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

Abstract

The invention relates to a communication method and device, equipment, a communication system, a storage medium and a program product. The method is executed by the terminal. The method comprises the following steps: receiving first information which is used for indicating the performance of an AI function; through the scheme of the invention, the terminal can obtain the performance of the AI function determined by the network side.

Description

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

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

[0002] In a communication system, artificial intelligence (AI) technology can be integrated, thereby providing users with better communication services.

[0003] SUMMARY

[0004] The present disclosure provides a communication method and apparatus, communication device, communication system, storage medium and program product.

[0005] According to a first aspect of embodiments of the present disclosure, a communication method is provided. The method is performed by a terminal. The method comprises: receiving first information, wherein the first information is used to indicate a performance of an AI function.

[0006] According to a second aspect of embodiments of the present disclosure, a communication method is provided. The method is performed by a network device. The method comprises: transmitting first information, wherein the first information is used to indicate a performance of an AI function.

[0007] According to a third aspect of embodiments of the present disclosure, a communication apparatus is provided. The apparatus is arranged in a terminal. The apparatus comprises a transceiver module. The transceiver module is configured to: receive first information, wherein the first information is used to indicate a performance of an AI function.

[0008] According to a fourth aspect of embodiments of the present disclosure, a communication apparatus is provided. The apparatus is arranged in a network device. The apparatus comprises a transceiver module. The transceiver module is configured to: transmit first information, wherein the first information is used to indicate a performance of an AI function.

[0009] According to a fifth aspect of embodiments of the present disclosure, a communication device is provided. The communication device comprises one or more processors, and a memory storing instructions. The instructions, when executed by the communication device, cause the communication device to implement the communication method according to the first aspect or the second aspect.

[0010] According to a sixth aspect of embodiments of the present disclosure, a communication system is provided. The communication system comprises a terminal and a network device. The terminal is configured to implement the communication method according to the first aspect. The network device is configured to implement the communication method according to the second aspect.

[0011] According to a seventh aspect of embodiments of the present disclosure, a storage medium is provided. The storage medium stores instructions. The instructions, when executed on a communication device, cause the communication device to perform the communication method according to the first aspect or the second aspect.

[0012] According to an eighth aspect of embodiments of the present disclosure, a program product is provided. The program product, when executed by a communication device, causes the communication device to perform the communication method according to the first aspect or the second aspect.

[0013] According to a ninth aspect of embodiments of the present disclosure, a computer program is provided. The computer program, when executed on a computer, causes the computer to perform the communication method according to the first aspect or the second aspect.

[0014] According to a tenth aspect of embodiments of the present disclosure, a chip or chip system is provided. The chip or chip system comprises processing circuitry. The processing circuitry is configured to perform the communication method according to the first aspect or the second aspect.

[0015] According to embodiments of the present disclosure, the performance of the AI function on the terminal side can be improved.

[0016] It should be understood that the general description above and the detailed description below are only exemplary and explanatory, and do not constitute a limitation on the embodiments of the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure and serve to explain the principles of the present disclosure together with the specification.

[0018] FIG. 1 is a schematic diagram of an architecture of a communication system according to embodiments of the present disclosure.

[0019] FIG. 2 is a schematic diagram of interactions of a communication method according to embodiments of the present disclosure.

[0020] FIG. 3 is a schematic diagram of a flow of a communication method according to embodiments of the present disclosure.

[0021] FIG. 4 is a schematic diagram of a flow of a communication method according to embodiments of the present disclosure.

[0022] FIG. 5 is a schematic diagram of interactions of a communication method according to embodiments of the present disclosure.

[0023] FIG. 6 is a schematic diagram of a structure of a communication apparatus according to embodiments of the present disclosure.

[0024] FIG. 7A is a schematic diagram of a structure of a communication device according to embodiments of the present disclosure.

[0025] FIG. 7B is a schematic diagram of a structure of a chip according to embodiments of the present disclosure. DETAILED DESCRIPTION

[0026] The embodiments of the present disclosure provide a communication method and apparatus, a communication device, a communication system, a storage medium and a program product.

[0027] In a first aspect, the embodiments of the present disclosure provide a communication method. The method is performed by a terminal. The method comprises: receiving first information, wherein the first information is used to indicate a performance of an AI function.

[0028] According to the present embodiment, the first information can be used to indicate the performance of the AI function to the terminal. In this way, in the case that the performance of the AI function on the terminal is monitored by the network, the terminal can obtain the performance of the AI function through the first information. That is, the terminal can obtain the performance of the AI function on itself according to the first information sent by the network device.

[0029] In combination with some embodiments of the first aspect, in some embodiments, the first information can be used to control a first model associated with the AI function, and the first model is an AI / machine learning (ML) model.

[0030] According to the present embodiment, the AI function can be implemented by the first model. Through the first information, the first model of the AI function can be controlled.

[0031] In combination with some embodiments of the first aspect, in some embodiments, controlling the first model can include at least one of the following: training, updating, activating, deactivating.

[0032] In combination with some embodiments of the first aspect, in some embodiments, the first information can include at least one of the following: first performance information used to indicate that the performance of the AI function does not meet a preset condition; second performance information used to indicate performance data of the AI function; third performance information used to indicate an expected performance of the AI function; first condition information used to indicate a network side condition associated with the performance of the AI function; second condition information used to indicate a terminal side condition associated with the performance of the AI function; first parameter information used to indicate a statistical parameter associated with the performance of the AI function; and first function information used to indicate the AI function.

[0033] According to the present embodiment, through the first performance information and / or the second performance information in the first information, the terminal can determine that the performance of the AI function does not meet the preset condition, thereby triggering further training of the first model of the AI function on the terminal. The third performance information, the first condition information, the second condition information and the first parameter information can be used to help implement the control of the first model.

[0034] In some embodiments combined with some embodiments of the first aspect, in some embodiments, the first performance information can comprise at least one bit, and a first value of the bit can be used to indicate that the performance of the AI function does not satisfy the preset condition.

[0035] According to the present embodiment, the value of one bit can be used to directly indicate that the performance of the AI function does not satisfy the preset condition, so that the terminal directly obtains that the performance of the AI function is poor.

[0036] In some embodiments combined with some embodiments of the first aspect, in some embodiments, the expected performance indicated by the third performance information can satisfy the preset condition.

[0037] In some embodiments combined with some embodiments of the first aspect, in some embodiments, the network side condition indicated by the first condition information can comprise at least one of: a network side condition in a case where the performance of the AI function does not satisfy the preset condition; a network side condition in a statistical process of the performance of the AI function.

[0038] In some embodiments combined with some embodiments of the first aspect, in some embodiments, the first condition information can comprise a first identifier, and the first identifier can be used to identify the network side condition associated with the performance of the AI function.

[0039] According to the present embodiment, the network side condition indicated by the first condition information can be represented by the first identifier. In a case where the network side condition is different, the first identifier is also different. In this way, the specific network side condition can not be included in the first information, but only the first identifier is used for identification. This significantly reduces the load of the first information.

[0040] In some embodiments combined with some embodiments of the first aspect, in some embodiments, the terminal side condition indicated by the second condition information can comprise at least one of: a terminal side condition in a case where the performance of the AI function does not satisfy the preset condition; a terminal side condition in a statistical process of the performance of the AI function.

[0041] In some embodiments combined with some embodiments of the first aspect, in some embodiments, the statistical parameter indicated by the first parameter information can comprise at least one of: a time length of the statistics; a time window of the statistics; a number of times of the statistics; a data volume of the statistics.

[0042] In some embodiments combined with some embodiments of the first aspect, in some embodiments, the first function information can comprise a second identifier, and the second identifier can be used to identify the AI function.

[0043] In some embodiments combined with some embodiments of the first aspect, in some embodiments, the performance of the AI function can correspond to at least one of: at least one network side condition; at least one terminal side condition.

[0044] In some embodiments combined with the first aspect, in some embodiments, the first information can be used to indicate at least one of: a combination of the performance of the AI function and at least one of the network side condition; a combination of the performance of the AI function and at least one of the terminal side condition.

[0045] In some embodiments combined with the first aspect, in some embodiments, the method can further include: training, according to the first information, a first model associated with the AI function.

[0046] In some embodiments combined with the first aspect, in some embodiments, the first information can include a first identifier, the first identifier being used to indicate the network side condition associated with the performance information; wherein the operation of training, according to the first information, the first model associated with the AI function can include: training the first model based on the training data in a case where the first identifier is consistent with a third identifier, wherein the second identifier is used to indicate the network side condition in a process of collecting the training data.

[0047] According to the embodiment, through comparison of the first identifier and the third identifier, it can be determined whether the training data is applicable to the training of the first model. More specifically, in a case where the first identifier is consistent with the third identifier, it indicates that the network side condition indicated by the first condition information is the same as the network side condition corresponding to the training data; and more specifically, it indicates that the network side condition in a case where the performance of the AI function is poor is the same as the network side condition corresponding to the training data. In this way, the first model trained based on the training data can be applicable to the network side condition in a case where the performance of the AI function is poor, so as to optimize the performance of the first model for such network side condition.

[0048] In some embodiments combined with the first aspect, in some embodiments, the method can further include: sending second information, wherein the second information is used to determine whether the terminal supports receiving the first information.

[0049] According to the embodiment, the terminal can send the second information to the network device to inform the network device that the terminal itself supports receiving the first information. In this way, the network device can send the first information in a case where the performance of the AI function is monitored, so as to control the first model on the terminal.

[0050] In a second aspect, the embodiments of the present disclosure provide a communication method. The method is performed by a network device. The method includes: sending first information, wherein the first information is used to indicate the performance of the AI function.

