WO2023179570A1

WO2023179570A1 - Channel feature information transmission method and apparatus, terminal, and network side device

Info

Publication number: WO2023179570A1
Application number: PCT/CN2023/082612
Authority: WO
Inventors: 任千尧
Original assignee: 维沃移动通信有限公司
Priority date: 2022-03-22
Filing date: 2023-03-20
Publication date: 2023-09-28
Also published as: CN116828499A

Abstract

The present application relates to the technical field of communications, and discloses a channel feature information transmission method and apparatus, a terminal, and a network side device. The channel feature information transmission method provided by embodiments of the present application comprises: the terminal inputs channel information into a first artificial intelligence (AI) network model and a second AI network model, and acquires first channel feature information outputted by the first AI network model and second channel feature information outputted by the second AI network model; and the terminal reports at least one of the first channel feature information and the second channel feature information to the network side device.

Description

Channel characteristic information transmission method, device, terminal and network side equipment

Cross-references to related applications

This application claims priority to Chinese Patent Application No. 202210289411.1 filed in China on March 22, 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 a channel characteristic information transmission method, device, terminal and network side equipment.

Background technique

With the development of science and technology, people have begun to study the application of artificial intelligence (AI) networks in communication systems. For example, communication data can be transmitted between network-side devices and terminals through AI network models. Currently, the AI network model used for encoding on the terminal side and the AI network model used for decoding on the network side are jointly trained. During training, encoding bits will not cause errors. However, during air interface transmission, if the channel information is large, , which may cause channel characteristic information to be lost.

Contents of the invention

Embodiments of the present application provide a channel characteristic information transmission method, device, terminal and network side equipment, which can solve the problem in related technologies that channel information is large and may be lost.

In the first aspect, a channel characteristic information transmission method is provided, including:

The terminal inputs the channel information into the first artificial intelligence AI network model and the second AI network model, and obtains the first channel feature information output by the first AI network model and the second channel feature output by the second AI network model. information;

The terminal reports at least one of the first channel characteristic information and the second channel characteristic information to the network side device.

In the second aspect, a channel characteristic information transmission method is provided, including:

The network side device receives the first channel characteristic information and the second channel characteristic information reported by the terminal. at least one item;

Wherein, the first channel characteristic information is output by the terminal through a first AI network model, and the second channel characteristic information is output by the terminal through a second AI network model.

In a third aspect, a channel characteristic information transmission device is provided, including:

An acquisition module, configured to input channel information into the first artificial intelligence AI network model and the second AI network model, and acquire the first channel feature information output by the first AI network model and the first channel feature information output by the second AI network model. Second channel characteristic information;

A reporting module is configured to report at least one of the first channel characteristic information and the second channel characteristic information to the network side device.

In a fourth aspect, a channel characteristic information transmission device is provided, including:

A receiving module, configured to receive at least one of the first channel characteristic information and the second channel characteristic information reported by the terminal;

In a fifth aspect, a terminal is provided. The terminal includes a processor and a memory. The memory stores programs or instructions that can be run on the processor. When the program or instructions are executed by the processor, the following implementations are implemented: The steps of the channel characteristic information transmission method described in one aspect.

In a sixth aspect, a terminal is provided, including a processor and a communication interface, wherein the processor is configured to input channel information into a first artificial intelligence AI network model and a second AI network model, and obtain the first AI The first channel characteristic information output by the network model and the second channel characteristic information output by the second AI network model, the communication interface is used to report the first channel characteristic information and the second channel characteristic information to the network side device at least one item of information.

In a seventh aspect, a network side device is provided. The network side device includes a processor and a memory. The memory stores programs or instructions that can be run on the processor. The program or instructions are executed by the processor. When implementing the steps of the channel characteristic information transmission method described in the second aspect.

In an eighth aspect, a network side device is provided, including a processor and a communication interface, the communication interface being configured to receive at least one of the first channel characteristic information and the second channel characteristic information reported by the terminal; wherein, the The first channel characteristic information is output by the terminal through the first AI network model, and the second channel characteristic information is output by the terminal through the second AI network model.

A ninth aspect provides a communication system, including: a terminal and a network side device. The terminal can be used to perform the steps of the channel characteristic information transmission method as described in the first aspect. The network side device can be used to perform the steps of the channel characteristic information transmission method as described in the first aspect. The steps of the channel characteristic information transmission method described in the second aspect.

In a tenth 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 channel characteristic information transmission method as described in the first aspect are implemented. , or implement the steps of the channel characteristic information transmission method described in the second aspect.

In an eleventh 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. Channel characteristic information transmission method, or implement the channel characteristic information transmission method as described in the second aspect.

In a twelfth 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 as described in the first aspect The channel characteristic information transmission method, or to implement the channel characteristic information transmission method as described in the second aspect.

In this embodiment of the present application, the terminal can input channel information into the first AI network model and the second AI network model respectively, and obtain the first channel characteristic information output by the first AI network model and the second channel output by the second AI network model. The characteristic information is reported to the network side device, and the network side device can process it through the corresponding third AI network model and the fourth AI network model respectively to restore the channel information. In this way, for longer channel information, terminals and network-side devices can process channel information through two sets of AI network models. Each set of AI network models processes a portion of the channel information to avoid transmission errors and loss of channel information.

Description of the drawings

Figure 1 is a block diagram of a wireless communication system applicable to the embodiment of the present application;

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

Figure 2a is one of the schematic diagrams of preprocessing channel information in a channel characteristic information transmission method provided by an embodiment of the present application;

Figure 2b is the second schematic diagram of preprocessing channel information in a channel characteristic information transmission method provided by an embodiment of the present application;

Figure 2c is the third schematic diagram of preprocessing channel information in a channel characteristic information transmission method provided by an embodiment of the present application;

Figure 2d is one of the schematic diagrams of the splicing operation in a channel characteristic information transmission method provided by an embodiment of the present application;

Figure 2e is a second schematic diagram of the splicing operation in a channel characteristic information transmission method provided by an embodiment of the present application;

Figure 2f is the third schematic diagram of the splicing operation in a channel characteristic information transmission method provided by an embodiment of the present application;

Figure 2g is a schematic diagram of the fourth splicing operation in a channel characteristic information transmission method provided by an embodiment of the present application;

Figure 3 is a flow chart of another channel characteristic information transmission method provided by an embodiment of the present application;

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

Figure 5 is a structural diagram of another channel characteristic information transmission device provided by an embodiment of the present application;

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

Figure 7 is a structural diagram of a terminal provided by an embodiment of the present application;

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

Detailed ways

The technical solutions in the embodiments of the present application will be clearly described below 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 radio access network device, a radio access network (Radio Access Network, RAN), a radio access network function or a wireless access network unit. Access network equipment may include base stations, Wireless Local Area Network (WLAN) access points or WiFi nodes, etc. Base stations Can be called Node B, Evolved Node B (eNB), access point, 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 Receiving Point (TRP) or some other appropriate term in the field, as long as the same technology is achieved Effect, the base station is not limited to specific technical terms. It should be noted that in the embodiment of this application, only the base station in the NR system is used as an example for introduction, and the specific type of the base station is not limited.

