WO2023272739A1

WO2023272739A1 - Channel decoding method, apparatus, training method for neural network model used for channel decoding, and apparatus

Info

Publication number: WO2023272739A1
Application number: PCT/CN2021/104367
Authority: WO
Inventors: 郑凤; 庞博文; 池连刚
Original assignee: 北京小米移动软件有限公司
Priority date: 2021-07-02
Filing date: 2021-07-02
Publication date: 2023-01-05
Also published as: CN115804067A

Abstract

The present disclosure relates to the field of communication, and provided within is a channel decoding method. A technical solution of the present disclosure, in essence, is: decoding, on the basis of a pre-trained neural network model, a received codeword to be decoded that is obtained after an information sequence undergoes channel transmission, so as to obtain a decoded codeword corresponding to the received codeword; wherein the pre-trained neural network model comprises a decoding generator, and the decoding generator decodes the received codeword and outputs the decoded codeword.

Description

Channel decoding method and device, neural network model training method and device for channel decoding

technical field

The present disclosure relates to the technical field of mobile communication, and in particular to a channel decoding method and device, and a training method and device for a neural network model used for channel decoding.

Background technique

With the commercialization of 5G technology, higher requirements are put forward for the data transmission rate and data transmission volume of wireless communication systems. Therefore, it is necessary to provide a channel decoding technology with lower bit error rate and higher data transmission rate. To support the transmission requirements of various business data of the wireless communication system.

Contents of the invention

The present disclosure proposes a channel decoding method and device, which realizes decoding of codewords based on a pre-trained neural network model, thereby providing a channel decoding method with low bit error rate, low decoding time and low decoding complexity. Degree channel decoding scheme. In addition, the present disclosure also proposes a training method and device for a neural network model for channel decoding, so as to obtain a neural network model that can be used for channel decoding through iterative training.

The embodiment of the first aspect of the present disclosure provides a training method for a neural network model for channel decoding, the neural network model includes a decoding generator and a decoding discriminator, and the method includes: obtaining information sequences including The information sequence training set of samples, and obtain the received codeword samples to be decoded based on the information sequence samples; use the received codeword samples as the input features of the decoding generator, and use the information sequence samples and the The decoded codeword sample output by the decoding generator is used as the input feature of the decoded discriminator, and whether the information sequence sample and the decoded codeword sample can be distinguished is used as the output of the decoded discriminator feature, perform iterative training to obtain the pre-trained neural network model.

Optionally, the obtaining a received codeword sample to be decoded based on the information sequence sample includes: obtaining an encoded codeword sample by an encoder based on the information sequence sample; modulating the encoded codeword sample to obtain modulating codeword samples; and inputting the modulated codeword samples into a noise-added channel to obtain received codeword samples transmitted through the channel.

Optionally, each round of training in iterative training includes: based on the received codeword samples obtained from the information sequence samples used for this round of training, the decoding code corresponding to the information sequence samples is obtained by the decoding generator word sample; based on the decoded codeword sample and the information sequence sample, determine whether the decoded codeword sample and the information sequence sample can be distinguished through the decoding discriminator; if it is determined that the decoded codeword sample and the information sequence sample can be distinguished codeword samples and the information sequence samples, update the decoding generator and the decoding discriminator through the backpropagation method, and repeat the above steps until all the information sequence samples used for this round of training have passed The next round of training starts after the decoding generator and the decoding discriminator process; and if it is determined that the decoding codeword sample and the information sequence sample cannot be distinguished, the iterative training is ended and the pre-training neural network model.

Optionally, the training target of the decoding discriminator and the decoding generator is expressed as:

Wherein, G represents the decoding generator, D represents the decoding discriminator, V(D, G) represents the difference between the information sequence samples and the decoding codeword samples,

Represents the training target of the decoding generator and the decoding discriminator, wherein the training target of the decoding generator is to be able to minimize the difference between the information sequence samples and the decoding codeword samples and The training goal of the decoding discriminator is to maximize the difference between the information sequence samples and the decoding codeword samples, x represents the information sequence samples input to the decoding discriminator, x～p _data (x) means that x obeys the data distribution input to the decoding generator, z means input noise, and z～p _z (z) means that z obeys the noise variable distribution input to the decoding generator,

represents a probability distribution.

Optionally, the decoding discriminator is updated using a gradient ascent method based on the following formula:

Wherein, D represents the decoding discriminator,

Represents the updated decoding discriminator, m represents the number of information sequence samples participating in the current round of training, x _i represents the information sequence samples input to the decoding discriminator for the ith time,

Indicates the information sequence samples last input to the decoder discriminator.

Optionally, the decoding generator is updated using a gradient descent method based on the following formula:

Wherein, G represents the decoding generator, D represents the decoding discriminator,

represents the updated decoding generator, m represents the number of information sequence samples participating in the current round of training, and y _i represents the received codeword samples input to the decoding generator for the ith time.

Optionally, the encoder is a low density parity check LDPC code encoder.

Optionally, the modulator is binary phase shift keying BPSK modulation.

Optionally, the noise adding channel is one of the following: additive white noise AWGN channel; and Rayleigh channel.

Optionally, the neural network model is a GAN model for generating an adversarial neural network.

The embodiment of the second aspect of the present disclosure provides a channel decoding method, including: based on the pre-trained neural network model, decoding the received codeword to be decoded after the information sequence is transmitted through the channel, so as to obtain the A decoding codeword corresponding to the received codeword; wherein, the pre-trained neural network model includes a decoding generator, and the decoding generator decodes the receiving codeword to output the decoding code Character.

Optionally, the neural network model further includes a decoding discriminator, and the pre-trained neural network model is obtained through the following process: obtaining an information sequence training set including information sequence samples, and obtaining the information sequence to be translated based on the information sequence samples. The received codeword sample of the code; take the received codeword sample as the input feature of the decoding generator, and use the information sequence sample and the decoded codeword sample output by the decoding generator as the decoding The input feature of the discriminator, and whether the information sequence sample can be distinguished from the decoded codeword sample is used as the output feature of the decoding discriminator, and iterative training is performed to obtain the pre-trained neural network model.

represents a probability distribution.

