WO2024051564A1

WO2024051564A1 - Information transmission method, ai network model training method, apparatus, and communication device

Info

Publication number: WO2024051564A1
Application number: PCT/CN2023/116030
Authority: WO
Inventors: 任千尧
Original assignee: 维沃移动通信有限公司
Priority date: 2022-09-07
Filing date: 2023-08-31
Publication date: 2024-03-14
Also published as: CN117715106A

Abstract

The present application relates to the technical field of communications, and discloses an information transmission method, an artificial intelligence (AI) network model training method, an apparatus, and a communication device. The information transmission method of embodiments of the present application comprises: a terminal collects AI training data, wherein the AI training data comprises first channel information of a target downlink channel, the AI training data is used for training a first AI network model and/or a second AI network model, the first AI network model is used for processing second channel information into first channel feature information, the second AI network model is used for restoring the first channel feature information into the second channel information, and the second channel information is channel information of the target downlink channel; the terminal sends the AI training data to a first device.

Description

Information transmission method, AI network model training method, device and communication equipment

Cross-references to related applications

This application claims priority to Chinese Patent Application No. 202211091873.9 filed in China on September 7, 2022, the entire content of which is incorporated herein by reference.

Technical field

This application belongs to the field of communication technology, and specifically relates to an information transmission method, artificial intelligence (Artificial Intelligence, AI) network model training method, device and communication equipment.

Background technique

In related technologies, methods for transmitting channel characteristic information using AI network models have been studied.

The AI network model may include an encoding part (i.e., encoding AI network model) and a decoding part (i.e., decoding AI network model). The encoding AI network model is used to encode channel information into channel feature information, and the decoding AI network model is used to convert the encoding AI network model into The channel characteristic information output by the network model is restored to channel information. In this way, in order to match the encoding AI network model and the decoding AI network model, the encoding AI network model and the decoding AI network model are usually jointly trained in the same device, and then jointly trained The obtained encoded AI network model is transmitted to the terminal, and the decoded AI network model obtained through joint training is transmitted to the base station.

In related technologies, the base station trains the encoding AI network model and the decoding AI network model based on pre-acquired training data. Since the pre-acquired training data is inconsistent with the actual channel, the trained encoding AI network model and decoding AI network model will be The matching degree with the actual channel is relatively low, thus reducing the accuracy of the encoding AI network model and the decoding AI network model in processing actual channel information.

Contents of the invention

Embodiments of the present application provide an information transmission method, AI network model training method, device and communication equipment, enabling the terminal to report actual estimated channel information to the base station, so that the base station uses the channel information as AI training data to train more accurately. High-end encoding AI network model and decoding AI network model.

In the first aspect, an information transmission method is provided, which method includes:

The terminal collects artificial intelligence AI training data, wherein the AI training data includes first channel information of the target downlink channel, and the AI training data is used to train the first AI network model and/or the second AI network model, and the third An AI network model is used to process the second channel information into the first channel characteristic information. The second AI network model is used to restore the first channel characteristic information into the second channel information. The second channel The information is the channel information of the target downlink channel;

The terminal sends the AI training data to the first device.

In a second aspect, an information transmission device is provided, applied to a terminal, and the device includes:

Collection module, used to collect artificial intelligence AI training data, wherein the AI training data includes the first channel information of the target downlink channel, and the AI training data is used to train the first AI network model and/or the second AI network model , the first AI network model is used to process the second channel information into the first channel characteristic information, and the second AI network model is used to restore the first channel characteristic information into the second channel information, so The second channel information is the channel information of the target downlink channel;

The first sending module is used to send the AI training data to the first device.

The third aspect provides an information transmission method, including:

The first device receives AI training data from the terminal, where the AI training data includes first channel information of the target downlink channel;

The first device trains a first AI network model and/or a second AI network model according to the AI training data, wherein the first AI network model is used to process second channel information into first channel feature information, The second AI network model is used to restore the first channel characteristic information to the second channel information, and the second channel information is the channel information of the target downlink channel.

In a fourth aspect, an information transmission device is provided, applied to the first device, and the device includes:

A first receiving module configured to receive AI training data from the terminal, where the AI training data includes first channel information of the target downlink channel;

A training module configured to train a first AI network model and/or a second AI network model according to the AI training data, wherein the first AI network model is used to process the second channel information into the first channel feature information, The second AI network model is used to restore the first channel characteristic information to the second channel information, and the second channel information is the channel information of the target downlink channel.

In a fifth aspect, a communication device is provided. The communication device includes a processor and a memory. The memory stores a program or instructions that can be run on the processor. The program or instructions are implemented when executed by the processor. The steps of the method described in the first aspect or the third aspect.

In a sixth aspect, a communication device is provided, including a processor and a communication interface, wherein the communication interface is used to collect artificial intelligence AI training data and send the AI training data to the first device, wherein the AI The training data includes the first channel information of the target downlink channel. The AI training data is used to train the first AI network model and/or the second AI network model. The first AI network model is used to process the second channel information into First channel characteristic information, the second AI network model is used to restore the first channel characteristic information to the second channel information, and the second channel information is the channel information of the target downlink channel; or,

The communication interface is used to receive AI training data from the terminal, where the AI training data includes first channel information of the target downlink channel; the processor is used to train a first AI network model and/or based on the AI training data. Or a second AI network model, wherein the first AI network model is used to process the second channel information into first channel characteristic information, and the second AI network model is used to restore the first channel characteristic information into The second channel information, The second channel information is the channel information of the target downlink channel.

In a seventh aspect, a communication system is provided, including: a terminal and a network side device. The terminal can be used to perform the steps of the information transmission method as described in the first aspect. The network side device can be used to perform the steps of the information transmission method as described in the third aspect. The steps of the information transmission method.

In an eighth aspect, a readable storage medium is provided. Programs or instructions are stored on the readable storage medium. When the programs or instructions are executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method are implemented as described in the first aspect. The steps of the method described in the third aspect.

In a ninth aspect, a chip is provided. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the method described in the first aspect. , or implement the method as described in the third aspect.

In a tenth 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 as described in the first aspect The steps of the information transmission method, or the computer program/program product is executed by at least one processor to implement the steps of the information transmission method as described in the third aspect.

In this embodiment of the present application, the terminal collects artificial intelligence AI training data, wherein the AI training data includes the first channel information of the target downlink channel, and the AI training data is used to train the first AI network model and/or the second AI network model, the first AI network model is used to process the second channel information into the first channel characteristic information, and the second AI network model is used to restore the first channel characteristic information into the second channel information, the second channel information is the channel information of the target downlink channel; the terminal sends the AI training data to the first device. In this way, the AI training data reported by the terminal to the first device includes actual estimated channel information. In this way, in the process of training the first AI network model and/or the second AI network model based on the AI training data, the first device, The degree of matching between the trained first AI network model and/or the second AI network model and the target downlink channel can be improved. In the process of using the first AI network model to compress the channel information of the target downlink channel into channel feature information, and/or using the second AI network model to restore the channel feature information of the target downlink channel into channel information, the first step can be improved. The compression coding accuracy of the AI network model and/or the decoding accuracy of the second AI network model can be improved and/or the matching degree between the first AI network model and the second AI network model can be improved, thereby reducing the occupancy of the channel information reporting process. resources, and improve the accuracy of the reporting process.

Description of the drawings

Figure 1 is a schematic structural diagram of a wireless communication system to which embodiments of the present application can be applied;

Figure 2 is a flow chart of an information transmission method provided by an embodiment of the present application;

Figure 3 is a flow chart of an AI network model training method provided by an embodiment of the present application;

Figure 4 is a schematic structural diagram of an information transmission device provided by an embodiment of the present application;

Figure 5 is a schematic structural diagram of an AI network model training device provided by an embodiment of the present application;

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

Figure 7 is a schematic diagram of the hardware structure of a terminal 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 with reference to the accompanying 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 the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art fall within the scope of protection of this application.

The terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects and are not used to describe a specific order or sequence. It is to be understood that the terms so used are interchangeable under appropriate circumstances so that the embodiments of the present application can be practiced in sequences other than those illustrated or described herein, and that "first" and "second" are distinguished objects It is usually one type, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and/or" in the description and claims indicates at least one of the connected objects, and the character "/" generally indicates that the related objects are in an "or" relationship.

It is worth pointing out that the technology described in the embodiments of this application is not limited to Long Term Evolution (LTE)/LTE Evolution (LTE-Advanced, LTE-A) systems, 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 (SC-FDMA) and other systems. The terms "system" and "network" in the embodiments of this application are often used interchangeably, and the described technology can be used not only for the above-mentioned systems and radio technologies, but also for other systems and radio technologies. The following description describes a New Radio (NR) system for example purposes, and NR terminology is used in much of the following description, but these techniques can also be applied to applications other than NR system applications, such as 6th ^generation Generation, 6G) communication system.

