WO2023125933A1

WO2023125933A1 - Ai network information transmission method and apparatus, and communication device

Info

Publication number: WO2023125933A1
Application number: PCT/CN2022/143951
Authority: WO
Inventors: 任千尧; 孙鹏; 塔玛拉卡拉盖施
Original assignee: 维沃移动通信有限公司
Priority date: 2021-12-31
Filing date: 2022-12-30
Publication date: 2023-07-06
Also published as: CN116419321A

Abstract

The present application belongs to the technical field of communications. Disclosed are an AI network information transmission method and apparatus, and a communication device. The AI network information transmission method in the embodiments of the present application comprises: a first end receiving AI network information sent by at least one second end, wherein the AI network information sent by each second end comprises at least one of a network structure and a network parameter.

Description

AI network information transmission method, device and communication equipment

Cross References to Related Applications

This application claims priority to Chinese Patent Application No. 202111672400.3 filed in China on December 31, 2021, the entire contents of which are hereby incorporated by reference.

technical field

The present application belongs to the technical field of communication, and in particular relates to an AI network information transmission method, device and communication equipment.

Background technique

Artificial Intelligence (AI) is a new technical science that researches and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. more and more widely. At present, people have begun to study the application of AI networks in communication systems. For example, communication data can be transmitted between network-side devices and terminals through AI networks. In a communication system, the entire AI network is usually transmitted together, resulting in a large system overhead.

Contents of the invention

Embodiments of the present application provide an AI network information transmission method, device, and communication equipment, which can solve the problem of relatively large transmission overhead of communication equipment in AI network transmission in the related art.

In the first aspect, an AI network information transmission method is provided, including:

The first end receives at least one AI network information sent by the second end;

Wherein, the AI network information sent by each second terminal includes at least one of network structure and network parameters.

In the second aspect, an AI network information transmission method is provided, including:

The second end sends AI network information to at least one first end;

Wherein, the AI network information includes at least one of network structure and network parameters.

In a third aspect, an AI network information transmission device is provided, including:

A receiving module, configured to receive AI network information sent by at least one second end;

In the fourth aspect, an AI network information transmission device is provided, including:

A sending module, configured to send AI network information to at least one first end;

In a fifth aspect, a communication device is provided, including a processor and a memory, the memory stores programs or instructions that can run on the processor, and when the programs or instructions are executed by the processor, the first The steps of the AI network information transmission method described in the aspect, or the steps of implementing the AI network information transmission method described in the second aspect.

A sixth aspect provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the AI network information transmission method as described in the first aspect are implemented , or implement the steps of the AI network information transmission method as described in the second aspect.

In the seventh aspect, there is provided a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the AI described in the first aspect The steps of the network information transmission method, or the steps of realizing the AI network information transmission method described in the second aspect.

In an eighth aspect, a computer program/program product is provided, the computer program/program product is stored in a storage medium, and the computer program/program product is executed by at least one processor to implement the method described in the first aspect The steps of the AI network information transmission method, or the steps of realizing the AI network information transmission method as described in the second aspect.

In the embodiment of the present application, the first end receives at least one AI network information sent by the second end, thereby making the transmission of AI network information between communication devices more flexible, and no longer limited to one-to-one sending and receiving; and , the AI network information sent by each second end includes at least one of the network structure and network parameters, and then there is no need to transmit the entire AI network including all network structures and network parameters together during the communication process. The network structure and network parameters can also be sent separately, which can effectively reduce the transmission overhead in the communication process. At the same time, the second end with different permissions can send different information of the AI network separately, further improving flexibility.

Description of drawings

FIG. 1 is a block diagram of a wireless communication system to which an embodiment of the present application is applicable;

FIG. 2 is a flow chart of an AI network information transmission method provided by an embodiment of the present application;

Fig. 3 is a flow chart of another AI network information transmission method provided by the embodiment of the present application;

FIG. 4 is a structural diagram of an AI network information transmission device provided by an embodiment of the present application;

Fig. 5 is a structural diagram of another AI network information transmission device provided by the embodiment of the present application;

FIG. 6 is a structural diagram of a communication device provided by an embodiment of the present application;

FIG. 7 is a structural diagram of a terminal provided in an embodiment of the present application;

FIG. 8 is a structural diagram of a network-side device provided by an embodiment of the present application;

FIG. 9 is a structural diagram of another network-side device provided by an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all of them. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in this application belong to the protection scope of this application.

The terms "first", "second" and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific sequence or sequence. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or described herein and that "first" and "second" distinguish objects. It is usually one category, and the number of objects is not limited. For example, there may be one or more first objects. In addition, "and/or" in the description and claims means at least one of the connected objects, and the character "/" generally means that the related objects are an "or" relationship.

It is worth pointing out that the technology described in the embodiment of this application is not limited to the Long Term Evolution (Long Term Evolution, LTE)/LTE-Advanced (LTE-Advanced, LTE-A) system, and can also be used in other wireless communication systems, such as code Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access, OFDMA), Single-carrier Frequency Division Multiple Access (Single-carrier Frequency Division Multiple Access, SC-FDMA) and other systems. The terms "system" and "network" in the embodiments of the present application are often used interchangeably, and the described technologies can be used for the above-mentioned systems and radio technologies as well as other systems and radio technologies. The following description describes the New Radio (NR) system for illustrative purposes, and uses NR terminology in most of the following descriptions, but these techniques can also be applied to applications other than NR system applications, such as the 6th generation (6 ^th Generation, 6G) communication system.

