阅读说明：本技术 一种基于深度学习的rs码置信传播译码方法 (RS code belief propagation decoding method based on deep learning ) 是由 张为 邹述铭 于 2019-09-12 设计创作，主要内容包括：本发明涉及一种一种基于深度学习的RS码置信传播译码方法,包括下列步骤：使用深度学习方法,根据RS码奇偶校验矩阵所对应的Tanner图搭建非全连接神经网络；将Tanner图中校验节点与变量节点的运算过程转化为神经网络中的神经元的运算过程,初始化奇偶校验矩阵参数为0或1作为可供训练的权重值,在对应的Tanner图中即为变量节点中的,在对应的神经网络中就是变量节点的层中使用深度学习优化的参数值,用于神经网络训练；将从噪声信道接收的比特级对数似然比(LLR)作为可靠度信息输入至神经网络；在LLR值进行求和运算后；将每一次迭代计算后的码字使用SSID算法进行随机长度的符号级位移。(The invention relates to an RS code belief propagation decoding method based on deep learning, which comprises the following steps: constructing a non-fully-connected neural network according to a Tanner graph corresponding to the RS code parity check matrix by using a deep learning method; converting the operation process of the check nodes and the variable nodes in the Tanner graph into the operation process of neurons in a neural network, initializing a parity check matrix parameter to be 0 or 1 as a weight value for training, wherein the parity check matrix parameter is in the variable nodes in the corresponding Tanner graph, and the parameter value of deep learning optimization is used in the layer of the variable nodes in the corresponding neural network for training the neural network; inputting bit-level log-likelihood ratios (LLRs) received from the noisy channel as reliability information to a neural network; after the LLR value is subjected to summation operation; and carrying out symbol-level displacement of random length on the code word after each iterative computation by using an SSID algorithm.)

1. A RS code belief propagation decoding method based on deep learning comprises the following steps:

(1) constructing a non-fully-connected neural network according to a Tanner graph corresponding to the RS code parity check matrix by using a deep learning method; the operation process of the check nodes and the variable nodes in the Tanner graph is converted into the operation process of the neurons in the neural network, the parity check matrix parameters are initialized to be 0 or 1 and serve as weight values for training, the corresponding Tanner graph is the variable nodes, and the deep learning optimization parameter values are used in the layer of the corresponding neural network, namely the variable nodes, and are used for training the neural network.

(2) Inputting bit-level log-likelihood ratios (LLRs) received from a noise channel into a neural network as reliability information, and calculating an output value of information bits of each layer through one-time iterative calculation of a belief propagation algorithm, namely through a variable node layer and a check node layer of the neural network; adding an output layer after each iterative computation, multiplying the LLR value received before the iterative computation and the LLR value returned by the check node in the iterative process by the parameters and performing addition computation, wherein the multiplied parameters after the LLR information summation output by the check node are the damping coefficients in the SSID algorithm, so that the damping coefficients can be used as unknown quantities to be trained by using a deep learning technology, and the optimal parameter values are obtained; after the LLR value is subjected to summation operation, a sigmoid function is used as an activation function so as to facilitate deep learning to train;

(3) carrying out symbol-level displacement of random length on the code word after each iterative computation by using an SSID algorithm, wherein the length of the displaced symbol is less than the whole length of the code word, inputting the displaced code word to a new neural network layer, including variable nodes and check nodes, and carrying out next iteration; the code words before and after the displacement all belong to the code words in the RS code;

(4) if the specified iterative computation times are reached, the code words are moved back to the initial position before decoding, the output predicted values are rounded and compared with the correct code words, the parameter training of the neural network is carried out on the basis of the set training period and the set learning rate, and the information of the parameters in the variable nodes is updated; if the iteration times are not reached, performing next iteration calculation;

(5) when the specified training period is reached, a final check matrix value can be obtained after the training process is finished; inputting the LLR value of the test information into a neural network, checking output code words after set iterative operation, and analyzing decoding effect; and changing the training period and the learning rate for multiple times to find the optimal parameters.