Wherein, D represents the decoding discriminator,

Optionally, the encoder is a low density parity check LDPC code encoder.

Optionally, the modulator is binary phase shift keying BPSK modulation.

The embodiment of the third aspect of the present disclosure provides a neural network model training device for channel decoding, the neural network model includes a decoding generator and a decoding discriminator, the device includes: an acquisition module, used Obtaining an information sequence training set including information sequence samples, and obtaining received codeword samples to be decoded based on the information sequence samples; and a training module, configured to use the received codeword samples as the decoding generator The input feature is to use the information sequence sample and the decoded code word sample output by the decoding generator as the input feature of the decoding discriminator, and to distinguish the information sequence sample from the decoded code Word samples are used as the output features of the decoding discriminator, and iterative training is performed to obtain the pre-trained neural network model.

The embodiment of the fourth aspect of the present disclosure provides a channel decoding device, including: a decoding module, used for receiving codewords to be decoded after the information sequence is transmitted through the channel based on the pre-trained neural network model Decoding is performed to obtain a decoding codeword corresponding to the received codeword; wherein, the pre-trained neural network model includes a decoding generator, and the decoding generator decodes the receiving codeword to output the decoded codeword.

The embodiment of the fifth aspect of the present disclosure provides an electronic device, including: a memory; a processor connected to the memory and configured to implement the above-mentioned embodiment of the first aspect by executing computer-executable instructions on the memory A neural network model training method for channel decoding or the channel decoding method described in the embodiment of the second aspect.

The embodiment of the sixth aspect of the present disclosure provides an electronic device, including: a model trainer, which is used to obtain a pre-trained neural network based on information sequence samples and received codeword samples corresponding to the information sequence samples to be decoded. A network model, wherein the neural network model includes a decoding generator and a decoding discriminator, the model trainer is used to use the received codeword sample as the input feature of the decoding generator, and the information sequence The sample and the decoded codeword sample output by the decoding generator are used as the input features of the decoding discriminator, and whether the information sequence sample and the decoded codeword sample can be distinguished is used as the decoding discrimination The output features of the device, perform iterative training to obtain the pre-trained neural network model; the decoder, the decoder is used to obtain the information sequence to be obtained after the information sequence is transmitted through the channel based on the pre-trained neural network model The decoded received codeword is decoded to obtain a decoded codeword corresponding to the received codeword.

The embodiment of the seventh aspect of the present disclosure provides a computer storage medium, wherein the computer storage medium stores computer-executable instructions; after the computer-executable instructions are executed by a processor, the above-mentioned embodiment of the first aspect can be implemented. A training method for a neural network model for channel decoding or the channel decoding method described in the embodiment of the second aspect.

An embodiment of the present disclosure also provides a training method and device for a neural network model for channel decoding, by obtaining an information sequence training set including information sequence samples and obtaining the received code to be decoded based on the information sequence samples This, and take the received codeword sample as the input feature of the decoding generator, take the information sequence sample and the decoded codeword sample output by the decoding generator as the input feature of the decoding discriminator, and use whether the information sequence sample can be distinguished And the decoded codeword samples are used as the output features of the decoded discriminator, and iterative training is performed to obtain the trained neural network model. The trained neural network model thus obtained can decode the received codeword to be decoded after the information sequence is transmitted through the channel to obtain the original information sequence.

The embodiment of the present disclosure provides a channel decoding method and device, which decodes the received codewords to be decoded after the information sequence is transmitted through the channel based on the pre-trained neural network model, so as to obtain the received codewords A corresponding decoding codeword, wherein the pre-trained neural network model includes a decoding generator for decoding the received codeword to output the decoding codeword. Therefore, the decoding of the codeword to be decoded can be realized through the pre-trained neural network model, thereby realizing a channel decoding scheme with low bit error rate, low decoding duration and low decoding complexity.

Additional aspects and advantages of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.

Description of drawings

The above and/or additional aspects and advantages of the present disclosure will become apparent and understandable from the following description of the embodiments in conjunction with the accompanying drawings, wherein:

FIG. 1 is a schematic flowchart of a training method for a neural network model for channel decoding according to an embodiment of the present disclosure;

FIG. 2 is a schematic flowchart of a training method for a neural network model for channel decoding according to an embodiment of the present disclosure;

FIG. 3 is a schematic flowchart of a training method for a neural network model for channel decoding according to an embodiment of the present disclosure;

FIG. 4 is a schematic flowchart of a channel decoding method according to an embodiment of the present disclosure;

FIG. 5 is a schematic structural diagram of a training method and device for a neural network model for channel decoding provided by an embodiment of the present disclosure;

FIG. 6 is a schematic structural diagram of a channel decoding device provided by an embodiment of the present disclosure;

FIG. 7 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure;

FIG. 8 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.

detailed description

Embodiments of the present disclosure are described in detail below, examples of which are illustrated in the drawings, in which the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present disclosure and should not be construed as limiting the present disclosure.

The commercialization of 5G has promoted the progress of human life style and education, made life more intelligent, and accelerated the process of social informatization, which also led to the exponential expansion of user data and system capacity. Therefore, for the new generation of wireless The data transmission rate and system capacity of the communication system put forward higher requirements.

In the existing channel decoding algorithm based on the belief propagation algorithm, after receiving the information sequence, all variable nodes receive the corresponding received value, and each variable node will transmit a reliability message to all the check nodes adjacent to it , each check node will process after receiving the reliability message, and will pass a new reliability message to all the variable nodes adjacent to it, this process can be regarded as an iteration, after an iteration, the Judgment, if the verification equation is satisfied, the decoding ends and the judgment result is output, otherwise iterates again, and so on until the verification equation is satisfied or the maximum number of iterations is reached. Due to the need for continuous iterations to achieve decoding, the existing channel decoding algorithm based on the belief propagation algorithm has high complexity, long decoding time, and complex implementation of decoding devices.