Fig. 1 shows a block diagram of a wireless communication system to which the embodiment of the present application is applicable. The wireless communication system includes a terminal 11 and a network side device 12 . Wherein, the terminal 11 can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer) or a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), a palmtop computer, a netbook, a super mobile personal computer (ultra-mobile personal computer, UMPC), mobile Internet device (Mobile Internet Device, MID), augmented reality (augmented reality, AR) / virtual reality (virtual reality, VR) equipment, robot, wearable device (Wearable Device) , Vehicle User Equipment (VUE), Pedestrian User Equipment (PUE), smart home (home equipment with wireless communication functions, such as refrigerators, TVs, washing machines or furniture, etc.), game consoles, personal computers (personal computer, PC), teller machine or self-service machine and other terminal side devices, wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart feet bracelets, smart anklets, etc.), smart wristbands, smart clothing, etc. It should be noted that, the embodiment of the present application does not limit the specific type of the terminal 11 . The network side device 12 may include an access network device or a core network device, where the access network device may also be called a radio access network device, a radio access network (Radio Access Network, RAN), a radio access network function, or a wireless network. access network unit. The access network equipment may include a base station, a wireless local area network (Wireless Local Area Network, WLAN) access point, or a WiFi node, etc., and the base station may be called a node B, an evolved node B (Evolved Node B, eNB), an access point, or a base station. Base Transceiver Station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home Node B, Home Evolved Node B, Transmitting Receiving Point (Transmitting Receiving Point, TRP) or some other suitable term in the field, as long as the same technical effect is achieved, the base station is not limited to specific technical terms. It should be noted that, in the embodiment of this application, only The base station in the NR system is taken as an example for introduction, and the specific type of the base station is not limited. Core network equipment may include but not limited to at least one of the following: core network nodes, core network functions, mobility management entities (Mobility Management Entity, MME), access mobility management functions (Access and Mobility Management Function, AMF), session management functions (Session Management Function, SMF), User Plane Function (UPF), Policy Control Function (Policy Control Function, PCF), Policy and Charging Rules Function (PCRF), edge application service Discovery function (Edge Application Server Discovery Function, EASDF), unified data management (Unified Data Management, UDM), unified data storage (Unified Data Repository, UDR), home subscriber server (Home Subscriber Server, HSS), centralized network configuration ( Centralized network configuration, CNC), network storage function (Network Repository Function, NRF), network exposure function (Network Exposure Function, NEF), local NEF (Local NEF, or L-NEF), binding support function (Binding Support Function, BSF), application function (Application Function, AF), etc. It should be noted that, in the embodiment of the present application, only the core network equipment in the NR system is used as an example for introduction, and the specific type of the core network equipment is not limited.

The following describes in detail the AI network information transmission method provided by the embodiment of the present application through some embodiments and application scenarios with reference to the accompanying drawings.

Please refer to Figure 2, Figure 2 is a flow chart of an AI network information transmission method provided in the embodiment of the present application, as shown in Figure 2, the method includes the following steps:

Step 201. The first terminal receives the AI network information sent by at least one second terminal; wherein, each AI network information sent by the second terminal includes at least one of network structure and network parameters.

Wherein, the first end and the second end are communication devices with sending and receiving functions. For example, the first end is a terminal, and the second end is an access network device (such as a base station); or, the first end is an access network device, and the second end is a terminal; or, the The first end is an access network device, and the second end is a core network device; or, the first end and the second end are different nodes of the terminal; or, the first end and the second end are access different nodes of the network access equipment; or, the first end and the second end are different nodes of the core network equipment, etc., and this application does not list them one by one.

In some embodiments, the first end may also be called a receiving end, and the second end may also be called a sending end. It should be noted that the first end and the second end are not specific, and the first end in the embodiment of the present application can also be used as the second end or the third end mentioned later in other scenarios. In the embodiment of the present application The second terminal can also be used as the first terminal or the third terminal in some scenarios. Similarly, the third terminal mentioned later in the embodiment of the present application can be used as the first terminal or the second terminal in some scenarios.

In this embodiment of the present application, the AI network information sent by the second end includes at least one of network structure and network parameters. Optionally, the AI network information may be the network structure and/or network parameters of a certain AI network or multiple AI networks. In some embodiments, the AI network may also be called an AI neural network, an AI model, or the like.

It should be noted that the first end can receive at least one AI network information sent by the second end, and each AI network information sent by the second end can include network structure and/or network parameters, and each AI network information sent by the second end Information can vary. For example, the first end receives three AI network information sent by the second end, one of the AI network information sent by the second end is the network structure of the first AI network, and the AI network information sent by the other second end is the first AI network information. The network parameters of the network, the last AI network information sent by the second end is the network structure and network parameters of the second AI network. Or, the first end receives part of the network structure and network parameters of an AI network sent by the second end; or, the first end receives the network structure or network parameters of an AI network sent by the second end, etc., this Application examples do not enumerate too much.

In the embodiment of the present application, the first end receives at least one AI network information sent by the second end, thereby making the transmission of AI network information between communication devices more flexible, and no longer limited to one-to-one sending and receiving; and, The AI network information sent by each second end includes at least one of the network structure and network parameters, and then there is no need to transmit the entire AI network including all network structures and network parameters together during the communication process. The structure and network parameters can also be sent separately, which can effectively reduce the transmission overhead in the communication process.

Optionally, the step 201 includes:

The first end receives at least one AI network information broadcast by the second end.

For example, the first end is a terminal, the second end is a base station, and the terminal may receive AI network information broadcast by at least one base station. Optionally, the base station can also broadcast the AI network information to at least one terminal. For the AI network information broadcast by the same base station, the AI network information received by each terminal is the same; for different base stations broadcast The AI network information can be different. For example, the AI network information broadcast by base station A is the network structure of the first AI network, and the AI network information broadcast by base station B is the network parameters of the first AI network.

Optionally, the step 201 may include:

The first end receives the AI network information sent by at least two second ends, wherein the AI network information sent by each second end is different;

After the first end receives the AI network information sent by at least two second ends, the method further includes:

The first end splices the received at least two pieces of AI network information.

For example, the first end is the terminal, and the second end is the base station. The terminal receives the AI network information sent by the two base stations. The AI network information sent by one base station is part of the network structure of the first AI network, and the AI network information sent by the other base station. The information is the remaining part of the network structure of the first AI network. After receiving the AI network information sent by the two base stations, the terminal splices part of the network structure of the first AI network structure with the remaining part of the network structure, and then The complete network structure of the first AI network is obtained by splicing.

