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International Conference on Dependable Systems and Their Applications (DSA)

A LDPC Decoding Algorithm based on Convolutional Neural Network

Jiamei Gao, Bo Zhang, Bin Wang, Yang Liu

Published Aug 1, 2022• 2 citations
Publisher

Abstract

At present, low density parity check code (LDPC) has been widely used in channel coding and decoding because of its excellent performance, but with the increase of code length, the complexity of decoding algorithm has became higher and higher. In view of the limitations of decoding algorithm and the rapid development of artificial intelligence technology, it has great research prospects to solve the above problems through deep neural network. Therefore, this paper mainly focuses on the design and improvement of LDPC decoding process, and proposes an LDPC decoding model based on DenseNet neural network structure, which improves the LPDC decoding performance by optimizing DenseNet neural network structure. This method can recover information at the decoding end, avoiding the limitations of traditional short decoding loop and high complexity of decoding algorithm. The simulation results show that the LDPC decoding algorithm based on DenseNet neural network structure improves the decoding performance.

DOI
10.1109/dsa56465.2022.00165

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At present, low density parity check code (LDPC) has been widely used in channel coding and decoding because of its excellent performance, but with the increase of code length, the complexity of decoding algorithm has became higher and higher. In view of the limitations of decoding algorithm and the rapid development of artificial intelligence technology, it has great research prospects to solve the above problems through deep neural network. Therefore, this paper mainly focuses on the design and improvement of LDPC decoding process, and proposes an LDPC decoding model based on DenseNet neural network structure, which improves the LPDC decoding performance by optimizing DenseNet neural network structure. This method can recover information at the decoding end, avoiding the limitations of traditional short decoding loop and high complexity of decoding algorithm. The simulation results show that the LDPC decoding algorithm based on DenseNet neural network structure improves the decoding performance.
BP decoding algorithm is one of the commonly used decoding algorithms for LDPC codes. To adapt LDPC codes to different 5G scenarios and further improve the decoding performance of short LDPC codes, a scheme combining model-driven deep learning with a traditional BP decoding algorithm is proposed. With the advantages of model-driven, this solution expands the decoding iteration process between the check node and the variable node into a neural network and proposes a parameter normalization optimization solution to solve the problem of the program with many training parameters, the edge weights of the optimized Tanner graph are re-assigned and bound. Simulation results show that the proposed scheme can improve the decoding performance of LDPC codes with short lengths.
In order to overcome the problems of high bit error rate, delay and complexity, research on CNN polar decoding algorithm is proposed. This method uses CNN as the decoder, analyzes the difference between the traditional algorithm and the CNN decoder, adjusts the network parameters, obtains the best parameters, makes the decoder optimal, and in the channel coding scheme, the encoding and decoding complexity of polarized codes is low, and it has been strictly proved that they can reach the Shannon limit. However, there are a lot of shortcomings in the existing traditional decoding algorithms, With the maturity of deep learning, the application of deep learning to the field of communication, deep learning has advantage of powerful computing, and neural network after training is static, once only need data through the network, so the deep learning was applied to the polarization code decoding process, can effectively reduce the decoding delay and improve the efficiency of decoding, this paper mainly studies convolution decoder of neural networks in all aspects of performance, can get the conclusion by experiment, with the increase of the number of iterations network decoding ability improved, error performance is getting better and better, and CNN has obvious advantages.
LDPC code is a widely-used high-performance error correction code. In recent years, LDPC decoding based on deep learning and neural networks becomes a research hotspot. Based on the (512,256) LDPC code of the CCSDS standard, this paper firstly studies the traditional decoding algorithms of SP, MS, NMS, and OMS, laying a foundation for the construction of neural networks. Then a data-driven (DD) decoding method is studied which adopts the information with its encoded, modulated and noise-added LDPC code as the training data within a Multi-layer Perceptron (MLP) neural network. In order to solve the problem of high bit error rate (BER) in data-driven method, the Neural Normalized Min-sum (NNMS) decoding in which the NMS algorithm is mapped to the neural network structure is proposed, achieving more excellent BER performance than that of NMS. The BER declines by 85.19% when channel SNR equals to 3.5 dB. Finally, improved training methods to enhance the SNR generalization ability of the NNMS network is studied.
LDPC codes, as an error correcting code close to the Shannon limit, are widely used in modern communication systems such as 5 G, satellite communication, deep space communication, and other fields. Traditional LDPC decoding algorithms, such as belief propagation (BP) algorithm and its variants, have achieved good performance, but face challenges such as high complexity and slow convergence, especially when dealing with long code lengths and high noise scenarios. The rise of deep learning has brought new opportunities for LDPC decoding, and its powerful features learning and pattern recognition capabilities are expected to break through the bottleneck of traditional decoding. Based on the overview of deep neural network decoding methods for LDPC codes by data-driven and model driven approaches, this paper analysis and comparison of error rate performance of short codeH(8,16)\mathbf{H} (8,16)H(8,16)matrix by convolutional neural networks (CNN), residual network (ResNet), and Kolmogorov-Arnold Network (KAN).
Low Density Parity Check(LDPC) code itself has a good performance and the application of LDPC in the communication field is extensive.LDPC codes have the characteristics of low bit error rate, easy adjustment, low decoding complexity and excellent decoding performance.LDPC is considered to be the "best performance" in the field of coding and decoding [1] .In this paper, we mainly introduce the theory of LDPC codes.And the use of the method of the LDPC code to do a further explanation.Several simple decoding schemes of LDPC are studied.Finally, using the method of function approximation to replace the confidence function to improve the BP LLR algorithm, and simulation experiments are carried out.
In this paper, we discuss the perspectives of utilizing deep neural networks (DNN) to decode Low-Density Parity Check (LDPC) codes. The main idea is to build a neural network to learn and optimize a conventional iterative decoder of LDPC codes. A DNN is based on Tanner graph, and the activation functions emulate message update functions in variable and check nodes. We impose a symmetry on weight matrices which makes it possible to train the DNN on a single codeword and noise realizations only. Based on the trained weights and the bias, we further quantize messages in such DNN-based decoder with 3-bit precision while maintaining no loss in error performance compared to the min-sum algorithm. We use examples to present that the DNN framework can be applied to various code lengths. The simulation results show that, the trained weights and bias make the iterative DNN decoder converge faster and thus achieve higher throughput at the cost of trivial additional decoding complexity.