Technical Field

The invention belongs to the field of error control coding in channel coding and decoding, and relates to a Reed-Solomon code (RS code) belief propagation soft-decision decoding algorithm using a deep learning technology.

Background

In recent years, with the development of information society and the continuous progress of communication technology, the requirement for data transmission reliability is increasing, and how to ensure reliable data transmission becomes one of the issues that must be paid attention to in communication system design. Since shannon proposed the channel coding theory in 1948, the application of error control coding has become a research hotspot of modern communication systems and memory systems. When transmitted information is transmitted from an information source and received by an information sink, random errors occur in the information in the transmission process due to non-ideal transmission channels, error control coding is a technology for checking and correcting the errors occurring in the information transmission process by using a coding and decoding technology in the digital communication process, and Reed-Solomon code (RS code) has the characteristics of strong error correction capability, simple structure and the like, and is widely applied to the fields of data storage, digital video broadcasting, deep space exploration, wireless communication and the like.

For code words such as BCH codes, RS codes and the like, the number of times of occurrence of 1 in a parity check matrix is large, the number of times of occurrence of 0 is small, and a matrix of code words which is 0 more and less than 1 in a check matrix such as LDPC codes belongs to a "non-sparse matrix", so that mutual association between nodes is easily generated, a "short-loop effect" is generated, and further, error information is mutually propagated between nodes in decoding, thereby causing decoding errors. The belief propagation decoding algorithm is suitable for code words with sparse parity check matrix, and has achieved excellent decoding performance on the LDPC code. Therefore, whether the belief propagation decoding algorithm can be combined with BCH codes, RS codes and other codes with non-sparse parity check matrixes to obtain better decoding effect becomes a direction for research.

In 2004, by using the cyclic characteristic of the RS code, the king Jiang et al performs symbol-level random shift on the codeword after each iteration of the belief propagation algorithm, and because the corresponding relationship between the shifted codeword and the original codeword and the check matrix Tanner graph is different, the "short-loop effect" generated by the "non-sparse" characteristic of the parity check matrix can be suppressed after multiple random shifts, and the occurrence of errors is reduced, which is called as a Stochastic shift iterative Decoding algorithm (SSID algorithm), and provides a more effective implementation method for the belief propagation Decoding algorithm of the RS code. The algorithm uses a bit-level Log-Likelihood Ratio (LLR) as input data of a decoder, and a Coefficient for improving code performance, called Damping Coefficient (Damping Coefficient), is used after the outer LLR values of the current iteration are summed at the output end, but the value is set by using an empirical value obtained through simulation, and an accurate mathematical derivation is not available. In 2016, Eliya Nachmani et al combined BCH codes with belief propagation decoding algorithm, and constructed a non-fully-connected neural network using deep learning technique, which uses 0,1 in parity check matrix as weight value for neural network training, and then can train to obtain optimal weight using deep learning algorithm (as shown in fig. 1). Compared with the belief propagation decoding algorithm directly used in the BCH code, the algorithm can improve the decoding accuracy while passing through fewer iterations, and reduce the operation complexity under the condition of almost the same decoding performance.

According to the invention, 0 and 1 in the dense parity check matrix of the RS code are quantized into parameters which can be optimized for deep learning training by using a deep learning technology, and then the optimal parameters can be trained by using a belief propagation decoding algorithm through repeated iterative computation, so that the decoding performance under a fixed iteration number is improved.

Disclosure of Invention

The invention aims to provide an RS code belief propagation decoding method capable of improving decoding performance under fixed iteration times, on the basis of decoding by using a belief propagation algorithm, the SSID algorithm is used for reducing error information on an RS code from mutual propagation among calculation nodes due to dense parity check matrixes and short-loop effect, so that decoding errors are generated, a non-fully-connected neural network is built by using a deep learning technology to simulate Tanner graph mapping corresponding to the parity check matrixes of the RS code, and 0,1 values used in the operation process are subjected to parameter quantization and training, so that optimal parameters are obtained and a decoding framework is formed. The technical scheme is as follows:

a RS code belief propagation decoding method based on deep learning comprises the following steps:

(1) constructing a non-fully-connected neural network according to a Tanner graph corresponding to the RS code parity check matrix by using a deep learning method; converting the operation process of the check nodes and the variable nodes in the Tanner graph into the operation process of neurons in a neural network, initializing a parity check matrix parameter to be 0 or 1 as a weight value for training, wherein the parity check matrix parameter is in the variable nodes in the corresponding Tanner graph, and the parameter value of deep learning optimization is used in the layer of the variable nodes in the corresponding neural network for training the neural network;

(2) inputting bit-level log-likelihood ratios (LLRs) received from a noise channel into a neural network as reliability information, and calculating an output value of information bits of each layer through one-time iterative calculation of a belief propagation algorithm, namely through a variable node layer and a check node layer of the neural network; adding an output layer after each iterative computation, multiplying the LLR value received before the iterative computation and the LLR value returned by the check node in the iterative process by the parameters and performing addition computation, wherein the multiplied parameters after the LLR information summation output by the check node are the damping coefficients in the SSID algorithm, so that the damping coefficients can be used as unknown quantities to be trained by using a deep learning technology, and the optimal parameter values are obtained; after the LLR value is subjected to summation operation, a sigmoid function is used as an activation function so as to facilitate deep learning to train;

(3) carrying out symbol-level displacement of random length on the code word after each iterative computation by using an SSID algorithm, wherein the length of the displaced symbol is less than the whole length of the code word, inputting the displaced code word to a new neural network layer, including variable nodes and check nodes, and carrying out next iteration; the code words before and after the displacement all belong to the code words in the RS code;

(4) if the specified iterative computation times are reached, the code words are moved back to the initial position before decoding, the output predicted values are rounded and compared with the correct code words, the parameter training of the neural network is carried out on the basis of the set training period and the set learning rate, and the information of the parameters in the variable nodes is updated; if the iteration times are not reached, performing next iteration calculation;

(5) when the specified training period is reached, a final check matrix value can be obtained after the training process is finished; inputting the LLR value of the test information into a neural network, checking output code words after set iterative operation, and analyzing decoding effect; and changing the training period and the learning rate for multiple times to find the optimal parameters.

Drawings

FIG. 1 is a diagram of a neural network architecture based on deep learning BCH (15-7) three-iteration computation

FIG. 2 is a flowchart of RS code belief propagation decoding algorithm based on deep learning

FIG. 3 simulation results of decoding using RS 7-5 codes

Detailed Description

The invention is mainly based on a belief propagation decoding algorithm, reduces errors generated by a short-loop effect through random displacement by using the basic characteristic of a cyclic code (the code words before and after the displacement are one of RS codes) of the RS code, and simultaneously builds a neural network by using a deep learning technology to perform parameter training so as to obtain the optimal parameters of a parity check matrix (and the optimal value of a damping coefficient), thereby reducing the iterative computation amount and improving the decoding performance under the fixed iteration times. The technical scheme is as follows:

(1) and (3) constructing a non-fully-connected neural network according to a Tanner graph corresponding to the RS code parity check matrix by using a deep learning method. The operation process of the check nodes and the variable nodes in the Tanner graph is converted into the operation process of the neurons in the neural network, the parity check matrix parameters are initialized to be 0 or 1 and serve as weight values for training, the corresponding Tanner graph is the variable nodes, and the deep learning optimization parameter values are used in the layer of the corresponding neural network, namely the variable nodes, and are used for training the neural network.

(2) The bit-level log-likelihood ratio (LLR) received from a noisy channel is input to a neural network as reliability information, and an output value of information bits of each layer is calculated through one iteration calculation of a belief propagation algorithm, namely through a variable node layer and a check node layer of the neural network. And adding an output layer after each iterative computation, multiplying the LLR value received before the iterative computation and the LLR value returned by the check node in the iterative process by the parameters, and performing addition computation, wherein the multiplied parameters after the LLR information is summed output by the check node are the damping coefficients in the SSID algorithm, so that the damping coefficients can be used as unknown quantities to be trained by using a deep learning technology, and the optimal parameter values are obtained. After the LLR values are summed, a sigmoid function is used as an activation function for training using deep learning.