[0051] According to the embodiment, the first information can be used to indicate the performance of the AI function to the terminal. In this way, in a case where the performance of the AI function on the terminal is monitored by the network, the terminal can obtain the performance of the AI function through the first information. That is, the first information can enable the terminal to obtain the performance of the AI function of the terminal itself from the network device.

[0052] In some embodiments combining with the second aspect, in some embodiments, the first information can be used for controlling a first model associated with the AI function, the first model being an AI / ML model.

[0053] In some embodiments combining with the second aspect, in some embodiments, the controlling the first model can comprise at least one of: training, updating, activating, deactivating.

[0054] In some embodiments combining with the second aspect, in some embodiments, the first information can comprise at least one of: first performance information indicating that a performance of the AI function does not satisfy a preset condition; second performance information indicating performance data of the AI function; third performance information indicating an expected performance of the AI function; first condition information indicating a network side condition associated with the performance of the AI function; second condition information indicating a terminal side condition associated with the performance of the AI function; first parameter information indicating a statistical parameter associated with the performance of the AI function; first function information indicating the AI function.

[0055] In some embodiments combining with the second aspect, in some embodiments, the first performance information can comprise at least one bit, a first value of the bit indicating that the performance of the AI function does not satisfy the preset condition.

[0056] In some embodiments combining with the second aspect, in some embodiments, the expected performance indicated by the third performance information can satisfy the preset condition.

[0057] In some embodiments combining with the second aspect, in some embodiments, the network side condition indicated by the first condition information can comprise at least one of: a network side condition in a case that the performance of the AI function does not satisfy the preset condition; a network side condition in a statistical process of the performance of the AI function.

[0058] In some embodiments combining with the second aspect, in some embodiments, the first condition information can comprise a first identifier, the first identifier being used for identifying the network side condition associated with the performance of the AI function.

[0059] In some embodiments combining with the second aspect, in some embodiments, the terminal side condition indicated by the second condition information can comprise at least one of: a terminal side condition in a case that the performance of the AI function does not satisfy the preset condition; a terminal side condition in a statistical process of the performance of the AI function.

[0060] In some embodiments combining with the second aspect, in some embodiments, the statistical parameter indicated by the first parameter information can comprise at least one of: a time length of the statistics; a time window of the statistics; a number of times of the statistics; a data amount of the statistics.

[0061] In some embodiments of the second aspect, the first function information can comprise a second identifier, the second identifier being used to identify the AI function.

[0062] In some embodiments of the second aspect, the performance of the AI function can correspond to at least one of: at least one network side condition; at least one terminal side condition.

[0063] In some embodiments of the second aspect, the first information can be used to indicate at least one of: a combination of the performance of the AI function and at least one of the network side conditions; a combination of the performance of the AI function and at least one of the terminal side conditions.

[0064] In some embodiments of the second aspect, the method further comprises: receiving second information, wherein the second information is used to determine that the terminal supports receiving the first information.

[0065] In a third aspect, the embodiments of the present disclosure provide a communication apparatus. The apparatus is arranged in a terminal. The apparatus comprises a transceiver module. The transceiver module is configured to: receive first information, wherein the first information is used to indicate a performance of an AI function.

[0066] In some embodiments of the third aspect, the first information can be used to control a first model associated with the AI function, the first model being an AI / ML model.

[0067] In some embodiments of the third aspect, the controlling of the first model can comprise at least one of: training, updating, activating, deactivating.

[0068] In some embodiments of the third aspect, the first information can comprise at least one of: first performance information used to indicate that the performance of the AI function does not satisfy a preset condition; second performance information used to indicate performance data of the AI function; third performance information used to indicate an expected performance of the AI function; first condition information used to indicate a network side condition associated with the performance of the AI function; second condition information used to indicate a terminal side condition associated with the performance of the AI function; first parameter information used to indicate a statistical parameter associated with the performance of the AI function; first function information used to indicate the AI function.

[0069] In some embodiments of the third aspect, the first performance information can comprise at least one bit, a first value of the bit being used to indicate that the performance of the AI function does not satisfy the preset condition.

[0070] In some embodiments of the third aspect, the expected performance indicated by the third performance information can satisfy the preset condition.

[0071] In some embodiments in combination with the third aspect, in some embodiments, the network-side condition indicated by the first condition information can comprise at least one of: a network-side condition in a case where the performance of the AI function does not satisfy a preset condition; a network-side condition in a statistical process of the performance of the AI function.

[0072] In some embodiments in combination with the third aspect, in some embodiments, the first condition information can comprise a first identifier, the first identifier being used to identify the network-side condition associated with the performance of the AI function.

[0073] In some embodiments in combination with the third aspect, in some embodiments, the terminal-side condition indicated by the second condition information can comprise at least one of: a terminal-side condition in a case where the performance of the AI function does not satisfy a preset condition; a terminal-side condition in a statistical process of the performance of the AI function.

[0074] In some embodiments in combination with the third aspect, in some embodiments, the statistical parameter indicated by the first parameter information can comprise at least one of: a time length of the statistics; a time window of the statistics; a number of times of the statistics; a data volume of the statistics.

[0075] In some embodiments in combination with the third aspect, in some embodiments, the first function information can comprise a second identifier, the second identifier being used to identify the AI function.

[0076] In some embodiments in combination with the third aspect, in some embodiments, the performance of the AI function can correspond to at least one of: at least one network-side condition; at least one terminal-side condition.

[0077] In some embodiments in combination with the third aspect, in some embodiments, the first information can be used to indicate at least one of: a combination of the performance of the AI function and at least one network-side condition; a combination of the performance of the AI function and at least one terminal-side condition.

[0078] In some embodiments in combination with the third aspect, in some embodiments, the apparatus can further comprise a processing module. The processing module is configured to: train, according to the first information, a first model associated with the AI function.

[0079] In some embodiments in combination with the third aspect, in some embodiments, the first information can comprise a first identifier, the first identifier being used to indicate the network-side condition associated with the performance information; wherein the processing module can be configured to: train, in a case where the first identifier is consistent with the third identifier, the first model based on training data, wherein the second identifier is used to indicate the network-side condition in a collection process of the training data.

[0080] In some embodiments of the third aspect, in some embodiments, the transceiver module can be further configured to: transmit second information, wherein the second information is used to determine whether the terminal supports receiving the first information.

[0081] In a fourth aspect, the embodiments of the present disclosure provide a communication apparatus. The apparatus is arranged in a network device. The apparatus comprises a transceiver module. The transceiver module is configured to: transmit first information, wherein the first information is used to indicate a performance of an AI function.

[0082] In some embodiments of the fourth aspect, in some embodiments, the first information can be used to control a first model associated with the AI function, the first model being an AI / ML model.

[0083] In some embodiments of the fourth aspect, in some embodiments, the control of the first model can comprise at least one of: training, updating, activating, deactivating.

[0084] In some embodiments of the fourth aspect, in some embodiments, the first information can comprise at least one of: first performance information used to indicate that the performance of the AI function does not satisfy a preset condition; second performance information used to indicate performance data of the AI function; third performance information used to indicate an expected performance of the AI function; first condition information used to indicate a network side condition associated with the performance of the AI function; second condition information used to indicate a terminal side condition associated with the performance of the AI function; first parameter information used to indicate a statistical parameter associated with the performance of the AI function; and first function information used to indicate the AI function.

[0085] In some embodiments of the fourth aspect, in some embodiments, the first performance information can comprise at least one bit, a first value of the bit being used to indicate that the performance of the AI function does not satisfy the preset condition.

[0086] In some embodiments of the fourth aspect, in some embodiments, the expected performance indicated by the third performance information can satisfy the preset condition.

[0087] In some embodiments of the fourth aspect, in some embodiments, the network side condition indicated by the first condition information can comprise at least one of: a network side condition in a case where the performance of the AI function does not satisfy the preset condition; and a network side condition in a statistical process of the performance of the AI function.

[0088] In some embodiments of the fourth aspect, in some embodiments, the first condition information can comprise a first identifier used to identify the network side condition associated with the performance of the AI function.

[0089] In some embodiments combining with the fourth aspect, in some embodiments, the terminal side condition indicated by the second condition information can comprise at least one of: a terminal side condition in a case that the performance of the AI function does not satisfy a preset condition; a terminal side condition in a statistical process of the performance of the AI function.

[0090] In some embodiments combining with the fourth aspect, in some embodiments, the statistical parameter indicated by the first parameter information can comprise at least one of: a time length of the statistics; a time window of the statistics; a number of times of the statistics; a data volume of the statistics.

[0091] In some embodiments combining with the fourth aspect, in some embodiments, the first function information can comprise a second identifier, the second identifier being used to identify the AI function.

[0092] In some embodiments combining with the fourth aspect, in some embodiments, the performance of the AI function can correspond to at least one of: at least one network side condition; at least one terminal side condition.

[0093] In some embodiments combining with the fourth aspect, in some embodiments, the first information can be used to indicate at least one of: at least one combination of the performance of the AI function and the network side condition; at least one combination of the performance of the AI function and the terminal side condition.

[0094] In some embodiments combining with the fourth aspect, in some embodiments, the transceiver module can be further configured to: receive second information, wherein the second information is used to determine that the terminal supports receiving the first information.

[0095] In a fifth aspect, the embodiments of the present disclosure provide a communication device. The communication device comprises one or more processors, and a memory storing instructions. The instructions, when executed by the communication device, cause the communication device to implement the communication method according to any one of the first aspect and possible implementation manners thereof.

[0096] In a sixth aspect, the embodiments of the present disclosure provide a communication device. The communication device comprises one or more processors, and a memory storing instructions. The instructions, when executed by the communication device, cause the communication device to implement the communication method according to any one of the second aspect and possible implementation manners thereof.