Figure 1 shows a block diagram of a wireless communication system to which embodiments of the present application are applicable. The wireless communication system includes a terminal 11 and a network side device 12. The terminal 11 may 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, or a super mobile personal computer. (ultra-mobile personal computer, UMPC), mobile Internet device (Mobile Internet Device, MID), augmented reality (AR)/virtual reality (VR) equipment, robots, wearable devices (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 anklets) 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 wireless access network device, a radio access network (Radio Access Network, RAN), or a wireless access network device. network function or wireless access network unit. Access network equipment may include a base station, a Wireless Local Area Network (WLAN) access point or a WiFi node, etc. The base station may be called a Node B, an Evolved Node B (eNB), an access point, a base transceiver station ( Base Transceiver Station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), home B-node, home evolved B-node, transmitting and receiving point ( Transmitting Receiving Point (TRP) or some other appropriate terminology 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 in the NR system The base station is introduced as an example, and the specific type of base station is not limited.

Artificial intelligence is currently widely used in various fields. There are many ways to implement AI network models, such as neural networks, decision trees, support vector machines, Bayesian classifiers, etc. This application takes a neural network as an example for explanation, but does not limit the specific type of AI network model.

In related technologies, the encoding AI network model is used on the terminal side to compress and encode the channel information, and the compressed and encoded channel characteristic information is reported. The decoding AI network model can be used on the network side to decode the received channel characteristic information. processing to recover channel information. Among them, the encoding AI network model needs to match the decoding AI network model. In related technologies, the encoding AI network model and the decoding AI network model are usually jointly trained on the network side to achieve an appropriate matching degree between the two. Among them, the AI training data used to train the coding AI network model and the decoding AI network model are usually pre-obtained data, or channel information collected by terminals or network-side devices dedicated to collecting training data, and are trained based on the AI training data. After obtaining the encoding AI network model and the decoding AI network model, the network side device then delivers the encoding AI network model to the terminal, so that the terminal encodes channel information based on the encoding AI network model delivered by the network side device. However, the encoding AI network model and decoding AI network model trained by the network side device based on pre-acquired data or channel information collected by a terminal or network side device dedicated to collecting training data may be different from the actual channel information of the terminal. In the case of matching, at this time, it may happen that the encoding AI network model has a low degree of compression of the actual channel information of the terminal, causing the compressed channel characteristic information to occupy a large amount of resources, or the encoding AI network model and the decoding AI network model have a negative impact on the terminal. The processing of actual channel information is not accurate enough, which reduces the accuracy of channel information reporting.

In this embodiment of the present application, the terminal will report the estimated channel information of the actual channel as AI training data to the training encoding AI network model (ie, the first AI network model in the embodiment of the present application) and/or the decoding AI network model. (i.e., the second AI network model in the embodiment of the present application). In this way, the encoding AI network model and/or the decoding AI network model trained by the first device based on the AI training data can be consistent with the actual channel status of the terminal. matching, thereby reducing the resources occupied by the channel information reporting process and improving the accuracy of the channel information reporting process.

The information transmission method, information transmission device, communication equipment, etc. provided by the embodiments of the present application will be described in detail through some embodiments and application scenarios with reference to the accompanying drawings.

Referring to Figure 2, an information transmission method provided by an embodiment of the present application is executed by a terminal, such as various types of terminals 11 listed in Figure 1. As shown in Figure 2, the information transmission method performed by the terminal may include the following steps:

Step 201: The terminal collects artificial intelligence AI training data, where the AI training data includes the target downlink signal. The first channel information of the channel, the AI training data is used to train the first AI network model and/or the second AI network model, the first AI network model is used to process the second channel information into the first channel characteristic information , the second AI network model is used to restore the first channel characteristic information to the second channel information, where the second channel information is the channel information of the target downlink channel.

The AI training data includes the first channel information of the target downlink channel. The terminal may use the actually estimated channel information of the target downlink channel as the AI training data, or the terminal may determine the actually estimated channel information of the target downlink channel. Preprocessing (such as at least one of filtering, quantification, normalization, orthogonalization and other processing methods for channel information), and the processed channel information is used as AI training data. For example: the first channel information is at least one of a precoding matrix and a channel matrix. For convenience of explanation, the embodiments of this application are summarized by taking the first channel information as a precoding matrix as an example. This does not constitute a detailed description. limited.

As an optional implementation, the AI training data includes at least one of the following:

The strongest M-layer precoding matrix among the N-layer precoding matrices, the channel information of the target downlink channel includes the N-layer precoding matrix, N is a positive integer, and M is a positive integer less than or equal to N;

The N-layer precoding matrix;

Among the N-layer precoding matrices, the precoding matrix indicated by third indication information, where the third indication information comes from the first device;

In the N-layer precoding matrix, the precoding matrix indicated by fourth indication information, where the fourth indication information is the indication information sent by the terminal to the first device in advance;

Among the N-layer precoding matrices, a precoding matrix that satisfies a preset condition.

Option 1, the strongest M-layer precoding matrix among the N-layer precoding matrices can be that the rank (Rank) N of the target downlink channel is greater than 1. At this time, the precoding matrix of the target downlink channel includes N layers, where the strength The larger the layer, the more effectively it can reflect the channel status of the target downlink channel. In this way, the strongest one is selected from the N-layer precoding matrix (such as the largest eigenvalue, the largest singular value, or the largest proportion of power, or the signal and The M layer with the largest Signal to Interference plus Noise Ratio (SINR) is used as AI training data. For example: the terminal reports the precoding matrix of the strongest layer.

It is worth mentioning that when the terminal reuses the conventional channel state information (CSI) report as AI training data, it can use the precoding matrix corresponding to layer 0 (layer 0) in the CSI report as AI training data. data.

Compared with selecting the entire N-layer precoding matrix, it can reduce the amount of AI training data and retain as much effective information in the N-layer precoding matrix as possible.

Option two, the terminal can also use the N-layer precoding matrix as AI training data, so that it can accurately reflect the real channel status of the target downlink channel.

Option 3: The terminal can receive the above-mentioned third indication information before sending the AI training data. The third indication information can indicate the precoding matrix that the terminal needs to report. In this way, the terminal only needs to report the precoding matrix of the specified layer according to the instructions of the first device. Encoding matrix is enough.

Option four: the terminal can choose which layers of precoding matrices to report, and report fourth indication information to indicate which layers of precoding matrices the terminal reports. The terminal may send the fourth instruction information before, after, or at the same time as the AI training data, which is not specifically limited here.

Option 5: The terminal can select the required precoding matrix based on whether each layer in the N-layer precoding matrix meets the preset conditions. Among them, the precoding matrix that meets the preset conditions can more effectively reflect the channel status of the target downlink channel.

Optionally, the preset conditions include at least one of the following:

Channel quality indicator: Channel quality indicator (CQI) is greater than or equal to the first threshold;

The signal to interference plus noise ratio SINR is greater than or equal to the second threshold;

The characteristic value is greater than or equal to the third threshold;

The singular value is greater than or equal to the fourth threshold.

In this embodiment, the terminal can filter and report which layer or layer of precoding matrices according to preset conditions. Compared with selecting the entire N-layer precoding matrix, it can reduce the amount of AI training data and minimize the amount of AI training data. It is possible to retain the effective information in the N-layer precoding matrix.

In a possible implementation, when the terminal selects which layer or layer of precoding matrices to report based on preset conditions, or reports the strongest M-layer precoding matrix among the N-layer precoding matrices, the terminal Fourth indication information may also be sent to the first device to indicate which layer or layer of the N-layer precoding matrix the precoding matrix reported by the terminal is.

Alternatively, the terminal can retain the true values of the elements in the precoding matrix of the layer that needs to be reported in the N-layer precoding matrix, and use 0 or other values for the elements in the precoding matrix of the layer that does not need to be reported in the N-layer precoding matrix. Instead, in this way, the first device can determine the corresponding physical meaning of each element according to the position of each element in the N-layer precoding matrix.

Step 202: The terminal sends the AI training data to the first device.

The first device may be any device used to train an AI network model, such as an access network device (such as a base station) or a core network device. For convenience of explanation, in this embodiment, the first device is a base station as an example. for example.

The above-mentioned first AI network model is used to process the second channel information into the first channel characteristic information. The first AI network model may be an encoding AI network side model. The encoding AI network side model is used to compress and encode the channel information into a channel. Feature information. Correspondingly, the above-mentioned second AI network model is used to restore the first channel characteristic information to the second channel information. The second AI network model may be a decoding AI that matches the first AI network side model. Network model. The decoding AI network model is used to decode the channel characteristic information processed by the encoding AI network side model to restore the channel information.

In implementation, the above-mentioned first device may train or adjust the first AI network model and/or the second AI network model according to the AI training data. For example, assuming that the first AI network model and the second AI network model are neural network models, Then the methods of training the first AI network model and/or the second AI network model include the following three methods:

Option 1: The neural network model includes a joint neural network model composed of the encoder of the terminal and the decoder of the base station, and is jointly trained by the network side device. After the training is completed, the base station sends the AI network model of the encoder to the terminal. In this solution, the first device includes a base station.

