Digital pre-distortion communication equipment based on GRU network and implementation method

Abstract

The invention provides a digital pre-distortion communication device based on a GRU network and an implementation method thereof. The method comprises the following steps: acquiring original data, and determining distorted digital signal data based on the original data. And preprocessing the data. A power amplifier model is established based on the preprocessed raw data and the preprocessed digital signal data. Pre-training the power amplifier model to obtain a pre-trained power amplifier model; and establishing a radio frequency link model of the communication equipment based on the pre-trained power amplifier model and the digital pre-distortion model of the communication equipment. And training the digital pre-distortion model on the basis of fixed parameters of the pre-trained power amplifier model to obtain a trained digital pre-distortion model. The trained digital pre-distortion model is deployed to a baseband chip of the communication equipment, digital pre-distortion in the communication process of the communication equipment is counteracted through the trained digital pre-distortion model, and according to the method provided by the invention, the digital pre-distortion communication technology is combined with reality to be used.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a digital predistortion communication device and an implementation method based on a GRU network. Background Art

[0002] With the development of communication technology, especially the development of new generation wireless systems such as 6G and Wi-Fi 7, higher requirements are placed on the linearization technology of broadband power amplifiers. Traditional DPD technology can no longer meet the growing communication capacity and data accuracy requirements. The development of deep learning technology has brought new breakthroughs to DPD technology, but the existing deep learning network is only studied from the perspective of network design, without combining it with the actual communication system. From the perspective of algorithm implementation such as training data collection, data preprocessing, model training, and model deployment, the large model of the existing technology is difficult to implement.

[0003] Therefore, how to combine the digital pre-distortion communication technology with the actual communication system and put it into practical use has become a technical problem that those skilled in the art need to solve urgently. Summary of the invention

[0004] Based on the above problems, the present application provides a digital predistortion communication device and implementation method based on a GRU network, the method comprising:

[0005] Acquiring original data, and determining distorted digital signal data based on the original data;

[0006] Preprocessing the original data to obtain preprocessed original data, and preprocessing the distorted digital signal data to obtain preprocessed digital signal data;

[0007] Establishing a power amplifier model based on the preprocessed raw data and the preprocessed digital signal data;

[0008] Pre-training the power amplifier model to obtain a pre-trained power amplifier model;

[0009] Establishing a radio frequency link model of the communication device based on the pre-trained power amplifier model and the digital pre-distortion model of the communication device;

[0010] Based on the radio frequency link model, the digital pre-distortion model is trained on the basis of fixing the parameters of the pre-trained power amplifier model to obtain a trained digital pre-distortion model;

[0011] The trained digital pre-distortion model is deployed in a baseband chip of a communication device, and the digital pre-distortion in the communication process of the communication device is offset by the trained digital pre-distortion model.

[0012] In a possible implementation manner, the acquiring original data and determining the distorted digital signal data based on the original data includes:

[0013] Generate an I / Q signal of the baseband of the communication device by a communication device, where I and Q represent two complex components in the communication process of the communication device, and the I / Q signal represents original data X, which is data to be sent in the baseband of the communication device;

[0014] Passing the original data X through a power amplifier of a communication device to obtain distorted original data;

[0015] The distorted original data is processed through the receiving link of the communication device to obtain the distorted digital signal data Y.

[0016] In a possible implementation manner, the preprocessing the original data to obtain preprocessed original data, and the preprocessing the distorted digital signal data to obtain preprocessed digital signal data include:

[0017] The time series data of the original data X is divided into frame data of the original data , and dividing the time series data of the distorted digital signal data Y into frame data of the distorted digital signal data , and The number of is T, and The step length is S, and >S;

[0018] Frame data based on raw data Sure Overlapping samples of the original data , t represents T The tth one in the figure, and the frame data based on the distorted digital signal data Sure Overlapping samples of distorted digital signal data , t represents T The tth one in ;

[0019] Determine amplitude data based on the frame data of the raw data , phase sinusoidal component and the phase cosine component .