[0097] In a seventh aspect, the embodiments of the present disclosure provide a communication system. The communication system comprises a terminal and a network device. The terminal is configured to implement the communication method according to the first aspect. The network device is configured to implement the communication method according to the second aspect.

[0098] In an eighth aspect, the embodiments of the present disclosure provide a storage medium. The storage medium stores instructions. The instructions, when executed on a communication device, cause the communication device to perform the communication method according to any one of the first aspect, the second aspect, and possible implementation manners thereof.

[0099] In a ninth aspect, an embodiment of the present disclosure provides a program product. The program product, when executed by a communication device, causes the communication device to perform the communication method according to any one of the first aspect, the second aspect, and possible implementation manners thereof.

[0100] In a tenth aspect, an embodiment of the present disclosure provides a computer program. The computer program, when running on a computer, causes the computer to perform the communication method according to any one of the first aspect, the second aspect, and possible implementation manners thereof.

[0101] In an eleventh aspect, an embodiment of the present disclosure provides a chip or chip system. The chip or chip system includes processing circuitry. The processing circuitry is configured to perform the communication method according to any one of the first aspect, the second aspect, and possible implementation manners thereof.

[0102] It can be understood that the above communication apparatus, communication device, communication system, storage medium, program product, computer program, chip, and chip system are all used to perform the method provided by the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved are referred to the beneficial effects in the corresponding method, which will not be described here.

[0103] Embodiments of the present disclosure provide a communication method and apparatus, a communication device, a communication system, a storage medium, and a program product. In some embodiments, the terms of the communication method, the information processing method, and the information transmission method can be replaced with each other, the terms of the communication apparatus, the communication device, the network device, the network function, and the network entity can be replaced with each other, and the terms of the communication system and the information processing system can be replaced with each other.

[0104] Embodiments of the present disclosure are not exhaustive, but only illustrate some embodiments, and are not specific limitations on the protection scope of the present disclosure. In the case of no contradiction, each step in an embodiment can be implemented as an independent embodiment, and the steps can be combined arbitrarily, for example, the scheme after removing some steps in an embodiment can also be implemented as an independent embodiment, and the order of the steps in an embodiment can be exchanged arbitrarily, in addition, the optional implementation manners in an embodiment can be combined arbitrarily; in addition, the embodiments can be combined arbitrarily, for example, the steps of different embodiments or parts or all of the steps of different embodiments can be combined arbitrarily, and an embodiment can be combined with the optional implementation manners of other embodiments.

[0105] In the embodiments of the present disclosure, the terms and / or descriptions between the embodiments are consistent if there is no special description and logical conflict, and can be referred to each other, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0106] The terminology used in the disclosure of the embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure.

[0107] In the embodiments of the present disclosure, unless otherwise specified, the elements expressed in singular form, such as "one", "a", "an", "the", "above", "said", "preceding", "this" and the like, can represent "one and only one", but also can represent "one or more", "at least one" and the like. For example, in the case of using articles such as "a", "an", "the" and the like in English in translation, the noun after the article can be understood as singular expression, but also can be understood as plural expression.

[0108] In the embodiments of the present disclosure, "plurality" means two or more than two.

[0109] In some embodiments, the terms "at least one (at least one, at least one, at least one)", "one or more" and the like can be replaced with each other.

[0110] In some embodiments, the writing manner of "at least one of A, B", "A and / or B", "A in one case, B in another case", "in response to a case A, in response to another 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 executed). When there are more branches of A, B, C and the like, it is similar to the above.

[0111] In some embodiments, the writing manner of "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 of A, B, C and the like, it is similar to the above.

[0112] The prefix words of "first", "second" and the like in the embodiments of the present disclosure are merely used to distinguish different description objects, and do not constitute limitation on the position, order, priority, quantity or content of the description objects. The description objects are described in the claims or embodiments, and should not be construed as redundant limitation because of the use of the prefix words. For example, the description object is "field", and the ordinal words before "field" 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 object is "level", and the ordinal words before "level" in "first level" and "second level" do not limit the priority between "levels". For another example, the quantity of the description object is not limited by the ordinal words, and can be one or more. For example, "first device", wherein the quantity of "device" can be one or more. In addition, the objects modified by different prefix words can be the same or different, for example, the description object is "device", 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 object is "information", and "second information" and "first information" can be the same information or different information, and the contents thereof can be the same or different.

[0113] In some embodiments, "including A", "containing A", "for indicating A", "carrying A" can be interpreted as directly carrying A, or indirectly indicating A.

[0114] In some embodiments, the terms of "in response to", "in response to determining", "in the case of", "when", "when", "if", "if" and the like can be replaced with each other.

[0115] In some embodiments, the terms of "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 lower than", "above" and the like can be replaced with each other, and the terms of "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.

[0116] In some embodiments, an apparatus or the like can be interpreted as an entity, and can also be interpreted as virtual, and the name thereof is not limited to the name described in the embodiments, and the terms "apparatus", "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.

[0117] In some embodiments, a "network" can be interpreted as an apparatus (for example, an access network device, a core network device, and the like) included in the network.

[0118] In some embodiments, the terms "access network device (AN device)", "radio access network device (RAN device)", "base station (BS)", "radio base station", "fixed station", "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission / reception point (TRP)", "panel", "antenna panel", "antenna array", "cell", "macro cell", "small cell", "femto cell", "pico cell", "sector", "cell group", "serving cell", "carrier", "component carrier", "bandwidth part (BWP)" and the like can be replaced with each other.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] In some embodiments, obtaining data, information, etc. can comply with laws and regulations of the country where the location is.

[0123] In some embodiments, data, information, etc. can be obtained after obtaining user consent.

[0124] 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 any element, any row, or any column can also be implemented as an independent embodiment.

[0125] FIG. 1 is a schematic diagram of an architecture of a communication system according to an embodiment of the present disclosure. As shown in FIG. 1, the communication system 100 includes a terminal 101 and a network device 102.

[0126] In some embodiments, the terminal 101 includes at least one of a mobile phone, a wearable device, an Internet of Things device, a car with communication function, 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, etc., but is not limited thereto.

[0127] In some embodiments, the network device 102 can include at least one of an access network device, a core network element.

[0128] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. In some embodiments, the access network device 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.

[0129] In some embodiments, the technical solutions of the present disclosure can be applicable to an open wireless access network (Open RAN) architecture, at this time, the interfaces between or within the access 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 through software or programs.

[0130] In some embodiments, the access network device can be composed of a central unit (CU) and a distributed unit (DU), where the CU can also be referred to as a control unit. The CU-DU structure can split the protocol layers of the access network device, and part of the protocol layer functions are controlled by the CU, and the remaining part or all of the protocol layer functions are distributed in the DU and controlled by the CU, but is not limited thereto.

[0131] In some embodiments, the core network element can be one device, or a plurality of devices or device groups. The network element can be virtual or physical. The core network includes, for example, at least one of an evolved packet core (EPC), a 5G core network (5GCN), and a next generation core (NGC).

[0132] In some embodiments, the above communication system 100 can be a 4G communication system or a 5G communication system. It should be noted that the communication system 100 can also be other communication systems, for example, a 6G communication system, and the embodiments of the present disclosure do not make specific limitations thereto.

[0133] Machine learning is one of the most important implementation methods of artificial intelligence technology. In machine learning, the training of a model can be implemented based on a large amount of training data. Through the trained model, an event can be predicted. In many fields, the model obtained through machine learning training can obtain accurate prediction results.

[0134] A wireless communication network can use AI models for prediction and inference, thereby improving the performance of the communication system. The training of AI models requires the collection of a large amount of data, and the data required for different application scenarios can be different. Application scenarios can include beam management, channel state indication (CSI) reporting, CSI compression, positioning, handover, mobility management, wireless resource management, and other mobile communication system processes.

[0135] In some embodiments, in the process of beam management, the UE can reduce the number of beams measured. In some embodiments, the UE or the base station can implement beam prediction through AI-based inference, thereby obtaining the optimal beam. In some embodiments, beam prediction can include spatial domain beam prediction and time domain beam prediction. In spatial domain beam prediction, the UE can perform beam measurement on part of the beams, and can predict the measurement results of other beams. In time domain beam prediction, the UE can predict future beam measurement results according to historical beam measurement results.

[0136] In some embodiments, in the process of CSI reporting, the UE can compress the CSI measurement results through AI and report the compressed CSI measurement results to the base station; after receiving the compressed CSI measurement results, the base station restores the original CSI measurement results through AI. This reduces the signaling load required in the reporting process. In some embodiments, the base station can also predict future CSI based on historical CSI measurement results reported by the UE.

[0137] In some embodiments, in the process of positioning, the UE can predict an accurate position through AI and based on limited measurement results.

[0138] In some embodiments, in mobility management procedure, the UE can predict the measurement results of a cell, a handover target cell or a mobility event by AI. In an example, the UE can predict the future measurement results of a cell. This can be referred to as time domain prediction. In an example, the UE can predict the measurement results of a cell which is not measured. This can be referred to as space domain prediction. In an example, the mobility event can include measurement reporting condition satisfied, handover failure, cell dwell time, radio link failure, etc.

[0139] In the use and inference procedure of AI, one or more AI models or one or more AI functionalities can be needed for inference and prediction. In an example, one AI functionality can implement one specific function. In an example, one AI functionality can include one or more AI models.

[0140] In some embodiments, the inference based on AI model and / or AI functionality can be run at the UE side or at the network side. For example, the inference based on AI model or AI functionality can be run in an access network device. For example, the inference based on AI model and / or AI functionality can be run in a core network element.

[0141] In some embodiments, in the case that the inference based on AI model and / or AI functionality is run at the UE side, the UE can use the result of inference to assist handover locally, for example, select a handover cell according to the result of inference, or scale a handover parameter. In some embodiments, the UE can also send the result of inference to the network side. In this case, the network side can select a handover target cell or configure a handover parameter according to the result of inference reported by the UE.