(3) And (3) performing symbol-level displacement of random length on the code word after each iterative calculation by using an SSID algorithm, wherein the length of the displaced symbol is less than the whole length of the code word, inputting the displaced code word to a new neural network layer, including variable nodes and check nodes, and performing the next iteration. The code words before and after the displacement all belong to the code words in the RS code, and the corresponding relations of the code word Tanner graphs before and after the displacement are different, so that the displacement process can be used for reducing the decoding error caused by the 'short-loop effect' due to the fact that the parity check matrix is dense.

(4) If the specified iterative computation times are reached, the code words are moved back to the initial positions before decoding, the output predicted values are rounded and compared with the correct code words, the parameter training of the neural network is carried out on the basis of the set training period and the set learning rate, and the information of the parameters in the variable nodes is updated. And if the specified iteration times are not reached, performing the next iteration calculation.

(5) When the specified training period is reached, the final check matrix value (including the damping coefficient) can be obtained after the training process is finished. And inputting the LLR value of the test information into a neural network, checking the output code word after set iterative operation, and analyzing the decoding effect. And changing the training period and the learning rate for multiple times to find the optimal parameters. The neural network trained by the parameters can improve the decoding performance under the condition of fixed iteration times, and can reduce the iterative operation time under the condition of ensuring that the decoding effect is close.

The work flow of the whole algorithm is shown in fig. 1, and the flow of the algorithm is described in detail below with reference to fig. 1:

(1) firstly, initializing a parity check matrix of a corresponding code word, and quantizing 0 and 1 values in the matrix into parameters for deep learning training. And constructing a non-fully-connected neural network according to the Tanner graph corresponding to the code word parity check matrix for parameter updating. The data required for the deep learning training set, including the correct RS codeword and the values of the simulated LLRs after passage through the channel, was prepared using Matlab software.

(2) Reliability information from the channel is received. The log-likelihood ratio (LLR) of the channel reliability information in the case of BPSK (binary phase shift keying) modulation of AWGN (additive white gaussian noise) channel is simulated using Matlab software, and is calculated as: (where σ is the variance value of the channel and y is the codeword soft information received after passing through the channel)

(3) For the first iteration (check node output equation), updating parameters of check node neurons of a Tanner graph corresponding to the parity check matrix by using a belief propagation algorithm:

(4) for the first iteration (variable node output equation), a belief propagation algorithm is used to update parameters of variable node neurons of a Tanner graph corresponding to a parity check matrix (where w is a weight value to be trained and is 1 during initialization), and a tanh function is also used as an activation function in deep learning and can be used for training neural network parameters:

(5) the output LLR value after the end of one iteration cycle is obtained by the following formula (wherein a sigmoid function is used as an activation function to facilitate deep learning to train the neural network parameters):

(6) and carrying out symbol-level random shift on the output LLR value, inputting the shifted code word to a neural network for second iterative computation by using the new LLR value after the shift, wherein the shifted code word also belongs to the code word in the RS code. And after the specified iteration times are finished, the code words are moved back to the original positions, the code words are compared with correct code words, a loss function is calculated by utilizing a deep learning technology, and the parameter values are optimized. The neural network training is completed after a specified iteration period is reached, and therefore the optimal parity check matrix parameter value and the optimal damping coefficient value are obtained, and the construction of a belief propagation decoding framework by using deep learning is completed.

Fig. 3 is an error rate curve obtained by comparing an SSID algorithm constructed by deep learning with a general belief propagation algorithm and an SSID algorithm and performing different iterations, taking an RS (7-5) code as an example. It can be seen that decoding is performed under the structure of the NN-SSID algorithm built by using the neural network, so that only a few iterations are used, and the decoding effect achieved by multiple iterations of the BP algorithm and the SSID algorithm is achieved.