Communication method, terminal, network device and storage medium

Abstract

The invention relates to a communication method, a terminal, network equipment and a storage medium. The communication method comprises: receiving first information sent by a network device, the first information being associated with a data set, the data set being used for a terminal to train an AI model, and the data set being pre-allocated with an association identifier; according to the invention, the association between the data set and the AI model is established by receiving the data set indicated by the network equipment and the association identifier pre-allocated by the data set, that is, the corresponding AI model can be accurately trained by using the data set, and the communication efficiency is improved.

Description

Communication method, terminal, network device and storage medium Technical Field

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

[0002] In recent years, artificial intelligence (AI) technology has achieved continuous breakthroughs in various fields. Its widespread application in various sectors has not only brought convenience to people's lives, but also promoted industrial upgrading in various industries. AI technology is also intersecting with other disciplines. For example, AI is being introduced to wireless air interfaces to explore how it can assist in improving wireless air interface transmission technology.

[0003] Summary of the Invention

[0004] How terminals should use data sets indicated by network devices to train AI models to improve communication efficiency is a technical problem that urgently needs to be solved.

[0005] The embodiments of the present disclosure provide a communication method, a terminal, a network device, and a storage medium.

[0006] According to a first aspect of an embodiment of the present disclosure, a communication method is proposed, comprising: receiving first information sent by a network device, wherein the first information is associated with a data set, the data set is used for terminal training of an AI model, and the data set is pre-assigned with an association identifier.

[0007] According to a second aspect of an embodiment of the present disclosure, a communication method is proposed, comprising: sending first information to a terminal, wherein the first information is associated with a data set, the data set is used for training an AI model by the terminal, and the data set is pre-assigned with an association identifier.

[0008] According to a third aspect of an embodiment of the present disclosure, a communication method is proposed, comprising: a network device sending first information to a terminal, wherein the first information is associated with a data set, the data set is used for the terminal to train an AI model, and the data set is pre-assigned with an association identifier; and the terminal receives the first information.

[0009] According to the fourth aspect of an embodiment of the present disclosure, a terminal is proposed, comprising: a transceiver module for receiving first information sent by a network device, wherein the first information is associated with a data set, the data set is used for training an AI model by the terminal, and the data set is pre-assigned with an association identifier.

[0010] According to the fifth aspect of an embodiment of the present disclosure, a network device is proposed, including: a transceiver module, used to send first information to a terminal, the first information is associated with a data set, the data set is used for the terminal to train an AI model, and the data set is pre-assigned with an association identifier.

[0011] According to a sixth aspect of an embodiment of the present disclosure, a terminal is proposed, comprising: one or more processors; wherein the terminal is configured to execute the first aspect and any one of the communication methods in the first aspect.

[0012] According to a seventh aspect of an embodiment of the present disclosure, a network device is proposed, comprising: one or more processors; wherein the network device is used to execute the second aspect and any one of the communication methods in the second aspect.

[0013] According to the eighth aspect of an embodiment of the present disclosure, a communication system is proposed, comprising a terminal and a network device, wherein the terminal is configured to implement the first aspect and any one of the communication methods in the first aspect, and the network device is configured to implement the second aspect and any one of the communication methods in the second aspect.

[0014] According to the ninth aspect of an embodiment of the present disclosure, a storage medium is proposed, which stores instructions. When the instructions are executed on a communication device, the communication device executes a communication method such as the first aspect and any one of the first aspects or the second aspect and any one of the second aspects.

[0015] The present disclosure establishes an association between a data set and an AI model by receiving a data set indicated by a network device and an association identifier pre-assigned to the data set, that is, the data set can be accurately used to train the corresponding AI model, thereby improving communication efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following drawings required for describing the embodiments are introduced. The following drawings are merely some embodiments of the present disclosure and do not impose specific limitations on the protection scope of the present disclosure.

[0017] FIG1 is a schematic diagram showing a communication system architecture according to an embodiment of the present disclosure.

[0018] FIG2 a is a schematic diagram showing interaction of a communication method according to an embodiment of the present disclosure.

[0019] FIG2 b is a schematic diagram showing interaction of a communication method according to an embodiment of the present disclosure.

[0020] FIG2c is a schematic diagram showing interaction of a communication method according to an embodiment of the present disclosure.

[0021] FIG3 a is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0022] FIG3 b is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0023] FIG3 c is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0024] FIG3 d is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0025] FIG4 a is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0026] FIG4 b is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0027] FIG4 c is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0028] FIG4 d is a flow chart showing a communication method according to an embodiment of the present disclosure.

[0029] FIG5 is a schematic diagram showing an interaction of a communication method according to an embodiment of the present disclosure.

[0030] FIG6 a is a schematic structural diagram of a terminal according to an embodiment of the present disclosure.

[0031] FIG6 b is a schematic structural diagram of a network device according to an embodiment of the present disclosure.

[0032] Fig. 7a is a schematic structural diagram of a communication device according to an exemplary embodiment.

[0033] FIG7 b is a schematic diagram showing a chip structure according to an exemplary embodiment. DETAILED DESCRIPTION

[0034] The embodiments of the present disclosure provide a communication method, a terminal, a network device, and a storage medium.

[0035] In a first aspect, an embodiment of the present disclosure proposes a communication method, comprising: receiving first information sent by a network device, wherein the first information is associated with a data set, the data set is used for terminal training of an AI model, and the data set is pre-assigned with an association identifier.

[0036] In the above embodiment, by receiving the data set indicated by the network device and the association identifier pre-assigned to the data set, the association between the data set and the AI ​​model is established, that is, the data set can be accurately used to train the corresponding AI model, thereby improving communication efficiency.

[0037] In some optional embodiments of the first aspect, the association identifier includes a first model identifier pre-assigned by the network device.

[0038] In the above embodiment, the association identifier pre-assigned by the network device to the data set can be a model identifier, that is, while establishing an association between the data set and the AI ​​model, the model identifier is also assigned to the AI ​​model to improve efficiency.

[0039] In some optional embodiments of the first aspect, the first model identifier of a data set corresponds to an AI model; or the first model identifiers of multiple data sets correspond to the same AI model.

[0040] In the above embodiment, the first model identifier of a data set can correspond to one AI model, that is, the AI ​​model can be trained using one data set. The first model identifiers of multiple data sets can also correspond to one AI model, that is, the AI ​​model can be trained using multiple data sets. This allows the AI ​​model to be trained flexibly and associated with one or more model identifiers, thereby improving communication efficiency.

[0041] In some optional embodiments of the first aspect, the AI ​​model training is completed and deployed on the terminal, and the method further includes: sending second information to the network device, where the second information is used to indicate a first model identifier corresponding to the AI ​​model trained and deployed on the terminal.

[0042] In the above embodiment, after the AI ​​model training is completed and deployed on the terminal, the terminal can directly report the first model identifier of the trained and deployed AI model without the need for additional instructions from the network side, thereby saving signaling consumption and improving communication efficiency.

[0043] In some optional embodiments of the first aspect, the second information is used to indicate multiple first model identifiers, and the method further includes: sending third information to the network device, where the third information is used to indicate an association between the multiple first model identifiers.

[0044] In the above embodiment, when an AI model is associated with multiple first model identifiers at the same time, the terminal can also report the association between multiple first model identifiers of the same AI model through third information, so as to save time and improve efficiency when performing subsequent operations such as switching AI models.

