / or second information associated with the third model.

38. A third node, comprising: The third processing module is configured to train a first model based on a third model and / or second information associated with the third model; The third transceiving module is configured to send the first information to a first node; The first information is used to trigger the first node to determine a model pairing relationship, the model pairing relationship being used to indicate at least one model pair, each model pair including the first model and a second model.

39. A fifth node, comprising: The fourth transceiving module is configured to receive first information and first data processed by a first model; The fourth processing module is configured to process the first data using a second model paired with the first model, the first information being used to indicate the first model, the second model being determined based on a model pairing relationship, the model pairing relationship being determined based on the first information, the model pairing relationship being used to indicate at least one model pair, each model pair including the first model and the second model.

40. A communication device, comprising: one or more processors; The communication device is configured to perform the communication method of any one of claims 1-35.

41. A communication system, comprising a first node, a second node, a third node, a fourth node, and a fifth node; The first node is configured to implement the communication method of any one of claims 1-8; the second node is configured to implement the communication method of any one of claims 9-17; the third node is configured to implement the communication method of any one of claims 18-27; the fourth node is configured to send a third model and / or second information associated with the third model to the third node; and the fifth node is configured to implement the communication method of any one of claims 28-35.

42. A storage medium, the storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the communication method of any one of claims 1-35.

43. A computer program product, comprising a computer program that, when executed by a processor, implements the communication method of any one of claims 1-35.

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