For this reason, channel decoding technology is in the evolution stage of a new generation technology. An important feature of the new generation of channel decoding technology is to maintain massive data transmission and maintain a low bit error rate while increasing the transmission rate. At the same time, different service types have different requirements for channel decoding technology according to performance requirements. For example, in eMBB service data, LDPC codes are used for long codes, and Polar codes are used for short codes. Therefore, it is necessary to provide a channel decoding scheme with lower bit error rate, faster decoding speed and lower decoding model complexity to support the transmission requirements of various service data.

The present disclosure proposes a channel decoding method and device, which realizes decoding of codewords based on a pre-trained neural network model, thereby providing a channel decoding method with low bit error rate, low decoding time and low decoding complexity. Degree channel decoding scheme. In addition, the present disclosure also proposes a training method and device for a neural network model for channel decoding, so as to obtain a neural network model that can be used for channel decoding through a training process.

The channel decoding method and device thereof provided by the present application will be described in detail below with reference to the accompanying drawings.

Fig. 1 shows a schematic flowchart of a training method for a neural network model for channel decoding according to an embodiment of the present disclosure. The neural network model includes a decoding generator and a decoding discriminator. As shown in Figure 1, the method includes the following steps.

Step S101, obtaining an information sequence training set including information sequence samples, and obtaining received codeword samples to be decoded based on the information sequence samples.

Wherein, the neural network model may be a Generative Adversarial Networks (GAN, Generative Adversarial Networks) model.

GAN is a feature learning method of artificial intelligence network. It converts the original data into a higher-level and more abstract expression through some simple but nonlinear models. As long as there are enough conversion combinations, very complex features can also be be learned. At present, GAN has been applied in computer vision, image processing, speech recognition, natural language processing and other fields and has achieved superior performance.

Wherein, the information sequence training set is a data set used for training the neural network model, which may include multiple information sequences. The information sequence may be, for example, a binary information sequence.

In addition, the received codeword samples are obtained based on the information sequence samples, and the received codeword samples are obtained after encoding. Therefore, the received codeword samples are codeword samples that need to be decoded.

Step S102, take the received codeword sample as the input feature of the decoding generator, use the information sequence sample and the decoded codeword sample output by the decoding generator as the input feature of the decoding discriminator, and determine whether the information sequence sample can be distinguished and the decoded codeword samples are used as the output features of the decoded discriminator, and iterative training is performed to obtain a pre-trained neural network model. Wherein, the decoding generator outputs decoded codeword samples based on the received codeword samples.

The decoding generator in the neural network model is used to decode the received codeword samples to be decoded and output the corresponding decoded codeword samples, while the decoding discriminator is used to compare the information sequence samples and the decoded codeword samples obtained from the decoding generator. codeword samples to distinguish. By performing an iterative training process with the output features capable of distinguishing information sequence samples and decoding codeword samples as the decoding discriminator, a trained neural network model can be obtained, wherein the decoding generator in the trained neural network model Be able to decode the received codeword obtained from the information sequence into a decoded codeword that cannot be distinguished from the information sequence by the decoded discriminator, i.e. the decoded codeword is the information sequence (at least from the perspective of the decoded discriminator) , that is to say, the decoding generator can restore the received codeword to be decoded into an information sequence after decoding.

According to an embodiment of the present invention, by obtaining an information sequence training set including information sequence samples and obtaining received codeword samples to be decoded based on the information sequence samples, and using the received codeword samples as input features of the decoding generator, to The information sequence samples and the decoded codeword samples output by the decoding generator are used as the input features of the decoding discriminator, and the ability to distinguish the information sequence samples and the decoded codeword samples as the output features of the decoding discriminator is used for iterative training to obtain a trained neural network model. The trained neural network model thus obtained can decode the received codeword to be decoded after the information sequence is transmitted through the channel to obtain the original information sequence.

FIG. 2 shows a schematic flowchart of a training method for a neural network model for channel decoding according to an embodiment of the present disclosure. Based on the embodiment shown in FIG. 1 , in this example embodiment, an information sequence based The samples obtain a specific implementation of the received codeword samples to be decoded.

As shown in FIG. 2 , step S101 shown in FIG. 1 may specifically include the following steps.

In step S201, based on the information sequence samples, codeword samples are obtained through an encoder.

An encoder may be used to encode information sequence samples to obtain encoded codeword samples. Wherein, the encoder can be an encoder used in a channel coding scheme in a mobile communication system, such as a low density parity check code (LDPC, Low Density Parity Check) encoder, a polar (Polar) code encoder, a Turbo code encoder device. Among them, since LDPC code is a linear block code with a sparse check matrix, its characteristics are completely determined by the parity check matrix, that is, it has a structured structure, which is more suitable for deep learning network learning. In other words, it is easier to obtain a neural network model suitable for decoding LDPC codes.

Step S202: Modulate the encoded codeword samples to obtain modulated codeword samples.

After the encoded codeword samples are obtained, modulated codeword samples can be obtained by modulating the encoded codeword samples. Wherein, for example, binary phase shift keying (BPSK, Binary Phase Shift Keying) modulation, frequency shift keying (FSK, Frequency Shift Keying) modulation and other modulation schemes can be used to modulate the encoded codeword samples.

Step S203, input the modulated codeword samples into the noise adding channel to obtain the received codeword samples transmitted through the channel.

The modulated codeword samples obtained through modulation can be transmitted through the noise-added channel to obtain received codeword samples, so that the received codeword samples are codewords with noise.

In an example embodiment, the noise-added channel may be an Additive White Gaussian Noise (AWGN) channel or a Rayleigh channel.

For example, in a specific example, an information sequence x with a length of L (wherein, the total length of the information sequence is L, and the length of the sequence containing information is less than or equal to L) can be input into the LDPC encoder to obtain a length of M (the length M can be greater than L or less than or equal to L, which depends on the codeword u used by the encoder), the codeword u is modulated by BPSK to obtain the codeword s, but the codeword s is input into the AWGN channel to obtain the noisy Codeword y, where y=s+n, n represents noise.