[0045] In some optional embodiments of the first aspect, the association identifier includes a data identifier pre-assigned by the network device.

[0046] In the above embodiment, the association identifier pre-assigned by the network device to the data set may be a data identifier, so that the terminal can flexibly use one or more data sets for model training.

[0047] In some optional embodiments of the first aspect, the data identifier of one data set corresponds to one AI model; or the data identifiers of multiple data sets correspond to the same AI model.

[0048] In the above embodiment, the terminal can use a single dataset to train an AI model. In this case, the data identifier of a dataset corresponds to one AI model, i.e., the AI ​​model is associated with one data identifier. Alternatively, the terminal can use multiple datasets to train an AI model. In this case, the data identifiers of multiple datasets correspond to the same AI model, i.e., the AI ​​model is associated with multiple data identifiers. The terminal can flexibly use one or more datasets to train an AI model. In some cases, this can improve the efficiency of training an AI model. For example, using a single dataset to train an AI model can be more efficient. In some cases, this can improve the performance of the AI ​​model. For example, using multiple datasets to train an AI model can result in higher performance.

[0049] In some optional embodiments of the first aspect, the AI ​​model training is completed and deployed on the terminal, and the method further includes: sending fourth information to the network device, wherein the fourth information is used to indicate the data identifier corresponding to the AI ​​model trained and deployed on the terminal.

[0050] In the above embodiment, when the AI ​​model training is completed and deployed on the terminal, the data identifier corresponding to the trained and deployed AI model can be reported to the network device through the fourth information. This allows the network device to know the data set used by the terminal to train the AI ​​model, and facilitates the subsequent network device to indicate the second model identifier to the terminal, thereby improving communication efficiency.

[0051] In some optional embodiments of the first aspect, the method further includes: receiving fifth information sent by a network device, where the fifth information is used to indicate a second model identifier corresponding to the AI ​​model.

[0052] In the above embodiment, the terminal can receive the second model identifier of the AI ​​model indicated by the network device through the fifth information, that is, the model identifier can be assigned to the AI ​​model that has been trained and deployed by the terminal, which is beneficial to subsequent performance testing, management and other operations of the AI ​​model, and improves communication efficiency.

[0053] In some optional embodiments of the first aspect, the fifth information is used to indicate a plurality of second model identifiers, and the plurality of second model identifiers correspond one-to-one to the plurality of data identifiers.

[0054] In the above embodiment, the network device can assign multiple second model identifiers to the AI ​​model trained and deployed by the terminal, and can assign multiple second model identifiers based on the multiple data identifiers reported by the terminal, that is, the multiple second model identifiers correspond one-to-one to the multiple data identifiers. This one-to-one correspondence is the same for each terminal, making subsequent management of the AI ​​model simpler and more efficient.

[0055] In some optional embodiments of the first aspect, the first information indicates the data set in at least one of the following ways: the first information includes the data set; the first information includes configuration information, and the configuration information is used by the terminal to collect the data set.

[0056] In the above embodiment, the network device can directly send the data set to the terminal, or send configuration information so that the terminal can use the configuration information to collect the data set on its own. This ensures that the model has relatively good performance during the inference phase of the AI ​​model.

[0057] In a second aspect, a communication method is provided, comprising: sending first information to a terminal, wherein the first information is associated with a data set, the data set is used for training an AI model by the terminal, and the data set is pre-assigned with an association identifier.

[0058] In some optional embodiments of the second aspect, the association identifier includes a first model identifier pre-assigned by the network device.

[0059] In some optional embodiments of the second aspect, the first model identifier of a data set corresponds to an AI model; or the first model identifiers of multiple data sets correspond to the same AI model.

[0060] In some optional embodiments of the second aspect, the AI ​​model training is completed and deployed on the terminal, and the method further includes: receiving second information sent by the terminal, where the second information is used to indicate a first model identifier corresponding to the AI ​​model trained and deployed on the terminal.

[0061] In some optional embodiments of the second aspect, the second information is used to indicate multiple first model identifiers, and the method further includes: receiving third information sent by the terminal, where the third information is used to indicate an association between the multiple first model identifiers.

[0062] In some optional embodiments of the second aspect, the association identifier includes a data identifier pre-assigned by the network device.

[0063] In some optional embodiments of the second aspect, the data identifier of one data set corresponds to one AI model; or the data identifiers of multiple data sets correspond to the same AI model.

[0064] In some optional embodiments of the second aspect, the AI ​​model training is completed and deployed on the terminal, and the method further includes: receiving fourth information sent by the terminal, where the fourth information is used to indicate a data identifier corresponding to the AI ​​model trained and deployed on the terminal.

[0065] In some optional embodiments of the second aspect, the method further includes: sending fifth information to the terminal, where the fifth information is used to indicate a second model identifier corresponding to the AI ​​model.

[0066] In some optional embodiments of the second aspect, the fifth information is used to indicate multiple second model identifiers, and the multiple second model identifiers correspond one-to-one to the multiple data identifiers.

[0067] In some optional embodiments of the second aspect, the first information indicates the data set in at least one of the following ways: the first information includes the data set; the first information includes configuration information, and the configuration information is used by the terminal to collect the data set.

[0068] According to a third aspect, a communication method is provided, comprising: a network device sending first information to a terminal, wherein the first information is associated with a data set, the data set is used for training an AI model by the terminal, and the data set is pre-assigned with an association identifier.

[0069] In a fourth aspect, a terminal is provided, comprising: a transceiver module for receiving first information sent by a network device, wherein the first information is associated with a data set, the data set is used for training an AI model by the terminal, and the data set is pre-assigned with an association identifier.

[0070] In a fifth aspect, a network device is provided, including: a transceiver module for sending first information to a terminal, wherein the first information is associated with a data set, the data set is used for training an AI model by the terminal, and the data set is pre-assigned with an association identifier.

[0071] In a sixth aspect, a terminal is provided, comprising: one or more processors; wherein the terminal is used to execute the first aspect and any one of the communication methods in the first aspect.

[0072] In a seventh aspect, a network device is provided, comprising: one or more processors; wherein the network device is used to execute the second aspect and any one of the communication methods in the second aspect.

[0073] In an eighth aspect, a communication system is provided, comprising a terminal and a network device, wherein the terminal is configured to implement the first aspect and any one of the communication methods in the first aspect, and the network device is configured to implement the second aspect and any one of the communication methods in the second aspect.

[0074] In the ninth aspect, a storage medium is provided, which stores instructions. When the instructions are executed on a communication device, the communication device executes a communication method such as the first aspect and any one of the first aspect or the second aspect and any one of the second aspect.

[0075] In a tenth aspect, an embodiment of the present disclosure proposes a program product. When the program product is executed by a communication device, the communication device executes the method described in the optional implementation manner of the first aspect or the second aspect.

[0076] In an eleventh aspect, an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the method described in the optional implementation of the first or second aspect.

[0077] In a twelfth aspect, an embodiment of the present disclosure provides a chip or a chip system, wherein the chip or chip system includes a processing circuit configured to execute the method described in the optional implementation of the first or second aspect.

