Communication method and apparatus, communication device, communication system, and storage medium

Abstract

Provided in the present disclosure are a communication method and apparatus, a communication device, a communication system, and a storage medium. The method comprises: a first entity training a first model, so as to obtain a trained first model; the first entity sending the trained first model to a second entity; the second entity sending the trained first model to a third entity; the third entity using the trained first model to support the transmission of an AI model between other NFs or entities. The performance of the communication system can be enhanced by means of an AI model.

Description

Communication method and device, communication equipment, communication system, and storage medium Technical Field

[0001] The present disclosure relates to the field of communication technologies, and in particular to communication methods and devices, communication equipment, communication systems, and storage media. Background Art

[0002] In the field of mobile communication networks, the introduction of AI (Artificial Intelligence) / ML (Machine Learning) into 3GPP (3rd Generation Partnership Project) can improve network performance, achieve network automation, and optimize energy efficiency.

[0003] Summary of the Invention

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

[0005] According to a first aspect of an embodiment of the present disclosure, a communication method is provided. The method is performed by a first entity and includes:

[0006] The first model is trained to obtain a trained first model; and the trained first model is sent to the second entity.

[0007] According to a second aspect of an embodiment of the present disclosure, a communication method is provided. The method is performed by a second entity and includes:

[0008] Get the trained first model.

[0009] According to a third aspect of an embodiment of the present disclosure, a communication method is provided. The method is performed by a third entity and includes:

[0010] Get the trained first model; use the trained first model

[0011] According to a fourth aspect of an embodiment of the present disclosure, a communication method is provided, which is performed by a communication system, the communication system including a first entity, a second entity, and a third entity. The method includes:

[0012] The first entity trains the first model to obtain a trained first model; the first entity sends the trained first model to the second entity; the second entity sends the trained first model to the third entity; and the third entity uses the trained first model.

[0013] According to a fifth aspect of an embodiment of the present disclosure, a first entity is provided, including:

[0014] The processing module is used to train the first model to obtain a trained first model; the transceiver module is used to send the trained first model to the second entity.

[0015] According to a sixth aspect of an embodiment of the present disclosure, a second entity is provided, including:

[0016] The transceiver module is used to obtain the trained first model.

[0017] According to a seventh aspect of the embodiments of the present disclosure, a third entity is proposed, including:

[0018] The transceiver module is used to obtain the trained first model; the processing module is used to use the trained first model.

[0019] According to an eighth aspect of an embodiment of the present disclosure, a communication device is provided, including:

[0020] A transceiver; a memory; and a processor, connected to the transceiver and the memory, respectively, configured to control the wireless signal reception and transmission of the transceiver by executing computer-executable instructions on the memory, and capable of implementing the communication method of any one of the first, second, third, and fourth aspects.

[0021] According to the ninth aspect of an embodiment of the present disclosure, a communication system is proposed, including a first entity, a second entity and a third entity, wherein the first entity is configured to implement the communication method of the first aspect, the second entity is configured to implement the communication method of the second aspect, and the third entity is configured to implement the communication method of the third aspect.

[0022] According to the tenth aspect of the embodiment of the present disclosure, a computer storage medium is proposed, which stores computer-executable instructions. After the computer-executable instructions are executed by the processor, the communication method of any one of the first aspect, the second aspect, the third aspect, and the fourth aspect can be implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or additional aspects and advantages of the present disclosure will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0024] [Corrected 23.08.2023 in accordance with Rule 91] FIG1 is a schematic diagram of the architecture of some communication systems provided by embodiments of the present disclosure;

[0025] FIG2 is an interactive diagram of a communication method provided by an embodiment of the present disclosure;

[0026] 3a-3b are flowcharts of a communication method provided in yet another embodiment of the present disclosure;

[0027] 4a-4b are flowcharts of a communication method provided in yet another embodiment of the present disclosure;

[0028] 5a-5c are flowcharts of a communication method provided in yet another embodiment of the present disclosure;

[0029] FIG6 is a flow chart of a communication method provided in yet another embodiment of the present disclosure;

[0030] FIG7 is an exemplary diagram of the architecture of a communication system provided by yet another embodiment of the present disclosure;

[0031] FIG8a is a schematic structural diagram of a first entity provided by an embodiment of the present disclosure;

[0032] FIG8b is a schematic structural diagram of a second entity provided by an embodiment of the present disclosure;

[0033] FIG8c is a schematic structural diagram of a third entity provided by an embodiment of the present disclosure;

[0034] FIG9a is a schematic structural diagram of a communication device provided by an embodiment of the present disclosure;

[0035] FIG9 b is a schematic structural diagram of a chip provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0036] The method disclosed in the present invention can be used to solve the technical problem that "the AI ​​model in the communication system can only be shared within the NWDAF and does not support sharing with other NFs, and AI technology is not used to enhance the communication system."

[0037] The embodiments of the present disclosure provide a communication method and apparatus, a communication device, a communication system, and a storage medium.

[0038] In a first aspect, an embodiment of the present disclosure proposes a communication method, which is performed by a first entity and includes: training a first model to obtain a trained first model; and sending the trained first model to a second entity.

[0039] In the above embodiment, the first entity can train an AI model and send the AI ​​model to the second entity. The AI ​​model can be transferred between the first entity and the second entity, solving the problem that the AI ​​model can only be shared within the NWDAF.

[0040] In combination with some embodiments of the first aspect, in some embodiments, training the first model to obtain a trained first model includes: collecting training data; and training the first model based on the training data to obtain a trained first model.

[0041] In the above embodiment, the first entity may collect data from the communication system as training data for model training to obtain an AI model.

[0042] In combination with some embodiments of the first aspect, in some embodiments, the first entity is at least one of a core network device, a terminal, an access network device, and a third-party server, and the second entity is at least one of a core network device, a terminal, an access network device, and a third-party server.

[0043] In the above embodiment, the AI ​​model can be obtained through training, which not only supports sharing of the AI ​​model within the NWDAF, but also supports sharing of the AI ​​model between other NFs or entities other than the NWDAF.

[0044] In combination with some embodiments of the first aspect, in some embodiments, the first model is used for at least one of the following: network data analysis; communication resource allocation; load balancing; positioning; interference management; beam management; channel state information CSI enhancement; network automation; network energy saving.