In order to better understand the technical solution of the present application, relevant concepts that may be involved in the embodiments of the present application are explained below.

According to information theory, accurate channel state information (CSI) is crucial to channel capacity. Especially for multi-antenna systems, the transmitter can optimize signal transmission based on CSI to better match the channel status. For example: channel quality indicator (CQI) can be used to select an appropriate modulation and coding scheme (MCS) to implement link adaptation; precoding matrix indicator (precoding matrix indicator (PMI)) can be used to implement Eigen beamforming (eigen beamforming) is used to maximize the strength of the received signal, or to suppress interference (such as inter-cell interference, interference between multiple users, etc.). Therefore, since multi-antenna technology (multi-input multi-output, MIMO) was proposed, CSI acquisition has always been a research hotspot.

Usually, network side equipment (such as a base station) sends a CSI reference signal (channel state information reference signal, CSI-RS) on certain time-frequency resources in a certain time slot (slot), and the terminal performs channel estimation based on the CSI-RS. Calculate the channel information in this slot, and feedback PMI to the base station through the codebook. The base station combines the channel information based on the codebook information fed back by the terminal. Before the next CSI report, the base station uses this to perform data precoding and multi-user scheduling.

In order to further reduce CSI feedback overhead, the terminal can change the PMI reported on each subband to report PMI based on delay. Since the channels in the delay domain are more concentrated, PMI with fewer delays can approximately represent the PMI of all subbands. That is, the delay field information will be compressed before reporting.

Similarly, in order to reduce overhead, the base station can precode the CSI-RS in advance and send the coded CSI-RS to the terminal. What the terminal sees is the channel corresponding to the coded CSI-RS. The terminal only needs to Select several ports with greater strength among the indicated ports and report these ports The coefficient corresponding to the mouth is enough.

Furthermore, in order to better compress channel information, neural network or machine learning methods can be used. Specifically, the terminal uses the AI network model to compress and encode the channel information, and the base station decodes the compressed content through the AI network model to restore the channel information. At this time, the base station's AI network model for decoding and the terminal's use The AI network model for coding needs to be jointly trained to achieve a reasonable matching degree. The terminal's AI network model for encoding and the base station's AI network model for decoding form a joint neural network model, which is jointly trained by the network side. After the training is completed, the base station sends the AI network model for encoding to the terminal. .

The terminal estimates CSI-RS, calculates channel information, uses the calculated channel information or original estimated channel information through the AI network model to obtain the coding result, and sends the coding result to the base station. The base station receives the coding result and inputs it into the AI network model. decode and recover the channel information.

Different channel environments have different degrees of compressible encoding of channel information, and the length of the encoded information is also different. For a certain target compression performance, simple channel information requires only a short code length, but complex channel information requires a longer code length. Generally speaking, different lengths of encoded information correspond to different AI network models, so multiple AI network models are needed to process different compression bit lengths, resulting in the need to train and configure multiple AI network models on the terminal and network side.

The channel characteristic information transmission method provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings through some embodiments and application scenarios.

Please refer to Figure 2. Figure 2 is a flow chart of a channel characteristic information transmission method provided by an embodiment of the present application. The execution subject of this method is a terminal. As shown in Figure 2, the method includes the following steps:

Step 201: The terminal inputs channel information into the first AI network model and the second AI network model, and obtains the first channel feature information output by the first AI network model and the second channel output by the second AI network model. Feature information.

Alternatively, the terminal may detect the Channel State Information Reference Signal (CSI-RS) or Tracking Reference Signal (TRS) at a location specified by the network side device, and perform channel estimation to obtain the channel Information, the terminal inputs the channel information to the first AI network model and the second AI network model respectively. The first AI network model analyzes the input channel information (such as the channel matrix of each subband, or the precoding of each subband). matrix) to perform compression encoding, output the first channel characteristic information, and the second AI network model performs compression encoding on the channel The information is compressed and encoded, and the second channel characteristic information is output. The first channel characteristic information and the second channel characteristic information may be channel state information (Channel State Information, CSI), or may be other information related to channel information. It should be noted that the channel information coding mentioned in the embodiments of this application is different from channel coding.

Step 202: The terminal reports at least one of the first channel characteristic information and the second channel characteristic information to the network side device.

For example, the terminal reports the first channel characteristic information and the second channel characteristic information to the network side device, or may also report only the first channel characteristic information. Based on the received first channel characteristic information and/or second channel characteristic information, the network side device inputs the first channel characteristic information and/or the second channel characteristic information into the corresponding AI network model for decoding processing to restore the channel information.

It should be noted that the network side device includes a third AI network model corresponding to the first AI network model and a fourth AI network model corresponding to the second AI network model. The network side device combines the first AI network model with the third AI network model. The network models are jointly trained, and the second AI network model and the fourth AI network model are jointly trained, so that the first AI network model matches the third AI network model, and the second AI network model matches the fourth AI network model. Matching, the output of the first AI network model is used as the input of the third AI network, the output of the second AI network model is used as the input of the fourth AI network model, and the output of the third AI network model is the input of the first AI network model Matching, the output of the fourth AI network model matches the input of the second AI network model, so that the channel information can be processed by the AI network model to realize channel information transmission between the terminal and the network side device.

In the embodiment of this application, after the terminal reports the first channel characteristic information output by the first AI network model to the network side device, the network side device inputs the first channel characteristic information into the third AI network model, and the third AI The network model decodes the first channel characteristic information to obtain part of the channel information; after the terminal reports the second channel characteristic information output by the second AI network model to the network side device, the network side device The information is input to the fourth AI network model. The fourth AI network model decodes the second channel characteristic information to obtain another part of the channel information. The network side device combines part of the channel information output by the third AI network model with the fourth AI network The other part of the channel information output by the model is combined to recover the complete channel information, which is also the channel information input to the first AI network model and the second AI network model. In this way, complete channel information can be restored through the third AI network model and the fourth AI network model of the network side device. For longer channel information, terminals and network-side devices can process channel information through two sets of AI network models. Each set of AI network models processes a portion of the channel information to avoid transmission errors and loss of channel information.