Option 2: The terminal trains the encoding AI network model, and the base station trains the decoding AI network model, and matches through the matching process, so that the terminal's encoding AI network model and the base station's decoding AI network model match each other. Among them, the matching process may include: the terminal uses the coding AI network model to encode the channel information, obtains the coded data (i.e., channel characteristic information), and sends the original channel information and the terminal's coded data to the base station, and the base station uses decoding The AI network model decodes the terminal's encoded data into corresponding channel information, and adjusts the decoding AI network model. In this solution, the first device includes a base station.

Option 3: The terminal trains the encoding AI network model and the decoding AI network model, and uses the encoding AI network model to encode the original channel information to obtain the encoded data, and then sends the original channel information and the terminal's encoded data to the base station. The base station trains the decoding AI network model based on the channel information and the terminal's encoding data. At this time, the base station does not need to train the encoding AI network model. In this solution, the first device includes a base station.

In the implementation, the time and/or frequency resources for the terminal to report AI training data may be indicated by the network side device or agreed in the agreement. For example, AI training data may be collected and reported periodically, or collected and reported at a designated time. AI training data.

As an optional implementation, before the terminal collects AI training data, the method further includes:

The terminal receives first instruction information from the first device, where the first instruction information is used to instruct the terminal to collect the AI training data.

In implementation, the first instruction information may instruct the terminal to collect and report the AI training data. For example, the base station instructs the terminal to activate the collection and reporting of AI training data through downlink control information (DCI). In this way, the terminal receives When the first instruction information is received, the collection and reporting of AI training data can be started immediately.

In a possible implementation, after receiving the above-mentioned first indication information, the terminal can determine whether it supports collecting and reporting AI training data based on its own status.

Optionally, the information transmission method also includes:

When the terminal determines that collection of the AI training data is supported based on the status information of the terminal, it sends first information to the first device, and the first information instructs the terminal to collect and report the AI training data;

and / or,

When the terminal determines that collection of the AI training data is not supported based on the status information of the terminal, it sends second information to the first device, and the second information indicates that the terminal will not collect or report it. The AI training data.

The status information of the terminal may include status information of the terminal that changes over time, such as location, moving speed, remaining power, user settings, etc.

In this embodiment, when the terminal receives the first instruction information or the first configuration information, it can determine whether it currently supports collecting and reporting the AI training data based on its actual situation. For example, if the user sets the terminal not to collect and reporting the AI training data, it is determined that the terminal does not support collecting and reporting the AI training data; when the terminal's power is low, it is determined that the terminal does not support collecting and reporting the AI training data; during the movement of the terminal, it is determined The terminal does not support collecting and reporting the AI training data, etc.

In one implementation, after the terminal sends the first information to the first device, the first device knows that the terminal can collect and report the AI training data, and thus only needs to wait for the terminal to report the AI training data.

In another implementation, after the terminal sends the second information to the first device, the first device knows that the terminal will not report AI training data, so it can re-select another terminal to collect and report AI training data, or use Other ways to obtain AI training data. For example: sending the first instruction information to other terminals that may support the collection of AI training data, etc.

The terminal receives first configuration information from the first device, where the first configuration information is used to configure target time domain and/or frequency domain resources;

The terminal collects AI training data, including:

The terminal collects AI training data in the target time domain and/or frequency domain resources.

Among them, the first configuration information can configure the terminal to collect and report the AI training data at a specified time or according to a specified period. For example, the base station controls it through Radio Resource Control (RRC) signaling or media access control layer. The unit (Medium Access Control Element, MAC CE) configures the time or period for the terminal to collect AI training data, so that the terminal can activate the collection and reporting of AI training data at the corresponding time.

This implementation is similar to the above-mentioned way for the terminal to collect AI training data based on the first instruction information from the first device. The difference is that in this implementation, the terminal collection can be configured through protocol agreement or network side pre-configuration. And/or report time domain and/or frequency domain resources of AI training data.

As an optional implementation, before the terminal receives the first indication information or the first configuration information from the first device, the method further includes:

The terminal sends target capability information to the first device, where the target capability information indicates the information reporting capability and/or data collection capability supported by the terminal.

The target capability information may be the communication capability information of the terminal. The target capability information is used by the first device to determine whether the terminal supports the collection of AI training data, and thereby sends the first indication information or the first instruction to the terminal that supports the collection of AI training data. Configuration information.

Optionally, the target capability information includes at least one of the following:

second indication information, the second indication information is used to indicate whether the terminal supports collecting the AI training data;

The maximum number of Channel State Information (CSI)-Reference Signal (RS) ports supported by the terminal;

The total number of ports of the terminal.

Option one, the second indication information can clearly indicate whether the terminal supports collecting the AI training data. For example, the terminal determines whether the terminal supports collecting and reporting AI training data based on its own capabilities and/or status information.

Option two, the maximum number of CSI-RS ports supported by the terminal can be used as a basis for the first device to determine whether the terminal supports collecting the AI training data. For example, the first device can determine the number of CSI-RS ports based on the maximum number of CSI-RS ports supported by the terminal. Whether the terminal can collect AI training data based on the existing CSI-RS port.

Option three, the total number of ports of the terminal can be used as a basis for the first device to determine whether the terminal supports collecting the AI training data. For example, the first device can use the number of all ports of the terminal to be greater than or equal to the ports required for collecting AI training data. If the number is high, it is judged that the terminal supports collecting AI training data.

In this embodiment, the first device can obtain the capability information of the terminal in advance, so that it can filter the terminal according to the capability information, and send the first instruction information or the first configuration information to the terminal that supports collecting AI training data. In this way, it can Reduce the probability that the first device instructs or configures a terminal that does not support the collection of AI training data to collect AI training data, thereby saving resources occupied in the process of instructing or configuring a terminal that does not support the collection of AI training data to collect AI training data, and reducing The delay in collecting and reporting AI training data.

Of course, the first device can also collect other methods to determine which terminal or terminals to send the first instruction information or the first configuration information, for example, obtain a priori information such as the terminal's location, resource configuration, etc., and determine whether the terminal is Support collecting AI training data, thereby sending first instruction information or first configuration information to a terminal that supports collecting AI training data.

As an optional implementation, the terminal collects AI training data, including:

The terminal collects AI training data based on the first port corresponding to the first reference signal resource or the CSI-RS port corresponding to the CSI-RS resource, wherein the first port includes a preconfigured port for collecting the AI training data. A port corresponding to the first reference signal resource, and the CSI-RS port includes a port used for CSI measurement.

In a possible implementation, the first port may be a port configured by the base station specifically for collecting AI training data. In this way, the terminal can perform channel estimation on the first reference signal resource corresponding to the first port to achieve AI collection. training data. The first reference signal resource may be a CSI-RS resource or may be a resource of other types of reference signals, which is not specifically limited here. In this embodiment, the base station can configure an additional first port for the terminal to collect channel estimation of AI training data.

In another possible implementation, the CSI-RS port can represent the port used for existing CSI measurement, that is, the terminal can reuse the CSI-RS port for normal CSI measurement.

In a possible implementation, the AI training data is carried in at least one of the following:

First uplink control information (Uplink Control Information, UCI), the first UCI carries the CSI report of the target downlink channel, and the CSI report is related to the first channel information;

A second UCI, the second UCI does not carry the CSI report of the target downlink channel;

Target physical uplink shared channel (Physical Uplink Shared Channel, PUSCH).

Option one, when the AI training data is carried in the first UCI, the AI training data can be used as part of the CSI report and reported through the first UCI. In this case, the CSI report and the first channel information The correlation can be understood as: the first channel information in the CSI report may be the original channel information of the CSI report, and the CSI report may be channel information in the form of a codebook or precoded channel information or coding processing based on the first AI network model The subsequent channel characteristic information.

Optionally, the AI training data includes first channel information in the CSI report, where the first channel information in the CSI report is precoding information.

In this embodiment, when the terminal uses a codebook to report CSI, the CSI report in the form of the codebook can be reused as AI training data. At this time, the terminal can no longer independently report AI training data, and the base station can receive the CSI reports serve as AI training data.

Option two, the above-mentioned second UCI is a UCI that does not carry the CSI report of the target downlink channel, that is, the AI training data is reported through the UCI independently of the CSI.

Option three: The above target PUSCH carries AI training data. The AI training data can be used as data content and reported through PUSCH.

In implementation, the terminal can flexibly report AI training data based on any one or more of the above options one to three, which are not specifically limited here.

As an optional implementation manner, in the case where the terminal has a first AI network model, the method further includes:

The terminal performs first processing on the first channel information based on the first AI network model to obtain second channel characteristic information;

The terminal sends the second channel characteristic information to the first device, where the AI training data and the second channel characteristic information are used to train the second AI network model.