[0078] It is understandable that the terminal, access network device, first network element, other network elements, core network device, communication system, storage medium, program product, computer program, chip, or chip system involved in each embodiment of the present disclosure are all used to perform the method proposed in the embodiment of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding method and will not be repeated here.

[0079] The present disclosure provides communication methods, devices, equipment, and storage media. In some embodiments, the terms "communication method," "information processing method," and "communication method" are interchangeable; the terms "communication device," "information processing device," and "communication device" are interchangeable; and the terms "information processing system," "communication system," and "communication system" are interchangeable.

[0080] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0081] In each embodiment of the present disclosure, unless otherwise specified or provided for, the terms and / or descriptions between the embodiments are consistent and may be referenced by each other. The technical environments in different embodiments may be combined to form new embodiments based on their inherent logical relationships.

[0082] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0083] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0084] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0085] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0086] In some embodiments, descriptions such as "at least one of A and B," "A and / or B," "A in one case, B in another case," or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.

[0087] In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

[0088] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for example, if the description object is "information", then the "first information" and "the performance of each AI model" can be the same information or different information, and their contents can be the same or different.

[0089] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0090] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0091] In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not less than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

[0092] In some embodiments, devices and equipment can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they can also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject", etc.

[0093] In some embodiments, "network" can be interpreted as devices included in the network, such as access network equipment, core network equipment, etc.

[0094] In some embodiments, "access network device (AN device)" may also be referred to as "radio access network device (RAN device)", "base station (BS)", "radio base station", "fixed station", and in some embodiments may also be understood as "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission and / or 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)", etc.

[0095] In some embodiments, "terminal" or "terminal device" may be referred to as "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, etc.

[0096] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0097] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0098] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0099] In recent years, the widespread adoption of fifth-generation mobile communication technology (5G) has brought tremendous changes to every aspect of people's lives. According to the International Telecommunication Union (ITU), 5G will permeate every aspect of future society, building a comprehensive, user-centric information ecosystem. 5G user experience rates can reach 100 megabits per second (Mbit / s) to 1 gigabit per second (Gbit / s), enabling premium services like mobile virtual reality. 5G peak rates can reach 10 Gbit / s to 20 Gbit / s, with traffic density reaching 10 megabits per second per square meter (Mbit / s / m²), effectively supporting massive numbers of IoT devices. 5G transmission latency can reach milliseconds, meeting the stringent requirements of connected vehicles and industrial control. 5G can support speeds of 500 kilometers per hour (km / h), ensuring a superior user experience even in high-speed rail environments. As a representative of new infrastructure, 5G will undoubtedly reshape the future information society.

[0100] Currently, AI technology is making continuous breakthroughs in numerous fields. The continued development of fields like intelligent voice and computer vision has not only brought a rich variety of applications to smart terminals, but has also found widespread application in education, transportation, home living, healthcare, retail, security, and other fields. While bringing convenience to people's lives, it is also promoting industrial upgrading across various industries. AI technology is also rapidly interpenetrating with other disciplines. Its development integrates knowledge from different disciplines while also providing new directions and methods for their development.

[0101] In some embodiments, a research project on artificial intelligence technology in wireless air interfaces has been established. This project aims to study how to introduce artificial intelligence technology in wireless air interfaces and explore how artificial intelligence technology can assist in improving wireless air interface transmission technology.

[0102] In some embodiments, in the research of wireless AI, application cases of artificial intelligence include: AI-based channel state information (CSI) enhancement, AI-based beam management, AI-based positioning, etc.

[0103] In some embodiments, during the AI ​​model training process, the data used is collected under certain conditions, which may include network configuration and some implementation strategies on the network side, such as beamforming implementation strategies, antenna virtualization, etc. During the inference phase of the AI ​​model, in order to ensure that the model has better performance, the conditions of the terminal during inference are preferably consistent with the conditions associated with the training data. In order to achieve the purpose of maintaining consistency, one method is that the terminal side collects data under the instruction of the network and then performs model training based on the collected data, or the network side directly sends a data set to the terminal side, and the terminal side performs model training based on the data set sent by the network.

[0104] How terminals should use data sets indicated by network devices to train AI models to improve communication efficiency is a technical problem that urgently needs to be solved.

[0105] FIG1 is a schematic diagram showing a communication system architecture according to an embodiment of the present disclosure.

[0106] As shown in FIG1 , a communication system 100 includes a terminal 101 and a network device 102 .

[0107] In some embodiments, the terminal 101 includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, 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 a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.

[0108] In some embodiments, the network device 102 may include at least one of an access network device and a core network device.

[0109] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a Wi-Fi system, but is not limited thereto.

[0110] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0111] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit. The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0112] In some embodiments, a core network device may be a device including one or more network elements, or may be multiple devices or device groups, each including all or part of the one or more network elements. The network element may be virtual or physical. The core network may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

[0113] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0114] The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1 , or a portion thereof, but are not limited thereto. The entities shown in FIG1 are illustrative only. The communication system may include all or part of the entities shown in FIG1 , or may include other entities outside of FIG1 . The number and form of the entities are arbitrary, and the entities may be physical or virtual. The connection relationships between the entities are illustrative only. The entities may be connected or disconnected, and the connection may be in any manner, including direct or indirect, wired or wireless.

[0115] The embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), future radio access (FRA), new radio access technology (RAT), new radio (NR), new radio access (NX), future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X), systems utilizing other communication methods, and next-generation systems based on and extending these methods. Furthermore, multiple systems may be combined (for example, a combination of LTE or LTE-A with 5G).

[0116] FIG2a is a schematic diagram illustrating an interaction of a communication method according to an embodiment of the present disclosure. As shown in FIG2a , the present disclosure embodiment relates to a communication method for use in a communication system 100, the method comprising:

[0117] Step S2101 , the network device 102 sends first information to the terminal 101 .

[0118] In some embodiments, the terminal 101 receives first information sent by the network device 102 .

[0119] In some embodiments, the first information is used to indicate a dataset. The dataset is used to train an AI model. The dataset is pre-assigned with an association identifier, which is used to indicate an association between the dataset and the AI ​​model.

[0120] Optionally, the first information indicating the data set may include the data set in the first information. For example, the network device may collect the data set and send the collected data set to the terminal via the first information, so that the terminal can use the data set to train the AI ​​model.

[0121] Optionally, the first information indicating the data set may include configuration information in the first information, and the configuration information is used by the terminal to collect the data set. That is, the network device may send the configuration information through the first information, and the terminal uses the configuration information to collect the data set on its own. The configuration information may include, for example, the measurement object, the resources for reporting the data set after collecting the data set, the reporting time, etc. For example, when the AI ​​model is used for positioning, the measurement object may be, for example, a positioning reference signal. When the AI ​​model is used for CSI enhancement, the measurement object may be, for example, a sounding reference signal. Of course, the present disclosure uses positioning reference signals and sounding reference signals as examples, but is not limited thereto.

[0122] In some embodiments, the pre-assigned association identifier of the data set may be a first model identity document (model ID).

[0123] In some embodiments, the first model ID of a data set corresponds to an AI model, or the first model IDs of multiple data sets correspond to the same AI model.

[0124] Optionally, the first model ID of a data set corresponds to one AI model. That is, a terminal can use a data set to train an AI model. The trained AI model is associated with the first model ID of the data set. That is, an AI model can be associated with one model ID.