Optionally, the length of the first channel characteristic information is a fixed value, and the length of the second channel characteristic information is a fixed value or a variable value. That is to say, the first AI network model is used to encode channel information into first channel characteristic information of a fixed length, and the length of the second channel characteristic information obtained by encoding the channel information through the second AI network model is not fixed. For example, it can be It is determined according to the length of the first channel characteristic information. Correspondingly, the length of the first channel feature information input by the third AI network model is fixed, the channel information generated by decoding the third AI network model can also be specific, and the second channel feature input by the fourth AI network model The information length is not fixed, so the length of the channel information output by the fourth AI network model is not fixed. The terminal and the network side device can pre-agree on the length of the first channel characteristic information, and then for channel information of different lengths, the fixed length channel information can be restored through encoding of the first AI network model and decoding of the third AI network model. , for example, the first channel characteristic information can be the more important channel information in the channel information, so that the loss of important channel information can be avoided; in addition, for channel information of different lengths, the terminal and network side equipment can be used for different lengths of channel information. According to the length of the second channel characteristic information corresponding to the information agreement, through the encoding of the second AI network model and the decoding of the fourth AI network model, channel information of different lengths can be recovered, so that different lengths of channel information can be more flexibly implemented. Encoding and decoding processing of length channel information.

Optionally, the second channel characteristic information may also be a fixed value. For example, the length of the first channel characteristic information and the length of the second channel characteristic information are the same, so that the network side device can restore half of the channel through the third AI network model. Information, the other half of the channel information is restored through the fourth AI network model, and the complete channel information is obtained through combination. The half of the channel information may be channel information of subbands 1, 3, 5, and 7, or channel information of subbands 1, 2, 3, and 4, or one polarized channel information, etc.

It should be noted that in this embodiment of the present application, the terminal may directly input the channel information into the first AI network model and the second AI network model without performing any processing. For example, please refer to Figure 2a. In a beam-delay network, the terminal may not perform any processing on the channel information, but input the channel matrix of each subband into the first AI network model for beam encoding. processing, and inputting the second AI network model for delay encoding processing, and converting the output channel characteristics Information is spliced. Alternatively, the terminal may also preprocess the channel information and then input it into the first AI network model and the second AI network model.

Optionally, the terminal inputs channel information into the first AI network model and the second AI network model, including at least one of the following:

The terminal inputs the channel information into the first AI network model after first preprocessing;

The terminal inputs the channel information to the second AI network model after undergoing second preprocessing.

For example, the terminal may perform a first preprocessing on the channel information and then input it into the first AI network model. For example, in a broadband subband network, the first preprocessing may be to calculate the sum of second moments of the channel matrices of all subbands; or, in a beam-delay network, the first preprocessing It can be to project the channel information onto an orthogonal discrete Fourier Transform (Discrete Fourier Transform, DFT) basis; etc.

Optionally, inputting the channel information into the second AI network model after second preprocessing includes any one of the following:

input the channel information to the first AI network model, and input the output of the first AI network model to the second AI network model;

The channel information is input to the first AI network model, and the output of the target network structure in the first AI network model is input to the second AI network model.

For example, the terminal inputs channel information into the first AI network model, uses the output of the first AI network as the input of the second AI network model, or uses the output of a certain network structure in the first AI network model as the second AI network model. As shown in Figure 2b, the output of the first AI network model after encoding and before quantization is used as the input of the second AI network model (subband coding in Figure 2b), so as to perform the second preprocessing on the channel information.

Alternatively, the second preprocessing is the same as the first preprocessing, that is, the terminal can input the channel information into the first AI network model and the second AI network model after the first preprocessing, as shown in Figure 2c, such as calculating The sum of the second moments of the channel matrices of all subbands is then input to the second AI network model (subband coding in Figure 2c).

In the embodiment of the present application, the first preprocessing may be performed only on the channel information input to the first AI network model, or the second preprocessing may be performed only on the channel information inputted to the second AI network model, or it may also be performed Channel information is directly input into the first AI network model and the second AI network without preprocessing. network model, or it may also be to perform first preprocessing on the channel information input to the first AI network model, and to perform second preprocessing on the channel information input to the second AI network model. In this way, the terminal can process channel information in different ways according to the conditions of the channel information, making the terminal's processing of channel information more flexible.

In the embodiment of this application, the first channel characteristic information and the second channel characteristic information may be independent of CSI. In this case, the first channel characteristic information and the second channel characteristic information may be independently reported to the network side device through the terminal; Alternatively, the first channel characteristic information and the second channel characteristic information may also be reported through CSI.

Optionally, when the first channel characteristic information and the second channel characteristic information are reported through CSI, the terminal reports the first channel characteristic information and the second channel characteristic information to the network side device. At least one item of information, including any of the following:

The terminal reports the first channel characteristic information to the network side device through the first CSI, and reports the second channel characteristic information to the network side device through the second CSI;

The terminal reports the first channel characteristic information and the second channel characteristic information to the network side device through the first CSI, where the first channel characteristic information is included in the first part of the first CSI, and The second channel characteristic information is included in the second part of the first CSI.

In one implementation, the terminal reports the first channel characteristic information and the second channel characteristic information respectively through two CSIs. For example, the first channel characteristic information is carried in the first CSI, and the third channel characteristic information is carried in the first CSI. The second channel characteristic information is carried in the second CSI.

In another implementation manner, the terminal may report the first channel characteristic information and the second channel characteristic information through one CSI. For example, the first channel characteristic information is carried in the first part of the first CSI (CSI Part1), and the second channel characteristic information is carried in the second part of the first CSI (CSI Part2). The first part is a fixed length part in the first CSI, and the second part is a variable length part in the first CSI.

Optionally, the first part (CSI Part1) also includes the length of the second channel characteristic information. The network side device may directly obtain the length of the second channel characteristic information from the CSI Part 1, and decode the first channel characteristic information and the second channel characteristic information in parallel. In this way, the network side device can more accurately decode the second channel characteristic information based on the length of the second channel characteristic information.

In this embodiment of the present application, the terminal may report only the first channel characteristic information to the network side device, or may also report the first channel characteristic information and the second channel characteristic information. Optionally, the terminal reports at least one of the first channel characteristic information and the second channel characteristic information to the network side device, including:

The terminal splices the first channel characteristic information and the second channel characteristic information to obtain target channel characteristic information;

The terminal reports the target channel characteristic information to the network side device.

That is to say, the terminal splices the first channel characteristic information and the second channel characteristic information, for example, splices the second channel characteristic information after the first channel characteristic information, and then reports the spliced first channel characteristic information and the spliced first channel characteristic information and the second channel characteristic information to the network side device. Second channel characteristic information.