According to the embodiment of the present invention, the received codeword samples to be decoded can be obtained by encoding, modulating, and adding noise to the channel of the information sequence samples.

FIG. 3 shows a schematic flowchart of a training method for a neural network model for channel decoding according to an embodiment of the present disclosure. Based on the embodiment shown in FIG. 1, in this example embodiment, it describes the iterative training The specific implementation of each round of training.

As shown in FIG. 3 , step S102 shown in FIG. 1 may specifically include the following steps.

Step S301, based on the received codeword samples obtained from the information sequence samples used for the current round of training, the decoded codeword samples corresponding to the information sequence samples are obtained through the decoding generator.

For each round of training, one or more information sequence samples can be used. For example, received codeword samples obtained from one or more information sequence samples may be input into the decoding generator to obtain one or more decoded codeword samples respectively corresponding to the one or more information sequence samples.

Step S302, based on the decoded codeword samples and the information sequence samples, determine whether the decoded codeword samples and the information sequence samples can be distinguished through the decoding discriminator.

One or more decoded codeword samples and corresponding one or more information sequence samples output from the decode generator are input into a decode discriminator, which determines whether the decoded codeword samples and the information sequence samples can be distinguished .

In an example embodiment, the training objectives of the decode discriminator and the decode generator can be expressed as:

Among them, G represents the decoding generator, D represents the decoding discriminator, V(D, G) represents the difference between the information sequence samples and the decoded codeword samples,

Represents the training objectives of the decoding generator and the decoding discriminator, where the training objective of the decoding generator is to minimize the difference between the information sequence samples and the decoded codeword samples, and the training objective of the decoding discriminator is to be able to maximize The difference between the information sequence sample and the decoded codeword sample is distinguished in the z region, x represents the input data of the decoding discriminator, x~p _data (x) represents that x obeys the data distribution input to the decoding generator, z Indicates the input noise, z～p _z (z) indicates that z obeys the noise variable distribution input to the decoding generator,

represents a probability distribution. Wherein, p _z (z) may obey a normal distribution.

The training goal of the decoding discriminator is to update the decoding discriminator while keeping the decoding generator unchanged, and the training target of the decoding generator is to analyze the information sequence samples based on the updated decoding discriminator. This is accomplished by updating the decoding generator to differentiate between the differences from the decoded codeword samples.

Step S303, if it is determined that the decoded codeword sample and the information sequence sample can be distinguished, update the decoding generator and the decoding discriminator through the back propagation method, and repeat the above steps until all the information sequence samples used for this round of training After being processed by the decoding generator and the decoding discriminator, the next round of training starts.

If the decoding discriminator can distinguish the decoded codeword samples from the information sequence samples, it means that the difference between the decoded codeword samples generated by the decoding generator from the received codeword samples and the information sequence samples can be distinguished by the decoding discriminator , that is, the decoding generator does not completely restore the information sequence to the decoding of the received codeword samples. Therefore, the current neural network model is not suitable for decoding the received codewords, and the decoding generator needs to be updated so that it can Learn a more suitable decoding algorithm, and at the same time need to update the decoding discriminator to enhance the discriminative ability of the decoding discriminator, and continuously update the decoding generator and the decoding discriminator through the training process, so as to finally reach the Nash equilibrium state.

For example, for a decoded codeword sample and an information sequence sample input to the decoding discriminator, if the decoding discriminator can distinguish the decoded codeword sample from the information sequence sample, then the decoding generator and the decoding The code discriminator is updated. Specifically, the decoding generator is updated by using the gradient descent method so that the updated decoding generator can minimize the difference between the information sequence samples and the decoded codeword samples, and the decoding The code discriminator is updated so that the updated decoding discriminator can maximize the difference between the information sequence samples and the decoded code word samples.

In an example embodiment, the decoding discriminator is updated using gradient ascent based on the following formula:

Among them, D represents the decoding discriminator,

Represents the updated decoding discriminator, m represents the number of information sequence samples participating in the current round of training, x _i represents the information sequence samples input to the decoding discriminator for the i time,

Indicates the information sequence samples input to the decoder discriminator last time.

In an example embodiment, the decoding generator is updated using gradient descent based on the following formula:

Among them, G represents the decoding generator, D represents the discriminator,

After updating the decoding generator and the decoding discriminator, use the updated decoding generator and the updated decoding discriminator to repeat the above steps until all the information sequence samples used for this round of training are decoded and generated After the discriminator and decode discriminator processing begins.

That is to say, after the decoding generator and the decoding discriminator are updated, if there are information sequences that have not been processed by the decoding generator and the decoding discriminator in one or more information sequence samples used for this round of training sample, then input the received codeword sample obtained from any of the unprocessed information sequence samples into the decoding generator to obtain the decoding codeword sample corresponding to the information sequence sample, and combine the information sequence sample and the decoding code Word samples are input into the decoding discriminator to determine that the two can be distinguished. If they can be distinguished, the decoding generator and the decoding discriminator are updated again, and so on, until one or more information used for this round of training There are no information sequence samples in the sequence samples that have not been processed by the decoding generator and the decoding discriminator, that is, all the information sequence samples used for this round of training have been used, and the next round of training can start.

Step S304, if it is determined that the decoded codeword sample and the information sequence sample cannot be distinguished, the iterative training is ended and a pre-trained neural network model is obtained.

If the decoding discriminator cannot distinguish the decoded codeword samples from the information sequence samples, it indicates that the decoding generator can completely restore the information sequence samples according to the received codeword samples. Therefore, the current neural network model is suitable for decoding the received codeword samples. Code, the iterative training ends, so as to obtain the pre-trained neural network model.

According to the embodiment of the present invention, in the iterative training, the decoding discriminator is continuously updated by the gradient ascending method and the decoding generator is continuously updated by the gradient descent method, so that it is possible to obtain A trained neural network model.

Fig. 4 shows a schematic flowchart of a channel decoding method according to an embodiment of the present disclosure. As shown in Fig. 4 , the method includes the following steps.