Optionally, the AI network information sent by the second end may also be a network parameter, or may also be a network parameter and a network structure. In the embodiment of the present application, the first end can receive at least two AI network information sent by the second end, and the AI network information sent by each second end is different, and the first end can splice the received AI network information, To obtain the network structure and/or network parameters of one or more AI networks. In this way, AI network information can be divided into multiple parts and sent to the first end through different second ends, which can effectively reduce the communication overhead of the second end.

It should be noted that, in a case where the first end receives the AI network information sent by at least two second ends, the at least two second ends may be different types of second ends. For example, the first end is a terminal, and the terminal may receive the network structure of the first AI network sent by the base station or the core network device, and receive the network parameters of the first AI network sent by other terminals.

Optionally, the step 201 may include:

The first terminal receives the AI network information sent by the second terminal at least twice;

After the first end receives the AI network information sent by the second end at least twice, the method further includes:

The first end splices the received AI network information of at least two times.

In the embodiment of the present application, the first end may also receive the AI network information sent by the second end at least twice, and splicing the received AI network information at least twice to obtain one or more AI network information network structure and/or network parameters. For example, the first end is a terminal, and the second end is a base station. The base station can be the first AI network sent through a physical random access channel (Physical Random Access Channel, PRACH) or a physical downlink control channel (Physical downlink control channel, PDCCH). The network structure of the first AI network is sent through the PDCCH or the physical downlink shared channel (Physical downlink shared channel, PDSCH), and the terminal splices the received network structure and network parameters to obtain a complete first AI network .

In the embodiment of the present application, after the step 201, the method may further include the following steps:

The first end sends the AI network information to at least one third end.

For example, the first end is a base station, the second end is a core network device, and the third end can be a terminal or a base station, that is, after the base station receives the AI network information sent by the core network device, it can transfer the The above AI network information is sent to the terminal or other base stations.

In this embodiment of the present application, after the first end receives the AI network information sent by at least one second end, the first end can send the AI network information to at least one third end. In this way, the transfer of AI network information between multiple communication devices can also be realized.

It should be noted that the third end receives the AI network information sent by the first end. In some scenarios, the third end as the receiving end can also implement the related solutions of the first end. For example, the third end receives at least one information sent by the second end. For AI network information, when the third terminal receives at least two AI network information sent by the second terminal, it splices the received at least two AI network information, etc., which will not be repeated here.

In the embodiment of the present application, the AI network information sent by the second end includes a network structure, and after the first end receives at least one AI network information sent by the second end, the method further includes:

The first end sends network parameters and the network structure to at least one third end.

For example, the AI network information sent by the second terminal to the first terminal is the network structure of the first AI network, and the first terminal stores the network parameters of the first AI network, or obtains the network of the first AI network from other communication devices Parameters, or the network parameters of the first AI network trained by the first end, the first end can send the network parameters and the network structure sent by the second end to the third end together, or can also send them separately The network parameters and network structure.

In this embodiment of the present application, the first end can send the network parameters and the network structure to the third end when it only receives the network structure sent by the second end.

Optionally, the AI network information sent by the second end includes network parameters and network structure, and after the first end receives at least one AI network information sent by the second end, the method further includes:

The first terminal updates at least one of the network parameters and network structure, and sends updated AI network information to at least one third terminal; wherein, the updated AI network information includes updated At least one of the network parameters and the updated network structure.

Exemplarily, after the first end receives the network structure and network parameters sent by the second end, the first end may only update the network parameters, such as modifying the network parameters, and the modified network parameters sending to the third end; or, supplementing the network parameters may also be performed, and sending the supplemented network parameters to the third end.

Alternatively, when the first end receives the network parameters and network structure sent by the second end, it can also update the network structure, for example, adding a new network structure on the basis of the network structure, or Based on the network structure, part of the network structure is deleted, or the network structure is modified; then the first end can send the updated network structure to the third end.

Alternatively, the first end updates both the received network parameters and the network structure, and sends the updated network structure and the updated network parameters to at least one third end.

Optionally, when the first end updates the network structure, the update includes at least one of the following:

adding at least one new network structure on the basis of the network structure;

Deleting at least one network structure based on the network structure;

Modify the network structure.

In the embodiment of the present application, when the network structure needs to be updated, the first end may only send the updated network structure without sending the entire network structure, which can effectively save the communication overhead between communication devices.

Optionally, in the case of adding at least one new network structure on the basis of the network structure, the updated AI network information includes first target information for indicating the updated network structure, so The first target information includes at least one of the following:

input information of the new network structure;

output information of the new network structure;

Network parameters of the new network structure;

the functionality of said new network structure;

an identification of said new network structure;

a corresponding relationship between the new network structure and the network structure;

Modification information of input information and output information of the network structure;

Modification information of some network parameters corresponding to the network structure.

It should be noted that, in some embodiments, the input information of the network structure can also be called the input or input parameters of the network structure, and the input information includes dimensions, identifiers and lengths; the output information of the network structure can also be called the Output or output parameters, the output information also includes dimensions, identifiers and lengths. The network parameters of the network structure include dimensions, identifiers, and values. The values include the value used to represent the non-zero position, the value used to represent the update position, the value used to represent the maximum position, and the value used to represent whether it is normalized. value etc.

Optionally, the information content of the target parameter includes at least one of the following: the dimension of the target parameter, the value of the target parameter, and the position of the updated value in the target parameter; wherein the target parameter includes at least one of the following Items: input information of the network structure, output information of the network structure, and network parameters.

For example, in the case where the first end needs to add at least one new network structure to the network structure, the updated AI network information sent by the first end further includes first target information, and the first target information includes Network parameters, the information content of the network parameters includes dimensions, values, and the position of the updated values in the network parameters, and then the third end adds a new network to the corresponding network structure based on the information content of the received network parameters structure.

Optionally, the target parameters may also include input information, output information, etc. of the network structure, which will not be described in detail here.