[0020] In a possible implementation manner, establishing a power amplifier model based on the preprocessed raw data and the preprocessed digital signal data includes:

[0021] Will and As the label data of the input and output of the power amplifier model, Establishing a power amplifier model as a loss function of the power amplifier model;

[0022] = ;

[0023] in, represents the loss function of the power amplifier model, and The number of is T, The power amplifier model corresponds to the t-th input and the tth input The output value obtained is express Overlapping samples of distorted digital signal data.

[0024] In a possible implementation manner, pre-training the power amplifier model to obtain a pre-trained power amplifier model includes:

[0025] Will In , and , and The data constitutes the input vector, and express The two complex components of ;

[0026] The input vector is used as the power amplifier model Input, As a power amplifier model Tags for power amplifier models Perform pre-training to obtain a pre-trained power amplifier model .

[0027] In a possible implementation, establishing a radio frequency link model of a communication device based on the pre-trained power amplifier model and a digital pre-distortion model of the communication device includes:

[0028] The RF link model It is expressed by the following formula:

[0029] + ;

[0030] in, represents the RF link model, represents the pre-trained power amplifier model, represents the digital pre-distortion model, The loss function is ;

[0031] The RF link model The output label data is , represents the gain of the desired output of the communication system, yes and Obtained through transformation;

[0032] Said The loss function is It is expressed by the following formula:

[0033] ;

[0034] in, express The loss function is Indicates the tth The output data, Represents the output label data corresponding to the t-th output data.

[0035] In a possible implementation, the step of training the digital predistortion model based on the radio frequency link model and on the basis of fixing the parameters of the pre-trained power amplifier model to obtain a trained digital predistortion model includes:

[0036] Will In , and , and The data constitutes the input vector, and express The two complex components of ;

[0037] Will and Transformed to , and As Output label data of

[0038] It is transformed by the following formula:

[0039] ;

[0040] in, and express The two complex components of and express The two complex components of , t represents the tth input;

[0041] train Model, in training When modeling, first fix The parameters of the model are only optimized The parameters of the model are trained to obtain a trained digital pre-distortion model.

[0042] In a possible implementation, the digital predistortion model of the communication device includes a GRU layer, a hidden layer, an activation layer, a connection layer, and an output layer;

[0043] In the GRU layer, input data , , and is represented as a one-dimensional column vector , the number of input neurons M of the GRU layer is 5*T, and the hidden layer of the GRU layer is set to 5*T;

[0044] The hidden layer is a fully connected layer, and the input and output are set to the same parameters as the hidden layer of the GRU layer;

[0045] The activation layer uses the relu activation function, and the data after the hidden layer is ;

[0046] The connection layer will and The data is spliced ​​and input to the output layer. The splicing result It is expressed as follows:

[0047] );

[0048] The output layer is a fully connected layer, and the input data of the output layer is , the output data is expressed as components and quantity .

[0049] The present application also provides a digital pre-distortion communication device based on a GRU network, wherein the baseband chip of the communication device is equipped with a trained digital pre-distortion model, and the digital pre-distortion during the communication process of the communication device is offset by the trained digital pre-distortion model.

[0050] The present application also provides an electronic device, the electronic device comprising a processor and a memory:

[0051] The memory is used to store a computer program and transmit the computer program to the processor;

[0052] The processor is used to execute the steps of the above-mentioned GRU network-based digital predistortion communication method according to the instructions in the computer program.

[0053] The present application also provides a computer-readable storage medium, which is used to store a computer program. When the computer program is executed by an electronic device, the steps of the above-mentioned digital pre-distortion communication method based on the GRU network are implemented.