[0045] In the above embodiments, the problem that AI technology is not used to enhance the communication system is solved. The AI ​​model can be used to enhance the performance of the communication system, including for at least one of network data analysis, communication resource allocation, load balancing, positioning, interference management, beam management, channel state information CSI enhancement, network automation, and network energy saving.

[0046] In a second aspect, an embodiment of the present disclosure proposes a communication method, which is executed by a second entity and includes: obtaining a trained first model.

[0047] In the above embodiment, the second entity can obtain the AI ​​model, solving the problem that the AI ​​model can only be shared within the NWDAF.

[0048] In combination with some embodiments of the second aspect, in some embodiments, obtaining the trained first model includes: receiving the trained first model sent by the first entity.

[0049] In the above embodiment, the second entity can receive the AI ​​model trained by the first entity, and the AI ​​model can be transferred between the first entity and the second entity, solving the problem that the AI ​​model can only be shared within the NWDAF.

[0050] In combination with some embodiments of the second aspect, in some embodiments, the method further includes: storing the trained first model.

[0051] In the above embodiment, the second entity may store the trained AI model.

[0052] In combination with some embodiments of the second aspect, in some embodiments, the method further includes: receiving a subscription message sent by a third entity, wherein the third entity is used to use the trained first model, and the subscription message is used to subscribe to the trained first model.

[0053] In the above embodiment, the third entity can obtain the trained AI model through subscription.

[0054] In combination with some embodiments of the second aspect, in some embodiments, the method further includes: sending the trained first model to a third entity.

[0055] In the above embodiment, the AI ​​model can be transferred between the second entity and the third entity, solving the problem that the AI ​​model can only be shared within the NWDAF.

[0056] In combination with some embodiments of the first aspect, in some embodiments, the first entity is at least one of a core network device, a terminal, an access network device, and a third-party server, and the second entity is at least one of a core network device, a terminal, an access network device, and a third-party server.

[0057] In the above embodiment, the AI ​​model can be transferred between the first entity and the second entity, which not only supports sharing of the AI ​​model within the NWDAF, but also supports sharing of the AI ​​model between other NFs or entities other than the NWDAF.

[0058] In combination with some embodiments of the first aspect, in some embodiments, the first model is used for at least one of the following: network data analysis; communication resource allocation; load balancing; positioning; interference management; beam management; channel state information CSI enhancement; network automation; network energy saving.

[0059] In the above embodiments, the problem that AI technology is not used to enhance the communication system is solved. The AI ​​model can be used to enhance the performance of the communication system, including for at least one of network data analysis, communication resource allocation, load balancing, positioning, interference management, beam management, channel state information CSI enhancement, network automation, and network energy saving.

[0060] In a third aspect, an embodiment of the present disclosure proposes a communication method, which is executed by a third entity and includes: obtaining a trained first model; and using the trained first model.

[0061] In the above embodiment, the third entity can obtain the AI ​​model, solving the problem that the AI ​​model can only be shared within the NWDAF.

[0062] In combination with some embodiments of the third aspect, in some embodiments, obtaining the trained first model includes: receiving the trained first model sent by the second entity.

[0063] In the above embodiment, the AI ​​model can be transferred between the second entity and the third entity, solving the problem that the AI ​​model can only be shared within the NWDAF.

[0064] In combination with some embodiments of the third aspect, in some embodiments, the method also includes at least one of the following: sending a request message to the second entity, the request message is used to request the second entity to send the trained first model; sending a subscription message to the second entity, the subscription message is used to subscribe to the trained first model.

[0065] In the above embodiment, it is clear that the third entity can obtain the AI ​​model through a request message or subscription.

[0066] In combination with some embodiments of the third aspect, in some embodiments, using the trained first model includes: using the trained first model to perform at least one of the following: network data analysis; communication resource allocation; load balancing; positioning; interference management; beam management; channel state information CSI enhancement; network automation; network energy saving.

[0067] In the above embodiment, the problem that AI technology is not used to enhance the communication system is solved. The third entity can use the AI ​​model to enhance the performance of the communication system, including for at least one of network data analysis, communication resource allocation, load balancing, positioning, interference management, beam management, channel state information CSI enhancement, network automation, and network energy saving.

[0068] According to a fourth aspect of an embodiment of the present disclosure, a communication method is provided, which is performed by a communication system, the communication system including a first entity, a second entity, and a third entity. The method includes:

[0069] The first entity trains the first model to obtain a trained first model; the first entity sends the trained first model to the second entity; the second entity sends the trained first model to the third entity; and the third entity uses the trained first model.

[0070] In the above embodiment, a communication system architecture supporting an AI framework is provided to solve the problem that AI models can only be shared within the NWDAF, support the transmission of AI models between other NFs or entities, and use AI models to enhance the performance of the communication system.

[0071] According to a fifth aspect of an embodiment of the present disclosure, a first entity is provided, including:

[0072] The processing module is used to train the first model to obtain a trained first model; the transceiver module is used to send the trained first model to the second entity.

[0073] According to a sixth aspect of an embodiment of the present disclosure, a second entity is provided, including:

[0074] The transceiver module is used to obtain the trained first model.

[0075] According to a seventh aspect of the embodiments of the present disclosure, a third entity is proposed, including:

[0076] The transceiver module is used to obtain the trained first model; the processing module is used to use the trained first model.

[0077] According to an eighth aspect of an embodiment of the present disclosure, a communication device is provided, including:

[0078] A transceiver; a memory; and a processor, connected to the transceiver and the memory, respectively, configured to control the wireless signal reception and transmission of the transceiver by executing computer-executable instructions on the memory, and capable of implementing the communication method of any one of the first, second, third, and fourth aspects.

[0079] According to the ninth aspect of an embodiment of the present disclosure, a communication system is proposed, including a first entity, a second entity and a third entity, wherein the first entity is configured to implement the communication method of the first aspect, the second entity is configured to implement the communication method of the second aspect, and the third entity is configured to implement the communication method of the third aspect.

[0080] In combination with some embodiments of the ninth aspect, in some embodiments, the first entity is used to train the first model, the second entity is used to store the trained first model, and the third entity is used to use the trained first model.