It should be noted that any AI network can be disassembled into a list of multiple nodes, and the entire AI network can be represented by the connection relationship between the input and output of each node. For example, a simple two-layer fully connected network can be represented by two nodes. The first node includes an input, a weight, and an output, and the second node also includes an input, a weight, and an output. If the first The input of a node is associated with the input of the entire AI network, the output of the first node is associated with the input of the second node, and the output of the second node is associated with the output of the entire AI network, the AI network can be described. The association can be using the same index or name, etc.

At the sending end (for example, the first end) wants to add a new network structure, such as adding a new fully connected node between two nodes, the new node also includes an input, a weight and an output, the sending end can It is to send the corresponding relationship between the new network structure and the historical network structure (such as the network structure of the second AI network) to the receiving end (such as the third end, the second end), after receiving the corresponding relationship, the receiving end can A new network structure is added to the network structure of the second AI network, and the association relationship between the input and output of the new network structure (or new node) and the input and output of the old network structure is reset.

In the embodiment of the present application, the corresponding relationship between the new network structure and the original network structure may refer to the corresponding relationship between each input and output of the new network and the input and output of the original network. For example, the original network structure includes the first node and the second node, and the new network structure is a newly added node. The corresponding relationship between the new network structure and the original network structure may include: 1. The input of the new node is associated with the output of the first node, 2. The output of the new node is associated with the input of the second node. In addition, modification information of the input information and output information of the original network structure may also be included, for example, the output of the first node is canceled from the input of the second node.

It should be noted that when the first end adds at least one new network structure to the original network structure, the updated AI network information sent by the first end may also include other information content, such as the function of the new network structure, The identification of the new network structure, the modification information of some network parameters, etc., are not described in detail in this embodiment.

Optionally, in the case of deleting at least one network structure on the network structure, the updated AI network information includes second target information for indicating the updated network structure, and the second Target information includes at least one of the following:

An identification of a first target network structure, where the first target network structure is a pruned network structure;

The corresponding relationship between the input information and the output information of the remaining network structure, where the remaining network structure is the remaining network structure after the first target network structure is deleted by the first end.

Exemplarily, if the first end wants to delete the original network structure, the first end may directly send the identifier of the first target network structure to be deleted to the third end, and may also send the ID of the remaining network structure. Correspondence between input information and output information.

For example, the original network structure of the first end includes the first node and the second node. If the deleted network structure is the second node, the AI network information sent by the first end may include: 1. The first node to be deleted The identification of the two nodes, 2. The relationship between the output of the first node and the total output of the original AI network. Furthermore, based on the received updated AI network information, the third end deletes the second node, and modifies the input-output relationship of the remaining network structure accordingly.

Optionally, in the case of modifying the network structure, the updated AI network information includes third target information for indicating the updated network structure, and the third target information includes at least the following one item:

The corresponding relationship between the second target network structure and the third target network structure, the second target network structure is a modified network structure, and the third target network structure is an unmodified network structure;

Increased network parameters;

Deleted network parameters;

Modified network parameters;

a function of said second target network structure;

An operator for the second target network structure.

In this embodiment of the present application, the updating of the second AI network may also be modifying at least one network structure of the second AI network. In this case, the AI network information sent by the second end may include modified The corresponding relationship between the network structure and the unmodified network structure, the functions and operators of the modified network structure, etc.

For example, the original network structure of the first end includes the first node and the second node, if the first end wants to modify the first node to a convolution node, including an input, an output, and three convolution kernels With three corresponding offsets, the updated AI network information can include: 1. The node that needs to be modified. If the input and output of the modified node are the same as before the modification, no indication can be made. 2. The function of the modified node Switching, such as switching from a fully connected node to a convolution node, 3. Three convolution kernels and corresponding three offsets. It should be noted that the functions included in each node are pre-agreed, depending on the way the model is expressed, and the specific node functions may vary according to the way the model is expressed, for example:

1. If the representation of the convolution node is not supported, several multiplication nodes and addition nodes can be used to represent the convolution node;

2. The convolution node can have multiple outputs, that is, each convolution kernel corresponds to one output;

3. An output of the convolution node can be expanded, for example, the input is a 16×32 matrix, and the output is a 3×16×32 matrix after convolution;

4. The length of an output of the convolution node can be changed, for example, the input is a 16×32 matrix, and after convolution, the output is a 16×96 matrix, or a 1×1536 matrix.

If the dimension of the output changes after being modified to a convolution node, the input dimension of the second node connected behind should also match it. If there is no special instruction at the first end, the receiving end (such as the third end) can be modified by default The dimension of the output of the first node after that is taken as the standard, and the dimension of the relevant weight matrix in the second node is modified. Before the new network parameters are issued, the parameters that do not exist are treated as 0.

It should be noted that the embodiment of the present application may also describe the network structure of the AI network through layers. In contrast, describing the AI network through nodes focuses on the structure of the AI network and the connection management between each node. For example, the third node may not be connected to the second node, but to the first node, and through The layer description network focuses on the function of the layer, and two adjacent layers are always connected to each other, and the input and output are corresponding.

Optionally, the AI network is described by layers. The AI network can be expressed as several layers, and each layer can have multiple nodes or multiple functions. When updating the network structure, it includes adding, deleting, and modifying layers. According to different AI network model representation methods, each layer can contain different functional characteristics, such as convolution, pooling, activation, etc., and the required weights are also different, which can be preset by the model representation method.

If the sending end (for example, the first end) wants to add a new layer, it needs to indicate the identity of the previous layer of the new layer, which is directly associated with the input layer, and at the same time indicate the function and weight information of the new layer.

If the sender wants to delete a layer, send the identifier of the deleted layer, and then update the dimensions of the input and output of the two newly connected layers after deletion and related network parameters.

If the sender wants to modify a layer, it can directly modify the function and dimension of this layer, or directly replace the original layer with a new layer.

Optionally, in the case where the updated AI network information includes updated network parameters, the updated network parameters include any of the following: network parameters corresponding to the updated AI network; original AI network before update The network parameters corresponding to the new network structure added; the network parameters corresponding to the network structure modified by the original AI network before the update.