[0142] In some embodiments, the performance of AI (e.g. of AI model and / or AI functionality) needs to be monitored. Generally, the monitoring of the performance of AI is performed by network device. In some embodiments, the performance of AI needs to be maintained above a certain level, if the performance of AI does not meet the expectation or the performance of AI decreases, the AI functionality and / or AI model can need to be replaced, or the AI functionality needs to be deactivated.

[0143] In some embodiments, the performance indicator of AI includes the accuracy of the prediction output of AI. In an example, the accuracy can depend on the difference between the prediction output of AI and the true value. In some embodiments, the performance indicator of AI can be obtained by statistics or multiple statistics in a period of time.

[0144] In some embodiments, in a beam management procedure, the performance indicator of AI can comprise at least one of: whether the predicted strongest beam is the actual strongest beam; a difference between a measurement of the predicted strongest beam and a measurement of the actual strongest beam; a difference between a predicted measurement of a beam and an actual measurement of the beam. In an example, the strongest beam can refer to a beam on which the signal quality is the best. For example, the strongest beam can correspond to the best wireless channel quality. For example, the strongest beam can correspond to the largest signal power.

[0145] In some embodiments, in a positioning procedure, the performance indicator of AI can comprise at least one of: a distance between a predicted position and an actual position; whether the predicted position is consistent with the actual position. In an example, the predicted position can be determined to be consistent with the actual position in a case that the distance between the predicted position and the actual position is less than a certain value.

[0146] In some embodiments, in a CSI reporting procedure, the performance indicator of AI can comprise a difference between a predicted CSI and an actual CSI.

[0147] In some embodiments, in a mobility management procedure, the performance indicator of AI can comprise at least one of: whether the predicted strongest cell is the actual strongest cell; a difference between a predicted measurement of a cell and an actual measurement of the cell; whether a predicted link failure occurs. In an example, the strongest cell can refer to a cell on which the signal quality is the best. For example, the strongest cell can correspond to the best wireless channel quality. For example, the strongest cell can correspond to the largest signal power.

[0148] In some embodiments, the inference based on the AI model and / or AI function can be run on the UE side, and the management of the AI model and / or AI function can be implemented on the network side. The network side can monitor the performance of AI on the UE, and can send indication information to the UE. In an example, the indication information can be used to instruct the UE to perform the management of the AI model and / or AI function. The management of the AI model and / or AI function can comprise: activation, deactivation, switching, training, updating, etc.

[0149] In some embodiments, the training of the AI model and / or AI function located on the UE side can be completed on the UE side.

[0150] In some embodiments, the AI model and / or AI function can achieve better performance under certain application conditions. In some embodiments, the application conditions can comprise network side conditions and UE side conditions.

[0151] In some embodiments, the UE side conditions can comprise at least one of:

[0152] - speed, e.g., the moving speed of the UE;

[0153] - power, e.g., the rated power of the UE, the maximum power of the UE;

[0154] - power, e.g., the rated power of the UE, the maximum power of the UE;

[0155] - power, e.g., the rated power of the UE, the maximum power of the UE;

[0156] - location, e.g., the geographical location of the UE, the location of the UE in a cell;

[0157] - service type, e.g., audio, video, multimedia, voice;

[0158] - antenna configuration, e.g., the number of ports of the antennas set on the UE;

[0159] - rotation speed, e.g., the rotation speed of the UE;

[0160] - storage space, e.g., the storage space for AI model and / or AI function in the UE, which can be measured by bits.

[0161] In some embodiments, the network side condition can comprise at least one of the following:

[0162] - cell type, e.g., macro cell, micro cell, dense urban cell;

[0163] - network deployment scenario, e.g., indoor, outdoor;

[0164] - wireless channel quality, e.g., which can be determined by reference signal receiving power (RSRP), reference signal receiving quality (RSRQ), signal to interference plus noise ratio (SINR), etc.;

[0165] - frequency of the cell;

[0166] - location of the cell;

[0167] - inter-base station distance;

[0168] - antenna configuration, e.g., number of ports, number of multiple input multiple output (MIMO) layers;

[0169] - transmit power, e.g., transmit power at the network side;

[0170] - numerology.

[0171] In some embodiments, the network side condition can be bound with an identifier (ID). The network side condition can be indicated by providing the ID.

[0172] In some embodiments, when the UE uses AI for inference, it can be determined whether the ID currently indicated by the network side is consistent with the ID provided by the network side when collecting training data of the AI model; if not consistent, it is determined that the network side condition is not met.

[0173] In some embodiments, during the operation of the communication system, the performance of AI on the UE can deteriorate. In this case, data in the case where the performance of AI is poor can be collected, and the AI model is further trained based on the collected data, so as to improve the performance of AI. Then, the UE needs to obtain the data confirmed by the network side as the poor performance of AI.

[0174] FIG. 2 is an interaction schematic diagram of a communication method according to an embodiment of the present disclosure. The mobility processing method related by the embodiment of the present disclosure can be applied to the communication system 100. As shown in FIG. 2, the communication method of the embodiment of the present disclosure includes steps S201 to S204.

[0175] In step S201, the terminal 101 sends second information to the network device 102.

[0176] In some embodiments, the network device 102 can receive the second information sent by the terminal 101.

[0177] In some embodiments, the second information can be used for the network device 102 to determine that the terminal 101 supports receiving the first information. In an example, according to the network device 102 receiving the second information, the network device 102 can determine that the terminal 101 supports receiving the first information. In an example, the network device 102 can determine that the terminal 101 supports receiving the first information according to specific information in the second information.

[0178] In some embodiments, the second information can be used to indicate that the terminal 101 supports receiving the first information.

[0179] In some embodiments, the name of the second information is not limited, which may, for example, be support information, capability information, capability reporting information, capability indication information, etc.

[0180] In some embodiments, the second information can comprise a preset field. The preset field can be used to indicate that the terminal 101 supports receiving the first information. In some embodiments, the preset field can comprise one or more bits. In an example, the preset field can comprise one bit. For example, when the bit is valued as 1, it indicates that the terminal 101 supports receiving the first information; when the bit is valued as 0, it indicates that the terminal 101 does not support receiving the first information. For example, when the bit is valued as 0, it indicates that the terminal 101 supports receiving the first information; when the bit is valued as 1, it indicates that the terminal 101 does not support receiving the first information.

[0181] In some embodiments, the second information can be carried in control plane signaling.

[0182] In step S202, the network device 102 monitors the performance of the AI function.

[0183] In some embodiments, the AI function can be implemented or run by the terminal 101.

[0184] In some embodiments, the number of AI functions running on the terminal 101 can be one or more.

[0185] In some embodiments, the AI function can comprise at least one of the following: beam management, CSI reporting, CSI compression, positioning, handover, mobility management, and resource management. It can be understood that the terminal 101 can also implement other AI functions, which are not specifically limited by the embodiments of the present disclosure.

[0186] In some embodiments, the terminal 101 can be deployed with a first model. The first model can be used to implement the AI function. In other words, the AI function can be implemented based on the first model. For example, the terminal 101 can run the first model, thereby implementing the AI function.

[0187] In some embodiments, the first model can comprise an AI / ML model. In some embodiments, the first model can be an AI / ML model. The AI / ML model can be used to implement the AI function.

[0188] In some embodiments, the number of first models can be one or more. Each first model can be an AI / ML model.

[0189] In some embodiments, the network device 102 can monitor the performance of the AI function on the terminal 101. In some embodiments, the network device 102 can determine the performance of the AI function. In an example, the network device 102 can obtain performance data related to the performance indicator of the AI function.

[0190] In some embodiments, the network device 102 can perform statistics on the performance of the AI function in the process of monitoring the performance of the AI function. In an example, the network device 102 can perform statistics on one or more performance indicators of the AI function to obtain performance data.

[0191] In some embodiments, the network device 102 can obtain corresponding network side conditions and / or terminal side conditions in the process of monitoring the performance of the AI function.

[0192] In some embodiments, the monitoring result can be obtained through step S202.

[0193] In some embodiments, the monitoring result can include performance data related to performance indicators of the AI function. In an example, the monitoring result can include performance data corresponding to one or more performance indicators of the AI function.

[0194] In some embodiments, the monitoring result can include whether the performance of the AI function meets a preset condition. In some embodiments, the network device 102 can compare the obtained performance data with the preset condition to determine whether the performance of the AI function meets or does not meet the preset condition. In this way, the monitoring result can indicate whether the performance of the AI function meets the preset condition.

[0195] In step S203, the network device 102 sends first information to the terminal 101.

[0196] In some embodiments, the terminal 101 can receive the first information.

[0197] In some embodiments, the network device 102 can send the first information to the terminal 101 according to the monitoring result in step S202.

[0198] In some embodiments, in the case that the monitoring result is obtained in step S202, the network device 102 can send the first information to the terminal 101 so that the terminal obtains the performance data of the AI function.

[0199] In some embodiments, in the case that the monitoring result indicates that the performance of the AI function does not meet the preset condition in step S202, the network device 102 can send the first information to the terminal 101 so that the terminal 101 obtains the performance data of the AI function or determines that the performance of the AI function does not meet the preset condition.

[0200] In some embodiments, the premise for the network device 102 to send the first information can be that the network device 102 receives second information from the terminal 101.

[0201] In some embodiments, the first information can be used to indicate the performance of the AI function.

[0202] In some embodiments, the first information can be used for controlling a first model associated with the AI function. In some embodiments, controlling the first model can comprise at least one of the following: training, updating, activating, deactivating, switching.

[0203] In some embodiments, the name of the first information is not limited, which can be, for example, an AI performance indication, an AI function indication, an AI control indication, and the like.