[0125] Optionally, the first model IDs of multiple data sets correspond to the same AI model. That is, the terminal can use multiple data sets to train the AI ​​model. The trained AI model is associated with the first model IDs of the multiple data sets. That is, the AI ​​model can be associated with multiple first model IDs.

[0126] In some embodiments, the pre-assigned association identifier of the data set may be a data set identity document (data set ID).

[0127] In some embodiments, the data identifier of a data set corresponds to an AI model, or the data identifiers of multiple data sets correspond to the same AI model.

[0128] Optionally, the data identifier of a data set corresponds to one AI model. That is, a terminal can use a data set to train an AI model. The trained AI model is associated with the data identifier of the data set. That is, an AI model can be associated with one data identifier.

[0129] Optionally, the data identifiers of multiple data sets correspond to the same AI model. That is, the terminal can use multiple data sets to train the AI ​​model. The trained AI model is associated with the data identifiers of multiple data sets. In other words, the AI ​​model can be associated with multiple data models.

[0130] In some embodiments, the trained AI model can be deployed on the terminal.

[0131] In some embodiments, the name of the first information is not limited, and it can be, for example, "configuration information", "instruction information", etc.

[0132] In step S2102, the terminal 101 reports the association identifier corresponding to the trained and deployed AI model to the network device 102.

[0133] In some embodiments, the network device 102 receives an association identifier corresponding to the AI ​​model that has been trained and deployed and reported by the terminal 101.

[0134] In some embodiments, the AI ​​model is trained and deployed on the terminal, and the terminal reports the association identifier corresponding to the trained and deployed AI model to the network device.

[0135] In some embodiments, if the trained AI model is associated with one association identifier, one association identifier is reported; if it is associated with multiple association identifiers, multiple association identifiers are reported.

[0136] In some embodiments, when the association identifier is the first model ID, the terminal 101 may send a second message to the network device 102 to report the first model ID corresponding to the AI ​​model trained and deployed by the terminal. The second information is used to indicate the first model identifier corresponding to the AI ​​model trained and deployed by the terminal.

[0137] In some embodiments, when the association identifier is a data identifier, terminal 101 may send fourth information to network device 102 to report the data identifier corresponding to the AI ​​model that the terminal has trained and deployed. The fourth information is used to indicate the data identifier corresponding to the AI ​​model that the terminal has trained and deployed.

[0138] In some embodiments, the first model ID reported by the terminal facilitates subsequent network device performance monitoring and management of the AI ​​model. Management may include, for example, activation, deactivation, switching, etc.

[0139] In some embodiments, the data identifier reported by the terminal can facilitate the subsequent allocation of a second model ID by the network device, and the allocated second model ID is used by the network device to monitor the performance, manage, and perform other operations on the AI ​​model.

[0140] FIG2b is a schematic diagram illustrating an interaction of a communication method according to an embodiment of the present disclosure. As shown in FIG2b , the present disclosure embodiment relates to a communication method for use in a communication system 100, the method comprising:

[0141] Step S2201: The network device 102 sends first information to the terminal 101.

[0142] In some embodiments, the terminal 101 receives first information sent by the network device 102 .

[0143] In some embodiments, the first information is used to indicate a dataset. The dataset is used to train an AI model. The dataset is pre-assigned with a first model ID, and the first model ID is used to indicate an association between the dataset and the AI ​​model.

[0144] The optional implementation of step S2201 can refer to the implementation of step S2101, and this disclosure will not go into details here.

[0145] Step S2202 , the terminal 101 sends second information to the network device 102 .

[0146] In some embodiments, the network device 102 receives the second information sent by the terminal 101 .

[0147] In some embodiments, the second information is used to indicate the first model identifier corresponding to the AI ​​model that has been trained and deployed by the terminal.

[0148] The optional implementation of step S2202 can refer to the implementation of step S2102, and this disclosure will not go into details here.

[0149] Step S2203 , the terminal 101 sends third information to the network device 102 .

[0150] In some embodiments, the network device 102 receives the third information sent by the terminal 101 .

[0151] In some embodiments, the third information is used to indicate an association between multiple first model identifiers.

[0152] In some embodiments, when the terminal uses multiple data sets to train an AI model, that is, the trained AI model is associated with multiple first model IDs. The terminal can report the association of multiple first model IDs to the network device. That is, it can be reported to the device which first model identifiers correspond to the same AI model, so that in subsequent management, such as activation, deactivation or model switching, the associated first model ID can be processed in an activation, deactivation or model switch operation. The processing time can be shorter than that of no association, that is, processing time can be saved and efficiency can be improved. For example, if the terminal reports the association between multiple first model identifiers, then when activating a certain AI model, only one first model ID needs to be obtained, and other first model IDs associated with the first model ID can be processed at the same time, thereby saving processing time and improving efficiency. Of course, the above situation is only exemplary and not limited to this.

[0153] In some embodiments, after receiving the fifth information, when the terminal needs to report the model information supported by the terminal, the terminal may report the first model ID.

[0154] In some embodiments, the name of the third information is not limited, and it can be, for example, "indication information", "associated information", etc.

[0155] Figure 2c is a schematic diagram illustrating an interaction of a communication method according to an embodiment of the present disclosure. As shown in Figure 2c, the present disclosure embodiment relates to a communication method for use in a communication system 100, the method comprising:

[0156] Step S2301: The network device 102 sends first information to the terminal 101.

[0157] In some embodiments, the terminal 101 receives first information sent by the network device 102 .

[0158] In some embodiments, the first information is used to indicate a dataset. The dataset is used to train an AI model. The dataset is pre-assigned with a data identifier, which is used to indicate an association between the dataset and the AI ​​model.

[0159] The optional implementation of step S2301 can refer to the implementation of step S2101, and this disclosure will not go into details here.

[0160] Step S2302 , the terminal 101 sends fourth information to the network device 102 .

[0161] In some embodiments, the network device 102 receives the fourth information sent by the terminal 101 .

[0162] In some embodiments, the fourth information is used to indicate the data identifier corresponding to the AI ​​model that has been trained and deployed by the terminal.

[0163] The optional implementation of step S2302 can refer to the implementation of step S2102, and this disclosure will not go into details here.

[0164] Step S2303 , the network device 102 sends the fifth information to the terminal 101 .

[0165] In some embodiments, the terminal 101 receives fifth information sent by the network device 102 .

[0166] In some embodiments, the fifth information is used to indicate the second model identifier corresponding to the AI ​​model.

[0167] In some embodiments, when there are multiple data identifiers corresponding to the AI ​​model reported by the terminal, the network device can allocate one or more second model IDs for the AI ​​model.

[0168] In some embodiments, the network device can assign a second model ID to the AI ​​model that has been trained and deployed by the terminal. When assigning one second model ID, the number of second model IDs required is relatively small, and the bit length of each second model ID can be relatively small. When assigning multiple second model IDs

[0169] In some embodiments, the network device can assign multiple second model IDs to AI models trained and deployed by the terminal. For example, when the terminal reports multiple data identifiers corresponding to the AI ​​model, each second model ID can correspond to a data identifier. That is, multiple data identifiers correspond to multiple second model IDs one by one, so that each terminal is applicable to this correspondence, making it easier to manage AI models.