Exemplarily, in the above-mentioned implementation involving the first end, the second end and the third end, the first end is a base station, the second end is a core network, and the third end is a terminal or an access network device or, the first end, the second end, and the third end are different nodes of the core network; or, the first end, the second end, and the third end are different terminals ; or, the first end, the second end, and the third end are different nodes of a terminal; or, the third end is a terminal, and the first end and the second end are access nodes network equipment.

Wherein, when the first end is an access network device, the second end is a core network device, and the third end is a terminal, the core network device passes through a non-access stratum (Non-Access Stratum) , NAS) message to send the AI network information, and the access network device sends the AI network information through a radio resource control (Radio Resource Control, RRC) message.

For a better understanding, the technical solutions provided by the present application are illustrated below through several specific examples.

Embodiment one

The core network device sends the network structure to multiple terminals through the NAS message, and the network structure sent to the multiple terminals is the same. According to the base station where the terminal is located, the base station sends the network parameters to its corresponding terminal through the RRC message. Optionally, the network parameters are terminal-specific (UE specific) network parameters.

Alternatively, the core network device sends UE-specific network structure to each terminal, and the base station sends UE-specific network parameters to the terminal.

Embodiment two

The core network device sends UE-specific network structure and network parameters to multiple terminals through NAS messages, and the base station sends updated network parameters to terminals through RRC messages. Optionally, the NAS message and the RRC message may be sent to the terminal at the same time.

Or, the core network device sends the network structure and network parameters to the base station through the NAS message. The modified network structure and network parameters are sent to the terminal.

Alternatively, the core network device sends the network structure and/or network parameters to the base station through a NAS message, and the base station sends the received network structure and/or network parameters to the terminal. Optionally, the base station can modify the NAS message and send it to the terminal , that is, modify the received network structure and/or network parameters and send it to the terminal, or the base station directly sends the received network structure and/or network parameters to the terminal through an RRC message. Wherein, if the base station receives network structure and network parameters, the base station may modify any one of them, or modify both the network structure and network parameters.

Embodiment Three

The AI network is training the core network equipment, and the core network equipment sends the network structure and network parameters of the AI network to multiple transmission and reception points (Transmission and Reception Point, TRP) and terminals. Wherein, the core network device may independently send the network structure for each terminal, and terminals with the same network structure may have different network parameters.

Optionally, after each TRP receives the network structure and network parameters, it can continue to independently train the AI network, and can also change the network parameters.

Optionally, the base station of the serving cell may also change the network parameters and send the changes to the corresponding access terminals.

In the technical solution provided by the embodiment of the present application, one sending end can send AI network information to at least one receiving end, and one receiving end can also receive AI network information sent by at least one sending end, so that communication devices can communicate with each other for AI network information The transmission is more flexible.

Please refer to Figure 3, Figure 3 is a flowchart of another AI network information transmission method provided by the embodiment of the present application, as shown in Figure 3, the method includes the following steps:

Step 301, the second end sends AI network information to at least one first end; wherein, the AI network information includes at least one of network structure and network parameters.

Wherein, the first end and the second end are communication devices with sending and receiving functions.

Optionally, the first end and the second end are any of the following:

The first end is an access network device, and the second end is a core network device;

The first end is a terminal, and the second end is a core network device or an access network device;

The first end and the second end are different nodes of core network equipment;

The first end and the second end are different nodes of the access network equipment;

The first end and the second end are different access network devices;

the first end and the second end are different terminals;

The first end and the second end are different nodes of a terminal.

In some embodiments, the first end may also be called a receiving end, and the second end may also be called a sending end. It should be noted that the first end and the second end are not specific, and the first end in the embodiment of this application can also be used as the second end or the third end mentioned in the above embodiment in other scenarios. The second end in the embodiment of the application may also serve as the first end or the third end mentioned in the above embodiments in some scenarios.

Optionally, the second end sends AI network information to at least one first end, including:

The second end broadcasts the AI network information to at least one first end.

For example, the first end is a terminal, the second end is a base station, and the base station may broadcast the AI network information to at least one terminal. Optionally, for the AI network information broadcast by the same base station, the AI network information received by each terminal may be the same; or, the base station may also send UE specific AI network information for the terminal.

The AI network information transmission method provided by the embodiment of this application is applied to the second end, corresponding to the AI network information transmission method applied to the first end provided in the embodiment of Figure 2 above, and the relevant steps in the embodiment of this application For the specific implementation process, reference may be made to the description in the method embodiment shown in FIG. 2 above, and details are not repeated here to avoid repetition.

In the embodiment of the present application, the second end can send AI network information to at least one first end, making the transmission of AI network information between communication devices more flexible and no longer limited to one-to-one transmission; in addition, the AI Network information includes at least one of the network structure and network parameters, and there is no need to transmit the entire AI network including all network structures and network parameters together during the communication process, and the network structure and network parameters of the AI network can be separated In this way, the transmission overhead in the communication process can be effectively reduced.

The AI network information transmission method provided in the embodiment of the present application may be executed by an AI network information transmission device. In the embodiment of the present application, the AI network information transmission device provided in the embodiment of the present application is described by taking the AI network information transmission device executing the AI network information transmission method as an example.

Please refer to FIG. 4. FIG. 4 is a structural diagram of an AI network information transmission device provided in an embodiment of the present application. As shown in FIG. 4, the AI network information transmission device 400 includes:

A receiving module 401, configured to receive AI network information sent by at least one second end;

Optionally, the receiving module 401 is also used for:

Receive AI network information broadcast by at least one second end.

Optionally, the receiving module 401 is also used for:

receiving AI network information sent by at least two second ends, wherein the AI network information sent by each second end is different;

The device also includes:

The first splicing module is configured to splice the received at least two pieces of AI network information.

Optionally, when the device receives the AI network information sent by at least two second terminals, the at least two second terminals are different types of second terminals.

Optionally, the receiving module 401 is also used for:

Receive the AI network information sent by the second end at least twice;

The device also includes:

The second splicing module is configured to splice the AI network information received at least twice.