Step S401, based on the pre-trained neural network model, decode the received codeword to be decoded obtained after the information sequence is transmitted through the channel, so as to obtain the decoded codeword corresponding to the received codeword.

Wherein, the pre-trained neural network model includes a decoding generator, and the decoding generator decodes the received codeword to output the decoded codeword.

In this embodiment, the received codeword obtained after the information sequence is transmitted through the channel can be input into the pre-trained neural network model, and the pre-trained neural network model decodes the received codeword through its decoding generator to output The decoded codeword corresponding to the received codeword.

Wherein, the neural network model may be a Generative Adversarial Networks (GAN, Generative Adversarial Networks) model. Using the pre-trained neural network model, especially the GAN model to realize channel decoding can bring the following advantages:

1. Low bit error rate: With the increase of training times, the decoding performance gradually approaches the maximum a posteriori probability performance, and the bit error rate is lower than that of the belief propagation algorithm.

2. Low decoding time: The channel decoding based on the neural network model will shorten the decoding time after the training of the neural network model is completed, which reduces the decoding delay.

3. Low complexity of decoding algorithm: GAN is based on backpropagation and does not require Markov chain, thus reducing the complexity of decoding algorithm.

According to the embodiment of the present invention, the decoding of the received codeword obtained after the initial codeword is transmitted through the channel is realized by using a pre-trained neural network model, thereby realizing a low bit error rate, low decoding time and low decoding time. Code complexity channel decoding scheme.

The pre-trained neural network model can be obtained based on the training method of the neural network model for channel decoding described above with reference to FIGS.

Corresponding to the training methods for the neural network model for channel decoding provided by the above embodiments, the present disclosure also provides a training device for the neural network model for channel decoding. The training device for the neural network model for channel decoding corresponds to the training method for the neural network model for channel decoding provided by the above-mentioned several embodiments, so in the implementation of the training method for the neural network model for channel decoding The method is also applicable to the training device for the neural network model for channel decoding provided in this embodiment, and will not be described in detail in this embodiment. Fig. 5 is a schematic structural diagram of a training device for a neural network model for channel decoding proposed according to the present disclosure.

FIG. 5 is a schematic structural diagram of a neural network model training device 500 for channel decoding provided by an embodiment of the present disclosure. The neural network model includes a decoding generator and a decoding discriminator.

As shown in Figure 5, the training device 500 for the neural network model of channel decoding comprises:

An acquisition module 501, configured to acquire an information sequence training set including information sequence samples, and obtain received codeword samples to be decoded based on the information sequence samples; and

The training module 502 is configured to use the received codeword samples as the input features of the decoding generator, and use the information sequence samples and the decoded codeword samples output by the decoding generator as the decoding discrimination The input feature of the discriminator, and whether the information sequence sample can be distinguished from the decoded codeword sample is used as the output feature of the decoding discriminator, and iterative training is performed to obtain the pre-trained neural network model.

In some embodiments, the obtaining module 501 is configured to: obtain coded codeword samples through an encoder based on information sequence samples; modulate coded codeword samples to obtain modulated codeword samples; and input modulated codeword samples into noise channel to obtain received codeword samples after channel transmission.

In some embodiments, the training module 502 is configured to: obtain the decoded codeword samples corresponding to the information sequence samples through the decoding generator based on the received codeword samples obtained from the information sequence samples used for the current round of training; Based on the decoded codeword samples and information sequence samples, determine whether the decoded codeword samples and information sequence samples can be distinguished through the decoding discriminator; if it is determined that the decoded codeword samples and information sequence samples can be distinguished, use the back propagation method The decoding generator and the decoding discriminator are updated, and the above steps are repeated until all information sequence samples used for this round of training have been processed by the decoding generator and the decoding discriminator, and then start downloading rounds of training; if it is determined that the decoded codeword samples and information sequence samples cannot be distinguished, the iterative training is ended and a pre-trained neural network model is obtained.

In some embodiments, the training objectives of the decoding discriminator and the decoding generator are expressed as:

represents a probability distribution.

In some embodiments, the decoding discriminator is updated using gradient ascent based on the following formula:

Wherein, D represents the decoding discriminator,

In some embodiments, the decoding generator is updated using a gradient descent method based on the following formula:

In some embodiments, the encoder is a low density parity check LDPC code encoder.

In some embodiments, the modulator is binary phase shift keyed BPSK modulation.

In some embodiments, the noise adding channel is one of: an additive white noise AWGN channel; and a Rayleigh channel.

In some embodiments, the neural network model is a GAN model.

FIG. 6 is a schematic structural diagram of a channel decoding apparatus 600 provided by an embodiment of the present disclosure.

As shown in FIG. 6, the device 600 includes: a decoding module 601, configured to decode the received codeword to be decoded after the information sequence is transmitted through the channel based on the pre-trained neural network model, to obtain A decoded codeword corresponding to the received codeword; wherein, the pre-trained neural network model includes a decoded generator, and the decoded generator decodes the received codeword to output the decoded codeword numbers.

By implementing this embodiment, based on the pre-trained neural network model, the received codeword to be decoded after the information sequence is transmitted through the channel is decoded to obtain the decoded codeword corresponding to the received codeword, wherein the pre-trained The neural network model of includes a decoding generator for decoding received codewords to output decoded codewords. Therefore, the decoding of the codeword to be decoded can be realized through the pre-trained neural network model, thereby realizing a channel decoding scheme with low bit error rate, low decoding time and low decoding complexity.

In some embodiments, the neural network model further includes a decoding discriminator, and the pre-trained neural network model is obtained through the following process: obtaining an information sequence training set including information sequence samples, and obtaining The received codeword sample to be decoded; the received codeword sample is used as the input feature of the decoding generator, and the information sequence sample and the decoded codeword sample output by the decoding generator are used as the Decoding the input features of the discriminator, and taking whether the information sequence sample and the decoded codeword sample can be distinguished as the output feature of the decoding discriminator, performing iterative training to obtain the pre-trained neural network model .