[0204] In some embodiments, the first information can be used for indicating at least one of the following: whether the performance of the AI function meets a preset condition; performance data (e.g., a specific numerical value or parameter) of the performance of the AI function; an application condition (e.g., a network side condition, a terminal side condition) of the AI function.

[0205] In some embodiments, the following at least one can be included: the first performance information, the second performance information, the third performance information, the first condition information, the second condition information, the first parameter information, the first function information.

[0206] In some embodiments, the first performance information can be used for indicating that the performance of the AI function does not meet the preset condition.

[0207] In some embodiments, the preset condition can be used to determine whether the performance of the AI function meets the requirement. For example, in the case that the performance of the AI function does not meet the preset condition, the performance of the AI function can be determined as not meeting the requirement or poor. For example, in the case that the performance of the AI function meets the preset condition, the performance of the AI function can be determined as meeting the requirement or good.

[0208] In some embodiments, the first performance information can comprise at least one bit. The bit can be used to indicate that the performance of the AI function does not meet the preset condition. In an example, the value of the bit is 1, indicating that the performance of the AI function does not meet the preset condition; the value of the bit is 0, indicating that the performance of the AI function meets the preset condition. In an example, the value of the bit is 0, indicating that the performance of the AI function does not meet the preset condition; the value of the bit is 1, indicating that the performance of the AI function meets the preset condition.

[0209] In some embodiments, the number of preset conditions can be one or more. In some embodiments, the preset conditions can be completely same, partially same, or completely different for different application scenarios and / or AI functions. In some embodiments, one application scenario can correspond to one or more preset conditions. In some embodiments, the preset conditions can include at least one of the following: the predicted strongest beam is the actual strongest beam; the difference between the measurement result of the predicted strongest beam and the measurement result of the actual strongest beam is less than a threshold; the difference between the predicted measurement result of the beam and the actual measurement result of the beam is less than a threshold. In some embodiments, the preset conditions can include at least one of the following: the distance between the predicted position and the actual position is less than a threshold; the predicted position is consistent with the actual position. In some embodiments, the preset conditions can include: the difference between the predicted CSI and the actual CSI is less than a threshold. In some embodiments, the preset conditions can include at least one of the following: the predicted strongest cell is the actual strongest cell; the difference between the predicted measurement result of the cell and the actual measurement result of the cell is less than a threshold; the predicted link failure determination occurs.

[0210] In some embodiments, the first performance information can include one or more bits. The number of bits of the first performance information can be equal to the number of the indicated performance indicators. Each bit can correspond to one performance indicator of the AI function. In an embodiment, for any bit, if the value of the bit is 1, it indicates that the performance of the corresponding performance indicator of the AI function does not meet the preset condition; if the value of the bit is 0, it indicates that the performance of the corresponding performance indicator of the AI function meets the preset condition. In an embodiment, for any bit, if the value of the bit is 0, it indicates that the performance of the corresponding performance indicator of the AI function does not meet the preset condition; if the value of the bit is 1, it indicates that the performance of the corresponding performance indicator of the AI function meets the preset condition.

[0211] In some embodiments, the second performance information can be used to indicate the performance data of the AI function. In some embodiments, the second performance information can be used to indicate the performance data associated with one or more performance indicators. The performance data can be collected and / or processed by the network device 102 in the monitoring process of the AI function.

[0212] In some embodiments, the second performance information can include the performance data associated with all the performance indicators collected and / or processed by the network device 102. In some embodiments, the network device 102 can determine the performance data associated with one or more performance indicators in the monitoring process of the AI function on the terminal 101, and include the performance data associated with these performance indicators in the second performance information.

[0213] In some embodiments, the second performance information can comprise performance data associated with part of the performance indicators collected and / or processed by the network device 102. In some embodiments, the network device 102 can determine the performance data associated with one or more performance indicators in the process of monitoring the AI function on the terminal 101. The network device 102 can include the performance data that does not satisfy the preset condition in the second performance information. In this case, the second performance information can only indicate the performance indicators whose corresponding performance data do not satisfy the preset condition.

[0214] In some embodiments, the performance data of the AI function can comprise the numerical value of the performance indicators associated with the AI function.

[0215] In some embodiments, the number of performance data can be one or more. In some embodiments, the performance data can be completely the same, partially the same, or completely different for different application scenarios and / or AI functions. In some embodiments, one application scenario can correspond to one or more performance data. In some embodiments, the performance data can comprise at least one of the following: the difference between the predicted measurement result of the strongest beam and the actual measurement result of the strongest beam; the difference between the predicted measurement result of the beam and the actual measurement result of the beam. In some embodiments, the performance data can comprise: the distance between the predicted position and the actual position is less than a threshold. In some embodiments, the performance data can comprise: the difference between the predicted CSI and the actual CSI is less than a threshold. In some embodiments, the performance data can comprise: the difference between the predicted measurement result of the cell and the actual measurement result of the cell.

[0216] In some embodiments, the third performance information can be used to indicate the expected performance of the AI function. In some embodiments, the third performance information can be used to indicate the expected performance data of the AI function.

[0217] In some embodiments, the expected performance data indicated by the third performance information can be the performance data that the network device 102 considers acceptable.

[0218] In some embodiments, the expected performance indicated by the third performance information satisfies the preset condition. In some embodiments, in the case where the expected performance satisfies the preset condition, the expected performance data indicated by the third performance information can be the performance data that the network device 102 considers acceptable. In an example, whether the expected performance data indicated by the third performance information is considered acceptable by the network device 102 can be determined based on the preset condition. In some embodiments, the preset condition associated with the third performance information and the preset condition associated with the first performance information can be the same. In some embodiments, the preset condition associated with the third performance information and the preset condition associated with the first performance information can be different.

[0219] In some embodiments, the first condition information can be used to indicate a network side condition associated with the performance of the AI function.

[0220] In some embodiments, the network side condition can be a network condition under which the AI function is applied. In some embodiments, the network side condition can be a network condition under which the AI function is applied and achieves good performance.

[0221] In some embodiments, the network side condition can include at least one of the following: a cell type, a network deployment scenario, a wireless channel quality, a frequency in which the cell is located, a location of the cell, an inter-base station distance, an antenna configuration, a transmission power, a numerology. It can be understood that the network side condition can also include other conditions, which are not limited in the embodiments of the present disclosure.

[0222] In some embodiments, the network side condition indicated by the first condition information can include at least one of the following: a network side condition under which the performance of the AI function does not satisfy a preset condition; a network side condition in a statistical process of the performance of the AI function.

[0223] In some embodiments, the network side condition under which the performance of the AI function does not satisfy the preset condition can be a network side condition under which the performance of the AI function is poor. In some embodiments, in at least a part of a statistical process of the performance of the AI function by the network device 102, the performance of the AI function can not satisfy the preset condition. In this case, the first condition information can be used to indicate a network side condition corresponding to the part of the statistical process in which the performance of the AI function does not satisfy the preset condition.

[0224] In some embodiments, the first condition information can include a first identifier. The first identifier can be used to identify the network side condition associated with the performance of the AI function. In this way, the first identifier can be used to indicate the network side condition.

[0225] In some embodiments, the first condition information can only include the first identifier.

[0226] In some embodiments, the first condition information can include the first identifier and at least one network side condition. The first identifier and the at least one network side condition in the first condition information are corresponding.

[0227] In some embodiments, the second condition information can be used to indicate a terminal side condition associated with the performance of the AI function.

[0228] In some embodiments, the terminal side condition can be a terminal condition under which the AI function is applied. In some embodiments, the terminal side condition can be a terminal condition under which the AI function is applied and achieves good performance.

[0229] In some embodiments, the terminal-side condition can comprise at least one of the following: speed, power, computing capability, location, service type, antenna configuration, rotation speed, storage space. It can be understood that the terminal-side condition can also comprise other conditions, which are not limited in the embodiments of the present disclosure.

[0230] In some embodiments, the terminal-side condition indicated by the second condition information can comprise at least one of the following: a terminal-side condition in a case where the performance of the AI function does not satisfy a preset condition; a terminal-side condition in a statistical process of the performance of the AI function.

[0231] In some embodiments, the terminal-side condition in a case where the performance of the AI function does not satisfy a preset condition can be a terminal-side condition in a case where the performance of the AI function is poor. In some embodiments, in at least a part of a statistical process of the performance of the AI function by the network device 102, the performance of the AI function can not satisfy a preset condition. In this case, the second condition information can be used to indicate a terminal-side condition corresponding to a part of the statistical process in which the performance of the AI function does not satisfy a preset condition.

[0232] In some embodiments, the first parameter information can be used to indicate a statistical parameter associated with the performance of the AI function.

[0233] In some embodiments, the statistical parameter can comprise at least one of the following: a time length of the statistics, a time window of the statistics, a number of times of the statistics, a data amount of the statistics.

[0234] In some embodiments, the time length of the statistics can be a length of time during which the network device 102 performs the statistics of the performance of the AI function. In some embodiments, the network device 102 can perform the statistics of the performance of the AI function continuously. In an example, the time length of the statistics can be a length of time during which the network device 102 continuously performs the statistics of the performance of the AI function. In some embodiments, the network device 102 can perform the statistics of the performance of the AI function discontinuously. In an example, the time length of the statistics can be a sum of lengths of time during which the network device 102 performs the statistics of the performance of the AI function each time. In an example, the time length of the statistics can be a length of time during which the network device 102 performs the statistics of the performance of the AI function once. In an example, the time length of the statistics can be a length of time between a first time when the network device 102 performs the statistics of the performance of the AI function and a last time when the network device 102 performs the statistics of the performance of the AI function.

[0235] In some embodiments, the time window of the statistics can be a time window in which the network device 102 performs the statistics of the performance of the AI function. In other words, the network device 102 performs the statistics of the performance of the AI function in the time window.