Among them, the terminal is pre-configured or trained to obtain the first AI network model.

In this embodiment, in addition to sending the original channel information to the first device, the terminal also sends to the first device second channel characteristic information obtained by compressing and encoding the first channel information using the first AI network model. . In this way, the first device can input the second channel characteristic information into the second AI network model, and train or adjust the second AI network model for the purpose of outputting the first channel information from the second AI network model, wherein based on the AI training data and the third Training the second AI network model using the second channel characteristic information can improve the matching degree between the second AI network model and the first AI network model already possessed by the terminal.

It should be noted that there is a one-to-one correspondence between the first channel information and the second channel characteristic information, that is, if a piece of first channel information is input to the first AI network model, then the first AI network model will output the first channel. second channel characteristic information corresponding to the information; if another piece of first channel information is input to the first AI network model, the first AI network model outputs second channel characteristic information corresponding to the other piece of first channel information. In order for the first device to learn the correspondence between the first channel information and the second channel characteristic information, the terminal may send the corresponding first channel information and the second channel characteristic information at the same time, or in the mutually related time domain and/or Or the first channel information and the second channel characteristic information corresponding to the first channel information are respectively transmitted on frequency domain resources, or the reported first channel information carries identification information of the corresponding second channel characteristic information, etc.

Optionally, the AI training data carries first identification information of the second channel characteristic information;

or,

The terminal sends the AI training data to the first device, including:

The terminal sends a target CSI report to the first device, where the target CSI report carries the second channel characteristic information and the AI training data; or,

The terminal sends the AI training data to the first device on time domain and/or frequency domain resources corresponding to the first identification information of the second channel characteristic information.

In case one, the terminal sends a target CSI report to the first device. The target CSI report carries the second channel characteristic information and the AI training data. The terminal may be using the first AI network model to perform CSI reporting, and The corresponding first channel information is added to the CSI and reported together with the AI-encoded second channel characteristic information.

After receiving the CSI report, the base station can obtain the first channel information and the corresponding AI-encoded second channel characteristic information. The base station can restore the second channel characteristic information to the first channel information by using the second AI network model as Purpose: train the second AI network model.

It is worth mentioning that in the implementation, when the base station obtains the first channel information and the second channel characteristic information at the same time, it can also directly use the first channel information as the channel information for subsequent scheduling. In this way, the accuracy of the scheduling process can be improved. Spend.

Case 2: When the terminal sends the AI training data to the first device on the time domain and/or frequency domain resources corresponding to the first identification information of the second channel characteristic information, the base station receives the AI training data respectively. data and second channel characteristic information, and can determine which first channel information in the AI training data corresponds to the corresponding relationship between the time domain and/or frequency domain resources of the received AI training data and the first identification information. The second channel characteristic information corresponds to the second AI network model, and the second AI network model can be trained for the purpose of restoring the second channel characteristic information to the first channel information.

The first identifier may be an identifier (Identity, ID) of the CSI report configuration (report configuration) or any other identifier that can directly or indirectly identify the second channel characteristic information, which is not specifically limited here.

Case 3: When the above-mentioned AI training data carries the first identification information of the second channel characteristic information, the base station can determine the first channel information in the AI training data based on the first identification information carried in the AI training data. The corresponding second channel characteristic information can then be used to train the second AI network model for the purpose of restoring the second channel characteristic information to the first channel information.

In a possible implementation, if the AI training data is carried in the second UCI or target PUSCH, and the terminal reports the second channel characteristic information through the CSI report, the second UCI or target PUSCH can be used in conjunction with the AI The CSI report corresponding to the training data is sent on the relevant time domain and/or frequency domain resources. In this way, when the first device receives the AI training data on the time domain and/or frequency domain resources, it can determine whether the AI training data is consistent with the time domain and/or frequency domain resources. Corresponds to the second channel characteristic information in the CSI report related to domain and/or frequency domain resources.

In another possible implementation, if the AI training data is carried in the second UCI or target PUSCH, and the terminal reports the second channel characteristic information through the CSI report, the second UCI or target PUSCH may carry the corresponding The first identification information of the CSI report. In this way, when the first device receives the second UCI or the target PUSCH, it can obtain the AI training data and the first identification information of the CSI report corresponding to the AI training data, thereby determining the AI training The data corresponds to the second channel characteristic information in the CSI report identified by the first identification information.

In a possible implementation, if the AI training data is carried in the first UCI, at this time, the first UCI also carries the second channel characteristic information corresponding to the AI training data. In this way, the first device receives By the time of the first UCI, Corresponding AI training data and second channel feature information can be obtained therefrom.

As an optional implementation, the AI training data includes a precoding matrix determined based on a first codebook, a first parameter of the first codebook is greater than a preset parameter, and the first parameter includes at least one of the following item:

Number of ports, number of delay paths, number of beams, and non-zero coefficient ratio.

The above-mentioned preset parameters may be the number of ports, the number of delay paths, the number of beams, and the non-zero coefficient ratio of the existing R16 codebook.

Among them, the number of ports, the number of delay paths, the number of beams, and the proportion of non-zero coefficients in the R16 codebook are shown in Table 1 below:

Table 1

Among them, Pv is used to calculate the number of delay paths as shown in Table 1 above, and the R16 codebook supports up to 32 ports.

In this embodiment, at least one of the number of ports, the number of delay paths, the number of beams, and the non-zero coefficient ratio of the first codebook may be greater than any combination of the number of ports and the number of delay paths in Table 1 above. , the number of beams, and the proportion of non-zero coefficients. For example: the first parameter of the first codebook in this implementation may be a parameter combination as shown in Table 2 below:

Table 2

That is to say, in this embodiment, a codebook with higher accuracy than the R16 codebook can be used to report AI training data. At this time, the AI training data can more accurately describe the channel status of the target downlink channel, so that based on the The AI network model trained with AI training data better matches the actual channel status of the target downlink channel.

In this embodiment of the present application, the terminal collects artificial intelligence AI training data, wherein the AI training data includes the first channel information of the target downlink channel, and the AI training data is used to train the first AI network model and/or the second AI network model, the first AI network model is used to process the second channel information into first channel characteristic information, the second The AI network model is used to restore the first channel characteristic information to the second channel information, where the second channel information is the channel information of the target downlink channel; the terminal sends the AI training to the first device data. In this way, the AI training data reported by the terminal to the first device includes actual estimated channel information. In this way, in the process of training the first AI network model and/or the second AI network model based on the AI training data, the first device, The degree of matching between the trained first AI network model and/or the second AI network model and the target downlink channel can be improved. In the process of using the first AI network model to compress the channel information of the target downlink channel into channel feature information, and/or using the second AI network model to restore the channel feature information of the target downlink channel into channel information, the first step can be improved. The compression coding accuracy of the AI network model and/or the decoding accuracy of the second AI network model can be improved and/or the matching degree between the first AI network model and the second AI network model can be improved, thereby reducing the occupancy of the channel information reporting process. resources, and improve the accuracy of the reporting process.

Please refer to Figure 3. An embodiment of the present application provides an AI network model training method, the execution subject of which is the first device. The first device may be a network-side device, such as the network-side device 12 listed in the embodiment as shown in Figure 1 or a core network device. For ease of explanation, in the embodiment of this application, the first device is a base station as an example. for example.

As shown in Figure 3, the AI network model training method executed by the first device may include the following steps:

Step 301: The first device receives AI training data from the terminal, where the AI training data includes first channel information of the target downlink channel.

The above-mentioned AI training data and first channel information have the same meaning as the AI training data and first channel information in the method embodiment shown in Figure 2, and are not specifically limited here.

Step 302: The first device trains a first AI network model and/or a second AI network model according to the AI training data, wherein the first AI network model is used to process the second channel information into a first channel. Characteristic information, the second AI network model is used to restore the first channel characteristic information to the second channel information, and the second channel information is the channel information of the target downlink channel.

In implementation, the base station can train the encoding AI network model based on the AI training data, or train the decoding AI network model based on the AI training data, or jointly train the encoding AI network model and the decoding AI network model based on the AI training data.

In the embodiment of the present application, the base station can obtain the channel information obtained by performing channel estimation on the target downlink channel from the terminal to train the encoding and/or decoding AI network model suitable for the target downlink channel, and can improve the first AI network model obtained by training. And/or the degree of matching between the second AI network model and the target downlink channel.

As an optional implementation, in the case where the first device trains the first AI network model based on the AI training data, the method further includes:

The first device sends relevant information of the first AI network model to the terminal.

In this embodiment, after completing the training of the coding AI network model, the base station delivers the trained coding AI network model to the terminal. In this way, the terminal can use the coding AI network model to estimate the Compress and encode the channel information and report the compressed and encoded channel characteristic information. After that, the base station can use the decoding AI network model that matches the encoding AI network model to restore the channel characteristic information to the original channel information. information, or the base station can also use a non-AI method to restore the channel characteristic information to the original channel information, for example, using a certain algorithm to restore the channel characteristic information to the original channel information.