Optionally, the device also includes:

A first sending module, configured to send the AI network information to at least one third terminal.

Optionally, the AI network information sent by the second end includes a network structure, and the device further includes:

A second sending module, configured to send network parameters and the network structure to at least one third end.

Optionally, the AI network information sent by the second end includes network parameters and network structure, and the device further includes:

An update module, configured to update at least one of the network parameters and network structure, and send the updated AI network information to at least one third end;

Wherein, the updated AI network information includes at least one of updated network parameters and updated network structure.

Optionally, the update module is also configured to perform at least one of the following:

Deleting at least one network structure based on the network structure;

Modify the network structure.

Optionally, the device is an access network device, the second end is a core network device, and the third end is a terminal or an access network device; or,

The device, the second end, and the third end are different nodes of core network equipment; or,

said means, said second end and said third end are distinct terminals; or,

The device, the second end and the third end are different nodes of a terminal; or,

The device is a terminal, and the second end and the third end are access network equipment.

In the embodiment of the present application, the device can receive AI network information sent by at least one second end, thereby making the transmission of AI network information between communication devices more flexible; and, the AI network information sent by each second end Including at least one of the network structure and network parameters, and then there is no need to transmit the entire AI network including all network structures and network parameters together during the communication process, and the network structure and network parameters of the AI network can also be sent separately. In this way, the transmission overhead in the communication process can be effectively reduced.

The AI network information transmission apparatus 400 in the embodiment of the present application may be an electronic device, such as an electronic device with an operating system, or a component of the electronic device, such as an integrated circuit or a chip. The electronic device may be a terminal, or other devices other than the terminal. Exemplarily, the terminal may include, but not limited to, the types of terminal 11 listed above, and other devices may be servers, Network Attached Storage (NAS), etc., which are not specifically limited in this embodiment of the present application.

The AI network information transmission device 400 provided in the embodiment of the present application can realize each process implemented in the method embodiment shown in FIG. 2 and achieve the same technical effect. To avoid repetition, details are not repeated here.

Please refer to FIG. 5. FIG. 5 is a structural diagram of another AI network information transmission device provided by the embodiment of the present application. As shown in FIG. 5, the AI network information transmission device 500 includes:

A sending module 501, configured to send AI network information to at least one first end;

Optionally, the sending module 501 is also used for:

Broadcast AI network information to at least one first end.

Optionally, the first end and the device are any of the following:

The first end is an access network device, and the device is a core network device;

The first end is a terminal, and the device is a core network device or an access network device;

The first end and the device are different nodes of core network equipment;

The first end and the device are different nodes of access network equipment;

The first end and the device are different access network devices;

the first end and the device are distinct terminals;

The first end and the device are different nodes of terminals.

In the embodiment of the present application, the device is capable of sending AI network information to at least one first end, so that the transmission of AI network information between communication devices is more flexible; in addition, the AI network information includes network structure and network parameters At least one, and then there is no need to transmit the entire AI network including all network structures and network parameters together during the communication process, and the network structure and network parameters of the AI network can be sent separately, which can effectively reduce the communication process. The transmission overhead in .

The AI network information transmission device 500 provided in the embodiment of the present application can realize each process implemented in the method embodiment shown in FIG. 3 and achieve the same technical effect. To avoid repetition, details are not repeated here.

Optionally, as shown in FIG. 6 , the embodiment of the present application further provides a communication device 600, including a processor 601 and a memory 602, and the memory 602 stores programs or instructions that can run on the processor 601. The When the programs or instructions are executed by the processor 601, the various steps of the embodiment of the AI network information transmission method described above in FIG. 2 or FIG. 3 can be realized, and the same technical effect can be achieved. To avoid repetition, details are not repeated here.

The embodiment of the present application also provides a terminal, and each implementation process and implementation manner of the above-mentioned method embodiment in FIG. 2 or FIG. 3 can be applied to the terminal embodiment, and can achieve the same technical effect. Specifically, FIG. 7 is a schematic diagram of a hardware structure of a terminal implementing an embodiment of the present application.

The terminal 700 includes, but is not limited to: a radio frequency unit 701, a network module 702, an audio output unit 703, an input unit 704, a sensor 705, a display unit 706, a user input unit 707, an interface unit 708, a memory 709, and a processor 710. At least some parts.

Those skilled in the art can understand that the terminal 700 may also include a power supply (such as a battery) for supplying power to various components, and the power supply may be logically connected to the processor 710 through the power management system, so as to manage charging, discharging, and power consumption through the power management system. Management and other functions. The terminal structure shown in FIG. 7 does not constitute a limitation on the terminal, and the terminal may include more or fewer components than shown in the figure, or combine some components, or arrange different components, which will not be repeated here.

It should be understood that, in this embodiment of the present application, the input unit 704 may include a graphics processing unit (Graphics Processing Unit, GPU) 7041 and a microphone 7042, and the graphics processor 7041 is used by the image capture device ( Such as the image data of the still picture or video obtained by the camera) for processing. The display unit 706 may include a display panel 7061, and the display panel 7061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 707 includes at least one of a touch panel 7071 and other input devices 7072 . The touch panel 7071 is also called a touch screen. The touch panel 7071 may include two parts, a touch detection device and a touch controller. Other input devices 7072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, switch buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

In the embodiment of the present application, the radio frequency unit 701 may transmit the downlink data from the network side device to the processor 710 for processing after receiving the downlink data; in addition, the radio frequency unit 701 may send uplink data to the network side device. Generally, the radio frequency unit 701 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.