[0054] Compared with the prior art, this application has the following beneficial effects:

[0055] The present invention provides a solution for the application of a digital pre-distortion algorithm based on an end-to-end deep learning network of actual communication equipment. The solution includes data acquisition and preprocessing, power amplifier model modeling, digital pre-distortion model modeling, model pre-training and deployment, and provides a digital pre-distortion algorithm model framework based on a GRU network. The digital pre-distortion function is realized through smaller network parameters, which is convenient for subsequent model deployment and lays a foundation for building a digital pre-distortion model based on a deep learning network. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0057] Figure 1 A flowchart of a digital predistortion communication method based on a GRU network provided in an embodiment of the present application;

[0058] Figure 2 A system principle block diagram of a digital predistortion communication device and implementation method based on a GRU network provided in an embodiment of the present application;

[0059] Figure 3 A network structure diagram of a digital pre-distortion model provided in an embodiment of the present application;

[0060] Figure 4 A schematic structural diagram of a digital predistortion communication device based on a GRU network provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] As described above, with the development of communication technology, especially the development of new generation wireless systems such as 6G and Wi-Fi 7, higher requirements are placed on the linearization technology of broadband power amplifiers. Traditional DPD technology can no longer meet the growing communication capacity and data accuracy requirements. The development of deep learning technology has brought new breakthroughs to DPD technology, but the existing deep learning network is only studied from the perspective of network design, without combining it with the actual communication system. From the perspective of algorithm implementation such as training data collection, data preprocessing, model training, and model deployment, the large model of the existing technology is difficult to implement.

[0062] In order to solve the above problems, the present invention proposes a digital pre-distortion communication device and implementation method based on a GRU network. The present invention provides a digital pre-distortion solution based on an end-to-end deep learning network of an actual communication device, which is used for broadband power amplifier modeling, digital pre-distortion model modeling, model pre-training and deployment. The solution includes data acquisition and pre-processing, power amplifier model modeling, digital pre-distortion model modeling, model pre-training and deployment, and provides a digital pre-distortion algorithm model framework based on a GRU network, which realizes the digital pre-distortion function through smaller network parameters, facilitates subsequent model deployment, and lays the foundation for building a digital pre-distortion model based on a deep learning network.

[0063] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0064] It is understandable that the method provided in the present application can be applied to a processing device, which is a processing device that can obtain original data and determine distorted digital signal data based on the original data, for example, a terminal device or server that can obtain original data and determine distorted digital signal data based on the original data. The method provided in the present application can be executed independently by a terminal device or a server, or it can be applied to a network scenario in which a terminal device and a server communicate, and is executed by the cooperation of a terminal device and a server. Among them, the terminal device can be a computer, a mobile phone and other devices. The server can be understood as an application server or a Web server. In actual deployment, the server can be an independent server or a cluster server.

[0065] Figure 1 A flowchart of a digital predistortion communication method based on a GRU network provided in the present application, the method comprising the following steps:

[0066] S101: Acquire original data, and determine distorted digital signal data based on the original data.

[0067] The processing device acquires original data and determines distorted digital signal data based on the original data.

[0068] In one possible implementation, the processing device may generate an I / Q signal of the baseband of the communication device through the communication device, where I and Q represent two complex components in the communication process of the communication device, and the I / Q signal represents the original data X, which is the data to be sent in the baseband of the communication device.

[0069] In a possible implementation, the original data X can be expressed by the following formula:

[0070] ;

[0071] in, Represents the nth original data, Represents the two components of the nth original data, n∈0, 1, 2, ..., N-1. For complex data, express The imaginary part of .

[0072] Raw data in the baseband of communication equipment After passing through the RF devices such as digital-to-analog converter (DAC), frequency conversion, power amplifier PA, and filter circuit in the transmission link, the data will be sent out through the antenna. After passing through the PA, the data will be distorted to obtain the distorted original data. At this time, the distorted original data needs to be connected to the receiving link of the communication system through the feedback circuit in the communication equipment.