[0236] In some embodiments, the time window can comprise: a start time, a time length, an end time. In an example, the time window can be determined by the start time and the time length. In an example, the time window can be determined by the start time and the end time. In an example, the time window can be determined by the time length and the end time. In some embodiments, the statistical parameter can comprise at least one of: the start time of the time window, the time length, the end time.

[0237] In some embodiments, the number of statistics can be a number of times that the network device 102 performs the statistics on the performance of the AI function.

[0238] In some embodiments, the amount of data of statistics can be an amount of data of performance data obtained by the network device 102 performing the statistics on the performance of the AI function. In some embodiments, the amount of data of statistics can be a number of performance indicator values obtained by the network device 102 performing the statistics on the performance indicator of the AI function.

[0239] In some embodiments, the first parameter information can comprise a statistical parameter corresponding to one or more performance indicators. In some embodiments, the statistical parameter indicated by the first parameter information can be same or different for different performance indicators. In an example, at least one of the time length of statistics, the time window of statistics, the number of statistics, the amount of data of statistics can be different for different performance indicators.

[0240] In some embodiments, the first function information can be used to indicate the AI function. In some embodiments, the first function information can be used to indicate the AI function implemented by the first model deployed on the terminal 101.

[0241] In some embodiments, the first function information can comprise a second identifier. The second identifier can be used to identify the AI function.

[0242] In some embodiments, the performance of the AI function can correspond to at least one of: at least one network side condition, at least one terminal side condition. In some embodiments, the performance indicator of the AI function can correspond to at least one of: at least one network side condition, at least one terminal side condition. In an example, the performance indicator of the AI function can correspond to one or more network side conditions. In an example, the performance indicator of the AI function can correspond to one or more terminal side conditions. In an example, the performance indicator of the AI function can correspond to one or more network side conditions and one or more terminal side conditions.

[0243] In some embodiments, the first information can be used to indicate at least one of: a combination of the performance of the AI function and at least one of the network side condition, a combination of the performance of the AI function and at least one of the terminal side condition. In some embodiments, the first information can be used to indicate at least one of: a combination of the performance indicator of the AI function and at least one of the network side condition, a combination of the performance indicator of the AI function and at least one of the terminal side condition. In an example, the first information can comprise: the first performance information and / or the second performance information, the first condition information. At this time, the first information can be used to indicate a combination of the performance indicator of the AI function and the network side condition. In an example, the first information can comprise: the first performance information and / or the second performance information, the second condition information. At this time, the first information can be used to indicate a combination of the performance indicator of the AI function and the terminal side condition. In an example, the first information can comprise: the first performance information and / or the second performance information, the first condition information, the second condition information. At this time, the first information can be used to indicate a combination of the performance indicator of the AI function and the network side condition and the terminal side condition.

[0244] In some embodiments, the number of AI functions running on the terminal 101 can be one or more, and each AI function can correspond to one first information. In some embodiments, each AI function running on the terminal 101 can correspond to a unique second identity. Different AI functions can be distinguished by different second identities.

[0245] In some embodiments, the first information can be carried and sent on the control plane. In some embodiments, the first information can be carried and sent on the user plane.

[0246] In step S204, the terminal 101 controls the first model.

[0247] In some embodiments, after receiving the first information, the terminal 101 can control the first model according to the first information.

[0248] In some embodiments, the control of the first model by the terminal 101 includes at least one of: training, updating, activating, deactivating.

[0249] In some embodiments, step S204 can include training the first model associated with the AI function according to the first information.

[0250] In some embodiments, the terminal 101 can determine, according to the first information, that the performance of the AI function does not meet the preset condition.

[0251] In some embodiments, the terminal 101 can determine, according to the first performance information, that the performance of the AI function does not satisfy a preset condition. In an example, the terminal 101 can determine, according to the first performance information, that one or more performance indicators of the AI function do not satisfy a preset condition. For example, if the value of a bit corresponding to a performance indicator in the first performance information is 1, the terminal 101 can determine that the performance indicator does not satisfy the preset condition. For example, the first performance information includes one bit, and the value of the bit is 1, the terminal 101 can determine that the performance of the AI function does not satisfy the preset condition.

[0252] In some embodiments, the terminal 101 can determine, according to the second performance information, that the performance of the AI function does not satisfy a preset condition. In an example, the terminal 101 can obtain performance data of one or more performance indicators of the AI function according to the second performance information; then, the terminal 101 can determine that the obtained performance data does not satisfy the preset condition. For example, the terminal 101 can compare the performance data obtained from the second performance information with a threshold value, and can determine that the performance indicator corresponding to the performance data does not satisfy the preset condition according to the comparison result.

[0253] In some embodiments, in a case where it is determined that the performance of the AI function does not satisfy the preset condition, the terminal 101 can determine to train the first model associated with the AI function.

[0254] In some embodiments, the terminal 101 can obtain training data and train the first model based on the training data.

[0255] In some embodiments, the corresponding network side condition can be identified by a third identifier during the collection of the training data.

[0256] In some embodiments, the terminal 101 can train the first model based on the training data in a case where the first identifier and the third identifier are consistent. In some embodiments, the first identifier is used to identify the network side condition in the process in which the network device 102 monitors the performance of the AI function, and the third identifier is used to identify the network side condition in the process of collecting the training data. Therefore, in a case where the first identifier and the third identifier are consistent, the collected training data is suitable for training the first model corresponding to the AI function. In other words, in a case where the first identifier and the third identifier are consistent, the first model trained based on the training data can be used in the application scenario of the network side condition corresponding to the first identifier. Conversely, in a case where the first identifier and the third identifier are inconsistent, the terminal 101 cannot train the first model based on the training data corresponding to the third identifier.

[0257] In some embodiments, the terminal 101 can update the original first model using the trained first model.

[0258] In some embodiments, the terminal 101 can deactivate and / or activate the first model in the process of AI function or first model switching. In an example, the terminal 101 can perform model switching under the AI function. For example, the terminal 101 can deactivate one of the first models and activate another one of the first models. In an example, the terminal 101 can perform switching of the AI function. For example, the terminal 101 can deactivate the first model of the AI function. For example, the terminal 101 can activate the first model of the AI function.

[0259] By the above steps S201 to S204, the communication method according to the embodiments of the present disclosure can be implemented.

[0260] In some embodiments, the names of information and the like are not limited to the names described in the embodiments, and the terms of "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codebook", "codeword", "code point", "bit", "data", "program", "chip", and the like can be replaced with each other.

[0261] In some embodiments, the terms of "radio", "wireless", "radio access network (RAN)", "access network (AN)", "RAN-based", and the like can be replaced with each other.

[0262] In some embodiments, the terms of "time", "time point", "time", "time position", and the like can be replaced with each other, and the terms of "duration", "period", "time window", "window", "time", and the like can be replaced with each other.

[0263] In some embodiments, "acquire", "obtain", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive" can be replaced with each other, and can be interpreted as receiving from other subjects, acquiring from protocols, acquiring from higher layers, obtaining by self-processing, autonomously implementing, and the like.

[0264] In some embodiments, the terms “sending”, “transmitting”, “reporting”, “issuing”, “transferring”, “bidirectional transferring”, “sending and / or receiving”, and the like can be replaced by each other.

[0265] In some embodiments, the terms “certain”, “preset”, “pre-set”, “set”, “indicated”, “certain”, “arbitrary”, “first”, and the like can be replaced by each other, and “certain A”, “preset A”, “pre-set A”, “set A”, “indicated A”, “certain A”, “arbitrary A”, “first A” can be interpreted as A specified in advance in a protocol or the like, or A obtained by setting, configuration, or indication, or a specific A, a certain A, an arbitrary A, or a first A, but are not limited thereto.

[0266] In some embodiments, the determination or judgment can be made by a value represented by 1 bit (0 or 1), or by a true or false value (Boolean value) represented by true or false, or by comparison of numerical values (for example, comparison with a predetermined value), but is not limited thereto.

[0267] The communication method related to the embodiments of the present disclosure can include at least one of steps S201 to S204. For example, step S203 can be implemented as an independent embodiment. It should be noted that the possible independent embodiments composed of one or more of steps S201 to S204 are not limited thereto.

[0268] In some embodiments, steps S201, S202, and S204 are optional, and one or more of these steps can be omitted or replaced in different embodiments.

[0269] In some embodiments, other optional implementations described before or after the description corresponding to FIG. 2 can be referred to.

[0270] FIG. 3 is a flow diagram of a communication method according to an embodiment of the present disclosure. The embodiments of the present disclosure relate to a communication method. The communication method is performed by the terminal 101. As shown in FIG. 3, the above method includes steps S301 to S303.

[0271] In step S301, the second information is sent.

[0272] The optional implementation of step S301 can refer to the optional implementation of step S201 of FIG. 2 and other associated parts in the embodiments related to FIG. 2, which will not be repeated here.

[0273] In some embodiments, the terminal 101 can send the second information to the network device 102, but is not limited thereto, and can send the second information to other subjects.

[0274] In some embodiments, the second information can be used to determine that the terminal 101 supports receiving the first information.

[0275] In step S302, the first information is acquired.

[0276] The optional implementation of step S302 can refer to the optional implementation of step S203 of FIG. 2 and other associated parts in the embodiments involved in FIG. 2, which will not be repeated here.

[0277] In some embodiments, the terminal 101 can receive the first information sent by the network device 102, but is not limited thereto, and can receive the first information sent by other subjects.

[0278] In some embodiments, the first information can be sent by the network device 102 according to the second information.

[0279] In step S303, the first model is controlled.

[0280] In some embodiments, the optional implementation of step S303 can refer to the optional implementation of step S204 of FIG. 2 and other associated parts in the embodiments involved in FIG. 2, which will not be repeated here.