[0170] In some embodiments, after receiving the fifth information, when the terminal needs to report the model information supported by the terminal, the terminal may report the second model ID.

[0171] FIG3a is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3a, the embodiment of the present disclosure relates to a communication method, which is executed by terminal 101 and includes:

[0172] Step S3101, obtain first information.

[0173] The optional implementation of step S3101 can refer to the optional implementation of step S2101 in Figure 2a and other related parts in the embodiment involved in Figure 2a, which will not be repeated here.

[0174] In some embodiments, the terminal 101 receives the first information sent by the network device 102, but is not limited thereto and may also receive the first information sent by other entities.

[0175] In some embodiments, terminal 101 obtains first information specified by a protocol.

[0176] In some embodiments, terminal 101 obtains the first information from upper layer(s).

[0177] In some embodiments, the terminal 101 performs processing to obtain the first information.

[0178] In some embodiments, step S3101 is omitted, and the terminal 101 autonomously implements the function indicated by the first information, or the above function is default or by default.

[0179] Step S3102: Report the association identifier corresponding to the AI ​​model that has been trained and deployed.

[0180] The optional implementation of step S3102 can refer to the optional implementation of step S2102 in Figure 2a and other related parts in the embodiment involved in Figure 2a, which will not be repeated here.

[0181] In some embodiments, the terminal 101 reports the association identifier corresponding to the trained and deployed AI model to the network device 102, but is not limited to this. The association identifier corresponding to the trained and deployed AI model can also be reported to other entities.

[0182] FIG3b is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3b, the embodiment of the present disclosure relates to a communication method, which is executed by terminal 101 and includes:

[0183] Step S3201, obtain first information.

[0184] The optional implementation of step S3201 can refer to the optional implementation of step S2201 in Figure 2b and other related parts in the embodiment involved in Figure 2b, which will not be repeated here.

[0185] In some embodiments, the terminal 101 receives the first information sent by the network device 102, but is not limited thereto and may also receive the first information sent by other entities.

[0186] In some embodiments, terminal 101 obtains first information specified by a protocol.

[0187] In some embodiments, terminal 101 obtains the first information from upper layer(s).

[0188] In some embodiments, the terminal 101 performs processing to obtain the first information.

[0189] In some embodiments, step S3201 is omitted, and the terminal 101 autonomously implements the function indicated by the first information, or the above function is default or by default.

[0190] Step S3202, sending the second information.

[0191] The optional implementation of step S3202 can refer to the optional implementation of step S2202 in Figure 2b and other related parts in the embodiment involved in Figure 2b, which will not be repeated here.

[0192] In some embodiments, the terminal 101 sends the second information to the network device 102, but is not limited thereto and the second information may also be sent to other entities.

[0193] Step S3203, sending the third information.

[0194] The optional implementation of step S3203 can refer to the optional implementation of step S2203 in Figure 2b and other related parts in the embodiment involved in Figure 2b, which will not be repeated here.

[0195] In some embodiments, the terminal 101 sends the third information to the network device 102, but is not limited thereto and the third information may also be sent to other entities.

[0196] FIG3c is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3c, the embodiment of the present disclosure relates to a communication method, which is executed by terminal 101 and includes:

[0197] Step S3301, obtain first information.

[0198] The optional implementation of step S3301 can refer to the optional implementation of step S2301 in Figure 2c and other related parts in the embodiment involved in Figure 2c, which will not be repeated here.

[0199] In some embodiments, the terminal 101 receives the first information sent by the network device 102, but is not limited thereto and may also receive the first information sent by other entities.

[0200] In some embodiments, terminal 101 obtains first information specified by a protocol.

[0201] In some embodiments, terminal 101 obtains the first information from upper layer(s).

[0202] In some embodiments, the terminal 101 performs processing to obtain the first information.

[0203] In some embodiments, step S3301 is omitted, and the terminal 101 autonomously implements the function indicated by the first information, or the above function is default or by default.

[0204] Step S3302, sending the fourth information.

[0205] The optional implementation of step S3302 can refer to the optional implementation of step S2302 in Figure 2c and other related parts in the embodiment involved in Figure 2c, which will not be repeated here.

[0206] In some embodiments, the terminal 101 sends the fourth information to the network device 102, but is not limited thereto, and the fourth information may also be sent to other entities.

[0207] Step S3303, obtain the fifth information.

[0208] The optional implementation of step S3303 can refer to the optional implementation of step S2303 in Figure 2c and other related parts in the embodiment involved in Figure 2c, which will not be repeated here.

[0209] In some embodiments, the terminal 101 receives the fifth information sent by the network device 102, but is not limited thereto and may also receive the fifth information sent by other entities.

[0210] In some embodiments, terminal 101 obtains fifth information specified by the protocol.

[0211] In some embodiments, terminal 101 obtains the fifth information from upper layer(s).

[0212] In some embodiments, terminal 101 performs processing to obtain the fifth information.

[0213] In some embodiments, step S3303 is omitted, and the terminal 101 autonomously implements the function indicated by the fifth information, or the above function is default or by default.

[0214] FIG3 d is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3 d , the embodiment of the present disclosure relates to a communication method, which is executed by terminal 101 and includes:

[0215] Step S3401, obtain first information.

[0216] The optional implementation of step S3401 can refer to the optional implementation of step S2301 in Figure 2c and other related parts in the embodiment involved in Figure 2c, which will not be repeated here.

[0217] In some embodiments, the terminal 101 receives the first information sent by the network device 102, but is not limited thereto and may also receive the first information sent by other entities.

[0218] In some embodiments, terminal 101 obtains first information specified by a protocol.

[0219] In some embodiments, terminal 101 obtains the first information from upper layer(s).

[0220] In some embodiments, the terminal 101 performs processing to obtain the first information.

[0221] In some embodiments, step S3401 is omitted, and the terminal 101 autonomously implements the function indicated by the first information, or the above function is default or by default.

[0222] In some embodiments, the association identifier includes a first model identifier pre-assigned by the network device.

[0223] In some embodiments, the first model identifier of a data set corresponds to one AI model, or the first model identifiers of multiple data sets correspond to the same AI model.

[0224] In some embodiments, the AI ​​model training is completed and deployed on the terminal, and the method further includes: sending second information to the network device, where the second information is used to indicate a first model identifier corresponding to the AI ​​model that has been trained and deployed on the terminal.

[0225] In some embodiments, the second information is used to indicate a plurality of first model identifiers, and the method further includes: sending third information to the network device, where the third information is used to indicate an association between the plurality of first model identifiers.

[0226] In some embodiments, the association identifier includes a data identifier pre-assigned by the network device.

[0227] In some embodiments, the data identifier of a data set corresponds to one AI model, or the data identifiers of multiple data sets correspond to the same AI model.

[0228] In some embodiments, the AI ​​model training is completed and deployed on the terminal, and the method further includes: sending fourth information to the network device, where the fourth information is used to indicate the data identifier corresponding to the AI ​​model that has been trained and deployed on the terminal.

[0229] In some embodiments, the method further includes: receiving fifth information sent by the network device, where the fifth information is used to indicate a second model identifier corresponding to the AI ​​model.

[0230] In some embodiments, the fifth information is used to indicate a plurality of second model identifiers, and the plurality of second model identifiers correspond one-to-one to the plurality of data identifiers.