The memory 709 can be used to store software programs or instructions as well as various data. The memory 709 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required by at least one function (such as a sound playing function, image playback function, etc.), etc. Furthermore, memory 709 may include volatile memory or nonvolatile memory, or, memory 709 may include both volatile and nonvolatile memory. Among them, the non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electronically programmable Erase Programmable Read-Only Memory (Electrically EPROM, EEPROM) or Flash. Volatile memory can be random access memory (Random Access Memory, RAM), static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic RAM, DRAM), synchronous dynamic random access memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDRSDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous connection dynamic random access memory (Synch link DRAM , SLDRAM) and Direct Memory Bus Random Access Memory (Direct Rambus RAM, DRRAM). The memory 709 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

The processor 710 may include one or more processing units; optionally, the processor 710 integrates an application processor and a modem processor, wherein the application processor mainly handles operations related to the operating system, user interface, and application programs, etc., Modem processors mainly process wireless communication signals, such as baseband processors. It can be understood that the foregoing modem processor may not be integrated into the processor 710 .

In an implementation manner of the embodiment of this application, the terminal 700 is the first end. A radio frequency unit 701, configured to receive AI network information sent by at least one second end;

Optionally, the radio frequency unit 701 is further configured to: receive AI network information broadcast by at least one second end.

Optionally, the radio frequency unit 701 is further configured to: receive AI network information sent by at least two second terminals, wherein the AI network information sent by each second terminal is different;

The processor 710 is configured to: splice the received at least two AI network information.

Optionally, in the case where the first end receives the AI network information sent by at least two second ends, the at least two second ends are second ends of different types.

Optionally, the radio frequency unit 701 is also configured to: receive the AI network information sent by the second end at least twice;

The processor 710 is configured to: splice the received AI network information of at least two times.

Optionally, the radio frequency unit 701 is further configured to: send the AI network information to at least one third end.

Optionally, the AI network information sent by the second end includes a network structure, and the radio frequency unit 701 is further configured to: send network parameters and the network structure to at least one third end.

Optionally, the AI network information sent by the second end includes network parameters and network structure, and the radio frequency unit 701 is further configured to: update at least one of the network parameters and network structure, and update the updated AI network information is sent to at least one third party;

Optionally, the update includes at least one of the following:

Deleting at least one network structure based on the network structure;

Modify the network structure.

Optionally, the second end is a core network device, and the third end is a terminal or an access network device; or,

the first end, the second end and the third end are different terminals; or,

The second end and the third end are access network devices.

In another implementation manner of the embodiment of this application, the terminal 700 is the second terminal. A radio frequency unit 701, configured to: send AI network information to at least one first end;

Optionally, the radio frequency unit 701 is further configured to: broadcast AI network information to at least one first end.

Optionally, the first end is an access network device or a core network device; or, the first end and the second end are different terminals.

In the embodiment of the present application, the network structure and network parameters of the AI network can be sent separately, so that the transmission overhead of the terminal during the communication process can be effectively reduced.

The embodiment of the present application also provides a network-side device. The various implementation processes and implementation methods of the above-mentioned method embodiments shown in FIG. 2 and FIG. 3 can be applied to the network-side device embodiment, and can achieve the same technical effect.

Specifically, the embodiment of the present application also provides a network side device. As shown in FIG. 8 , the network side device 800 includes: an antenna 81 , a radio frequency device 82 , a baseband device 83 , a processor 84 and a memory 85 . The antenna 81 is connected to a radio frequency device 82 . In the uplink direction, the radio frequency device 82 receives information through the antenna 81, and sends the received information to the baseband device 83 for processing. In the downlink direction, the baseband device 83 processes the information to be sent and sends it to the radio frequency device 82 , and the radio frequency device 82 processes the received information and sends it out through the antenna 81 .

The method performed by the network side device in the above embodiments may be implemented in the baseband device 83, where the baseband device 83 includes a baseband processor.

The baseband device 83 can include at least one baseband board, for example, a plurality of chips are arranged on the baseband board, as shown in FIG. The program executes the network device operations shown in the above method embodiments.

The network side device may also include a network interface 86, such as a common public radio interface (common public radio interface, CPRI).

Specifically, the network-side device 800 in this embodiment of the present disclosure further includes: instructions or programs stored in the memory 85 and operable on the processor 84, and the processor 84 calls the instructions or programs in the memory 85 to execute FIG. 4 or FIG. 5 The methods executed by each module shown in the figure achieve the same technical effect, so in order to avoid repetition, they are not repeated here.

Specifically, the embodiment of the present application also provides another network side device. As shown in FIG. 9 , the network side device 900 includes: a processor 901 , a network interface 902 and a memory 903 . Wherein, the network interface 902 is, for example, a common public radio interface (common public radio interface, CPRI).

Specifically, the network-side device 900 in this embodiment of the present disclosure further includes: instructions or programs stored in the memory 903 and executable on the processor 901, and the processor 901 calls the instructions or programs in the memory 903 to execute FIG. 4 or FIG. 5 The methods executed by each module shown in the figure achieve the same technical effect, so in order to avoid repetition, they are not repeated here.

The embodiment of the present application also provides a readable storage medium, the readable storage medium may be nonvolatile or volatile, the readable storage medium stores programs or instructions, and the programs or instructions are stored in When executed by the processor, each process of the above-mentioned method embodiment shown in FIG. 2 or FIG. 3 can be realized, and the same technical effect can be achieved. To avoid repetition, details are not repeated here.

Wherein, the processor is the processor in the terminal described in the foregoing embodiments. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk, and the like.

The embodiment of the present application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the above-mentioned Figure 2 or Figure 3. Each process of the method embodiment described above can achieve the same technical effect, so in order to avoid repetition, details are not repeated here.

It should be understood that the chip mentioned in the embodiment of the present application may also be called a system-on-chip, a system-on-chip, a system-on-a-chip, or a system-on-a-chip.

The embodiment of the present application further provides a computer program/program product, the computer program/program product is stored in a non-transitory readable storage medium, and the computer program/program product is executed by at least one processor to realize The various processes of the above method embodiment shown in FIG. 2 or FIG. 3 can achieve the same technical effect, and will not be repeated here to avoid repetition.

The embodiment of the present application also provides a communication system, including: a terminal and a network-side device, the terminal can be used to perform the steps of the method described in Figure 2 above, and the network-side device can be used to perform the method described in Figure 3 above Alternatively, the terminal may be used to perform the steps of the method described in FIG. 3 above, and the network side device may be used to perform the steps of the method described in FIG. 2 above.