In some embodiments, the obtaining the received codeword samples to be decoded based on the information sequence samples includes: obtaining encoded codeword samples by an encoder based on the information sequence samples; and modulating the encoded codeword samples obtaining modulation codeword samples; and inputting the modulation codeword samples into a noise-added channel to obtain received codeword samples transmitted through the channel.

In some embodiments, each round of training in the iterative training includes: based on the received codeword samples obtained from the information sequence samples used for the current round of training, the decoding generator corresponding to the information sequence samples is obtained by the decoding generator. Codeword samples; based on the decoded codeword samples and the information sequence samples, determine whether the decoded codeword samples and the information sequence samples can be distinguished through the decoding discriminator; if it is determined that the information sequence samples can be distinguished The decoding codeword samples and the information sequence samples are updated by the back propagation method to the decoding generator and the decoding discriminator, and repeat the above steps until all the information sequence samples are used for this round of training Start the next round of training after being processed by the decoding generator and the decoding discriminator; and if it is determined that the decoding codeword samples and the information sequence samples cannot be distinguished, end the iterative training and obtain the Pretrained neural network models.

represents a probability distribution.

Wherein, D represents the decoding discriminator,

In some embodiments, the modulator is binary phase shift keyed BPSK modulation.

In some embodiments, the neural network model is a GAN model.

According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a computer-readable storage medium. As shown in FIG. 7 , it is a block diagram of an electronic device according to an embodiment of the present disclosure. Electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are by way of example only, and are not intended to limit implementations of the disclosure described and/or claimed herein.

As shown in FIG. 7 , the electronic device includes: one or more processors 710 , a memory 720 , and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and can be mounted on a common motherboard or otherwise as desired. The processor may process instructions executed within the electronic device, including instructions stored in or on the memory, to display graphical information of a GUI on an external input/output device such as a display device coupled to an interface. In other implementations, multiple processors and/or multiple buses may be used with multiple memories and multiple memories, if desired. Likewise, multiple electronic devices may be connected, with each device providing some of the necessary operations (eg, as a server array, a set of blade servers, or a multi-processor system). In FIG. 7, a processor 710 is taken as an example.

The memory 720 is a non-transitory computer-readable storage medium provided in the present disclosure. Wherein, the memory stores instructions executable by at least one processor, so that the at least one processor executes the data transmission method provided in the present disclosure. The non-transitory computer-readable storage medium of the present disclosure stores computer instructions, and the computer instructions are used to cause a computer to execute the data transmission method provided by the present disclosure.

As a non-transitory computer-readable storage medium, the memory 720 can be used to store non-transitory software programs, non-transitory computer-executable programs and modules, such as program instructions/modules corresponding to the data transmission method in the embodiments of the present disclosure. The processor 710 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions and modules stored in the memory 720, that is, implements the data transmission method in the above method embodiments.

The memory 720 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required by at least one function; the data storage area may store data created according to the use of the positioning electronic device, and the like. In addition, the memory 720 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. Optionally, the storage 720 may optionally include storages that are set remotely relative to the processor 710, and these remote storages may be connected to the positioning electronic device through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The electronic device may further include: an input device 730 and an output device 740 . The processor 710, the memory 720, the input device 730, and the output device 740 may be connected via a bus or in other ways, and connection via a bus is taken as an example in FIG. 7 .

The input device 730 can receive input numbers or character information, and generate key signal input related to user settings and function control of the positioning electronic equipment, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more Input devices such as mouse buttons, trackballs, joysticks, etc. The output device 740 may include a display device, an auxiliary lighting device (eg, LED), a tactile feedback device (eg, a vibration motor), and the like. The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, and a plasma display. In some implementations, the display device may be a touch screen.

Various implementations of the systems and techniques described herein can be implemented in digital electronic circuitry, integrated circuit systems, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs executable and/or interpreted on a programmable system including at least one programmable processor, the programmable processor Can be special-purpose or general-purpose programmable processor, can receive data and instruction from storage system, at least one input device, and at least one output device, and transmit data and instruction to this storage system, this at least one input device, and this at least one output device an output device.

These computing programs (also referred to as programs, software, software applications, or codes) include machine instructions for a programmable processor and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine language calculation program. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or means for providing machine instructions and/or data to a programmable processor ( For example, magnetic disks, optical disks, memories, programmable logic devices (PLDs), including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.

To provide for interaction with the user, the systems and techniques described herein can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user. ); and a keyboard and pointing device (eg, a mouse or a trackball) through which a user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and can be in any form (including Acoustic input, speech input or, tactile input) to receive input from the user.

The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., as a a user computer having a graphical user interface or web browser through which a user can interact with embodiments of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system can be interconnected by any form or medium of digital data communication, eg, a communication network. Examples of communication networks include: Local Area Network (LAN), Wide Area Network (WAN) and the Internet.

A computer system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other.

Those skilled in the art can also understand that various illustrative logical blocks and steps listed in the embodiments of the present application can be implemented by electronic hardware, computer software, or a combination of both. Whether such functions are implemented by hardware or software depends on the specific application and overall system design requirements. Those skilled in the art may use various methods to implement the described functions for each specific application, but such implementation should not be understood as exceeding the protection scope of the embodiments of the present application.

FIG. 8 shows a block diagram of another electronic device according to an embodiment of the present disclosure.

As shown in FIG. 8 , an electronic device 800 includes a model trainer 810 and a decoder 820 .

Wherein, the model trainer 810 is used to obtain a pre-trained neural network model based on information sequence samples and received codeword samples corresponding to the information sequence samples to be decoded, wherein the neural network model includes a decoding generator and decoding discriminator.

Specifically, the model trainer is used to use the received codeword samples as the input features of the decoding generator, and use the information sequence samples and the decoded codeword samples output by the decoding generator as the decoding The input feature of the discriminator, and whether the information sequence sample can be distinguished from the decoded codeword sample is used as the output feature of the decoding discriminator, and iterative training is performed to obtain the pre-trained neural network model.

The decoder 820 is configured to decode the received codeword to be decoded after the information sequence is transmitted through the channel based on the pre-trained neural network model, so as to obtain a decoding code corresponding to the received codeword Character.