[0231] In some embodiments, the first information indicates the data set in at least one of the following ways: the first information includes the data set. The first information includes configuration information, and the configuration information is used by the terminal to collect the data set.

[0232] FIG4a is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4a , the present disclosure embodiment relates to a communication method, which is executed by a network device 102 and includes:

[0233] Step S4101, sending the first information.

[0234] The optional implementation of step S4101 can refer to the optional implementation of step S2101 in Figure 2a and other related parts in the embodiment involved in Figure 2a, which will not be repeated here.

[0235] In some embodiments, the network device 102 sends the first information to the terminal 101, but is not limited thereto and may also send the first information to other entities.

[0236] Step S4102: Obtain the association identifier corresponding to the trained and deployed AI model.

[0237] The optional implementation of step S4102 can refer to the optional implementation of step S2102 in Figure 2a and other related parts in the embodiment involved in Figure 2a, which will not be repeated here.

[0238] In some embodiments, the network device 102 receives an association identifier corresponding to an AI model that has been trained and deployed and reported by the terminal 101, but is not limited to this. It can also receive an association identifier corresponding to an AI model that has been trained and deployed and reported by other entities.

[0239] In some embodiments, the network device 102 obtains an association identifier corresponding to the trained and deployed AI model from the upper layer(s).

[0240] In some embodiments, the network device 102 performs processing to obtain an association identifier corresponding to the trained and deployed AI model.

[0241] In some embodiments, step S4102 is omitted, and the network device 102 autonomously implements the function indicated by the associated identifier corresponding to the trained and deployed AI model, or the above function is default or by default.

[0242] FIG4 b is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4 b , the present disclosure embodiment relates to a communication method, which is executed by the network device 102 and includes:

[0243] Step S4201, sending the first information.

[0244] The optional implementation of step S4201 can refer to the optional implementation of step S2201 in Figure 2b and other related parts in the embodiment involved in Figure 2b, which will not be repeated here.

[0245] In some embodiments, the network device 102 sends the first information to the terminal 101, but is not limited thereto and may also send the first information to other entities.

[0246] Step S4202, obtain the second information.

[0247] The optional implementation of step S4202 can refer to the optional implementation of step S2202 in Figure 2b and other related parts in the embodiment involved in Figure 2b, which will not be repeated here.

[0248] In some embodiments, the network device 102 receives the second information sent by the terminal 101, but is not limited thereto and may also receive the second information sent by other entities.

[0249] In some embodiments, the network device 102 obtains second information specified by the protocol.

[0250] In some embodiments, the network device 102 obtains the second information from upper layer(s).

[0251] In some embodiments, the network device 102 performs processing to obtain the second information.

[0252] In some embodiments, step S4202 is omitted, and the network device 102 autonomously implements the function indicated by the second information, or the above function is default or by default.

[0253] Step S4203, obtain third information.

[0254] The optional implementation of step S4203 can refer to the optional implementation of step S2203 in Figure 2b and other related parts in the embodiment involved in Figure 2b, which will not be repeated here.

[0255] In some embodiments, the network device 102 receives the third information sent by the terminal 101, but is not limited thereto and may also receive the third information sent by other entities.

[0256] In some embodiments, the network device 102 obtains third information specified by the protocol.

[0257] In some embodiments, the network device 102 obtains the third information from upper layer(s).

[0258] In some embodiments, the network device 102 performs processing to obtain the third information.

[0259] In some embodiments, step S4203 is omitted, and the network device 102 autonomously implements the function indicated by the third information, or the above function is default or by default.

[0260] FIG4c is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4c, the embodiment of the present disclosure relates to a communication method, which is executed by the network device 102, and the method includes:

[0261] Step S4301, sending the first information.

[0262] The optional implementation of step S4301 can refer to the optional implementation of step S2301 in Figure 2c and other related parts in the embodiment involved in Figure 2c, which will not be repeated here.

[0263] In some embodiments, the network device 102 sends the first information to the terminal 101, but is not limited thereto and may also send the first information to other entities.

[0264] Step S4302, obtain the fourth information.

[0265] The optional implementation of step S4302 can refer to the optional implementation of step S2302 in Figure 2c and other related parts in the embodiment involved in Figure 2c, which will not be repeated here.

[0266] In some embodiments, the network device 102 receives the fourth information sent by the terminal 101, but is not limited thereto and may also receive the fourth information sent by other entities.

[0267] In some embodiments, the network device 102 obtains fourth information specified by the protocol.

[0268] In some embodiments, the network device 102 obtains the fourth information from upper layer(s).

[0269] In some embodiments, the network device 102 performs processing to obtain the fourth information.

[0270] In some embodiments, step S4302 is omitted, and the network device 102 autonomously implements the function indicated by the fourth information, or the above function is default or by default.

[0271] Step S4303, sending the fifth information.

[0272] The optional implementation of step S4303 can refer to the optional implementation of step S2303 in Figure 2c and other related parts in the embodiment involved in Figure 2c, which will not be repeated here.

[0273] In some embodiments, the network device 102 sends the fifth information to the terminal 101, but is not limited thereto and may also send the fifth information to other entities.

[0274] FIG4 d is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4 d , the embodiment of the present disclosure relates to a communication method, which is executed by the network device 102 and includes:

[0275] Step S4401, sending the first information.

[0276] The optional implementation of step S4401 can refer to the optional implementation of step S2301 in Figure 2c and other related parts in the embodiment involved in Figure 2c, which will not be repeated here.

[0277] In some embodiments, the network device 102 sends the first information to the terminal 101, but is not limited thereto and may also send the first information to other entities.

[0278] FIG5 is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG5 , the embodiment of the present disclosure relates to a communication method, and the method includes:

[0279] Step S5101: The network device 102 sends first information to the terminal 101.

[0280] The optional implementation of step S5101 can be found in S2101 of FIG. 2 a and other related parts of the embodiment involved in FIG. 2 a , which will not be described in detail here.

[0281] In some embodiments, the above method may include the method of the above embodiments related to the communication system 100, the terminal 101, the network device 102, etc., which will not be repeated here.

[0282] Step S5102: Terminal 101 receives first information.

[0283] The optional implementation of step S5102 can be found in S2101 of FIG. 2 a and other related parts of the embodiment involved in FIG. 2 a , which will not be described in detail here.

[0284] The present disclosure also provides a communication method as follows:

[0285] In some embodiments, the network side sends a data collection configuration to the terminal or the network side sends a data set to the terminal, instructing the terminal to perform model training based on the collected data.

[0286] In some embodiments, the network side may be understood as a network device. The configuration may be configuration information.

[0287] In some embodiments, the network also pre-assigns a model ID to the data trained based on the collected data or the sent data set. At this time, when the terminal performs model training, there are several implementation methods:

[0288] 1) For the model ID assigned by the network, each model ID can only correspond to one AI model. That is, the terminal can only perform model training based on a data set configured by the network.

[0289] 2) Corresponding to the model ID assigned by the network, multiple model IDs can correspond to the same AI model. This means that the terminal can perform model training based on multiple data sets configured by the network.

[0290] In some embodiments, when a terminal trains a model and deploys it, it reports the assigned model ID to the network. If an AI model is associated with multiple model IDs, the terminal reports multiple model IDs.