As an optional implementation, before the first device receives the AI training data from the terminal, the method further includes:

The first device sends first instruction information to the terminal, where the first instruction information is used to instruct the terminal to collect the AI training data.

The above-mentioned first instruction information has the same meaning and function as the first instruction information in the method embodiment shown in Figure 2, and will not be described again here.

The first device sends first configuration information to the terminal, where the first configuration information is used to configure target time domain and/or frequency domain resources, where the AI training data is in the target time domain and/or frequency domain. /Or collected from frequency domain resources.

The above-mentioned first configuration information has the same meaning and function as the first configuration information in the method embodiment shown in Figure 2, and will not be described again here.

As an optional implementation manner, before the first device sends the first indication information or the first configuration information to the terminal, the method further includes:

The first device receives target capability information from the terminal, wherein the target capability information indicates the information reporting capability and/or data collection capability supported by the terminal;

The first device sends first indication information or first configuration information to the terminal, including:

When the first device determines that the terminal supports collecting and reporting the AI training data according to the target capability information, the first device sends first indication information or first configuration information to the terminal.

The maximum number of channel state information reference signal CSI-RS ports supported by the terminal;

The total number of ports of the terminal.

The above target capability information has the same meaning and function as the target capability information in the method embodiment shown in Figure 2, and will not be described again here.

In this embodiment, the first device obtains the target capability information of the terminal in advance, and accordingly selects a terminal that supports collecting and reporting the AI training data to perform the action of collecting and reporting the AI training data.

In a first optional implementation, the method further includes:

The first device receives first information from the terminal, wherein the first information instructs the terminal to collect and report the AI training data;

and / or,

The first device receives second information from the terminal, and the second information indicates that the terminal will not collect or report the AI training data.

It is worth mentioning that when the base station receives the first information from the terminal, it can receive AI training data from the terminal.

In a possible implementation, when the base station receives the second information from the terminal, it can select other terminals to collect and report AI training data until it receives the first information from the terminal, for example: the base station sends data to other terminals. The terminal sends the first instruction information or the first configuration information, and receives the first information from the other terminal. Of course, if the other terminal still sends the second information to the base station, the base station continues to look for a device that can support collection and reporting of the AI training. Data terminal.

Alternatively, when the base station receives the second information from the terminal, it can obtain AI training data in other ways, such as using pre-stored data to train the encoding and/or decoding AI network model, which will not be described in detail here.

The first device configures a first port corresponding to a first reference signal resource and/or a CSI-RS port corresponding to a CSI-RS resource for the terminal, where the first port is dedicated to collecting the AI training data. The port corresponding to the first reference signal resource, and the CSI-RS port is a port used for CSI measurement and collection of the AI training data.

The above-mentioned first reference signal resource, first port, and CSI-RS port have the same meaning and function as the first reference signal resource, first port, and CSI-RS port in the method embodiment shown in Figure 2. Here, No longer.

As an optional implementation, the AI training data is carried in at least one of the following:

First uplink control information UCI, the first UCI carries a CSI report of the target downlink channel, and the CSI report is related to the first channel information;

Target physical uplink shared channel PUSCH.

As an optional implementation manner, the AI training data includes first channel information in the CSI report, where the first channel information in the CSI report is precoding information.

In the implementation, the base station can receive the CSI report and AI training data from the terminal. At this time, the base station can schedule the target downlink channel based on the more accurate first channel information in the AI training data. In this way, the base station scheduling target downlink channel can be improved. Channel reliability.

The first device receives the second channel characteristic information from the terminal, wherein the second channel characteristic information is obtained by first processing the first channel information based on the first AI network model;

The first device trains the first AI network model and/or the second AI network model according to the AI training data, including:

The first device trains the second AI network model according to the AI training data and the second channel characteristic information.

Wherein, the first device trains the second AI according to the AI training data and the second channel characteristic information. The network model may input the two channel characteristic information into the second AI network model, and train the second AI network model for the purpose of outputting the first channel information corresponding to the second channel characteristic information from the second AI network model. In this way, the degree of matching between the trained second AI network model and the first AI network model already possessed by the terminal can be improved. The first AI network model already owned by the terminal may be trained by the terminal based on the estimated actual channel information, or may be an AI network model previously trained and delivered by the base station, which is not specifically limited here.

As an optional implementation, the AI training data carries the first identification information of the second channel characteristic information;

or,

The first device receives AI training data from the terminal, including:

The first device receives a target CSI report from the terminal, where the target CSI report carries the second channel characteristic information and the AI training data; or,

The first device receives the AI training data from the terminal on time domain and/or frequency domain resources corresponding to the first identification information of the second channel characteristic information.

As an optional implementation manner, the first channel information is at least one of a precoding matrix and a channel matrix.

The N-layer precoding matrix;

As an optional implementation, the preset conditions include at least one of the following:

The channel quality indicator CQI is greater than or equal to the first threshold;

The characteristic value is greater than or equal to the third threshold;

The singular value is greater than or equal to the fourth threshold.

The AI network model training method executed by the first device provided by the embodiment of the present application corresponds to the information transmission method executed by the terminal, and can improve the training of the first AI network model and/or the second AI network model and the target downlink channel. degree of matching.

In order to facilitate the explanation of the information transmission method provided by the embodiment of the present application, the first device is a base station as an example to illustrate the present application. The information transmission method and AI network model training method provided by the embodiment may include the following steps:

Step 1. The base station instructs the terminal through DCI to activate the measurement and reporting of AI training data. Alternatively, the base station configures the time or period for measuring AI training data through RRC/MACCE signaling, and the terminal activates the measurement and reporting of AI training data at the corresponding time. .

Optionally, after receiving the DCI indication or RRC/MACCE signaling, the terminal can feedback to the base station the status of whether it supports measuring and reporting AI training data. For example, if the terminal has insufficient power, it will report that it cannot perform measurement and report AI training data. ; If the user does not agree to the report, the report cannot perform measurement and report AI training data; if the terminal is in a high-speed motion state, the channel measurement may be inaccurate at this time, and the report cannot perform measurement and report AI training data.

Step 2: The terminal measures the channel information and calculates the corresponding precoding matrix.

Scenario 1

The terminal uses the R-16TypeII codebook to report CSI, where CSI can include Precoding Matrix Indicator (PMI), Channel Quality Indicator (CQI), Rank Indicator (RI), etc. At this time The base station may not yet have a decoding AI network model, or the base station's decoding AI network model may not match the terminal's encoding AI network model.

First, if the terminal does not encode the AI network model, the terminal can incorporate the precoding matrix information into the CSI as AI training data and report it together. After the base station obtains the CSI, it can solve the PMI for scheduling and solve the AI training data. The data is used for AI model training, and after training, the encoded AI network model is sent to the terminal. Alternatively, after the base station obtains the CSI, it can decode the AI training data for AI model training. After training, the encoded AI network model is sent to the terminal. The base station can also use the AI training data to replace PMI as the precoding information for scheduling.

It is worth mentioning that the terminal can also send AI training data to the base station through PUSCH. After the AI network model is trained, the base station then sends the encoded AI network model to the terminal. Or when the first device is a node other than the base station (such as a core network), the terminal can also send the AI training data to the node through PUSCH. After the AI network model is trained, the node can encode the AI The network model is sent to the terminal, and the decoded AI network model is sent to the base station.

Secondly, for the situation where the terminal has a coding AI network model, the terminal has trained its own coding AI network model, but the base station does not have a decoding AI network model or the base station’s decoding AI network model does not match the terminal’s coding AI network model. . At this time, the terminal can send the corresponding channel characteristic information encoded by the coded AI network model while sending the AI training data. The specific sending method is similar to the above-mentioned sending method of the AI training data, and will not be described again here.

Scenario 2

The terminal uses the encoding AI network model to report PMI. At this time, the terminal's encoding AI network model has matched the base station's decoding AI network model and can work. However, because there are remaining resources at this time, the terminal can still report AI training data, or because the time is too long and the channel changes, the encoding AI network model and the decoding AI network model need to be rematched. At this time, the terminal collects and reports AI training data data.

Among them, since the terminal has used the coded AI network model to report PMI, the terminal will use the corresponding original channel information The information is reported as AI training data, that is, the AI training data and the PMI in the CSI report correspond to each other, and the reported PMI is the encoding result of the AI training data.

In implementation, the terminal can report AI training data in a CSI report or through other UCI. For example, the base station instructs the terminal to report the time-frequency position of the UCI carrying AI training data. This time-frequency position is consistent with the CSI report configuration ID. Correspondence, that is, when the base station receives the coded PMI from the CSI, it can receive the AI training data corresponding to the UCI at the corresponding time-frequency position, thereby determining the correspondence between the AI training data and the PMI; or, the terminal passes PUSCH Report AI training data, and the AI training data also carries CSI report configuration ID. The base station obtains one or more AI training data by receiving PUSCH, and finds the corresponding CSI based on the CSI report configuration ID carried by the AI training data, and then It is determined that the PMI in the CSI is the coded information corresponding to the AI training data.