[0081] According to the tenth aspect of the embodiment of the present disclosure, a computer storage medium is proposed, which stores computer-executable instructions. After the computer-executable instructions are executed by the processor, the communication method of any one of the first aspect, the second aspect, the third aspect, and the fourth aspect can be implemented.

[0082] In the eleventh 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 first aspect and the optional implementation of the first aspect, the second aspect and the optional implementation of the second aspect, and the third aspect and the optional implementation of the third aspect.

[0083] In the twelfth aspect, an embodiment of the present disclosure proposes a computer program, which, when running on a computer, enables the computer to execute the method described in the first aspect and its optional implementation, the second aspect and its optional implementation, and the third aspect and its optional implementation.

[0084] In a thirteenth aspect, an embodiment of the present disclosure provides a chip or a chip system, which includes a processing circuit configured to execute the method described in the optional implementation of the first, second, and third aspects above.

[0085] It is understandable that the first entity, second entity, third entity, communication system, storage medium, program product, computer program, chip, or chip system described above are all used to perform the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here.

[0086] The present disclosure provides a communication method. In some embodiments, the terms "communication method" and "information processing method" are interchangeable, "information transmission device" and "information processing device" and "communication device" are interchangeable, and "information processing system" and "communication system" are interchangeable.

[0087] 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.

[0088] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0089] 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.

[0090] 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.

[0091] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0092] In some embodiments, the terms "at least one of", "at least one of", "at least one of", "one or more", "a plurality of", "multiple", etc. can be used interchangeably.

[0093] In the embodiments of the present disclosure, descriptions such as "at least one of A and B," "A and / or B," "A in one case, B in another case," and "in response to one case A, in response to another case B" may include the following technical solutions depending on the circumstances: 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.

[0094] 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.

[0095] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] In some embodiments, devices, etc. can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", and "subject" can be used interchangeably.

[0100] In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

[0101] In some embodiments, the terms "access network device (AN device)", "radio access network device (RAN device)", "base station (BS)", "radio base station" "fixed station", "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission / reception point (TRP)", "panel", "antenna panel", "antenna array", "cell", "macro cell", "small cell", "femto cell", "pico cell", "sector", "cell group", "carrier", "component carrier", "bandwidth part (BWP)" and the like may be used interchangeably.

[0102] In some embodiments, the terms "terminal", "terminal device", "user equipment (UE)", "user terminal", "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc. can be used interchangeably.

[0103] In some embodiments, the access network device, the core network device, or the network device can be replaced by a terminal. For example, the various embodiments of the present disclosure can also be applied to a structure in which the communication between the access network device, the core network device, or the network device and the terminal is replaced by communication between multiple terminals (for example, it can also be called device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, it can also be set as a structure in which the terminal has all or part of the functions of the access network device. In addition, language such as "uplink" and "downlink" can also be replaced by language corresponding to communication between terminals (for example, "side"). For example, uplink channels, downlink channels, etc. can be replaced by side channels, and uplinks, downlinks, etc. can be replaced by side links.

[0104] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, the core network device, or the network device may have a structure that has all or part of the functions of the terminal.

[0105] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0106] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0107] 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.

[0108] FIG1 is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.

[0109] As shown in FIG. 1 , a communication system 100 may include a first entity 101 , a second entity 102 , and a third entity 103 .

[0110] In some embodiments, the first entity is used to collect training data and train the AI ​​model based on the training data, such as the first entity is an AI models training entity.

[0111] In some embodiments, the first entity may be at least one of a core network device, a terminal, an access network device, and a third-party server.

[0112] In some embodiments, the first entity is NWDAF (Network Data Analytics Function), which can be specifically AnLF (Analytics logical function) or MTLF (Model Training logical function). The first entity can also be other new core network devices, which is not limited in this disclosure.

[0113] In some embodiments, the first entity collects data from other necessary core network devices (other necessary NFs) or user equipment UE or access network equipment or OAM (Operation Administration and Maintenance, or network management).

[0114] In some embodiments, the second entity is used to store the AI ​​model and receive the trained AI model from the first entity, such as the second entity is an AI models storage entity.

[0115] In some embodiments, the second entity may be at least one of a core network device, a terminal, an access network device, and a third-party server.

[0116] In some embodiments, the second entity may be an NRF (Network Repository Function) or other new core network equipment.

[0117] In some embodiments, the third entity is an entity that uses an AI model, such as the third entity is an AI models inference entity.

[0118] In some embodiments, the third entity may be at least one of a core network device, a terminal, an access network device, and a third-party server.

[0119] In some embodiments, a core network device may be a single device including one or more network elements, or may be multiple devices or device groups, each including all or part of one or more network elements. A 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), or a Next Generation Core (NGC).

[0120] In some embodiments, the above-mentioned one or more network elements may include, for example: Policy Control Function (PCF), Application Function (AF), Network Application Function (NAF), Authentication and Key management for Applications Anchor Function (AAnF), Bootstrapping Server Functionality (BSF), Access and Mobility Management Function (AMF), User Plane Function (UPF), Session Management Function (SMF), Mobility Management Entity (MME), etc.

[0121] In some embodiments, the above-mentioned PCF, AF, NAF, AAnF, BSF, AMF, UPF, SMF, and MME are used for "function limitation", and the names are not limited to this.

[0122] In some embodiments, the above-mentioned PCF, AF, NAF, AAnF, BSF, AMF, UPF, SMF, and MME can be independent of the core network equipment.

[0123] In some embodiments, the above-mentioned PCF, AF, NAF, AAnF, BSF, AMF, UPF, SMF, and MME may be part of the core network equipment.

[0124] In some embodiments, the terminal 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.

[0125] 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 wireless fidelity (WiFi) system, but is not limited thereto.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The following embodiments of the present disclosure may be applied to the communication system 100 shown in Figure 1, or a portion thereof, but are not limited thereto. The entities shown in Figure 1 are illustrative only. The communication system may include all or part of the entities shown in Figure 1, or may include other entities outside of Figure 1. The number and form of the entities may be arbitrary. The connection relationship between the entities is illustrative only. The entities may be connected or disconnected, and the connection may be in any manner, including direct or indirect, wired or wireless.