The present application also provides a readable storage medium on which instructions are stored, and when the instructions are executed by a computer, the functions of any one of the above method embodiments are realized.

The present application also provides a computer program product, which implements the functions of any one of the above method embodiments when executed by a computer.

In the above embodiments, all or part of them may be implemented by software, hardware, firmware or any combination thereof. When implemented using software, it may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer programs. When the computer program is loaded and executed on the computer, all or part of the processes or functions according to the embodiments of the present application will be generated. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable devices. The computer program can be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program can be downloaded from a website, computer, server or data center Transmission to another website site, computer, server or data center by wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or a data center integrated with one or more available media. The available medium may be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a high-density digital video disc (digital video disc, DVD)), or a semiconductor medium (for example, a solid state disk (solid state disk, SSD)) etc.

Those of ordinary skill in the art can understand that: the first, second and other numbers involved in this application are only for convenience of description, and are not used to limit the scope of the embodiments of this application, and also indicate the sequence.

At least one in this application can also be described as one or more, and multiple can be two, three, four or more, and this application does not make a limitation. In this embodiment of the application, for a technical feature, the technical feature is distinguished by "first", "second", "third", "A", "B", "C" and "D", etc. The technical features described in the "first", "second", "third", "A", "B", "C" and "D" have no sequence or order of magnitude among the technical features described.

The corresponding relationships shown in the tables in this application can be configured or predefined. The values of the information in each table are just examples, and may be configured as other values, which are not limited in this application. When configuring the corresponding relationship between the information and each parameter, it is not necessarily required to configure all the corresponding relationships shown in the tables. For example, in the table in this application, the corresponding relationship shown in some rows may not be configured. For another example, appropriate deformation adjustments can be made based on the above table, for example, splitting, merging, and so on. The names of the parameters shown in the titles of the above tables may also adopt other names understandable by the communication device, and the values or representations of the parameters may also be other values or representations understandable by the communication device. When the above tables are implemented, other data structures can also be used, for example, arrays, queues, containers, stacks, linear tables, pointers, linked lists, trees, graphs, structures, classes, heaps, hash tables or hash tables can be used Wait.

Predefined in this application can be understood as defining, predefining, storing, prestoring, prenegotiating, preconfiguring, curing, or prefiring.

Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present application.

Those skilled in the art can clearly understand that for the convenience and brevity of the description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

The above is only a specific implementation of the application, but the scope of protection of the application is not limited thereto. Anyone familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the application. Should be covered within the protection scope of this application. Therefore, the protection scope of the present application should be determined by the protection scope of the claims.

Claims

A method for training a neural network model for channel decoding, wherein the neural network model includes a decoding generator and a decoding discriminator, and the method includes:

obtaining an information sequence training set including information sequence samples, and obtaining received codeword samples to be decoded based on the information sequence samples; and

Using the received codeword samples as input features of the decoding generator, using the information sequence samples and the decoded codeword samples output by the decoding generator as input features of the decoding discriminator, and Taking whether the information sequence sample can be distinguished from the decoded codeword sample as an output feature of the decoding discriminator, iterative training is performed to obtain the pre-trained neural network model.
The method according to claim 1, wherein said obtaining a received codeword sample to be decoded based on said information sequence sample comprises:

Obtaining encoded codeword samples through an encoder based on the information sequence samples;

modulating the encoded codeword samples to obtain modulated codeword samples; and

The modulated codeword samples are input into a noise adding channel to obtain received codeword samples transmitted through the channel.
The method according to claim 1, wherein each round of training in the iterative training comprises:

Based on the received codeword samples obtained from the information sequence samples used for the current round of training, the decoded codeword samples corresponding to the information sequence samples are obtained by the decoding generator;

determining whether the decoded codeword samples and the information sequence samples can be distinguished by the decoding discriminator based on the decoded codeword samples and the information sequence samples;

If it is determined that the decoded codeword sample and the information sequence sample can be distinguished, update the decoded generator and the decoded discriminator through the backpropagation method, and repeat the above steps until used for this round of training All the information sequence samples of are processed by the decoding generator and the decoding discriminator to start the next round of training; and

If it is determined that the decoded codeword samples and the information sequence samples cannot be distinguished, the iterative training is ended and the trained neural network model is obtained.
The method according to claim 3, wherein the training target of the decoding discriminator and the decoding generator is expressed as:

Wherein, G represents the decoding generator, D represents the decoding discriminator, V(D, G) represents the difference between the information sequence samples and the decoding codeword samples,
Represents the training target of the decoding generator and the decoding discriminator, wherein the training target of the decoding generator is to be able to minimize the difference between the information sequence samples and the decoding codeword samples and The training goal of the decoding discriminator is to maximize the difference between the information sequence samples and the decoding codeword samples, x represents the information sequence samples input to the decoding discriminator, x～p data (x) means that x obeys the data distribution input to the decoding generator, z means input noise, and z～p z (z) means that z obeys the noise variable distribution input to the decoding generator,
represents a probability distribution.
The method according to claim 3, wherein the decoding discriminator is updated using a gradient ascending method based on the following formula:

Wherein, D represents the decoding discriminator,
Represents the updated decoding discriminator, m represents the number of information sequence samples participating in the current round of training, x i represents the information sequence samples input to the decoding discriminator for the ith time,
Indicates the information sequence samples last input to the decoder discriminator.
The method according to claim 3, wherein the decoding generator is updated using a gradient descent method based on the following formula:

Wherein, G represents the decoding generator, D represents the decoding discriminator,
represents the updated decoding generator, m represents the number of information sequence samples participating in the current round of training, and y i represents the received codeword samples input to the decoding generator for the ith time.
The method according to claim 2, wherein the encoder is a Low Density Parity Check (LDPC) code encoder.
The method according to claim 2, characterized in that the modulator is binary phase shift keying BPSK modulation.
The method according to claim 2, wherein the noise adding channel is one of the following:

Additive white noise AWGN channel; and

Rayleigh channel.
The method according to any one of claims 1-9, wherein the neural network model is a GAN model for generating an adversarial neural network.
A channel decoding method, characterized in that, comprising:

Based on the pre-trained neural network model, decoding the received codeword to be decoded obtained after the information sequence is transmitted through the channel, so as to obtain the decoded codeword corresponding to the received codeword;

Wherein, the pre-trained neural network model includes a decoding generator, and the decoding generator decodes the received codeword to output the decoded codeword.
The method according to claim 10, the neural network model also includes a decoding discriminator, and the pre-trained neural network model is obtained through the following process:

obtaining an information sequence training set including information sequence samples, and obtaining received codeword samples based on the information sequence samples; and

Using the received codeword samples as input features of the decoding generator, using the information sequence samples and the decoded codeword samples output by the decoding generator as input features of the decoding discriminator, and Taking whether the information sequence sample can be distinguished from the decoded codeword sample as an output feature of the decoding discriminator, iterative training is performed to obtain the pre-trained neural network model.
The method according to claim 12, wherein said obtaining the received codeword samples to be decoded based on said information sequence samples comprises:

Obtaining encoded codeword samples through an encoder based on the information sequence samples;

modulating the encoded codeword samples to obtain modulated codeword samples; and

The modulated codeword samples are input into a noise adding channel to obtain received codeword samples transmitted through the channel.
The method according to claim 12, wherein each round of training in the iterative training comprises:

Based on the received codeword samples obtained from the information sequence samples used for the current round of training, the decoded codeword samples corresponding to the information sequence samples are obtained by the decoding generator;

determining whether the decoded codeword samples and the information sequence samples can be distinguished by the decoding discriminator based on the decoded codeword samples and the information sequence samples;

If it is determined that the decoded codeword sample and the information sequence sample can be distinguished, update the decoded generator and the decoded discriminator through the backpropagation method, and repeat the above steps until used for this round of training All the information sequence samples of are processed by the decoding generator and the decoding discriminator to start the next round of training; and

If it is determined that the decoded codeword samples and the information sequence samples cannot be distinguished, the iterative training is ended and the trained neural network model is obtained.
The method according to claim 14, wherein the training objectives of the decoding discriminator and the decoding generator are expressed as:

Wherein, G represents the decoding generator, D represents the decoding discriminator, V(D, G) represents the difference between the information sequence samples and the decoding codeword samples,
Represents the training target of the decoding generator and the decoding discriminator, wherein the training target of the decoding generator is to be able to minimize the difference between the information sequence samples and the decoding codeword samples and The training goal of the decoding discriminator is to maximize the difference between the information sequence samples and the decoding codeword samples, x represents the information sequence samples input to the decoding discriminator, x～p data (x) means that x obeys the data distribution input to the decoding generator, z means input noise, and z～p z (z) means that z obeys the noise variable distribution input to the decoding generator,
represents a probability distribution.
The method according to claim 14, wherein the decoding discriminator is updated using a gradient ascending method based on the following formula:

Wherein, D represents the decoding discriminator,
Represents the updated decoding discriminator, m represents the number of information sequence samples participating in the current round of training, x i represents the information sequence samples input to the decoding discriminator for the ith time,
Indicates the information sequence samples last input to the decoder discriminator.
The method according to claim 14, wherein the decoding generator is updated using a gradient descent method based on the following formula:

Wherein, G represents the decoding generator, D represents the decoding discriminator,
represents the updated decoding generator, m represents the number of information sequence samples participating in the current round of training, and y i represents the received codeword samples input to the decoding generator for the ith time.
The method according to any one of claims 11-17, wherein the neural network model is a GAN model for generating an adversarial neural network.
A training device for a neural network model for channel decoding, characterized in that the neural network model includes a decoding generator and a decoding discriminator, and the device includes:

An acquisition module, configured to acquire an information sequence training set including information sequence samples, and obtain received codeword samples to be decoded based on the information sequence samples; and

A training module, configured to use the received codeword samples as the input features of the decoding generator, and use the information sequence samples and the decoded codeword samples output by the decoding generator as the decoding discriminator The input feature of the input feature, and whether the information sequence sample can be distinguished from the decoded codeword sample is used as the output feature of the decoding discriminator, and iterative training is performed to obtain the pre-trained neural network model.
A channel decoding device is characterized in that it comprises:

The decoding module is used to decode the received codewords to be decoded after the information sequence is transmitted through the channel based on the pre-trained neural network model, so as to obtain the decoded codewords corresponding to the received codewords;

Wherein, the pre-trained neural network model includes a decoding generator, and the decoding generator decodes the received codeword to output the decoded codeword.
An electronic device, comprising:

memory;

A processor, connected to the memory, configured to implement the method according to any one of claims 1-10 by executing computer-executable instructions on the memory.
An electronic device, comprising:

memory;

A processor, connected to the memory, configured to implement the method according to any one of claims 11-18 by executing computer-executable instructions on the memory.
An electronic device comprising:

A model trainer, configured to obtain a pre-trained neural network model based on information sequence samples and received codeword samples to be decoded corresponding to the information sequence samples, wherein the neural network model includes a decoding generator and a decoding A code discriminator, the model trainer is used to use the received codeword samples as the input features of the decoding generator, and use the information sequence samples and the decoded codeword samples output by the decoding generator as the The input feature of the decoding discriminator, and whether the information sequence sample and the decoding codeword sample can be distinguished as the output feature of the decoding discriminator, perform iterative training to obtain the pre-trained neural network Model;

a decoder, the decoder is configured to decode the received codeword to be decoded after the information sequence is transmitted through the channel based on the pre-trained neural network model, so as to obtain the codeword corresponding to the received codeword The decoding codeword of .
A computer storage medium, wherein the computer storage medium stores computer-executable instructions; after the computer-executable instructions are executed by a processor, the method according to any one of claims 1-10 can be implemented.
A computer storage medium, wherein the computer storage medium stores computer-executable instructions; after the computer-executable instructions are executed by a processor, the method according to any one of claims 11-18 can be implemented.