[0291] In some embodiments, when a terminal reports a model ID, if multiple model IDs are associated with the same AI model, the terminal can further report the association of the multiple model IDs. Subsequently, when performing a model switch, the associated model IDs can be processed in less time than when no model IDs are associated.

[0292] In some embodiments, the network side sends a data collection configuration to the terminal, or the network side sends a data collection configuration to the terminal, instructing the terminal to perform model training based on the collected data.

[0293] In some embodiments, the network also pre-assigns an identifier, such as a dataset ID, to data trained based on collected data or a transmitted dataset.

[0294] In some embodiments, when the terminal performs model training, there are several implementation methods:

[0295] 1) For data identifiers assigned by the network, each data identifier can only correspond to one AI model. This means that the terminal can only perform model training based on a dataset configured by the network.

[0296] 2): Corresponding to the data identifier assigned by the network side, multiple data identifiers can correspond to the same AI model. That is, the terminal side can perform model training based on multiple data sets configured by the network.

[0297] In some embodiments, when the model is trained and deployed on the terminal side, the terminal side initiates the first process.

[0298] In some embodiments, the terminal reports the deployed model information to the network, including the data identification information used by the model. If the model is associated with only one data identification, the terminal only reports one data identification information. If the model is associated with multiple data identifications, the terminal reports multiple data identification information.

[0299] In some embodiments, the network side assigns a corresponding model ID to the terminal. In the case where a model is associated with multiple data identification information, the network side has the following two methods.

[0300] 1): The network side only allocates one model ID

[0301] 2): The network side assigns multiple model IDs to each model. Multiple model IDs correspond to data identifiers.

[0302] In some embodiments, after the terminal side obtains the model ID, when it is necessary to report the supported model information, the terminals report the model ID assigned by the network.

[0303] Figure 6a is a schematic diagram of the structure of a terminal proposed in an embodiment of the present disclosure. As shown in Figure 6a, terminal 6100 may include a transceiver module 6101. In some embodiments, the transceiver module 6101 is configured to receive first information sent by a network device, where the first information indicates a dataset used for training an AI model at the terminal, and the dataset is pre-assigned with an association identifier that indicates the association between the dataset and the AI ​​model.

[0304] In some embodiments, the association identifier includes a first model identifier pre-assigned by the network device.

[0305] In some embodiments, the first model identifier of a data set corresponds to one AI model, or the first model identifiers of multiple data sets correspond to the same AI model.

[0306] In some embodiments, the AI ​​model training is completed and deployed on the terminal, and the transceiver module 6101 is also used to: send second information to the network device, and the second information is used to indicate the first model identifier corresponding to the AI ​​model trained and deployed on the terminal.

[0307] In some embodiments, the second information is used to indicate multiple first model identifiers, and the transceiver module 6101 is further used to: send third information to the network device, where the third information is used to indicate the association between the multiple first model identifiers.

[0308] In some embodiments, the association identifier includes a data identifier pre-assigned by the network device.

[0309] In some embodiments, the data identifier of a data set corresponds to one AI model, or the data identifiers of multiple data sets correspond to the same AI model.

[0310] In some embodiments, the AI ​​model training is completed and deployed on the terminal, and the method further includes: sending fourth information to the network device, where the fourth information is used to indicate the data identifier corresponding to the AI ​​model that has been trained and deployed on the terminal.

[0311] In some embodiments, the transceiver module 6101 is further used to: receive fifth information sent by the network device, where the fifth information is used to indicate the second model identifier corresponding to the AI ​​model.

[0312] In some embodiments, the fifth information is used to indicate a plurality of second model identifiers, and the plurality of second model identifiers correspond one-to-one to the plurality of data identifiers.

[0313] In some embodiments, the first information indicates the data set in at least one of the following ways: the first information includes the data set. The first information includes configuration information, and the configuration information is used by the terminal to collect the data set.

[0314] In some embodiments, the terminal 6100 may further include a processing module 6102. For example, the processing module 6102 may be used to train an AI model or perform other operations.

[0315] Figure 6b is a schematic diagram of the structure of a network device proposed in an embodiment of the present disclosure. As shown in Figure 6b, network device 6200 may include a transceiver module 6201. The transceiver module 6201 is configured to send a first message to a terminal. The first message indicates a dataset used by the terminal to train an AI model. The dataset is pre-assigned with an association identifier, which indicates the association between the dataset and the AI ​​model.

[0316] In some embodiments, the association identifier includes a first model identifier pre-assigned by the network device.

[0317] In some embodiments, the first model identifier of a data set corresponds to one AI model, or the first model identifiers of multiple data sets correspond to the same AI model.

[0318] In some embodiments, the AI ​​model training is completed and deployed on the terminal, and the transceiver module 6201 is also used to: receive second information sent by the terminal, and the second information is used to indicate the first model identifier corresponding to the AI ​​model that has been trained and deployed on the terminal.

[0319] In some embodiments, the second information is used to indicate multiple first model identifiers, and the method further includes: receiving third information sent by the terminal, where the third information is used to indicate an association between the multiple first model identifiers.

[0320] In some embodiments, the association identifier includes a data identifier pre-assigned by the network device.

[0321] In some embodiments, the data identifier of a data set corresponds to one AI model, or the data identifiers of multiple data sets correspond to the same AI model.

[0322] In some embodiments, the AI ​​model training is completed and deployed on the terminal, and the transceiver module 6201 is also used to: receive fourth information sent by the terminal, and the fourth information is used to indicate the data identifier corresponding to the AI ​​model that has been trained and deployed on the terminal.

[0323] In some embodiments, the transceiver module 6201 is further used to: send fifth information to the terminal, where the fifth information is used to indicate the second model identifier corresponding to the AI ​​model.

[0324] In some embodiments, the fifth information is used to indicate a plurality of second model identifiers, and the plurality of second model identifiers correspond one-to-one to the plurality of data identifiers.

[0325] In some embodiments, the first information indicates the data set in at least one of the following ways: the first information includes the data set. The first information includes configuration information, and the configuration information is used by the terminal to collect the data set.

[0326] In some embodiments, the network device 6200 may further include a processing module 6202 .

[0327] Figure 7a is a schematic diagram of the structure of a communication device 7100 proposed in an embodiment of the present disclosure. Communication device 7100 can be a network device, a terminal, a chip, a chip system, or a processor that supports a network device implementing any of the above methods, or a chip, a chip system, or a processor that supports a terminal implementing any of the above methods. Alternatively, the network device can be an access network device, a core network device, or the like. Alternatively, the terminal can be a user equipment, or the like. Communication device 7100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0328] As shown in Figure 7a, communication device 7100 includes one or more processors 7101. Processor 7101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, while the central processing unit can be used to control the communication device, execute programs, and process program data. Communication device 7100 is used to perform any of the above methods. Optionally, the communication device can be a base station, a baseband chip, a terminal device, a terminal device chip, a DU or CU, etc.

[0329] In some embodiments, the communication device 7100 further includes one or more memories 7102 for storing instructions. Optionally, all or part of the memories 7102 may be located outside the communication device 7100.

[0330] In some embodiments, the communication device 7100 further includes one or more transceivers 7103. When the communication device 7100 includes one or more transceivers 7103, the transceiver 7103 performs the communication step S2101 such as sending and / or receiving in the above method, and the processor 7101 performs other steps.