[0130] Regarding AI / ML frameworks, relevant technologies have been researched and identified use cases for some AI / ML models. However, these technologies can only share AI / ML models between NWDAFs (Network Data Analytics Functions) and do not support sharing with other NFs (Network Functions). Therefore, AI technology has not been used to enhance communication systems.

[0131] FIG2 is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG2 , the embodiment of the present disclosure relates to a communication method for use in a communication system 100, the method comprising:

[0132] Step 2101: The first entity 101 collects training data.

[0133] In some embodiments, the first entity 101 is configured to collect training data and train the first model based on the training data.

[0134] In some embodiments, the first model is an AI model, and the first entity 101 is used to collect training data and train the AI ​​model based on the training data, such as the first entity 101 is an AI model training entity.

[0135] In some embodiments, the first entity 101 may be at least one of a core network device, a terminal, an access network device, and a third-party server.

[0136] In some embodiments, the first entity 101 is an NWDAF, which may specifically be an AnLF or a MTLF. The first entity 101 may also be other new core network devices, which is not limited in the present disclosure.

[0137] In some embodiments, the first entity 101 collects necessary data through other core network devices or user equipment UE or access network devices or OAM.

[0138] In some embodiments, the first entity 101 collects data through other necessary core network devices, for example, the first entity 101 collects necessary data from UPF, PCF, etc.

[0139] In some embodiments, the first entity 101 collects necessary data through access network equipment, for example, the first entity 101 collects necessary data from gNB, etc.

[0140] Step 2102: The first entity 101 trains the first model based on the training data to obtain a trained first model.

[0141] In some embodiments, the first entity 101 trains the AI ​​model by collecting necessary data from other core network devices or user equipment UE or access network devices or OAM.

[0142] Exemplarily, the first entity is NWDAF, which trains the AI ​​model by collecting necessary data from other network functions NFs (such as gNB, UPF, PCF, etc.) or OAM or UE.

[0143] In some embodiments, the first model is used for at least one of: network data analysis; communication resource allocation; load balancing; positioning; interference management; beam management; channel state information (CSI) enhancement; network automation; and network energy saving.

[0144] Exemplarily, the first model is used for network energy conservation. The communication network system, through first entity 101, collects user access data to train the first model. The first model can predict the number of user accesses within one or more future time periods. If the number of users at some base stations is currently low, the communication network system can shut down the servers corresponding to the base stations to achieve network energy conservation. Step 2103: First entity 101 sends the first model to second entity 102.

[0145] In some embodiments, the second entity 102 is configured to store the first model and receive the trained first model from the first entity.

[0146] In some embodiments, the first model is an AI model, and the second entity 102 is used to store the AI ​​model and receive the trained AI model from the first entity, such as the second entity is an AI model storage entity.

[0147] In some embodiments, the second entity 102 is at least one of a core network device, a terminal, an access network device, and a third-party server.

[0148] In some embodiments, the second entity 102 may be an NRF (Network Repository Function), an OAM, an NWDAF, or other new core network devices.

[0149] Exemplarily, the first entity is NWDAF, which sends the trained AI model to NRF or other new NF for storing the AI ​​model.

[0150] In step 2104 , the second entity 102 stores the trained first model.

[0151] In some embodiments, the first entity 101 sends the trained first model to the second entity 102 . The second entity 102 receives the trained first model and is responsible for storing the first model.

[0152] In some embodiments, each time the first entity 101 trains a new AI model, it sends the new AI model to the second entity 102 for storage.

[0153] In an embodiment of the present disclosure, it includes: the third entity 103 obtains the trained first model.

[0154] In some embodiments, the third entity 103 is an entity using the first model.

[0155] In some embodiments, the first model is an AI model, and the third entity 103 is an entity that uses the AI ​​model, such as the third entity 103 is an AI model reasoning entity.

[0156] In some embodiments, the third entity 103 may be at least one of a core network device, a terminal, an access network device, and a third-party server.

[0157] In some embodiments, the third entity 103 may be a core network device, such as an OAM or other NFs.

[0158] In some embodiments, the third entity 103 may be an access network device, such as a 5G radio access network NG-RAN.

[0159] In some embodiments, the third entity 103 may obtain the trained first model by including two solutions: step 2105a + step 2106 and step 2105b + step 2106.

[0160] Step 2105a: The third entity 103 sends a subscription message to the second entity 102.

[0161] The subscription message is used to subscribe to the trained first model.

[0162] In some embodiments, subscription is a messaging pattern, specifically referred to as the Publish-Subscribe pattern or the Observer model, which defines a one-to-many relationship between objects, allowing multiple observer objects to simultaneously monitor a subject object. When a change occurs to an object, all dependent objects are notified. For example, third entity 103 subscribes to a trained first model by sending a subscription message to second entity 102. When the trained first model changes, second entity 102 notifies all third entities 103 that have subscribed to the trained first model.

[0163] Step 2105b: The third entity 103 sends a request message to the second entity 102.

[0164] The request message is used to request the second entity 102 to send the trained first model.

[0165] In some embodiments, step 2105a and step 2105b may be performed in an interchanged order or simultaneously.

[0166] Step 2106 : The second entity 102 sends the trained first model to the third entity 103 .

[0167] In some embodiments, the third entity 103 may request the AI ​​model from the second entity 102 , and the second entity 102 may send the AI ​​model to the third entity 103 in response to the request of the third entity 103 .

[0168] For example, the UE or (R)AN or other NF may request an AI model from the NRF or other new NF responsible for AI storage, and the NRF or other new NF responsible for AI storage may send the AI ​​model to the UE or (R)AN or other NF in response to the request.

[0169] In some embodiments, the third entity 103 can subscribe to the AI ​​model from the second entity 102. After receiving the new version of the AI ​​model sent by the first entity 101, the second entity 102 can autonomously send the new version of the AI ​​model to the third entity 103 or notify the third entity 103 to obtain the new version of the AI ​​model.