[0281] In some embodiments, the control of the first model can be performed according to the first information.

[0282] The communication method involved in the embodiments of the present disclosure can include at least one of steps S301 to S303. For example, step S302 can be implemented as an independent embodiment. It should be noted that the possible independent embodiments composed of one or more of steps S301 to S303 are not limited thereto.

[0283] In some embodiments, steps S301 and S303 are optional, and one or more of these steps can be omitted or replaced in different embodiments.

[0284] FIG. 4 is a flow diagram of a communication method according to an embodiment of the present disclosure. The embodiments of the present disclosure relate to a communication method. The communication method is performed by the network device 102. As shown in FIG. 4, the above method includes steps S401 to S403.

[0285] In step S401, the second information is acquired.

[0286] The optional implementation of step S401 can refer to the optional implementation of step S201 in FIG. 2 and other associated parts in the embodiments related by FIG. 2, which will not be repeated here.

[0287] In some embodiments, the network device 102 can receive the second information sent by the terminal 101, but is not limited thereto, and can also receive the first information sent by other subjects.

[0288] In some embodiments, the second information can be used to determine that the terminal 101 supports receiving the first information.

[0289] In step S402, the performance of the AI function is monitored.

[0290] The optional implementation of step S402 can refer to the optional implementation of step S202 in FIG. 2 and other associated parts in the embodiments related by FIG. 2, which will not be repeated here.

[0291] In step S403, the first information is sent.

[0292] The optional implementation of step S403 can refer to the optional implementation of step S203 in FIG. 2 and other associated parts in the embodiments related by FIG. 2, which will not be repeated here.

[0293] In some embodiments, the network device 102 can send the first information to the terminal 101, but is not limited thereto, and can also send the first information to other subjects.

[0294] In some embodiments, the first information can be used for the terminal 101 to control the first model.

[0295] The communication method related by the embodiments of the present disclosure can include at least one of steps S401 to S403. For example, step S403 can be implemented as an independent embodiment. It should be noted that the possible independent embodiments composed of one or more of steps S401 to S403 are not limited thereto.

[0296] In some embodiments, steps S401 and S402 are optional, and one or more of these steps can be omitted or replaced in different embodiments.

[0297] FIG. 5 is a flow diagram of a communication method according to an embodiment of the present disclosure. The embodiments of the present disclosure relate to a communication method. As shown in FIG. 5, the above method includes step S501.

[0298] In step S501, the network device 102 sends first information to the terminal 101.

[0299] The optional implementation of step S501 can refer to the optional implementation of step S203 in FIG. 2 and other associated parts in the embodiments related to FIG. 2, which are not described herein again.

[0300] Hereinafter, the technical solutions of the embodiments of the present disclosure are exemplarily described through specific embodiments.

[0301] In some embodiments, the embodiments of the present disclosure relate to an AI updating method, so that the UE (i.e., terminal) can obtain poor performance data from the network (i.e., network device), thereby retraining the AI and improving the performance of the AI.

[0302] In some embodiments, the AI updating method relates to the UE side.

[0303] In some embodiments, the UE can receive first information sent by the network. The first information indicates a performance indicator of an AI function.

[0304] In some embodiments, the performance indicator can be one bit (i.e., first performance information) to indicate poor performance. In some embodiments, the performance indicator can also be a specific performance indicator value (i.e., second performance information).

[0305] In some embodiments, the AI function can be one or more AI models.

[0306] In some embodiments, the first information can also indicate network condition information corresponding to the performance indicator (i.e., first condition information).

[0307] In some embodiments, the network condition can be indicated by an ID (i.e., first identifier).

[0308] In some embodiments, the network condition information can be the network condition when the performance is poor, or the network condition when the performance indicator is counted.

[0309] In some embodiments, the first information can also indicate UE condition information corresponding to the performance indicator (i.e., second condition information).

[0310] In some embodiments, the UE condition information can be the UE condition when the performance is poor, or the UE condition when the performance indicator is counted.

[0311] In some embodiments, the first information can also indicate parameters for counting the performance indicator (i.e., first parameter information), including time length, time window, and quantity.

[0312] In some embodiments, the first information can also indicate an expected performance indicator (i.e., third performance information).

[0313] In some embodiments, the expected performance indicator value is a performance indicator value that the network considers acceptable.

[0314] In some embodiments, the performance indicator can correspond to one or more UE conditions or network conditions, and can indicate a combination of groups of performance indicators and UE conditions or network conditions.

[0315] In some embodiments, the first information can indicate an AI function (i.e., first function information) to which the performance indicator corresponds.

[0316] In some embodiments, the AI function can be indicated by an ID (i.e., second identifier).

[0317] In some embodiments, the UE can run multiple AI functions, and the network can respectively indicate the performance indicators of each AI function.

[0318] In some embodiments, the UE sends second indication (i.e., second information) to the network for indicating whether the UE supports receiving the first information sent by the network.

[0319] In some embodiments, the UE re-trains the AI model according to the first information to improve AI performance.

[0320] In some embodiments, the AI updating method involves the network side.

[0321] In some embodiments, the network monitors the performance of the AI function and sends the first information to the UE.

[0322] In some embodiments, the second indication sent by the UE is received. In some embodiments, if the UE supports receiving the first information sent by the network, the network sends the first information to the UE.

[0323] In the embodiments of the present disclosure, part or all of the steps, and optional implementation manners thereof, can be combined with part or all of the steps in other embodiments, or can be combined with optional implementation manners in other embodiments.

[0324] The embodiments of the present disclosure also provide a communication apparatus for implementing any of the above methods. For example, the embodiments of the present disclosure provide a communication apparatus including units or modules for implementing each step performed by a terminal in any of the above methods. For example, the embodiments of the present disclosure provide a communication apparatus including units or modules for implementing each step performed by a network device in any of the above methods.

[0325] 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 units or modules are realized by the design of the logical relationship of elements in the circuit; for another 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 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.

[0326] 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, a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), a digital signal processor (DSP), etc. In another implementation, the processor can implement certain functions through a logical relationship of hardware circuits, and the logical relationship of the hardware circuits is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by a special-purpose integrated circuit or a programmable logic device, 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), etc.

[0327] FIG. 6 is a structural schematic diagram of a communication apparatus provided by the embodiments of the present disclosure. As shown in FIG. 6, the communication apparatus 600 can include at least one of a transceiver module 601 and a processing module 602.

[0328] In some embodiments, the communication apparatus 600 can be the terminal 101. In some embodiments, the transceiver module 601 can be configured to receive first information, where the first information is used to indicate the performance of the AI function. Optionally, the transceiver module 601 can be configured to perform at least one of the communication steps (for example, steps S201 and S203) of transmitting and / or receiving performed by the terminal 101 in any of the above methods, and details are not described herein again. Optionally, the processing module 602 can be configured to perform at least one of the steps (for example, step S204) other than the communication steps of transmitting and receiving performed by the terminal 101 in any of the above methods.

[0329] In some embodiments, the communication device 600 can be the network device 102. In some embodiments, the transceiver module 601 can be configured to transmit the first information, where the first information is used to indicate the performance of the AI function. Optionally, the transceiver module 601 can be configured to perform at least one of the communication steps (e.g., steps S201, S203) of transmitting and / or receiving performed by the network device 102 in any of the above methods, which will not be repeated here. Optionally, the processing module 602 can be configured to perform at least one of the steps (e.g., step S202) other than the communication steps of transmitting and receiving performed by the network device 102 in any of the above methods.

[0330] In some embodiments, the transceiver module can include a transmitting module and / or a receiving module. The transmitting module and the receiving module can be separate or integrated together. Optionally, the transceiver module can be mutually replaced with the transceiver.

[0331] In some embodiments, the processing module can be one module or include multiple sub-modules. Optionally, the multiple sub-modules perform all or part of the steps required to be performed by the processing module. Optionally, the processing module can be mutually replaced with the processor.

[0332] FIG. 7A is a structural schematic diagram of a communication device according to embodiments of the present disclosure. The communication device 7100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user equipment, etc.), a chip, a chip system, or a processor supporting the network device to implement any of the above methods, or a chip, a chip system, or a processor supporting the terminal to implement any of the above methods. The communication device 7100 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.

[0333] As shown in FIG. 7A, the communication device 7100 includes one or more processors 7101. The processor 7101 can be a general-purpose processor or a special-purpose processor, etc., for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (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 7100 is used to implement any of the above methods. Optionally, the one or more processors 7101 are used to invoke instructions to cause the communication device 7100 to implement any of the above methods.

[0334] In some embodiments, the communication device 7100 further includes one or more transceivers 7102. When the communication device 7100 includes one or more transceivers 7102, the transceiver 7102 performs at least one of the communication steps (for example, steps S201, S203, but not limited to) in the above-described methods, and the processor 7101 performs at least one of the other steps (for example, steps S202, S204). In alternative embodiments, the transceiver can include a receiver and / or a transmitter, which can be separate or integrated together. Alternatively, the terms transceiver, transceiving unit, transceiver, transceiving circuit, interface circuit, interface, etc. can be replaced with each other, 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.

[0335] In some embodiments, the communication device 7100 further includes one or more memories 7103 for storing data. Alternatively, all or part of the memory 7103 can also be outside the communication device 7100. In alternative embodiments, the communication device 7100 can include one or more interface circuits 7104. Alternatively, the interface circuit 7104 is connected with the memory 7103, and the interface circuit 7104 can be used to receive data from the memory 7103 or other devices, and can be used to send data to the memory 7103 or other devices. For example, the interface circuit 7104 can read the data stored in the memory 7103 and send the data to the processor 7101.