[0073] The distorted original data passes through the filtering circuit of the receiving link, low noise amplifier (LAN), frequency conversion, analog-to-digital conversion (ADC) and reaches the baseband of the communication device. The signal at this time is the distorted digital signal data Y. The distorted digital signal data Y can be expressed by the following formula:

[0074] ;

[0075] in, Represents the nth distorted digital signal data, Represents the two components of the nth distorted digital signal data, n∈0, 1, 2, ..., N-1. For complex data, express The imaginary part of .

[0076] S102: performing data preprocessing on the original data to obtain preprocessed original data, and performing data preprocessing on the distorted digital signal data to obtain preprocessed digital signal data.

[0077] The processing device performs data preprocessing on the original data to obtain preprocessed original data, and preprocesses the distorted digital signal data to obtain preprocessed digital signal data.

[0078] In a possible implementation, the processing device may divide the time series data of the original data X into frame data of the original data , and dividing the time series data of the distorted digital signal data Y into frame data of the distorted digital signal data , and The number of is T, and The step length is S, and >S.

[0079] Frame data based on raw data Sure Overlapping samples of the original data , t represents T The tth one in the figure, and the frame data based on the distorted digital signal data Sure Overlapping samples of distorted digital signal data , t represents T The tth one in ;

[0080] [t]= ;

[0081] in, [t] represents the overlapping sample of the t-th original data, s represents the s-th in the step size S, Indicates the sth The weight, Indicates the sth Another component of .

[0082] Determine amplitude data based on frame data of raw data , phase sinusoidal component and the phase cosine component .

[0083] ;

[0084] ;

[0085] ;

[0086] in, and express Corresponding to the two components of t.

[0087] S103: Establishing a power amplifier model based on the preprocessed raw data and the preprocessed digital signal data.

[0088] The processing device establishes a power amplifier model based on the preprocessed raw data and the preprocessed digital signal data.

[0089] In a possible implementation, the processing device may and As label data for the input and output of the power amplifier model, As input, As the output label data, Establishing a power amplifier model as a loss function of the power amplifier model;

[0090] = ;

[0091] in, represents the loss function of the power amplifier model, and The number of is T, The power amplifier model corresponds to the t-th input and the tth input The output value obtained is express Overlapping samples of distorted digital signal data.

[0092] S104: Pre-train the power amplifier model to obtain a pre-trained power amplifier model.

[0093] The processing device pre-trains the power amplifier model to obtain a pre-trained power amplifier model.

[0094] In a possible implementation, the processing device may In , and , and The data constitutes the input vector, and express The two complex components of ;

[0095] The input vector is used as the power amplifier model Input, As a power amplifier model Tags for power amplifier models Perform pre-training to obtain a pre-trained power amplifier model .

[0096] S105: Establishing a radio frequency link model of the communication device based on the pre-trained power amplifier model and the digital pre-distortion model of the communication device.

[0097] The processing device establishes a radio frequency link model of the communication device based on the pre-trained power amplifier model and the digital pre-distortion model of the communication device.

[0098] In one possible implementation, the RF link model It is expressed by the following formula:

[0099] + ;

[0100] in, represents the RF link model, represents the pre-trained power amplifier model, represents the digital pre-distortion model, The loss function is ;

[0101] The RF link model The output label data is , represents the gain of the desired output of the communication system, yes and Obtained through transformation;

[0102] Said The loss function is It is expressed by the following formula:

[0103] ;

[0104] in, express The loss function is Indicates the tth The output data, Represents the output label data corresponding to the t-th output data.

[0105] In a possible implementation, a digital predistortion model of a communication device includes a GRU layer, a hidden layer, an activation layer, a connection layer, and an output layer;

[0106] In the GRU layer, input data , , and is represented as a one-dimensional column vector , the number of input neurons M of the GRU layer is 5*T, and the hidden layer of the GRU layer is set to 5*T;

[0107] The hidden layer is a fully connected layer, and the input and output are set to the same parameters as the hidden layer of the GRU layer;

[0108] The activation layer uses the relu activation function, and the data after the hidden layer is ;

[0109] The connection layer will and The data is spliced ​​and input to the output layer. The splicing result It is expressed as follows:

[0110] );

[0111] The output layer is a fully connected layer, and the input data of the output layer is , the output data is expressed as components and quantity .