[0170] Exemplarily, the first entity is NWDAF, and the UE or (R)AN or other NF subscribes to the AI ​​model from the NRF or other new NF responsible for AI storage. Whenever the NWDAF trains a new version of the AI ​​model, the NRF or other new NF responsible for AI storage will trigger a procedure to update the AI ​​model in the UE or (R)AN or other NF. The NRF or other new NF responsible for AI storage notifies the UE or (R)AN or other NF that subscribes to the AI ​​model, and delivers the new version of the AI ​​model after the AI ​​model is updated to the UE or (R)AN or other NF.

[0171] In some embodiments, a first network device is further included, and the first network device is responsible for forwarding the subscription notification of the second entity 102 to the third entity 103 , or is responsible for forwarding the request message sent by the second entity 102 to the third entity 103 .

[0172] In some embodiments, the first network device may be an access and mobility management function AMF network element.

[0173] For example, if the second entity is NRF and the third entity is UE, the UE sends a request message, the request message is used to request NRF to send the AI ​​model, the AMF forwards the request message sent by the UE to the NRF, and the NRF responds to the request message and sends the AI ​​model to the UE through the AMF.

[0174] For example, if the second entity is NRF and the third entity is UE, the UE subscribes to the AI ​​model to NRF. When there is a new version of the AI ​​model, NRF sends a subscription notification, and AMF forwards the subscription notification of NRF to the UE. The UE obtains the new version of the AI ​​model according to the subscription notification. If the subscription notification may include a server address, the UE establishes a connection to the server based on the server address to obtain the new version of the AI ​​model, or the NRF directly sends the new version of the AI ​​model to the UE through AMF.

[0175] Step 2107: The third entity 103 uses the trained first model.

[0176] In some embodiments, using the trained first model includes: using the trained first model to perform at least one of the following: network data analysis; communication resource allocation; load balancing; positioning; interference management; beam management; channel state information CSI enhancement; network automation; network energy saving.

[0177] Exemplarily, for the first model to be used for network energy saving, the communication network system collects user access data through the first entity 101 to train the first model. The first model can be used to predict the access number of the current user. The first entity 101 sends the first model to the second entity 102, and the second entity 102 sends the first model to the third entity 103 that subscribes to / requests the first model. The third entity 103 predicts the current number of users based on the first model. If the number of users is small, the third entity 103 can shut down the corresponding network server to achieve network energy saving.

[0178] The communication method involved in the embodiment of the present disclosure may include at least one of steps S2101 to S2107. For example, steps 2101 to 2104 may be implemented as independent embodiments, and steps S2101 to S2106 may be implemented as independent embodiments, but are not limited thereto.

[0179] In some embodiments, steps 2101-2102, step S2105a, step S2105b, and step S2106 are optional steps, and one or more of these steps may be omitted or replaced in different embodiments.

[0180] FIG3a is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG3a, the present disclosure embodiment relates to a communication method, which is executed by a first entity 101 and includes:

[0181] Step 3101: Collect training data.

[0182] The optional implementation of step S3101 can refer to the optional implementation of step S2101 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0183] Step 3102: Train the first model based on the training data to obtain a trained first model.

[0184] The optional implementation of step S3102 can refer to the optional implementation of step S2102 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0185] Step 3103: Send the trained first model to the second entity.

[0186] The optional implementation of step S3103 can refer to the optional implementation of step S2103 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0187] In some embodiments, the first entity is at least one of a core network device, a terminal, an access network device, and a third-party server, and the second entity is at least one of a core network device, a terminal, an access network device, and a third-party server.

[0188] In some embodiments, the first model is used for at least one of: network data analysis; communication resource allocation; load balancing; positioning; interference management; beam management; channel state information CSI enhancement; network automation; and network energy saving.

[0189] The communication method involved in the embodiment of the present disclosure may include at least one of steps S3101 to S3103.

[0190] In some embodiments, step 3101 and step 3102 are optional steps, and one or more of these steps may be omitted or replaced in different embodiments.

[0191] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0192] 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 the first entity 101 and includes:

[0193] Step 3201: Train the first model to obtain a trained first model.

[0194] The optional implementation of step S3201 can refer to the optional implementation of step S2101 and step S2102 in Figure 2, and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0195] In some embodiments, training the first model to obtain a trained first model includes: collecting training data; and training the first model based on the training data to obtain a trained first model.

[0196] Step 3202: Send the trained first model to the second entity.

[0197] The optional implementation of step S3103 can refer to the optional implementation of step S2103 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0198] In some embodiments, the first entity is at least one of a core network device, a terminal, an access network device, and a third-party server, and the second entity is at least one of a core network device, a terminal, an access network device, and a third-party server.

[0199] In some embodiments, the first model is used for at least one of: network data analysis; communication resource allocation; load balancing; positioning; interference management; beam management; channel state information CSI enhancement; network automation; and network energy saving.

[0200] The communication method involved in the embodiment of the present disclosure may include at least one of steps S3101 to S3102.

[0201] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0202] FIG4a is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4a , the embodiment of the present disclosure relates to a communication method, which is executed by the second entity 102 and includes:

[0203] Step 4101: Receive a trained first model sent by a first entity.

[0204] The optional implementation of step 4101 can refer to the optional implementation of step 2103 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0205] In some embodiments, the first entity is at least one of a core network device, a terminal, an access network device, and a third-party server, and the second entity is at least one of a core network device, a terminal, an access network device, and a third-party server.

[0206] In some embodiments, the first model is used for at least one of: network data analysis; communication resource allocation; load balancing; positioning; interference management; beam management; channel state information CSI enhancement; network automation; and network energy saving.

[0207] Step 4102: Store the trained first model.

[0208] The optional implementation of step 4102 can refer to the optional implementation of step 2104 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0209] Step 4103: Receive a subscription message sent by a third entity.

[0210] The third entity is used to use the trained first model, and the subscription message is used to subscribe to the trained first model.

[0211] The optional implementation of step 4103 can refer to the optional implementation of step 2105a in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0212] Step 4104: Send the trained first model to the third entity.

[0213] The optional implementation of step 4104 can refer to the optional implementation of step 2106 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0214] The communication method involved in the embodiment of the present disclosure may include at least one of steps S4101 to S4104. Step 4101+step 4102+step 4104 may be implemented as an independent embodiment, but is not limited thereto.