For the information transmission method provided by the embodiments of the present application, the execution subject may be an information transmission device. In the embodiment of the present application, an information transmission device performing an information transmission method is used as an example to illustrate the information transmission device provided by the embodiment of the present application.

Please refer to Figure 4. An information transmission device provided by an embodiment of the present application can be a device in a terminal. As shown in Figure 4, the information transmission device 400 can include the following modules:

Collection module 401, used to collect artificial intelligence AI training data, wherein the AI training data includes the first channel information of the target downlink channel, and the AI training data is used to train the first AI network model and/or the second AI network Model, the first AI network model is used to process the second channel information into the first channel characteristic information, and the second AI network model is used to restore the first channel characteristic information into the second channel information, The second channel information is the channel information of the target downlink channel;

The first sending module 402 is used to send the AI training data to the first device.

Optionally, the information transmission device 400 also includes:

The second receiving module is configured to receive first instruction information from the first device, where the first instruction information is used to instruct the terminal to collect the AI training data.

Optionally, the information transmission device 400 also includes:

A third receiving module, configured to receive first configuration information from the first device, where the first configuration information is used to configure target time domain and/or frequency domain resources;

The collection module 401 is specifically used for:

Collect AI training data in the target time domain and/or frequency domain resources.

Optionally, the information transmission device 400 also includes:

The second sending module is configured to send target capability information to the first device, where the target capability information indicates the information reporting capability and/or data collection capability supported by the terminal.

The total number of ports of the terminal.

Optionally, the information transmission device 400 also includes:

A third sending module, configured to send first information to the first device when it is determined that collection of the AI training data is supported based on the status information of the terminal, where the first information instructs the terminal to collect and Report the AI training data;

and / or,

The fourth sending module is used for the terminal to send second information to the first device when it is determined that the collection of the AI training data is not supported based on the status information of the terminal, and the second information indicates that the AI training data is not supported. The terminal will not collect or report the AI training data.

Optionally, the collection module 401 is specifically used to:

AI training data is collected based on the first port corresponding to the first reference signal resource or the CSI-RS port corresponding to the CSI-RS resource, where the first port includes a preconfigured first reference for collecting the AI training data. A port corresponding to the signal resource, and the CSI-RS port includes a port used for CSI measurement.

Optionally, the AI training data is carried in at least one of the following:

Target physical uplink shared channel PUSCH.

Optionally, in the case where the terminal has the first AI network model, the information transmission device 400 further includes:

A first processing module, configured to perform first processing on the first channel information based on the first AI network model to obtain second channel characteristic information;

A fifth sending module, configured to send the second channel characteristic information to the first device, where the AI training data and the second channel characteristic information are used to train the second AI network model.

or,

The first sending module 402 is specifically used for:

Send a target CSI report to the first device, where the target CSI report carries the second channel characteristic information and the AI training data; or,

The AI training data is sent to the first device on the time domain and/or frequency domain resources corresponding to the first identification information of the second channel characteristic information.

Optionally, the AI training data includes a precoding matrix determined based on a first codebook, a first parameter of the first codebook is greater than a preset parameter, and the first parameter includes at least one of the following:

Optionally, the first channel information is at least one of a precoding matrix and a channel matrix.

Optionally, the AI training data includes at least one of the following:

The N-layer precoding matrix;

Optionally, the preset conditions include at least one of the following:

The characteristic value is greater than or equal to the third threshold;

The singular value is greater than or equal to the fourth threshold.

The information transmission device 400 provided by the embodiment of the present application can implement various processes implemented by the terminal in the method embodiment as shown in Figure 2, and can achieve the same beneficial effects. To avoid duplication, details will not be described here.

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

For the AI network model training method provided by the embodiments of the present application, the execution subject may be an AI network model training device. In the embodiment of the present application, the AI network model training device executed by the AI network model training method is used as an example to illustrate the AI network model training device provided by the embodiment of the present application.

Please refer to Figure 5. An AI network model training device provided by an embodiment of the present application can be a device in the first device. As shown in Figure 5, the AI network model training device 500 can include the following modules:

The first receiving module 501 is configured to receive AI training data from the terminal, where the AI training data includes first channel information of the target downlink channel;

Training module 502, used to train the first AI network model and/or the second AI network model according to the AI training data, wherein the first AI network model is used to process the second channel information into the first channel characteristic information. , the second AI network model is used to restore the first channel characteristic information to the second channel information, where the second channel information is the channel information of the target downlink channel.

Optionally, in the case where the first device trains the first AI network model according to the AI training data, the AI network model training device 500 further includes:

A sixth sending module is configured to send relevant information of the first AI network model to the terminal.

Optionally, the AI network model training device 500 also includes:

A seventh sending module is configured to send first instruction information to the terminal, where the first instruction information is used to instruct the terminal to collect the AI training data.

Optionally, the AI network model training device 500 also includes:

The eighth sending module is configured to send first configuration information to the terminal, where the first configuration information is used to configure target time domain and/or frequency domain resources, where the AI training data is Collected from domain and/or frequency domain resources.

Optionally, the AI network model training device 500 also includes:

A fourth receiving module, configured to receive target capability information from the terminal, where the target capability information indicates the information reporting capability and/or data collection capability supported by the terminal;

The seventh sending module or the eighth sending module is specifically used for:

When it is determined based on the target capability information that the terminal supports collecting and reporting the AI training data, first indication information or first configuration information is sent to the terminal.

The total number of ports of the terminal.

Optionally, the AI network model training device 500 also includes:

A fifth receiving module, configured to receive first information from the terminal, where the first information instructs the terminal to collect and report the AI training data;

and / or,

A sixth receiving module is configured to receive second information from the terminal, where the second information indicates that the terminal will not collect or report the AI training data.

Optionally, the AI network model training device 500 also includes:

A configuration module configured to configure a first port corresponding to the first reference signal resource and/or a CSI-RS port corresponding to the CSI-RS resource for the terminal, wherein the first port is dedicated to collecting the AI training data. The port corresponding to the first reference signal resource, and the CSI-RS port is a port used for CSI measurement and collection of the AI training data.

Optionally, the AI training data is carried in at least one of the following:

Target physical uplink shared channel PUSCH.

Optionally, when the terminal has a first AI network model, the AI network model training device 500 also includes include:

A sixth receiving module, configured to receive the second channel characteristic information from the terminal, wherein the second channel characteristic information is obtained by performing the first processing on the first channel information based on the first AI network model. ;

The training module 502 is specifically used for:

The second AI network model is trained according to the AI training data and the second channel characteristic information.

Optionally, the AI training data carries the first identification information of the second channel characteristic information, or the first receiving module 501 specifically uses:

Receive a target CSI report from the terminal, where the target CSI report carries the second channel characteristic information and the AI training data;

or,

The AI training data from the terminal is received on time domain and/or frequency domain resources corresponding to the first identification information of the second channel characteristic information.

Optionally, the AI training data includes at least one of the following:

The N-layer precoding matrix;

Optionally, the preset conditions include at least one of the following:

The characteristic value is greater than or equal to the third threshold;

The singular value is greater than or equal to the fourth threshold.

The AI network model training device in the embodiment of the present application may be an electronic device, such as an electronic device with an operating system, or may be a component in the electronic device, such as an integrated circuit or chip. The electronic device may be a network side device. For example, the terminal may include but is not limited to the types of network side devices 12 listed above, which are not specifically limited in the embodiments of this application.

The AI network model training device 500 provided by the embodiment of the present application can implement the method embodiment shown in Figure 3 Each process implemented by the first device can achieve the same beneficial effects, and will not be described again in order to avoid repetition.

Optionally, as shown in Figure 6, this embodiment of the present application also provides a communication device 600, which includes a processor 601 and a memory 602. The memory 602 stores programs or instructions that can be run on the processor 601, for example. , when the communication device 600 is a terminal, when the program or instruction is executed by the processor 601, each step of the method embodiment shown in Figure 2 is implemented, and the same technical effect can be achieved. When the communication device 600 is the first device, when the program or instruction is executed by the processor 601, each step of the method embodiment shown in Figure 3 is implemented, and the same technical effect can be achieved. To avoid duplication, the details will not be described here.

An embodiment of the present application also provides a terminal, including a processor and a communication interface. The communication interface is used to collect artificial intelligence AI training data and send the AI training data to the first device, where the AI training data includes target downlink The first channel information of the channel, the AI training data is used to train the first AI network model and/or the second AI network model, the first AI network model is used to process the second channel information into the first channel characteristic information , the second AI network model is used to restore the first channel characteristic information to the second channel information, where the second channel information is the channel information of the target downlink channel.