Optionally, the terminal splices the first channel characteristic information and the second channel characteristic information to obtain target channel characteristic information, including any one of the following:

The terminal splices the second channel characteristic information after the first channel characteristic information to obtain target channel characteristic information;

The terminal interleaves and splices the first channel characteristic information and the second channel characteristic information to obtain target channel characteristic information;

The terminal splices the second channel characteristic information after the first channel characteristic information, and performs a third operation on the spliced first channel characteristic information, the second channel characteristic information and the preset scrambling code sequence. In one operation, the target channel characteristic information is obtained;

The terminal performs a second operation on the second channel characteristic information and the first channel characteristic information to obtain third channel characteristic information, and splices the third channel characteristic information after the first channel characteristic information, Obtain target channel characteristic information.

Please refer to Figure 2d. In the figure, A is the first channel characteristic information, and B is the second channel characteristic information. The terminal splices the second channel characteristic information after the first channel characteristic information to obtain the target channel characteristic information (that is, the spliced first channel characteristic information and second channel characteristic information). The terminal reports the target channel characteristic information to the network side device. The network side device may use a corresponding method to decode the target channel characteristic information into the first channel characteristic information and the second channel characteristic information, and then input the third AI network model and the second channel characteristic information respectively. The fourth AI network model performs decoding processing.

Please refer to Figure 2e. In the figure, A is the first channel characteristic information, B is the second channel characteristic information, and finally After the terminal interleaves and splices the first channel characteristic information and the second channel characteristic information, the target channel characteristic information is obtained. The interleaving and splicing may include randomly inserting the second channel characteristic information into the first channel characteristic information. Optionally, the first channel characteristic information may include the same identifier, and the second channel characteristic information may include the same identifier, and the network side device may decode the target channel characteristic information into the first channel characteristic information and the third channel characteristic information by identifying the identifier. The second channel characteristic information is then input into the third AI network model and the fourth AI network model respectively for decoding processing.

Please refer to Figure 2f. In the figure, A is the first channel characteristic information, and B is the second channel characteristic information. The terminal splices the second channel characteristic information after the first channel characteristic information, and combines the spliced first channel characteristic information and the second channel characteristic information. The second channel characteristic information is multiplied by the preset scrambling code sequence to obtain the target channel characteristic information. Wherein, the preset scrambling code sequence may be configured by a network side device, and then, after receiving the target channel characteristic information reported by the terminal, the network side device processes the target channel characteristic information based on the preset scrambling code sequence to obtain The first channel characteristic information and the second channel characteristic information are then input into the third AI network model and the fourth AI network model respectively for decoding processing.

Optionally, the length of the first channel characteristic information is related to the preset scrambling code sequence. For example, the relationship between the length of the first channel characteristic information and the preset scrambling sequence may be a network side device configuration or a protocol agreement.

Please refer to Figure 2g. In the figure, A is the first channel characteristic information, and B is the second channel characteristic information. The terminal can calculate the difference between the second channel characteristic information and the first channel characteristic information to obtain the third channel characteristic information. The three channel characteristic information is spliced after the first channel characteristic information, and then the target channel characteristic information is obtained and reported. The network side device processes the target channel characteristic information accordingly to obtain the first channel characteristic information and the second channel characteristic information, and then inputs the third AI network model and the fourth AI network model respectively for decoding processing.

Optionally, the terminal reports the target channel characteristic information to the network side device, including:

The terminal quantifies the target channel characteristic information through a quantization network, and reports the quantized target channel characteristic information to the network side device.

Wherein, the quantization network is an AI quantification network model, and the terminal is splicing the first channel characteristic information and the second channel characteristic information, and quantizing the spliced first channel characteristic information and the second channel characteristic information together through the quantization network, Then report it to the network side device. Optionally, the first channel characteristic information may be output after quantization and post-processing by the first AI network model, and the second channel characteristic information may be The characteristic information can be output after quantization processing by the second AI network model, that is, the first channel characteristic information and the second channel characteristic information before splicing have been quantized once, and the target channel characteristic information obtained after splicing can be further processed by the quantization network. Perform quantification processing. In the embodiments of the present application, by performing quantization processing on the channel characteristic information, the capacity of the channel characteristic information can be effectively compressed and simplified.

Optionally, the obtaining the first channel characteristic information output by the first AI network model and the second channel characteristic information output by the second AI network model includes:

Obtaining the first channel characteristic information output by the first AI network model before quantization and the second channel characteristic information output by the second AI network model before quantization;

The terminal reports at least one of the first channel characteristic information and the second channel characteristic information to the network side device, including:

In this embodiment, the terminal splices together the first channel characteristic information output before the first AI network model is quantized and the second channel characteristic information output before the second AI network model is quantified, and then performs the spliced first channel characteristic information and second channel characteristic information are quantified, and the quantized first channel characteristic information and second channel characteristic information are reported to the network side device to compress and simplify the capacity of the channel characteristic information.

In this embodiment of the present application, the terminal reports at least one of the first channel characteristic information and the second channel characteristic information to the network side device, including:

The terminal reports the first channel characteristic information to the network side device, and discards the second channel characteristic information.

It should be noted that the terminal may discard the second channel characteristic information in the process of reporting the first channel characteristic information and the second channel characteristic information to the network side device; or the terminal may discard the second channel characteristic information before reporting, That is, only the first channel characteristic information is reported. In this embodiment, for example, when channel resources are insufficient, the terminal discards the second channel characteristic information and reports only the first channel characteristic information to ensure that the network side device can at least receive the first channel characteristic information and pass The third AI network model decodes the first channel characteristic information to restore part of Channel information to prevent all channel information from being lost.

Optionally, the terminal includes a first AI network model and at least a second AI network model, and the terminal inputs channel information into the first AI network model and the second AI network model, including:

The terminal inputs channel information into the first AI network model and the target second AI network model, and the at least one second AI network model includes the target second AI network model:

Wherein, the target second AI network model is determined by at least one of the following:

channel environment;

Instructions from the network side device.

It should be noted that the first AI network model and at least one second AI network model are trained by the network side device and sent to the terminal. The network side device may send instruction information to the terminal to instruct the terminal which second AI network model to use. , or the terminal may adaptively select an appropriate second AI network model as the target second AI network model based on the channel environment. In this way, the terminal becomes more flexible in selecting the second AI network model.

Further, the terminal inputs the channel information into the first AI network model to obtain the first channel characteristic information, and inputs the channel information into the target second AI network model to obtain the second channel characteristic information. The terminal may report the first channel characteristic information in CSI Part1 The length of the channel characteristic information and the second channel characteristic information, the second channel characteristic information is reported in CSI Part2.

Optionally, the first AI network model corresponds to the third AI network model of the network side device, and the second AI network model corresponds to the fourth AI network model of the network side device;

The input of the third AI network model is the first channel characteristic information, and the input of the fourth AI network model includes any one of the following:

The second channel characteristic information;

the first channel characteristic information and the second channel characteristic information;

The output of the third AI network model and the second channel characteristic information.