[0215] In some embodiments, step 4102, step 4103, and step 4104 are optional steps, and one or more of these steps may be omitted or replaced in different embodiments.

[0216] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, and the optional methods or optional examples can be arbitrarily combined and can be arbitrarily combined with other embodiments or examples.

[0217] 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 performed by the second entity 102 and includes:

[0218] Step 4201: Obtain a trained first model.

[0219] The optional implementation of step S4201 can refer to the optional implementation of step S2103 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0220] In some embodiments, obtaining the trained first model includes: receiving the trained first model sent by the first entity.

[0221] In some embodiments, the method further includes storing the trained first model.

[0222] In some embodiments, the method further includes: receiving a subscription message sent by a third entity, wherein the third entity is used to use the trained first model, and the subscription message is used to subscribe to the trained first model.

[0223] In some embodiments, the method further includes: sending the trained first model to a third entity.

[0224] In some embodiments, the first entity is at least one of a core network device, a terminal, an access network device, and a third-party server, and the second entity is at least one of a core network device, a terminal, an access network device, and a third-party server.

[0225] In some embodiments, the first model is used for at least one of: network data analysis; communication resource allocation; load balancing; positioning; interference management; beam management; channel state information CSI enhancement; network automation; and network energy saving.

[0226] FIG5a is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG5a, the embodiment of the present disclosure relates to a communication method, which is executed by a third entity 103 and includes:

[0227] Step 5101: Send a request message to the second entity.

[0228] The request message is used to request the second entity to send the trained first model.

[0229] The optional implementation of step 5101 can refer to the optional implementation of step 2105b in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0230] Step 5102: Receive the trained first model sent by the second entity.

[0231] The optional implementation of step 5102 can refer to the optional implementation of step 2106 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0232] Step 5103: Use the trained first model.

[0233] The optional implementation of step 5103 can refer to the optional implementation of step 2107 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0234] In some embodiments, using the trained first model includes: using the trained first model to perform at least one of the following: network data analysis; communication resource allocation; load balancing; positioning; interference management; beam management; channel state information CSI enhancement; network automation; network energy saving.

[0235] The communication method involved in the embodiment of the present disclosure may include at least one of steps S5101 to S5103. Step 5102 and step 5103 may be implemented as independent embodiments, but are not limited thereto.

[0236] In some embodiments, step 5101 and step 5102 are optional steps, and one or more of these steps may be omitted or replaced in different embodiments.

[0237] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, and the optional methods or optional examples can be arbitrarily combined and can be arbitrarily combined with other embodiments or examples.

[0238] FIG5b is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG5b, the embodiment of the present disclosure relates to a communication method, which is executed by a third entity 103 and includes:

[0239] Step 5201: Send a subscription message to the second entity.

[0240] The subscription message is used to subscribe to the trained first model.

[0241] The optional implementation of step 5201 can refer to the optional implementation of step 2105a in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0242] Step 5202: Receive the trained first model sent by the second entity.

[0243] The optional implementation of step 5202 can refer to the optional implementation of step 2106 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0244] Step 5203: Use the trained first model.

[0245] The optional implementation of step 5203 can refer to the optional implementation of step 2107 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0246] In some embodiments, using the trained first model includes: using the trained first model to perform at least one of the following: network data analysis; communication resource allocation; load balancing; positioning; interference management; beam management; channel state information CSI enhancement; network automation; network energy saving.

[0247] The communication method involved in the embodiment of the present disclosure may include at least one of steps S5201 to S5203. Step 5202 and step 5203 may be implemented as independent embodiments, but are not limited thereto.

[0248] In some embodiments, step 5201 and step 5202 are optional steps, and one or more of these steps may be omitted or replaced in different embodiments.

[0249] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, and the optional methods or optional examples can be arbitrarily combined and can be arbitrarily combined with other embodiments or examples.

[0250] FIG5c is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG5c, the present disclosure embodiment relates to a communication method, which is executed by a third entity 103 and includes:

[0251] Step 5301: Obtain the trained first model.

[0252] The subscription message is used to subscribe to the trained first model.

[0253] The optional implementation of step 5301 can be found in the optional implementation of step 2105a, step 2105b, step 2106 in Figure 2, and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0254] In some embodiments, obtaining the trained first model includes: receiving the trained first model sent by the second entity.

[0255] In some embodiments, the method further includes at least one of the following: sending a request message to the second entity, the request message being used to request the second entity to send the trained first model; and sending a subscription message to the second entity, the subscription message being used to subscribe to the trained first model.

[0256] Step 5302: Use the trained first model.

[0257] The optional implementation of step 5302 can refer to the optional implementation of step 2107 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0258] In some embodiments, using the trained first model includes: using the trained first model to perform at least one of the following: network data analysis; communication resource allocation; load balancing; positioning; interference management; beam management; channel state information CSI enhancement; network automation; network energy saving.

[0259] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, and the optional methods or optional examples can be arbitrarily combined and can be arbitrarily combined with other embodiments or examples.

[0260] FIG6 is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG6 , the embodiment of the present disclosure relates to a communication method for use in a communication system 100, the method comprising:

[0261] Step 6101: The first entity trains the first model to obtain a trained first model.

[0262] For optional implementations of step 6101, please refer to the optional implementations of step 2101 and step 2102 in Figure 2, and other related parts in the embodiment involved in Figure 2. In some optional embodiments, step 6101 may include step 3101 and step 3102 in Figure 3a and the optional implementations of Figures 3a-3b, and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0263] Step 6102: The first entity sends the trained first model to the second entity.

[0264] For optional implementations of step 6102, see the optional implementations of step 2103 in FIG2 and other related parts of the embodiment involved in FIG2. In some optional embodiments, step 6102 may include step 3103 in FIG3a, step 4101 in FIG4a, and optional implementations of FIG3a-3b, FIG4a-4b, and other related parts of the embodiment involved in FIG2, which are not further described here.

[0265] Step 6103: The second entity sends the trained first model to the third entity.

[0266] For optional implementations of step 6103, see the optional implementations of step 2106 in FIG2 and other related parts in the embodiment involved in FIG2. In some optional embodiments, step 6103 may include step 4104 in FIG4a, step 5102 in FIG5a, step 5202 in FIG5b, as well as the optional implementations of FIG4a-4b, FIG5a-5b, and other related parts in the embodiment involved in FIG2, which are not further described here.