[0336] The communication device 7100 described in the above embodiments can be a network device or a terminal, but the scope of the communication device 7100 described in the present disclosure is not limited thereto, and the structure of the communication device 7100 can not be limited by FIG. 7A. 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 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, etc.; (6) others, etc.

[0337] FIG. 7B is a structural schematic diagram of a chip according to an embodiment of the present disclosure. For the case where the communication device 7100 can be a chip or a chip system, the structural schematic diagram of the chip 7200 shown in FIG. 7B can be referred to, but is not limited thereto.

[0338] The chip 7200 comprises one or more processors 7201. The chip 7200 is configured to perform any of the above methods.

[0339] In some embodiments, the chip 7200 further comprises one or more interface circuits 7202. Optionally, the terms interface circuit, interface, transceiver pin, and the like can replace each other. In some embodiments, the chip 7200 further comprises one or more memories 7203 configured to store data. Optionally, all or part of the memory 7203 can be outside the chip 7200. Optionally, the interface circuit 7202 is connected with the memory 7203, the interface circuit 7202 can be configured to receive data from the memory 7203 or other devices, and the interface circuit 7202 can be configured to send data to the memory 7203 or other devices. For example, the interface circuit 7202 can read the data stored in the memory 7203 and send the data to the processor 7201.

[0340] In some embodiments, the interface circuit 7202 performs at least one of the communication steps (for example, steps S201, S203, but not limited to) such as sending and / or receiving in the above methods. The interface circuit 7202 performing the communication steps such as sending and / or receiving in the above methods means that the interface circuit 7202 performs data interaction between the processor 7201, the chip 7200, the memory 7203, or the transceiver device. In some embodiments, the processor 7201 performs at least one of the other steps (for example, steps S202, S204, but not limited to).

[0341] The modules and / or devices described in each of the embodiments of the virtual device, the physical device, the chip, and the like can be combined or separated as appropriate. Optionally, part or all of the steps can also be performed by multiple modules and / or devices in cooperation, which is not limited here.

[0342] The embodiments of the present disclosure further propose a storage medium, and the storage medium stores instructions. When the instructions run on the communication device 7100, the communication device 7100 performs any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer readable storage medium, but is not limited to this, and it can also be a storage medium readable by other devices. Optionally, the storage medium can be a non-transitory storage medium, but is not limited to this, and it can also be a transitory storage medium.

[0343] The embodiments of the present disclosure further propose a program product, and the program product is executed by the communication device 7100, so that the communication device 7100 performs any of the above methods. Optionally, the program product is a computer program product.

[0344] The embodiments of the present disclosure further provide a computer program, which, when running on a computer, enables the computer to perform any of the above methods.

[0345] Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the present application cover any and all variations of the application that come within the scope of the following claims and their equivalents. It is intended that the specification and examples be considered exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0346] It is to be understood that the application is not limited to particular details described herein and as illustrated in the figures 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, therefore, be determined not with reference to the above description, but instead with reference to the appended claims, along with their full scope of equivalents.

Claims

1. A communication method performed by a terminal, wherein, The method comprises: receiving first information, wherein the first information is used to indicate performance of an artificial intelligence (AI) function.

2. The method of claim 1, wherein, The first information is used to control a first model associated with the AI function, the first model being an AI / machine learning (ML) model.

3. The method of claim 2, wherein, Controlling the first model comprises at least one of the following: training, updating, activating, deactivating.

4. The method of any one of claims 1 to 3, wherein, The first information comprises at least one of the following: first performance information used to indicate that the performance of the AI function does not meet a preset condition; second performance information used to indicate performance data of the AI function; third performance information used to indicate expected performance of the AI function; first condition information used to indicate a network-side condition associated with the performance of the AI function; second condition information used to indicate a terminal-side condition associated with the performance of the AI function; first parameter information used to indicate a statistical parameter associated with the performance of the AI function; first function information used to indicate the AI function.

5. The method of claim 4, wherein, The first performance information comprises at least one bit, and a first value of the bit is used to indicate that the performance of the AI function does not meet the preset condition.

6. The method of claim 4 or 5, wherein, The expected performance indicated by the third performance information meets the preset condition.

7. The method of any one of claims 4 to 6, wherein, The network-side condition indicated by the first condition information comprises at least one of the following: a network-side condition in a case where the performance of the AI function does not meet the preset condition; a network-side condition in a statistical process of the performance of the AI function.

8. The method of any one of claims 4 to 7, wherein, The first condition information comprises a first identifier used to identify the network-side condition associated with the performance of the AI function.

9. The method of any one of claims 4 to 8, wherein, The terminal-side condition indicated by the second condition information comprises at least one of the following: a terminal-side condition in a case where the performance of the AI function does not meet the preset condition; a terminal-side condition in a statistical process of the performance of the AI function.

10. The method of any one of claims 4 to 9, wherein, The statistical parameter indicated by the first parameter information comprises at least one of the following: a time length of statistics; a time window of statistics; a number of statistics; a data amount of statistics.

11. The method of any one of claims 4 to 10, wherein, The first function information comprises a second identifier used to identify the AI function.

12. The method of any one of claims 4 to 11, wherein, The performance of the AI function corresponds to at least one of the following: at least one network-side condition; at least one terminal-side condition.

13. The method of any one of claims 4 to 12, wherein, The first information is used to indicate at least one of the following: at least one combination of the performance of the AI function and a network-side condition; at least one combination of the performance of the AI function and a terminal-side condition.

14. The method of any one of claims 1 to 13, wherein, The method further comprises: training, according to the first information, a first model associated with the AI function.

15. The method of claim 13, wherein, The first information comprises a first identifier used to indicate a network-side condition associated with performance information; wherein the training, according to the first information, of the first model associated with the AI function comprises: training, based on training data, of the first model in a case where the first identifier and a third identifier are consistent, wherein the third identifier is used to indicate a network-side condition in a collection process of the training data.

16. The method of any one of claims 1 to 15, wherein, The method further comprises: sending second information, wherein the second information is used to determine that the terminal supports receiving the first information.

17. A communication method performed by a network device, wherein, The method comprises: sending first information, wherein the first information is used to indicate an artificial intelligence (AI) function.

18. The method of claim 17, wherein, The first information is used to control a first model associated with the AI function, the first model being an AI / machine learning (ML) model.

19. The method of claim 18, wherein, Controlling the first model comprises at least one of the following: training, updating, activating, deactivating.

20. The method of any one of claims 17-19, wherein, The first information comprises at least one of the following: first performance information used to indicate that a performance of the AI function does not satisfy a preset condition; second performance information used to indicate performance data of the AI function; third performance information used to indicate an expected performance of the AI function; first condition information used to indicate a network-side condition associated with the performance of the AI function; second condition information used to indicate a terminal-side condition associated with the performance of the AI function; first parameter information used to indicate a statistical parameter associated with the performance of the AI function; first function information used to indicate the AI function.

21. The method of claim 20, wherein, The first performance information comprises at least one bit, and a first value of the bit is used to indicate that the performance of the AI function does not satisfy the preset condition.

22. The method of claim 20 or 21, wherein, The expected performance indicated by the third performance information satisfies the preset condition.

23. The method of any one of claims 20-22, wherein, The network-side condition indicated by the first condition information comprises at least one of the following: a network-side condition in a case where the performance of the AI function does not satisfy the preset condition; a network-side condition in a statistical process of the performance of the AI function.

24. The method of any one of claims 20-23, wherein, The first condition information comprises a first identifier used to identify the network-side condition associated with the performance of the AI function.

25. The method of any one of claims 20-24, wherein, The terminal-side condition indicated by the second condition information comprises at least one of the following: a terminal-side condition in a case where the performance of the AI function does not satisfy the preset condition; a terminal-side condition in a statistical process of the performance of the AI function.

26. The method of any one of claims 20-25, wherein, The statistical parameter indicated by the first parameter information comprises at least one of the following: a time length of statistics; a time window of statistics; a number of times of statistics; a data amount of statistics.

27. The method of any one of claims 20 to 26, wherein, The first function information comprises a second identifier used to identify the AI function.

28. The method of any one of claims 20 to 27, wherein, The performance of the AI function corresponds to at least one of the following: at least one network-side condition; at least one terminal-side condition.

29. The method of any one of claims 20 to 28, wherein, The first information is used to indicate at least one of the following: at least one combination of the performance of the AI function and a network-side condition; at least one combination of the performance of the AI function and a terminal-side condition.

30. The method of any one of claims 17-29, wherein, The method further comprises: receiving second information, wherein the second information is used to determine whether the terminal supports receiving the first information.

31. A communication device, arranged at a terminal, wherein The apparatus comprises: a transceiver module configured to receive first information, wherein the first information is used to indicate a performance of an artificial intelligence (AI) function.

32. A communication device configured to be disposed in a network device, wherein, The apparatus comprises: a transceiver module configured to send first information, wherein the first information is used to indicate a performance of an artificial intelligence (AI) function.

33. A communication device comprising: one or more processors; a memory storing instructions; wherein the instructions, when executed by the communication device, cause the communication device to implement the communication method according to any one of claims 1 to 16.

34. A communication device comprising: one or more processors; memory storing instructions; wherein the instructions, when executed on the communication device, cause the communication device to implement the communication method of any of claims 17-30.

35. A communication system comprising a terminal and a network device; wherein the terminal configured to implement the communication method of any of claims 1-16, and the network device configured to implement the communication method of any of claims 17-30.

36. A storage medium storing instructions, wherein, when the instructions are run on a communication device, cause the communication device to implement at least one of: the communication method of any of claims 1-16; the communication method of any of claims 17-30.

37. A computer program product comprising instructions, wherein, when the instructions are run on a communication device, cause the communication device to implement at least one of: the communication method of any of claims 1-16; the communication method of any of claims 17-30.

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