In the embodiment of this application, the first AI network model and the third AI network model are jointly trained through the network side device, the second AI network model and the fourth AI network model are jointly trained through the network side device, and the third AI network model is The input is the output of the first AI network model, and the input of the fourth AI network model may include only the output of the second AI network model, or may include the output of the first AI network model and the output of the second AI network model, Or it can also include a third AI network The output of the model and the output of the second AI network model. In this way, the third AI network model can independently decode based on the first channel characteristic information, and the fourth AI network model can decode based on the second characteristic information alone, or rely on the first channel characteristic information for decoding. For example, the first channel characteristic information is broadband information, and the second channel characteristic information is subband information. At this time, the fourth AI network model cannot decode based on the subband information only, but needs to rely on the broadband information for decoding. In this way, the network side device can adopt different decoding methods based on the channel characteristic information, making the network side device more flexible in decoding the channel characteristic information.

It should be noted that the network side device implements independent decoding of the first channel characteristic information through the third AI network model, and adds 0 to the positions that are not included in the first channel characteristic information, or supplements other agreed values to ensure that the third AI network The model is able to output recovered channel information.

Optionally, the second channel characteristic information includes N information blocks, the N information blocks are arranged in a preset order, and N is an integer greater than 1;

When the fourth AI network model decodes the second channel characteristic information, the i+1-th information block among the N information blocks is decoded based on the i-th information block, and i is less than N integer.

In the embodiment of the present application, the second channel characteristic information can be divided into N information blocks. When the fourth AI network model decodes these N information blocks, each information block relies on its previous information block for decoding, or These N information blocks can be decoded individually to flexibly implement the decoding process of the second channel characteristic information.

Optionally, when the terminal uploads the first channel characteristic information and the second channel characteristic information, it may discard the second channel characteristic information; in the case of discarding the second channel characteristic information, the terminal will The N information blocks are discarded in the reverse order of the preset order, that is, the N information blocks are discarded from back to front in the preset order. The number of discards may be the terminal according to the channel resources. to decide. Furthermore, only part of the second channel characteristic information may be discarded, and the undiscarded part can still be reported to the network side device. When the network side device decodes the received second channel characteristic information based on the fourth AI network model, For the discarded part, add 0 or other agreed values to its position to ensure that the channel information can be decoded and recovered.

In this embodiment of the present application, the network side device can update the first AI network model and the second AI network model. For example, the two AI network models can be updated separately, or only the second AI network model can be updated. 2. AI network model, and send the updated AI network model to the terminal. Correspondingly, the third AI network model needs to be updated synchronously with the first AI network model, and the fourth AI network model needs to be updated synchronously with the second AI network model to ensure that the terminal and network side equipment can pass the channel implemented by the corresponding AI network model. Encoding and decoding of information.

It should be noted that when the network side device updates the second AI network model, it can adjust the length of the output of the second AI network model, that is, the length of the second channel characteristic information, that is, the length of the second channel characteristic information is Variable value, the length can be selected by the user, so that different lengths of the second channel characteristic information can be flexibly set for different channel information.

Please refer to Figure 3. Figure 3 is a flow chart of another channel characteristic information transmission method provided by an embodiment of the present application. The execution subject of this method is a network-side device. As shown in Figure 3, the method includes the following steps:

Step 301: The network side device receives at least one of the first channel characteristic information and the second channel characteristic information reported by the terminal.

In this embodiment of the present application, the network side device includes a third AI network model corresponding to the first AI network model, and a fourth AI network model corresponding to the second AI network model. After the terminal reports the first channel characteristic information output by the first AI network model to the network side device, the network side device inputs the first channel characteristic information into the third AI network model, and the third AI network model The characteristic information is decoded to obtain part of the channel information; after the terminal reports the second channel characteristic information output by the second AI network model to the network side device, the network side device inputs the second channel characteristic information into the fourth AI network model, the fourth AI network model decodes the second channel characteristic information to obtain another part of the channel information, and the network side device combines part of the channel information output by the third AI network model with another part of the channel output by the fourth AI network model By combining the information, complete channel information can be recovered, which is also the channel information input to the first AI network model and the second AI network model. In this way, the complete channel information can be restored through the third AI network model and the fourth AI network model of the network side device. For longer channel information, the terminal and the network side device can respectively use two sets of AI network models to recover the channel information. To process channel information, each group of AI network models processes a portion of the channel information to avoid transmission errors and loss of channel information.

In this embodiment of the present application, the length of the first channel characteristic information is a fixed value, and the length of the second channel characteristic information is a fixed value or a variable value. That is to say, the first AI network model is used to encode channel information into first channel characteristic information of a fixed length, and the length of the second channel characteristic information obtained by encoding the channel information through the second AI network model is not fixed. For example, it can be It is determined according to the length of the first channel characteristic information. The terminal and the network side device can pre-agree on the length of the first channel characteristic information, and then for channel information of different lengths, the fixed length channel information can be restored through encoding of the first AI network model and decoding of the third AI network model. , for example, the first channel characteristic information can be the more important channel information in the channel information, so that the loss of important channel information can be avoided; in addition, for channel information of different lengths, the terminal and network side equipment can be used for different lengths of channel information. According to the length of the second channel characteristic information corresponding to the information agreement, through the encoding of the second AI network model and the decoding of the fourth AI network model, channel information of different lengths can be recovered, so that different lengths of channel information can be more flexibly implemented. Encoding and decoding processing of length channel information.

Optionally, when the first channel characteristic information and the second channel characteristic information are reported through CSI, the network side device receives the first channel characteristic information and the second channel characteristic information reported by the terminal. At least one item, including any of the following:

The network side device receives the first channel characteristic information reported by the terminal through the first CSI, and the second channel characteristic information reported through the second CSI;

The network side device receives the first channel characteristic information and the second channel characteristic information reported by the terminal through the first CSI, wherein the first channel characteristic information is included in the first part of the first CSI, and the second channel characteristic information The information is contained in the second part of the first CSI.

Optionally, the first part also includes the length of the second channel characteristic information.

It should be noted that the specific process of the above steps may be described in detail with reference to the method embodiment described in FIG. 2 , which will not be described again in this embodiment.

Optionally, the network side device receives at least one of the first channel characteristic information and the second channel characteristic information reported by the terminal, including:

The network side device receives the target channel characteristic information reported by the terminal, where the target channel characteristic information is the channel characteristic information obtained by splicing the first channel characteristic information and the second channel characteristic information by the terminal.

Among them, the terminal implements the splicing of the first channel characteristic information and the second channel characteristic information. The process may be the specific description in the method embodiment described with reference to Figure 2, and will not be described again in this embodiment.

Optionally, the network side device includes a third AI decoding network model corresponding to the first AI network model, and a fourth AI network model corresponding to the second AI network model;

Wherein, the input of the third AI network model is the first channel characteristic information, and the input of the fourth AI network model includes any one of the following:

The second channel characteristic information;

The output of the first AI decoding network model and the second channel characteristic information.