[0112] S106: Based on the radio frequency link model, the digital pre-distortion model is trained on the basis of fixing the parameters of the pre-trained power amplifier model to obtain a trained digital pre-distortion model.

[0113] The processing device trains the digital pre-distortion model based on the radio frequency link model and on the basis of fixing the parameters of the pre-trained power amplifier model to obtain a trained digital pre-distortion model.

[0114] In a possible implementation, the processing device may In , and , and The data constitutes the input vector, and express The two complex components of ;

[0115] Will and Transformed to , and As Output label data of

[0116] It is transformed by the following formula:

[0117] ;

[0118] in, and express The two complex components of and express The two complex components of , t represents the tth input;

[0119] train Model, in training When modeling, first fix The parameters of the model are only optimized The parameters of the model are trained to obtain a trained digital pre-distortion model.

[0120] S107: deploying the trained digital pre-distortion model to the baseband chip of the communication device, and offsetting the digital pre-distortion in the communication process of the communication device by using the trained digital pre-distortion model.

[0121] The processing device deploys the trained digital pre-distortion model to the baseband chip of the communication device, and offsets the digital pre-distortion in the communication process of the communication device through the trained digital pre-distortion model.

[0122] The method provided in this application includes data acquisition and preprocessing, power amplifier model modeling, digital pre-distortion model modeling, model pre-training and deployment, and provides a digital pre-distortion algorithm model framework based on a GRU network. The digital pre-distortion function is realized through smaller network parameters, which is convenient for subsequent model deployment and lays the foundation for building a digital pre-distortion model based on a deep learning network.

[0123] In actual model deployment, the actual application only needs to use the digital pre-distortion model of the communication equipment The model is deployed in the baseband chip of the communication equipment to offset the digital pre-distortion of the communication equipment and realize digital pre-distortion based on the deep learning network.

[0124] Next, we will further explain it in combination with practical applications. Figure 2 A system principle block diagram of a digital predistortion communication device and implementation method based on a GRU network provided in an embodiment of the present application,

[0125] Figure 2 It is divided into two parts: model training server and communication equipment.

[0126] The model training server includes five modules: data acquisition, data preprocessing, power amplifier model construction, digital predistortion model construction, model pretraining and deployment. Data acquisition and model deployment require data interaction with communication equipment, and the functions of other modules are completed in the model training server.

[0127] In order to realize the digital pre-distortion function in communication equipment, it is necessary to add corresponding data acquisition and model deployment modules to the application layer and baseband layer of the communication equipment. In the actual application process, the data acquisition module of the model training server initiates the data acquisition process, controls the communication equipment to complete the original transmission data and distortion data acquisition, and then performs data preprocessing, power amplifier model construction, and digital pre-distortion model construction in sequence until the digital pre-distortion model training is completed. The trained model parameters are deployed to the digital pre-distortion model of the device baseband layer through the pre-trained model deployment module.

[0128] The overall processing steps of the system are as follows:

[0129] Step 1: The data acquisition module in the model training server initiates the data acquisition process. The data acquisition module of the communication device starts the data acquisition process and sends the original data sent by the baseband layer. And the received data The data collection is completed for the model training server;

[0130] Step 2: The data preprocessing module in the model training server processes the data into , amplitude data , phase sinusoidal component , phase cosine component ;

[0131] Step 3: Build the power amplifier model in the model training server;

[0132] Step 4: Build a digital pre-distortion model in the model training server;

[0133] Step 5: Pre-train the power amplifier model through the model pre-training and deployment module in the model training server, and then train the digital pre-distortion model based on the trained power amplifier model;

[0134] Step 6: Through model pre-training and deployment in the model training server, the digital pre-distortion model parameters are configured to the baseband layer of the communication device to implement the digital pre-distortion function in the baseband layer.