[0267] Step 6104: The third entity uses the trained first model.

[0268] For optional implementations of step 6101, see the optional implementations of step 2107 in Figure 2 and other related parts of the embodiment involved in Figure 2. In some optional embodiments, step 6104 may include the optional implementations of Figures 5a-5c and other related parts of the embodiment involved in Figure 2, which will not be repeated here.

[0269] In some embodiments, the above method may include the method described in the above embodiments of the communication system side, the first entity side, the second entity side, the second entity side, etc., which will not be repeated here.

[0270] The present disclosure provides an AI-enabled 5G system, including the following entities:

[0271] AI model training entity (AI training entity), AI model storage entity is a logical entity responsible for training AI models by collecting data from other necessary NFs or UE or OAM. In the 5G system, the AI ​​model storage entity can be the network data analysis function NWDAF or other new network functions NF;

[0272] AI model storage entity: a logical entity responsible for storing AI models. In 5GS, it can be a network storage function (NRF) or other new network functions (NF).

[0273] AI model inference entity (AI inference entity), an AI model inference entity is an entity that uses an AI model. The AI ​​model inference entity can be a user equipment UE and a (radio) access network (R)AN such as a base station gNB.

[0274] As shown in FIG7 , the above system is exemplarily introduced by taking the AI ​​model training entity as NWDAF, the AI ​​model storage entity as NRF, and the AI ​​model reasoning entity as UE and (R)AN as an example.

[0275] In some embodiments, the NWDAF trains the AI ​​model by collecting necessary data from other NFs (e.g., gNB, UPF, PCF, etc.) or OAM or UE. When the AI ​​model training is completed, the NWDAF sends the trained AI model to the NRF or other new NF for storing the AI ​​model.

[0276] In some embodiments, the NRF receives the trained AI model from the NWDAF and may trigger a procedure to update the AI ​​model in the UE, (R)AN, or other NF.

[0277] In some embodiments, the UE or RAN may request the AI ​​model from the NRF (or other new NF responsible for AI storage), or the NRF may deliver the AI ​​model after the AI ​​model is updated to the UE or RAN or other NF.

[0278] In some embodiments, the UE or RAN may perform services based on the AI ​​model.

[0279] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a user equipment in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0280] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units or modules by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0281] In the embodiment of the present disclosure, the processor is a circuit with signal processing capability. In one implementation, the processor can be a circuit with instruction reading and execution capability, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP); in another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit, and the logical relationship of the above hardware circuit is fixed or reconfigurable, such as a hardware circuit implemented by a processor as an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0282] Figure 8a is a schematic diagram of the structure of the first entity proposed in the embodiment of the present disclosure. As shown in Figure 8a, the first entity includes: a processing module for training the first model to obtain a trained first model; a transceiver module for sending the trained first model to the second entity. Optionally, the above-mentioned transceiver module is used to execute the steps related to "sending the trained first model to the second entity" performed by the first entity 101 in any of the above methods, which will not be repeated here. Optionally, the above-mentioned processing module is used to execute the steps related to "training the first model to obtain a trained first model" performed by the first entity 101 in any of the above methods, which will not be repeated here.

[0283] Figure 8b is a schematic diagram of the structure of the second entity proposed in an embodiment of the present disclosure. As shown in Figure 8b, the second entity includes a transceiver module for obtaining the trained first model. Optionally, the transceiver module is used to perform the steps related to "obtaining the trained first model" performed by second entity 102 in any of the above methods, and will not be further described here.

[0284] Figure 8c is a schematic diagram of the structure of the third entity proposed in an embodiment of the present disclosure. As shown in Figure 8c, the third entity includes: a transceiver module for obtaining the trained first model; and a processing module for using the trained first model. Optionally, the transceiver module is used to execute the steps related to "obtaining the trained first model" executed by the third entity 103 in any of the above methods, which are not repeated here. Optionally, the processing module is used to execute the steps related to "obtaining and using the trained first model" executed by the third entity 103 in any of the above methods, which are not repeated here.

[0285] Figure 9a is a schematic diagram of the structure of a communication device 9100 proposed in an embodiment of the present disclosure. Communication device 9100 can be a first network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user equipment, etc.), a chip, a chip system, or a processor that supports a network device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal to implement any of the above methods. Communication device 9100 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.

[0286] As shown in Figure 9a, the communication device 9100 includes one or more processors 9101. The processor 9101 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, and the central processing unit can be used to control the communication device (such as a base station, baseband chip, terminal device, terminal device chip, DU or CU, etc.), execute programs, and process program data. The processor 9101 is used to call instructions to enable the communication device 9100 to perform any of the above methods.

[0287] In some embodiments, the communication device 9100 further includes one or more memories 9102 for storing instructions. Optionally, all or part of the memories 9102 may be located outside the communication device 9100.

[0288] In some embodiments, the communication device 9100 further includes one or more transceivers 9103. When the communication device 9100 includes one or more transceivers 9103, the communication steps such as sending and receiving in the above method are performed by the transceiver 9103, and the other steps are performed by the processor 9101.

[0289] In some embodiments, a transceiver may include a receiver and a transmitter, which 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.

[0290] Optionally, the communication device 9100 further includes one or more interface circuits 9104, which are connected to the memory 9102. The interface circuits 9104 can be used to receive signals from the memory 9102 or other devices, and can be used to send signals to the memory 9102 or other devices. For example, the interface circuits 9104 can read instructions stored in the memory 9102 and send the instructions to the processor 9101.

[0291] The communication device 9100 described in the above embodiment may be a network device or a terminal, but the scope of the communication device 9100 described in the present disclosure is not limited thereto, and the structure of the communication device 9100 may not be limited by FIG. 9a. 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.

[0292] FIG9b is a schematic diagram of the structure of a chip 9200 according to an embodiment of the present disclosure. If the communication device 9100 can be a chip or a chip system, reference can be made to the schematic diagram of the structure of the chip 9200 shown in FIG9b , but the present disclosure is not limited thereto.