The method also includes:

The network side device decodes the second channel characteristic information through the fourth AI network model, wherein the i+1-th information block among the N information blocks is decoded based on the i-th information block, i is an integer less than N.

Optionally, the method also includes:

The network side device updates the first AI network model and the second AI network model; or,

The network side device updates the second AI network model.

Optionally, updating the second AI network model includes adjusting the length of the second channel characteristic information.

In this embodiment of the present application, the network side device can update the first AI network model and the second AI network model. For example, the two AI network models can be updated separately, or only the second AI network model can be updated, and The updated AI network model is sent to the terminal. Correspondingly, the third AI network model needs to be updated synchronously with the first AI network model, and the fourth AI network model needs to be updated synchronously with the second AI network model to ensure that the terminal and network side equipment can pass the channel implemented by the corresponding AI network model. Encoding and decoding of information.

Optionally, when the network side device updates the second AI network model, it can adjust the length of the output of the second AI network model, that is, the length of the second channel characteristic information, that is, the length of the second channel characteristic information is Variable value, the length can be selected by the user, which can flexibly target Different channel information sets different lengths of the second channel characteristic information.

The channel characteristic information method provided by the embodiment of the present application is applied to network-side equipment. The related concepts and specific implementation processes involved can be referred to the detailed description in the embodiment of the channel characteristic information transmission method applied to the terminal in Figure 2 above. In order to avoid duplication , no further details will be given in this embodiment.

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

Please refer to Figure 4. Figure 4 is a channel characteristic information transmission device provided by an embodiment of the present application. As shown in Figure 4, the channel characteristic information transmission device 400 includes:

Acquisition module 401, used to input channel information into the first artificial intelligence AI network model and the second AI network model, and obtain the first channel feature information output by the first AI network model and the output of the second AI network model. The second channel characteristic information;

The reporting module 402 is configured to report at least one of the first channel characteristic information and the second channel characteristic information to the network side device.

Optionally, the acquisition module 401 is also used to perform at least one of the following:

Input the channel information into the first AI network model after first preprocessing;

The channel information is input into the second AI network model after the second preprocessing.

Optionally, the acquisition module 401 is also used to perform any of the following:

Optionally, when the first channel characteristic information and the second channel characteristic information are reported through CSI, the reporting module 402 is further configured to perform any one of the following:

Report the first channel characteristic information to the network side device through the first CSI, and report the second channel characteristic information to the network side device through the second CSI;

The first channel characteristic information and the second channel characteristic information are reported to the network side device through the first CSI, where the first channel characteristic information is included in the first part of the first CSI, and the second channel characteristic information is Channel characteristic information is included in the second part of the first CSI.

Optionally, the reporting module 402 includes:

A splicing unit, configured to splice the first channel characteristic information and the second channel characteristic information to obtain target channel characteristic information;

A reporting unit is configured to report the target channel characteristic information to the network side device.

Optionally, the splicing unit is used to perform any of the following:

splicing the second channel characteristic information after the first channel characteristic information to obtain target channel characteristic information;

Interleave and splice the first channel characteristic information and the second channel characteristic information to obtain target channel characteristic information;

splicing the second channel characteristic information after the first channel characteristic information, and performing a first operation on the spliced first channel characteristic information, the second channel characteristic information and a preset scrambling sequence, Obtain target channel characteristic information;

Perform a second operation on the second channel characteristic information and the first channel characteristic information to obtain third channel characteristic information, and splice the third channel characteristic information after the first channel characteristic information to obtain the target channel Feature information.

Optionally, the length of the first channel characteristic information is related to the preset scrambling code sequence.

Optionally, the reporting unit is also used to:

The target channel characteristic information is quantified through a quantization network, and the quantized target channel characteristic information is reported to the network side device.

Optionally, the acquisition module 401 is also used to:

The reporting module 402 is also used to:

Splicing the first channel characteristic information and the second channel characteristic information to obtain target channel characteristic information;

Optionally, the length of the first channel characteristic information is a fixed value, and the length of the second channel characteristic information is The length of the message is either a fixed value or a variable value.

Optionally, the reporting module 402 is also used to:

Report the first channel characteristic information to the network side device, and discard the second channel characteristic information.

Optionally, the device includes a first AI network model and at least a second AI network model, and the acquisition module 401 is also used to:

Channel information is input into the first AI network model and the target second AI network model, and the at least one second AI network model includes the target second AI network model:

channel environment;

Instructions from the network side device.

The second channel characteristic information;

Optionally, when the second channel characteristic information is discarded, the N information blocks are discarded in reverse order of the preset order.

In the embodiment of this application, for longer channel information, the device and the network side device can process the channel information through two sets of AI network models respectively. Each set of AI network models processes a part of the channel information to avoid channel information transmission errors and losses.

The channel characteristic information transmission device in the embodiment of the present application may be an electronic device, such as one with an operating system Electronic equipment operating systems can also be components in electronic equipment, such as integrated circuits or chips. 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.

The channel characteristic information transmission device provided by the embodiment of the present application can realize each process implemented by the terminal in the method embodiment of Figure 2, and achieve the same technical effect. To avoid duplication, the details will not be described here.

Please refer to Figure 5. Figure 5 is another channel characteristic information transmission device provided by an embodiment of the present application. As shown in Figure 5, the channel characteristic information transmission device 500 includes:

The receiving module 501 is configured to receive at least one of the first channel characteristic information and the second channel characteristic information reported by the terminal;

Optionally, when the first channel characteristic information and the second channel characteristic information are reported through CSI, the receiving module 501 is further configured to perform any one of the following:

Receive the first channel characteristic information reported by the terminal through the first CSI, and the second channel characteristic information reported through the second CSI;

Receive first channel characteristic information and second channel characteristic information reported by the terminal through the first CSI, wherein the first channel characteristic information is included in the first part of the first CSI, and the second channel characteristic information is included in in the second part of the first CSI.

Optionally, the receiving module 501 is also used to:

Receive target channel characteristic information reported by the terminal, where the target channel characteristic information is channel characteristic information obtained by splicing the first channel characteristic information and the second channel characteristic information by the terminal.

Optionally, the length of the first channel characteristic information is a fixed value, and the length of the second channel characteristic information is a fixed value or a variable value.

Optionally, the device includes a third AI decoding network model corresponding to the first AI network model, and a fourth AI network model corresponding to the second AI network model;

The second channel characteristic information;

The device also includes a decoding module for:

The second channel characteristic information is decoded through the fourth AI network model, wherein the i+1-th information block among the N information blocks is decoded based on the i-th information block, i is less than N integer.