[0135] Figure 3 A network structure diagram of a digital predistortion model provided in an embodiment of the present application. During actual training or use, the processing device can process the input data into , , , The input data is input to the GRU layer to complete the time series feature extraction. The time series feature data is input to the hidden layer and the activation layer to further extract features and perform nonlinear processing. The data input to the activation layer is concatenated with the original data to obtain richer information and then input to the output layer together. The output layer outputs the digital pre-distortion processed , data.

[0136] The present application also provides a Figure 4 The structure diagram of the digital pre-distortion communication device based on the GRU network is shown, and the digital pre-distortion communication device 400 includes:

[0137] An acquisition module 401 is used to acquire original data and determine distorted digital signal data based on the original data;

[0138] The preprocessing module 402 is used to perform data preprocessing on the original data to obtain preprocessed original data, and to preprocess the distorted digital signal data to obtain preprocessed digital signal data;

[0139] A first establishing module 403, configured to establish a power amplifier model based on the preprocessed raw data and the preprocessed digital signal data;

[0140] A pre-training module 404, configured to pre-train the power amplifier model to obtain a pre-trained power amplifier model;

[0141] A second establishing module 405 is used to establish a radio frequency link model of the communication device based on the pre-trained power amplifier model and the digital pre-distortion model of the communication device;

[0142] A training module 406, configured to train the digital pre-distortion model based on the radio frequency link model and on the basis that the parameters of the pre-trained power amplifier model are fixed to obtain a trained digital pre-distortion model;

[0143] The deployment module 407 is used to deploy the trained digital pre-distortion model to the baseband chip of the communication device, and offset the digital pre-distortion in the communication process of the communication device through the trained digital pre-distortion model.

[0144] The present invention provides a solution for the application of a digital pre-distortion algorithm based on an end-to-end deep learning network of actual communication equipment. The solution includes data acquisition and preprocessing, power amplifier model modeling, digital pre-distortion model modeling, model pre-training and deployment, and provides a digital pre-distortion algorithm model framework based on a GRU network. The digital pre-distortion function is realized through smaller network parameters, which is convenient for subsequent model deployment and lays a foundation for building a digital pre-distortion model based on a deep learning network.

[0145] An embodiment of the present application also provides a digital pre-distortion communication device based on a GRU network, wherein the device includes a memory and a processor, the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device performs the steps of the digital pre-distortion communication method based on the GRU network described in any embodiment of the present application.

[0146] In practical applications, the computer-readable storage medium may be any combination of one or more computer-readable media, which may be a computer-readable signal medium or a computer-readable storage medium.

[0147] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0148] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0149] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0150] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0151] It should be noted that each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is merely schematic, in which the unit described as a separate component may or may not be physically separated, and the component prompted as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0152] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A digital predistortion communication method based on a GRU network, characterized in that: include: Acquiring original data, and determining distorted digital signal data based on the original data; Preprocessing the original data to obtain preprocessed original data, and preprocessing the distorted digital signal data to obtain preprocessed digital signal data; Establishing a power amplifier model based on the preprocessed raw data and the preprocessed digital signal data; Pre-training the power amplifier model to obtain a pre-trained power amplifier model; Establishing a radio frequency link model of the communication device based on the pre-trained power amplifier model and the digital pre-distortion model of the communication device; Based on the radio frequency link model, the digital pre-distortion model is trained on the basis of fixing the parameters of the pre-trained power amplifier model to obtain a trained digital pre-distortion model; The trained digital pre-distortion model is deployed in a baseband chip of a communication device, and the digital pre-distortion in the communication process of the communication device is offset by the trained digital pre-distortion model.

2. The method according to claim 1, characterized in that The obtaining of original data and determining distorted digital signal data based on the original data comprises: Generate an I / Q signal of the baseband of the communication device by a communication device, where I and Q represent two complex components in the communication process of the communication device, and the I / Q signal represents original data X, which is data to be sent in the baseband of the communication device; Passing the original data X through a power amplifier of a communication device to obtain distorted original data; The distorted original data is processed through the receiving link of the communication device to obtain the distorted digital signal data Y.