[0331] In some embodiments, a transceiver may include a receiver and / or a transmitter. The receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, and transceiver circuit may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0332] In some embodiments, the communication device 7100 may include one or more interface circuits 7104. Optionally, the interface circuit 7104 is connected to the memory 7102. The interface circuit 7104 may be configured to receive signals from the memory 7102 or other devices, and may be configured to send signals to the memory 7102 or other devices. For example, the interface circuit 7104 may read instructions stored in the memory 7102 and send the instructions to the processor 7101.

[0333] The communication device 7100 described in the above embodiment may 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 may not be limited by FIG. 7a. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0334] FIG7 b is a schematic diagram of the structure of a chip 7200 according to an embodiment of the present disclosure. If the communication device 7100 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 7200 shown in FIG7 b , but the present disclosure is not limited thereto.

[0335] The chip 7200 includes one or more processors 7201 , and the chip 7200 is configured to execute any of the above methods.

[0336] In some embodiments, the chip 7200 further includes one or more interface circuits 7202. Optionally, the interface circuit 7202 is connected to the memory 7203. The interface circuit 7202 can be used to receive signals from the memory 7203 or other devices, and can be used to send signals to the memory 7203 or other devices. For example, the interface circuit 7202 can read instructions stored in the memory 7203 and send the instructions to the processor 7201.

[0337] In some embodiments, the interface circuit 7202 executes the communication step S2101 of sending and / or receiving in the above method, and the processor 7201 executes other steps.

[0338] In some embodiments, terms such as interface circuit, interface, transceiver pin, and transceiver may be used interchangeably.

[0339] In some embodiments, the chip 7200 further includes one or more memories 7203 for storing instructions. Alternatively, all or part of the memories 7203 may be located outside the chip 7200.

[0340] The present disclosure also proposes a storage medium having instructions stored thereon. When the instructions are executed on the communication device 7100, the communication device 7100 executes any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto and may also be a temporary storage medium.

[0341] The present disclosure also provides a program product, which, when executed by the communication device 7100, enables the communication device 7100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0342] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

Claims

1. A communication method, characterized in that, The method includes: Receiving first information sent by a network device, where the first information is associated with a dataset, the dataset is used for a terminal to train an AI model, and the dataset is pre-assigned with an association identifier.

2. The method according to claim 1, characterized in that, The association identifier includes a first model identifier pre-assigned by the network device.

3. The method according to claim 2, characterized in that, The first model identifier of one dataset corresponds to one AI model; or The first model identifiers of multiple datasets correspond to the same AI model.

4. The method according to any one of claims 2-3, characterized in that After the AI model is trained and deployed on the terminal, the method further includes: Sending second information to the network device, where the second information is used to indicate the first model identifier corresponding to the AI model trained and deployed on the terminal.

5. The method according to claim 4, wherein When the second information is used to indicate multiple first model identifiers, the method further includes: Sending third information to the network device, where the third information is used to indicate the association between the multiple first model identifiers.

6. The method according to claim 1, wherein The association identifier includes a data identifier pre-assigned by the network device.

7. The method according to claim 6, wherein The data identifier of one dataset corresponds to one AI model; or The data identifiers of multiple datasets correspond to the same AI model.

8. The method according to claim 6, wherein After the AI model is trained and deployed on the terminal, the method further includes: Sending fourth information to the network device, where the fourth information is used to indicate the data identifier corresponding to the AI model trained and deployed on the terminal.

9. The method according to claim 7, characterized in that, The method further includes: Receiving fifth information sent by the network device, where the fifth information is used to indicate the second model identifier corresponding to the AI model.

10. The method according to claim 9, wherein When the fifth information is used to indicate multiple second model identifiers, the multiple second model identifiers are in one-to-one correspondence with multiple data identifiers.

11. The method according to claim 1, wherein The first information indicates the dataset in at least one of the following ways: The dataset is included in the first information; Configuration information is included in the first information, and the configuration information is used for the terminal to collect the dataset.

12. A communication method, characterized in that, The method includes: Sending first information to a terminal, where the first information is associated with a dataset, the dataset is used for the terminal to train an AI model, and the dataset is pre-assigned with an association identifier.

13. The method according to claim 12, characterized in that, The association identifier includes a first model identifier pre-assigned by the network device.

14. The method according to claim 13, wherein The first model identifier of one dataset corresponds to one AI model; or The first model identifiers of multiple datasets correspond to the same AI model.

15. The method according to any one of claims 13-14, characterized in that, After the AI model is trained and deployed on the terminal, the method further includes: Receiving second information sent by the terminal, where the second information is used to indicate the first model identifier corresponding to the AI model trained and deployed on the terminal.

16. The method according to claim 15, characterized in that, When the second information is used to indicate multiple first model identifiers, the method further includes: Receiving third information sent by the terminal, where the third information is used to indicate the association between the multiple first model identifiers.

17. The method according to claim 12, wherein The association identifier includes a data identifier pre-assigned by the network device.

18. The method according to claim 17, wherein The data identifier of one dataset corresponds to one AI model; or The data identifiers of multiple datasets correspond to the same AI model.

19. The method according to claim 17, wherein After the AI model is trained and deployed on the terminal, the method further includes: Receiving fourth information sent by the terminal, where the fourth information is used to indicate the data identifier corresponding to the AI model trained and deployed on the terminal.

20. The method according to claim 18, wherein The method further includes: Send a fifth piece of information to the terminal, where the fifth piece of information is used to indicate the second model identifier corresponding to the AI model.

21. The method according to claim 20, characterized in that, The fifth piece of information is used to indicate a plurality of second model identifiers, and the plurality of second model identifiers correspond one-to-one with a plurality of data identifiers.

22. The method according to claim 12, wherein The first piece of information indicates the data set in at least one of the following ways: The data set is included in the first piece of information; Configuration information is included in the first piece of information, and the configuration information is used for the terminal to collect the data set.

23. A communication method, characterized in that, The method includes: A network device sends the first piece of information to the terminal. The first piece of information is associated with a data set, and the data set is used for the terminal to train an AI model. The data set is pre-allocated with an associated identifier; The terminal receives the first piece of information.

24. A terminal, characterized in that, It includes: A transceiver module, configured to receive the first piece of information sent by the network device. The first piece of information is associated with a data set, and the data set is used for the terminal to train an AI model. The data set is pre-allocated with an associated identifier.

25. A network device, characterized in that, It includes: A transceiver module, configured to send the first piece of information to the terminal. The first piece of information is associated with a data set, and the data set is used for the terminal to train an AI model. The data set is pre-allocated with an associated identifier.

26. A terminal, characterized in that, It includes: One or more processors; Wherein, the processor is configured to execute the communication method according to any one of claims 1-11.

27. A network device, characterized in that, It includes: One or more processors; Wherein, the processor is configured to execute the communication method according to any one of claims 12-22.

28. A communication system, characterized in that, It includes a terminal and a network device. Among them, the terminal is configured to implement the communication method according to any one of claims 1-11, and the network device is configured to implement the communication method according to any one of claims 12-22.

29. A storage medium, the storage medium stores instructions, characterized in that, When the instruction runs on the communication device, the communication device is caused to execute the communication method according to any one of claims 1-11 or 12-22.

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