Optionally, the device further includes an update module for:

Update the first AI network model and the second AI network model; or,

Update the second AI network model.

In the embodiment of this application, for long channel information, the terminal and the device can process the channel information through two sets of AI network models respectively. Each set of AI network models processes a part of the channel information to avoid the transmission of the channel information. Errors and losses.

The channel characteristic information transmission device provided by the embodiment of the present application can implement each process implemented by the network side device in the method embodiment of Figure 3, and achieve the same technical effect. To avoid duplication, the details will not be described here.

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 described in Figure 2 is implemented, and the same technical effect can be achieved. When the communication device 600 is a network-side device, when the program or instruction is executed by the processor 601, each step of the method embodiment described in FIG. 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 processor is configured to input channel information into a first artificial intelligence AI network model and a second AI network model, and obtain the first AI network model. The first channel characteristic information output and the second channel characteristic information output by the second AI network model, the communication interface is used to report the first channel characteristic information and the second channel characteristic information to the network side device. at least one of. This terminal embodiment corresponds to the above-mentioned terminal-side method embodiment. Each implementation process and implementation manner of the above-mentioned method embodiment can be applied to this terminal embodiment, and can achieve the same technical effect. 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. Additionally, memory 709 may include volatile memory or non-volatile memory, or memory 709 may include both volatile and non-volatile memory. Among them, 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-mentioned modem processor may not be integrated into the processor 710.

Among them, the processor 710 is used to input channel information into the first artificial intelligence AI network model and the second AI network model, and obtain the first channel characteristic information output by the first AI network model and the second AI network The second channel characteristic information output by the model;

The radio frequency unit 701 is configured to report at least one of the first channel characteristic information and the second channel characteristic information to the network side device.

Optionally, the processor 710 is also configured to perform at least one of the following:

The channel information is input to the second AI network model after the second preprocessing.

Optionally, the processor 710 is also used to perform any of the following:

Optionally, when the first channel characteristic information and the second channel characteristic information are reported through CSI, the radio frequency unit 701 is also configured to perform any one of the following:

Optionally, the processor 710 is also used to:

The radio frequency unit 701 is also configured to report the target channel characteristic information to the network side device.

Optionally, the processor 710 is also used to perform any of the following:

Optionally, the radio frequency unit 701 is also used to:

Optionally, the processor 710 is also used to:

The radio frequency unit 701 is also used for:

Optionally, the radio frequency unit 701 is also used to:

Optionally, the terminal includes a first AI network model and at least a second AI network model, and the processor 710 is also used to:

channel environment;

Instructions from the network side device.

The second channel characteristic information;

In the embodiment of this application, for longer channel information, the terminal and the network side device can process the channel information through two sets of AI network models respectively. Each set of AI network models correspondingly processes a part of the channel information to avoid channel information transmission errors and losses.

An embodiment of the present application also provides a network side device, including a processor and a communication interface. The communication interface is used to receive at least one of first channel characteristic information and second channel characteristic information reported by the terminal. The first channel characteristic information The information is output by the terminal through the first AI network model, and the second channel characteristic information is output by the terminal through the second AI network model. This network-side device embodiment corresponds to the above-mentioned network-side device method embodiment. Each implementation process and implementation manner of the above-mentioned method embodiment can be applied to this network-side device embodiment, and can achieve the same technical effect.

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

The method performed by the network side device in the above embodiment can be implemented in the baseband device 83, which includes a baseband processor.

The baseband device 83 may include, for example, at least one baseband board on which multiple chips are disposed, as shown in FIG. Program to perform the network device operations shown in the above method embodiments.

The network side device may also include a network interface 86, which is, for example, a common public radio interface (CPRI).

Specifically, the network side device 800 in this embodiment of the present invention also includes: instructions or programs stored in the memory 85 and executable on the processor 84. The processor 84 calls the instructions or programs in the memory 85 to execute the various operations shown in Figure 5. The method of module execution and achieving the same technical effect will not be described in detail here to avoid duplication.

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 described in Figure 2 is implemented, or Each process of the method embodiment described in Figure 3 above can achieve the same technical effect. To avoid repetition, it 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 to implement the method described in Figure 2. Each process of the example, or each process of implementing the method embodiment described in Figure 3 above, and can achieve the same technical effect, will not be described again here to avoid repetition.

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.

Embodiments of the present application further provide a computer program/program product. The computer program/program product is stored in a storage medium. The computer program/program product is executed by at least one processor to implement the method described in Figure 2 above. Each process of the embodiment, or each process of implementing the above method embodiment described in Figure 3, 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 channel characteristic information transmission method as shown in Figure 2. The network side device can be used to perform the steps of the channel characteristic information transmission method as shown in the above figure. The steps of the channel characteristic information transmission method described in 3.

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 qualified by the statement "includes a..." is not excluded from the inclusion of that element, There are additional identical elements present in the method, article or apparatus. 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

A method for transmitting channel characteristic information, including:

The terminal inputs the channel information into the first artificial intelligence AI network model and the second AI network model, and obtains the first channel feature information output by the first AI network model and the second channel feature output by the second AI network model. information;

The terminal reports at least one of the first channel characteristic information and the second channel characteristic information to the network side device.
The method according to claim 1, wherein the terminal inputs channel information into the first AI network model and the second AI network model, including at least one of the following:

The terminal inputs the channel information into the first AI network model after first preprocessing;

The terminal inputs the channel information to the second AI network model after undergoing second preprocessing.
The method according to claim 2, wherein the input of the channel information into the second AI network model after second preprocessing includes any one of the following:

input the channel information to the first AI network model, and input the output of the first AI network model to the second AI network model;

The channel information is input to the first AI network model, and the output of the target network structure in the first AI network model is input to the second AI network model.
The method according to claim 1, wherein when the first channel characteristic information and the second channel characteristic information are reported through channel state information CSI, the terminal reports the first channel characteristic information to the network side device. At least one of the channel characteristic information and the second channel characteristic information includes any one of the following:

The terminal reports the first channel characteristic information to the network side device through the first CSI, and reports the second channel characteristic information to the network side device through the second CSI;

The terminal reports the first channel characteristic information and the second channel characteristic information to the network side device through the first CSI, where the first channel characteristic information is included in the first part of the first CSI, and The second channel characteristic information is included in the second part of the first CSI.
The method of claim 4, wherein the first part further includes a length of the second channel characteristic information.
The method according to claim 1, wherein the terminal reports at least one of the first channel characteristic information and the second channel characteristic information to the network side device, including:

The terminal splices the first channel characteristic information and the second channel characteristic information to obtain target channel characteristic information;

The terminal reports the target channel characteristic information to the network side device.
The method according to claim 6, wherein the terminal splices the first channel characteristic information and the second channel characteristic information to obtain target channel characteristic information, including any one of the following:

The terminal splices the second channel characteristic information after the first channel characteristic information to obtain target channel characteristic information;

The terminal interleaves and splices the first channel characteristic information and the second channel characteristic information to obtain target channel characteristic information;

The terminal splices the second channel characteristic information after the first channel characteristic information, and performs a third operation on the spliced first channel characteristic information, the second channel characteristic information and the preset scrambling code sequence. In one operation, the target channel characteristic information is obtained;

The terminal performs a second operation on the second channel characteristic information and the first channel characteristic information to obtain third channel characteristic information, and splices the third channel characteristic information after the first channel characteristic information, Obtain target channel characteristic information.
The method according to claim 7, wherein the length of the first channel characteristic information is related to the preset scrambling code sequence.
The method according to claim 6, wherein the terminal reports the target channel characteristic information to the network side device, including:

The terminal quantifies the target channel characteristic information through a quantization network, and reports the quantized target channel characteristic information to the network side device.
The method according to claim 1, wherein said obtaining the first channel characteristic information output by the first AI network model and the second channel characteristic information output by the second AI network model includes:

Obtaining the first channel characteristic information output by the first AI network model before quantization and the second channel characteristic information output by the second AI network model before quantization;

The terminal reports at least one of the first channel characteristic information and the second channel characteristic information to the network side device, including:

The terminal splices the first channel characteristic information and the second channel characteristic information to obtain target channel characteristic information;

The terminal quantifies the target channel characteristic information through a quantization network, and reports the quantized target channel characteristic information to the network side device.
The method according to any one of claims 1 to 10, wherein the length of the first channel characteristic information is a fixed value, and the length of the second channel characteristic information is a fixed value or a variable value.
The method according to any one of claims 1-10, wherein the terminal reports at least one of the first channel characteristic information and the second channel characteristic information to the network side device, including:

The terminal reports the first channel characteristic information to the network side device, and discards the second channel characteristic information.
The method according to any one of claims 1 to 10, wherein the terminal includes a first AI network model and at least a second AI network model, and the terminal inputs channel information into the first AI network model and The second AI network model includes:

The terminal inputs channel information into the first AI network model and the target second AI network model, and the at least one second AI network model includes the target second AI network model:

Wherein, the target second AI network model is determined by at least one of the following:

channel environment;

Instructions from the network side device.
The method according to any one of claims 1 to 10, wherein the first AI network model corresponds to the third AI network model of the network side device, and the second AI network model corresponds to the fourth AI of the network side device. network model;

The input of the third AI network model is the first channel characteristic information, and the input of the fourth AI network model includes any one of the following:

The second channel characteristic information;

the first channel characteristic information and the second channel characteristic information;

The output of the first AI decoding network model and the second channel characteristic information.
The method according to claim 14, wherein the second channel characteristic information includes N information blocks, the N information blocks are arranged in a preset order, and N is an integer greater than 1;

When the fourth AI network model decodes the second channel characteristic information, the i+1-th information block among the N information blocks is decoded based on the i-th information block, and i is less than N integer.
The method according to claim 15, wherein when the second channel characteristic information is discarded, the N information blocks are discarded in reverse order of the preset order.
A method for transmitting channel characteristic information, including:

The network side device receives at least one of the first channel characteristic information and the second channel characteristic information reported by the terminal;

Wherein, the first channel characteristic information is output by the terminal through a first AI network model, and the second channel characteristic information is output by the terminal through a second AI network model.
The method according to claim 17, wherein when the first channel characteristic information and the second channel characteristic information are reported through channel state information CSI, the network side device receives the first channel reported by the terminal. At least one of the characteristic information and the second channel characteristic information, including any one of the following:

The network side device receives the first channel characteristic information reported by the terminal through the first CSI, and the second channel characteristic information reported through the second CSI;

The network side device receives the first channel characteristic information and the second channel characteristic information reported by the terminal through the first CSI, wherein the first channel characteristic information is included in the first part of the first CSI, and the second channel characteristic information The information is contained in the second part of the first CSI.
The method according to claim 18, wherein the first part further includes a length of the second channel characteristic information.
The method according to claim 17, wherein the network side device receives at least one of the first channel characteristic information and the second channel characteristic information reported by the terminal, including:

The network side device receives the target channel characteristic information reported by the terminal, where the target channel characteristic information is the channel characteristic information obtained by splicing the first channel characteristic information and the second channel characteristic information by the terminal.
The method according to claim 17, wherein the length of the first channel characteristic information is a fixed value, and the length of the second channel characteristic information is a fixed value or a variable value.
The method of claim 17, wherein the network side device includes a third AI decoding network model corresponding to the first AI network model, and a fourth AI network model corresponding to the second AI network model. ;

Wherein, the input of the third AI network model is the first channel characteristic information, and the input of the fourth AI network model includes any one of the following:

The second channel characteristic information;

the first channel characteristic information and the second channel characteristic information;

The output of the first AI decoding network model and the second channel characteristic information.
The method according to claim 22, wherein the second channel characteristic information includes N information blocks, the N information blocks are arranged in a preset order, and N is an integer greater than 1;

The method also includes:

The network side device decodes the second channel characteristic information through the fourth AI network model, wherein the i+1-th information block among the N information blocks is decoded based on the i-th information block, i is an integer less than N.
The method of claim 22, wherein the method further includes:

The network side device updates the first AI network model and the second AI network model; or,

The network side device updates the second AI network model.
The method of claim 24, wherein the updating of the second AI network model includes adjusting a length of the second channel characteristic information.
A device for transmitting channel characteristic information, including:

An acquisition module, configured to input channel information into the first artificial intelligence AI network model and the second AI network model, and acquire the first channel feature information output by the first AI network model and the first channel feature information output by the second AI network model. Second channel characteristic information;

A reporting module is configured to report at least one of the first channel characteristic information and the second channel characteristic information to the network side device.
A device for transmitting channel characteristic information, including:

A receiving module, configured to receive at least one of the first channel characteristic information and the second channel characteristic information reported by the terminal;

Wherein, the first channel characteristic information is output by the terminal through a first AI network model, and the second channel characteristic information is output by the terminal through a second AI network model.
A terminal, including a processor and a memory, the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, any one of claims 1-16 is implemented. The steps of the channel characteristic information transmission method.
A network-side device includes a processor and a memory. The memory stores programs or instructions that can be run on the processor. When the program or instructions are executed by the processor, any of claims 17-25 is implemented. The steps of the channel characteristic information transmission method described in one item.
A readable storage medium on which programs or instructions are stored. When the programs or instructions are executed by a processor, the steps of the channel characteristic information transmission method according to any one of claims 1-16 are implemented. , or implement the steps of the channel characteristic information transmission method as described in any one of claims 17-25.