It should be noted that, in this document, the term "comprising", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. Functions are performed, for example, the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation. Based on this understanding, the essence of the technical solution of this application or the part that contributes to related technologies can be embodied in the form of computer software products, which are stored in a storage medium (such as ROM/RAM, disk, CD) contains several instructions to enable a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in various embodiments of the present application.

The embodiments of the present application have been described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementations. The above-mentioned specific implementations are only illustrative and not restrictive. Those of ordinary skill in the art will Under the inspiration of this application, without departing from the purpose of this application and the scope of protection of the claims, many forms can also be made, all of which belong to the protection of this application.

Claims

An artificial intelligence AI network information transmission method, comprising:

The first end receives at least one AI network information sent by the second end;

Wherein, the AI network information sent by each second terminal includes at least one of network structure and network parameters.
The method according to claim 1, wherein the first terminal receives at least one AI network information sent by the second terminal, comprising:

The first end receives at least one AI network information broadcast by the second end.
The method according to claim 1, wherein the first terminal receives at least one AI network information sent by the second terminal, comprising:

The first end receives the AI network information sent by at least two second ends, wherein the AI network information sent by each second end is different;

After the first end receives the AI network information sent by at least two second ends, the method further includes:

The first end splices the received at least two pieces of AI network information.
The method according to claim 3, wherein, when the first terminal receives the AI network information sent by at least two second terminals, the at least two second terminals are different types of second terminals.
The method according to claim 1, wherein the first terminal receives at least one AI network information sent by the second terminal, comprising:

The first terminal receives the AI network information sent by the second terminal at least twice;

After the first end receives the AI network information sent by the second end at least twice, the method further includes:

The first end splices the received AI network information of at least two times.
The method according to claim 1, wherein, after the first terminal receives at least one AI network information sent by the second terminal, the method further comprises:

The first end sends the AI network information to at least one third end.
The method according to claim 1, wherein the AI network information sent by the second end includes a network structure, and after the first end receives at least one AI network information sent by the second end, the method further includes:

The first end sends network parameters and the network structure to at least one third end.
The method according to claim 1, wherein the AI network information sent by the second end includes network parameters and network structure, and after the first end receives at least one AI network information sent by the second end, the method further include:

The first end updates at least one of the network parameters and network structure, and sends the updated AI network information to at least one third end;

Wherein, the updated AI network information includes at least one of updated network parameters and updated network structure.
The method according to claim 8, wherein, when the first end updates the network structure, the update includes at least one of the following:

adding at least one new network structure on the basis of the network structure;

Deleting at least one network structure based on the network structure;

Modify the network structure.
The method according to any one of claims 6-9, wherein the first end is an access network device, the second end is a core network device, and the third end is a terminal or an access network device ;or,

The first end, the second end, and the third end are different nodes of core network equipment; or,

the first end, the second end and the third end are different terminals; or,

The first end, the second end, and the third end are different nodes of a terminal; or,

The first end is a terminal, and the second end and the third end are access network devices.
An AI network information transmission method, comprising:

The second end sends AI network information to at least one first end;

Wherein, the AI network information includes at least one of network structure and network parameters.
The method according to claim 11, wherein the second terminal sends AI network information to at least one first terminal, comprising:

The second end broadcasts the AI network information to at least one first end.
The method according to claim 11, wherein the first end and the second end are any of the following:

The first end is an access network device, and the second end is a core network device;

The first end is a terminal, and the second end is a core network device or an access network device;

The first end and the second end are different nodes of core network equipment;

The first end and the second end are different nodes of the access network equipment;

The first end and the second end are different access network devices;

the first end and the second end are different terminals;

The first end and the second end are different nodes of a terminal.
An AI network information transmission device, comprising:

A receiving module, configured to receive AI network information sent by at least one second end;

Wherein, the AI network information sent by each second terminal includes at least one of network structure and network parameters.
The device according to claim 14, wherein the receiving module is also used for:

Receive AI network information broadcast by at least one second end.
The device according to claim 14, wherein the receiving module is also used for:

receiving AI network information sent by at least two second ends, wherein the AI network information sent by each second end is different;

The device also includes:

The first splicing module is configured to splice the received at least two pieces of AI network information.
The device according to claim 16, wherein, when the device receives the AI network information sent by at least two second terminals, the at least two second terminals are different types of second terminals.
The device according to claim 14, wherein the receiving module is also used for:

Receive the AI network information sent by the second end at least twice;

The device also includes:

The second splicing module is configured to splice the AI network information received at least twice.
The apparatus according to claim 14, wherein said apparatus further comprises:

A first sending module, configured to send the AI network information to at least one third terminal.
The device according to claim 14, wherein the AI network information sent by the second end includes a network structure, and the device further comprises:

A second sending module, configured to send network parameters and the network structure to at least one third end.
The device according to claim 14, wherein the AI network information sent by the second end includes network parameters and network structure, and the device further comprises:

An update module, configured to update at least one of the network parameters and network structure, and send the updated AI network information to at least one third end;

Wherein, the updated AI network information includes at least one of updated network parameters and updated network structure.
The device according to claim 21, wherein the update module is further configured to perform at least one of the following:

adding at least one new network structure on the basis of the network structure;

Deleting at least one network structure based on the network structure;

Modify the network structure.
An AI network information transmission device, comprising:

A sending module, configured to send AI network information to at least one first end;

Wherein, the AI network information includes at least one of network structure and network parameters.
The device according to claim 23, wherein the sending module is further configured to:

Broadcast AI network information to at least one first end.
A communication device, comprising a processor and a memory, the memory stores programs or instructions that can run on the processor, and when the programs or instructions are executed by the processor, any one of claims 1-10 is implemented The steps of the AI network information transmission method described in claim 1, or the steps of the AI network information transmission method described in any one of claims 11-13.
A readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the AI network information transmission method according to any one of claims 1-10 are realized , or realize the steps of the AI network information transmission method described in any one of claims 11-13.