[0293] The chip 9200 includes one or more processors 9201, and the processor 9201 is used to call instructions so that the chip 9200 executes any of the above methods.

[0294] In some embodiments, chip 9200 further includes one or more interface circuits 9202, which are connected to memory 9203. Interface circuits 9202 can be used to receive signals from memory 9203 or other devices, and can be used to send signals to memory 9203 or other devices. For example, interface circuit 9202 can read instructions stored in memory 9203 and send the instructions to processor 9201. Optionally, the terms interface circuit, interface, transceiver pin, and transceiver are interchangeable.

[0295] In some embodiments, the chip 9200 further includes one or more memories 9203 for storing instructions. Alternatively, all or part of the memories 9203 may be located outside the chip 9200.

[0296] The present disclosure also proposes a storage medium having instructions stored thereon, which, when executed on the communication device 9100, causes the communication device 9100 to execute 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.

[0297] The present disclosure also provides a program product, which, when executed by the communication device 9100, enables the communication device 9100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0298] 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.

[0299] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer programs. When the computer program is loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present disclosure are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0300] The correspondences shown in the tables of the present disclosure can be configured or predefined. The values ​​of the information in each table are merely examples and can be configured to other values, which are not limited by the present disclosure. When configuring the correspondences between information and parameters, it is not necessarily required to configure all the correspondences shown in each table. For example, in the tables of the present disclosure, the correspondences shown in certain rows may not be configured. For another example, appropriate deformation adjustments can be made based on the above tables, such as splitting, merging, etc. The names of the parameters shown in the titles of the above tables may also adopt other names that can be understood by the communication device, and the values ​​or representations of the parameters may also adopt other values ​​or representations that can be understood by the communication device. When implementing the above tables, other data structures may also be used, such as arrays, queues, containers, stacks, linear lists, pointers, linked lists, trees, graphs, structures, classes, heaps, hash tables or hash tables, etc.

[0301] The predefined in the present disclosure may be understood as defined, predefined, stored, pre-stored, pre-negotiated, pre-configured, solidified, or pre-burned.

[0302] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0303] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0304] The above description is merely a specific embodiment of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A communication method, characterized in that: The method is performed by a first entity, and comprises: Training the first model to obtain a trained first model; The trained first model is sent to a second entity.

2. The method according to claim 1, characterized in that The step of training the first model to obtain a trained first model includes: Collect training data; Based on the training data, the first model is trained to obtain a trained first model.

3. The method according to claim 1 or 2, characterized in that: The first entity is at least one of a core network device, a terminal, an access network device, and a third-party server, and the second entity is at least one of a core network device, a terminal, an access network device, and a third-party server.

4. The method according to any one of claims 1 to 3, characterized in that The first model is used for at least one of the following: Network data analysis; Communication resource allocation; Load balancing; position; Interference management; Beam management; Channel state information CSI enhancement; Network automation; Network energy saving.

5. A communication method, characterized in that: The method is performed by a second entity, and the method includes: Get the trained first model.

6. The method according to claim 5, characterized in that The obtaining of the trained first model comprises: The trained first model sent by the first entity is received.

7. The method according to claim 5 or 6, characterized in that: The method further comprises: The trained first model is stored.

8. The method according to any one of claims 5 to 7, characterized in that The method comprises: Receive a subscription message sent by a third entity, The third entity is used to use the trained first model, and the subscription message is used to subscribe to the trained first model.

9. The method according to any one of claims 5 to 8, characterized in that The method comprises: The trained first model is sent to a third entity.

10. The method according to any one of claims 5 to 9, characterized in that The first entity is at least one of a core network device, a terminal, an access network device, and a third-party server, and the second entity is at least one of a core network device, a terminal, an access network device, and a third-party server.

11. The method according to any one of claims 5 to 10, characterized in that The first model is used for at least one of the following: Network data analysis; Communication resource allocation; Load balancing; position; Interference management; Beam management; Channel state information CSI enhancement; Network automation; Network energy saving.

12. A communication method, characterized in that: The method is performed by a third entity, and the method includes: Obtaining a trained first model; The trained first model is used.

13. The method according to claim 12, characterized in that The obtaining of the trained first model comprises: The trained first model is received from the second entity.

14. The method according to claim 13, characterized in that The method further comprises at least one of the following: Sending a request message to the second entity, where the request message is used to request the second entity to send the trained first model; A subscription message is sent to the second entity, where the subscription message is used to subscribe to the trained first model.

15. The method according to claim 12, characterized in that The using the trained first model comprises: Use the trained first model to perform at least one of the following: Network data analysis; Communication resource allocation; Load balancing; position; Interference management; Beam management; Channel state information CSI enhancement; Network automation; Network energy saving.

16. A communication method, characterized in that: The method is performed by a communication system, the communication system includes a first entity, a second entity and a third entity, and the method includes: The first entity trains the first model to obtain a trained first model; The first entity sends the trained first model to the second entity; The second entity sends the trained first model to the third entity; The third entity uses the trained first model.

17. A first entity, characterized in that: include: A processing module, used for training the first model to obtain a trained first model; A transceiver module is used to send the trained first model to the second entity.

18. A second entity, characterized in that: include: The transceiver module is used to obtain the trained first model.

19. A third entity, characterized in that: include: A transceiver module, used to obtain a trained first model; A processing module is used to use the trained first model.

20. A communication device, wherein: include: Transceiver; Memory; A processor is connected to the transceiver and the memory, respectively, and is configured to control the wireless signal reception and transmission of the transceiver by executing computer executable instructions on the memory, and can implement any one of the methods of claims 1-16.

21. A communication system, characterized in that: Includes a first entity, a second entity and a third entity, wherein the first entity is configured to implement the method of any one of claims 1 to 4, the second entity is configured to implement the method of any one of claims 5 to 11, and the third entity is configured to implement the method of any one of claims 12 to 15.

22. The method according to claim 21, characterized in that The first entity is used to train a first model, the second entity is used to store the trained first model, and the third entity is used to use the trained first model.

23. A computer storage medium, wherein: The computer storage medium stores computer executable instructions; after the computer executable instructions are executed by the processor, the method described in any one of claims 1 to 16 can be implemented.

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