3. The method according to claim 2, characterized in that The preprocessing of the original data to obtain preprocessed original data, and the preprocessing of the distorted digital signal data to obtain preprocessed digital signal data include: The time series data of the original data X is divided into frame data of the original data , and dividing the time series data of the distorted digital signal data Y into frame data of the distorted digital signal data , and The number of is T, and The step length is S, and >S; Frame data based on raw data Sure Overlapping samples of the original data , t represents T The tth one in the above, and the frame data based on the distorted digital signal data Sure Overlapping samples of distorted digital signal data , t represents T The tth one in ; Determine amplitude data based on the frame data of the raw data , phase sinusoidal component and the phase cosine component .

4. The method according to claim 3, characterized in that The establishing of a power amplifier model based on the preprocessed raw data and the preprocessed digital signal data comprises: Will As input to the power amplifier model, As the label data output by the power amplifier model, Establishing a power amplifier model as a loss function of the power amplifier model; = ； in, represents the loss function of the power amplifier model, and The number of is T, The power amplifier model corresponds to the t-th input and the tth input The output value obtained is express Overlapping samples of distorted digital signal data.

5. The method according to claim 4, characterized in that The pre-training the power amplifier model to obtain a pre-trained power amplifier model comprises: Will In , and , and The data constitutes the input vector, and express The two complex components of ; The input vector is used as the power amplifier model Input, As a power amplifier model Tags for power amplifier models Perform pre-training to obtain a pre-trained power amplifier model .

6. The method according to claim 5, characterized in that The step of establishing a radio frequency link model of the communication device based on the pre-trained power amplifier model and the digital pre-distortion model of the communication device comprises: The RF link model It is expressed by the following formula: + ； in, represents the RF link model, represents the pre-trained power amplifier model, represents the digital pre-distortion model, The loss function is ; The RF link model The output label data is , represents the gain of the desired output of the communication system, yes and Obtained through transformation; Said The loss function is It is expressed by the following formula: ； in, express The loss function is Indicates the tth The output data, Represents the output label data corresponding to the t-th output data.

7. The method according to claim 6, characterized in that The step of training the digital pre-distortion model based on the radio frequency link model and on the basis of fixing the parameters of the pre-trained power amplifier model to obtain a trained digital pre-distortion model includes: Will In , and , and The data constitutes the input vector, and express The two complex components of ; Will and Transformed to , and As Output label data of It is transformed by the following formula: ； in, and express The two complex components of and express The two complex components of , t represents the tth input; train Model, in training When modeling, first fix The parameters of the model are only optimized The parameters of the model are trained to obtain a trained digital pre-distortion model.

8. The method according to claim 1, characterized in that The digital predistortion model of the communication device includes a GRU layer, a hidden layer, an activation layer, a connection layer and an output layer; In the GRU layer, input data , , and is represented as a one-dimensional column vector , the number of input neurons M of the GRU layer is 5*T, and the hidden layer of the GRU layer is set to 5*T; The hidden layer is a fully connected layer, and the input and output are set to the same parameters as the hidden layer of the GRU layer; The activation layer uses the relu activation function, and the data after the hidden layer is ; The connection layer will and The data is spliced ​​and input to the output layer. The splicing result It is expressed as follows: )； The output layer is a fully connected layer, and the input data of the output layer is , the output data is expressed as components and quantity .

9. A digital predistortion communication device based on a GRU network, characterized in that: include: The baseband chip of the communication device is equipped with a trained digital pre-distortion model, and the digital pre-distortion in the communication process of the communication device is offset by the trained digital pre-distortion model.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, wherein when the computer program is executed by a processor, the digital pre-distortion communication method based on the GRU network as described in any one of claims 1 to 8 is implemented.

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