Specifically, FIG. 7 is a schematic diagram of the hardware structure of a terminal that implements 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, a processor 710, etc. 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) that supplies power to various components. The power supply may be logically connected to the processor 710 through a power management system, thereby managing 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. The terminal may include more or fewer components than shown in the figure, or some components may be combined or arranged differently, which will not be described again here.

It should be understood that in the embodiment of the present application, the input unit 704 may include a graphics processing unit (Graphics Processing Unit, GPU) 7041 and a microphone 7042. The graphics processor 7041 is responsible for the image capture device (GPU) in the video capture mode or the image capture mode. Process the image data of still pictures or videos obtained by cameras (such as cameras). The display unit 706 may include a display panel 7061, which 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 a touch panel 7071 and at least one of other input devices 7072 . Touch panel 7071, also called 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 keys, switch keys, etc.), trackballs, mice, and joysticks, which will not be described again here.

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

Memory 709 may 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 for at least one function (such as a sound playback function, Image playback function, etc.) etc. In addition, save 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), electrically removable memory. Erase programmable read-only memory (Electrically EPROM, EEPROM) or flash memory. 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 link dynamic random access memory (Synch link DRAM) , SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DRRAM). Memory 709 in embodiments 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, where the application processor mainly handles operations related to the operating system, user interface, application programs, etc., Modem processors mainly process wireless communication signals, such as baseband processors. It can be understood that the above modem processor may not be integrated into the processor 710.

Among them, the radio frequency unit 701 is used to collect artificial intelligence AI training data, wherein the AI training data includes the first channel information of the target downlink channel, and the AI training data is used to train the first AI network model and/or the second AI network model, the first AI network model is used to process the second channel information into the first channel characteristic information, and the second AI network model is used to restore the first channel characteristic information into the second channel Information, the second channel information is the channel information of the target downlink channel;

The radio frequency unit 701 is also used to send the AI training data to the first device.

Optionally, before performing the collection of AI training data, the radio frequency unit 701 is also used to:

Receive first instruction information from the first device, where the first instruction information is used to instruct the terminal to collect the AI training data.

Receive first configuration information from the first device, where the first configuration information is used to configure target time domain and/or frequency domain resources;

The collection of AI training data performed by the radio frequency unit 701 includes:

The radio frequency unit 701 collects AI training data in the target time domain and/or frequency domain resources.

Optionally, before performing the receiving of the first indication information or the first configuration information from the first device, the radio frequency unit 701 is also configured to:

Send target capability information to the first device, where the target capability information indicates the information reporting capability and/or data collection capability supported by the terminal.

The total number of ports of the terminal.

Optionally, the processor 710 is also configured to control the radio frequency unit 701 to send the first information to the first device when it is determined that collection of the AI training data is supported according to the status information of the terminal. A message instructs the terminal to collect and report the AI training data;

and / or,

The processor 710 is also configured to control the radio frequency unit 701 to send second information to the first device when it is determined according to the status information of the terminal that collection of the AI training data is not supported, and the second information indicates The terminal will not collect or report the AI training data.

Optionally, the collection of AI training data performed by the radio frequency unit 701 includes:

The radio frequency unit 701 collects AI training data based on the first port corresponding to the first reference signal resource or the CSI-RS port corresponding to the CSI-RS resource, where the first port includes a preconfigured port for collecting the AI training data. A port corresponding to the first reference signal resource, and the CSI-RS port includes a port used for CSI measurement.

Optionally, the AI training data is carried in at least one of the following:

Target physical uplink shared channel PUSCH.

Optionally, when the terminal has the first AI network model:

The processor 710 is also configured to perform first processing on the first channel information based on the first AI network model to obtain second channel characteristic information;

The radio frequency unit 701 is also configured to send the second channel characteristic information to the first device, where the AI training data and the second channel characteristic information are used to train the second AI network model.

or,

The sending of the AI training data to the first device performed by the radio frequency unit 701 includes:

Optionally, the AI training data includes at least one of the following:

The N-layer precoding matrix;

Optionally, the preset conditions include at least one of the following:

The characteristic value is greater than or equal to the third threshold;

The singular value is greater than or equal to the fourth threshold.

The terminal 700 provided by the embodiment of the present application can implement various processes performed by the information transmission device as shown in Figure 3, and can achieve the same beneficial effects. To avoid duplication, the details will not be described again.

Embodiments of the present application also provide a network side device. The network side device may be an access network device or a core network device. The network side device includes a communication interface and a processor, where the communication interface is used to receive AI from the terminal. training data, wherein the AI training data includes first channel information of the target downlink channel; the processor is configured to train a first AI network model and/or a second AI network model according to the AI training data, wherein the The first AI network model is used to process the second channel information into the first channel characteristic information. The second AI network model is used to restore the first channel characteristic information into the second channel information. The second The channel information is the channel information of the target downlink channel.

This network side device embodiment corresponds to the method embodiment shown in Figure 3. Each implementation process and implementation manner of the method embodiment shown in Figure 3 can be applied to this network side device embodiment, and can achieve the same technical effect.

Embodiments of the present application also provide a readable storage medium. Programs or instructions are stored on the readable storage medium. When the program or instructions are executed by a processor, each process of the method embodiment shown in Figure 2 or Figure 3 is implemented. , and can achieve the same technical effect, so to avoid repetition, they will not be described again here.

Wherein, the processor is the processor in the terminal described in the above embodiment. The readable storage medium includes computer readable storage media, such as computer read-only memory ROM, random access memory RAM, magnetic disk or optical disk, etc.

An 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. The processor is used to run programs or instructions. The implementation is as shown in Figure 2 or Figure 3. Each process of the method embodiment is shown, and the same technical effect can be achieved. To avoid repetition, the details will not be described here.

It should be understood that the chips mentioned in the embodiments of this application may also be called system-on-chip, system-on-a-chip, system-on-chip or system-on-chip, etc.

The embodiment of the present application further provides a computer program/program product, 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 Figure 2 or Figure 3 Each process of the method embodiment shown can achieve the same technical effect. To avoid repetition, it will not be described again here.

Embodiments of the present application also provide a communication system, including: a terminal and a network side device. The terminal can be used to perform the steps of the information transmission method as shown in Figure 2. The network side device can be used to perform the steps of the information transmission method as shown in Figure 3. The steps of the AI network model training method shown below.

It should be noted that, in this document, the terms "comprising", "comprises" or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or device that includes a series of elements not only includes those elements, It also includes other elements not expressly listed or inherent in the process, method, article or apparatus. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article or apparatus that includes 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, but may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. Functions may be performed, for example, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better. implementation. Based on this understanding, the technical solution of the present application can be embodied in the form of a computer software product that is essentially or contributes to the existing technology. The computer software product is stored in a storage medium (such as ROM/RAM, disk , CD), including several instructions to cause a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of this application.

The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, 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 Inspired by this application, many forms can be made without departing from the purpose of this application and the scope protected by the claims, all of which fall within the protection of this application.

Claims

An information transmission method including:

The terminal collects artificial intelligence AI training data, wherein the AI training data includes first channel information of the target downlink channel, and the AI training data is used to train the first AI network model and/or the second AI network model, and the third An AI network model is used to process the second channel information into the first channel characteristic information. The second AI network model is used to restore the first channel characteristic information into the second channel information. The second channel The information is the channel information of the target downlink channel;

The terminal sends the AI training data to the first device.
The method according to claim 1, wherein before the terminal collects AI training data, the method further includes:

The terminal receives first instruction information from the first device, where the first instruction information is used to instruct the terminal to collect the AI training data.
The method according to claim 1, wherein before the terminal collects AI training data, the method further includes:

The terminal receives first configuration information from the first device, where the first configuration information is used to configure target time domain and/or frequency domain resources;

The terminal collects AI training data, including:

The terminal collects AI training data in the target time domain and/or frequency domain resources.
The method according to claim 2 or 3, wherein before the terminal receives the first indication information or the first configuration information from the first device, the method further includes:

The terminal sends target capability information to the first device, where the target capability information indicates the information reporting capability and/or data collection capability supported by the terminal.
The method according to claim 4, wherein the target capability information includes at least one of the following:

second indication information, the second indication information is used to indicate whether the terminal supports collecting the AI training data;

The maximum number of channel state information reference signal CSI-RS ports supported by the terminal;

The total number of ports of the terminal.
The method according to claim 2 or 3, wherein the method further includes:

When the terminal determines that collection of the AI training data is supported based on the status information of the terminal, it sends first information to the first device, and the first information instructs the terminal to collect and report the AI training data;

and / or,

When the terminal determines that collection of the AI training data is not supported based on the status information of the terminal, it sends second information to the first device, and the second information indicates that the terminal will not collect or report it. The AI training data.
The method according to claim 1, wherein the terminal collects AI training data, including:

The terminal collects AI training data based on the first port corresponding to the first reference signal resource or the CSI-RS port corresponding to the CSI-RS resource, wherein the first port includes a preconfigured port for collecting the AI training data. A port corresponding to the first reference signal resource, and the CSI-RS port includes a port used for CSI measurement.
The method according to any one of claims 1 to 3, wherein the AI training data is carried in at least one of the following:

First uplink control information UCI, the first UCI carries a CSI report of the target downlink channel, and the CSI report is related to the first channel information;

A second UCI, the second UCI does not carry the CSI report of the target downlink channel;

Target physical uplink shared channel PUSCH.
The method according to claim 8, wherein the AI training data includes first channel information in the CSI report, wherein the first channel information in the CSI report is precoding information.
The method according to any one of claims 1 to 3, wherein, in the case where the terminal has a first AI network model, the method further includes:

The terminal performs first processing on the first channel information based on the first AI network model to obtain second channel characteristic information;

The terminal sends the second channel characteristic information to the first device, where the AI training data and the second channel characteristic information are used to train the second AI network model.
The method according to claim 10, wherein the AI training data carries first identification information of the second channel characteristic information;

or,

The terminal sends the AI training data to the first device, including:

The terminal sends a target CSI report to the first device, where the target CSI report carries the second channel characteristic information and the AI training data; or,

The terminal sends the AI training data to the first device on time domain and/or frequency domain resources corresponding to the first identification information of the second channel characteristic information.
The method according to any one of claims 1 to 3, wherein the AI training data includes a precoding matrix determined based on a first codebook, the first parameter of the first codebook is greater than a preset parameter, so The first parameter includes at least one of the following:

Number of ports, number of delay paths, number of beams, and non-zero coefficient ratio.
The method according to any one of claims 1 to 3, wherein the first channel information is at least one of a precoding matrix and a channel matrix.
The method of claim 13, wherein the AI training data includes at least one of the following:

The strongest M-layer precoding matrix among the N-layer precoding matrices, the channel information of the target downlink channel includes the N-layer precoding matrix, N is a positive integer, and M is a positive integer less than or equal to N;

The N-layer precoding matrix;

Among the N-layer precoding matrices, the precoding matrix indicated by third indication information, where the third indication information comes from the first device;

In the N-layer precoding matrix, the precoding matrix indicated by fourth indication information, where the fourth indication information is the indication information sent by the terminal to the first device in advance;

Among the N-layer precoding matrices, a precoding matrix that satisfies a preset condition.
The method according to claim 14, wherein the preset conditions include at least one of the following:

The channel quality indicator CQI is greater than or equal to the first threshold;

The signal to interference plus noise ratio SINR is greater than or equal to the second threshold;

The characteristic value is greater than or equal to the third threshold;

The singular value is greater than or equal to the fourth threshold.
An artificial intelligence AI network model training method, including:

The first device receives AI training data from the terminal, where the AI training data includes first channel information of the target downlink channel;

The first device trains a first AI network model and/or a second AI network model according to the AI training data, wherein the first AI network model is used to process second channel information into first channel feature information, The second AI network model is used to restore the first channel characteristic information to the second channel information, and the second channel information is the channel information of the target downlink channel.
The method of claim 16, wherein in the case where the first device trains a first AI network model based on the AI training data, the method further includes:

The first device sends relevant information of the first AI network model to the terminal.
The method of claim 16, wherein before the first device receives the AI training data from the terminal, the method further includes:

The first device sends first instruction information to the terminal, where the first instruction information is used to instruct the terminal to collect the AI training data.
The method of claim 16, wherein before the first device receives the AI training data from the terminal, the method further includes:

The first device sends first configuration information to the terminal, where the first configuration information is used to configure target time domain and/or frequency domain resources, where the AI training data is in the target time domain and/or frequency domain. /Or collected from frequency domain resources.
The method according to claim 18 or 19, wherein before the first device sends the first indication information or the first configuration information to the terminal, the method further includes:

The first device receives target capability information from the terminal, wherein the target capability information indicates the information reporting capability and/or data collection capability supported by the terminal;

The first device sends first indication information or first configuration information to the terminal, including:

When the first device determines that the terminal supports collecting and reporting the AI training data according to the target capability information, the first device sends first indication information or first configuration information to the terminal.
The method of claim 20, wherein the target capability information includes at least one of the following:

second indication information, the second indication information is used to indicate whether the terminal supports collecting the AI training data;

The maximum number of channel state information reference signal CSI-RS ports supported by the terminal;

The total number of ports of the terminal.
The method according to claim 18 or 19, wherein the method further includes:

The first device receives first information from the terminal, wherein the first information instructs the terminal to collect and report the AI training data;

and / or,

The first device receives second information from the terminal, and the second information indicates that the terminal will not collect or report the AI training data.
The method of claim 16, wherein before the first device receives the AI training data from the terminal, the method further includes:

The first device configures a first port corresponding to a first reference signal resource and/or a CSI-RS port corresponding to a CSI-RS resource for the terminal, where the first port is dedicated to collecting the AI training data. The port corresponding to the first reference signal resource, and the CSI-RS port is a port used for CSI measurement and collection of the AI training data.
The method according to any one of claims 16 to 19, wherein the AI training data is carried in at least one of the following:

First uplink control information UCI, the first UCI carries a CSI report of the target downlink channel, and the CSI report is related to the first channel information;

A second UCI, the second UCI does not carry the CSI report of the target downlink channel;

Target physical uplink shared channel PUSCH.
The method of claim 24, wherein the AI training data includes first channel information in the CSI report, wherein the first channel information in the CSI report is precoding information.
The method according to any one of claims 16 to 19, wherein, in the case where the terminal has a first AI network model, the method further includes:

The first device receives the second channel characteristic information from the terminal, wherein the second channel characteristic information is obtained by first processing the first channel information based on the first AI network model;

The first device trains the first AI network model and/or the second AI network model according to the AI training data, including:

The first device trains the second AI network model according to the AI training data and the second channel characteristic information.
The method according to claim 26, wherein the AI training data carries first identification information of the second channel characteristic information;

or,

The first device receives AI training data from the terminal, including:

The first device receives a target CSI report from the terminal, where the target CSI report carries the second channel characteristic information and the AI training data; or,

The first device receives the AI training data from the terminal on time domain and/or frequency domain resources corresponding to the first identification information of the second channel characteristic information.
The method according to any one of claims 16 to 19, wherein the AI training data includes a precoding matrix determined based on a first codebook, the first parameter of the first codebook is greater than a preset parameter, so The first parameter includes at least one of the following:

Number of ports, number of delay paths, number of beams, and non-zero coefficient ratio.
The method according to any one of claims 16 to 19, wherein the first channel information is at least one of a precoding matrix and a channel matrix.
The method of claim 29, wherein the AI training data includes at least one of the following:

The strongest M-layer precoding matrix among the N-layer precoding matrices, the channel information of the target downlink channel includes the N-layer precoding matrix, N is a positive integer, and M is a positive integer less than or equal to N;

The N-layer precoding matrix;

Among the N-layer precoding matrices, the precoding matrix indicated by third indication information, where the third indication information comes from the first device;

In the N-layer precoding matrix, the precoding matrix indicated by fourth indication information, where the fourth indication information is the indication information sent by the terminal to the first device in advance;

Among the N-layer precoding matrices, a precoding matrix that satisfies a preset condition.
The method according to claim 30, wherein the preset conditions include at least one of the following:

The channel quality indicator CQI is greater than or equal to the first threshold;

The signal to interference plus noise ratio SINR is greater than or equal to the second threshold;

The characteristic value is greater than or equal to the third threshold;

The singular value is greater than or equal to the fourth threshold.
An information transmission device, applied to a terminal, the device includes:

Collection module, used to collect artificial intelligence AI training data, wherein the AI training data includes the first channel information of the target downlink channel, and the AI training data is used to train the first AI network model and/or the second AI network model , the first AI network model is used to process the second channel information into the first channel characteristic information, and the second AI network model is used to restore the first channel characteristic information into the second channel information, so The second channel information is the channel information of the target downlink channel;

The first sending module is used to send the AI training data to the first device.
An artificial intelligence AI network model training device, applied to the first device, the device includes:

A first receiving module configured to receive AI training data from the terminal, where the AI training data includes first channel information of the target downlink channel;

A training module, used to train the first AI network model and/or the second AI network model according to the AI training data, Wherein, the first AI network model is used to process the second channel information into the first channel characteristic information, and the second AI network model is used to restore the first channel characteristic information into the second channel information, The second channel information is the channel information of the target downlink channel.
A communication device, including a processor and a memory, the memory stores a program or instructions that can be run on the processor, and when the program or instructions are executed by the processor, any one of claims 1 to 15 is implemented. The steps of the information transmission method described in the item, or the steps of implementing the artificial intelligence AI network model training method as described in any one of claims 16 to 31.
A readable storage medium that stores programs or instructions that, when executed by a processor, implement the steps of the information transmission method as claimed in any one of claims 1 to 15, or The steps of implementing the artificial intelligence AI network model training method according to any one of claims 16 to 31.