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Computers & Electrical Engineering

Improved NSC decoding algorithm for polar codes based on multi-in-one neural network

Xiumin Wang, Jun Li, Zhuoting Wu, Jinlong He, Yue Zhang, Liang Shan

Published Jul 18, 2020• 8 citations
Publisher
DOI
10.1016/j.compeleceng.2020.106758

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In recent years, polar codes under deep learning have become a research hotspot because of their outstanding decoding performance. The neural network-based polar codes confidence propagation decoding algorithm can improve the decoding performance significantly, but the computational complexity and delay of the neural network-based polar codes confidence propagation decoding algorithm still need to be improved. In this paper, the theory of polar codes coding and the traditional polar codes confidence propagation decoding algorithm are introduced. The polar codes confidence propagation decoding algorithm based on deep neural network and recurrent neural network and the advantages and disadvantages of different decoding algorithms are mainly discussed, and the computational complexity of these algorithms is analyzed. According to the offset minimum sum approximation and scaling offset minimum sum approximation, the polar codes confidence propagation decoding algorithm based on recurrent neural network is optimized. Simulation results show that, compared with the traditional polar codes confidence propagation decoding algorithm, the decoding algorithm improves the decoding performance, reduces the computational complexity and reduces the iteration times of the algorithm. Compared with the polar codes confidence propagation decoding algorithm based on deep neural network and recurrent neural network, the multiplication and memory overhead of the algorithm are greatly reduced while the decoding performance of the algorithm is guaranteed.
Neural Network Decoders (NNDs) have been recently considered and investigated as an alternative to the classical algorithms for decoding Polar Codes. In particular, a class of partitioned decoders has been proposed where modified Successive Cancellation (SC) or Belief Propagation (BP) algorithms exploit the neural network decoding of short sublocks. Although NNDs work as one-shot decoders with small decoding latency, the performance of these decoders, in comparison with classic approaches, still suffers from some losses; this fact limits their application to codes with short/medium length. In this article we introduce the novel Neural Successive Cancellation List (NSCL) decoder, as a partitioned version of the SC decoder operating on a list of L multiple decoding candidates that are built with the decoded sub-blocks obtained from a set of NNDs working in parallel at each partition. This decoder significantly improves the BER performance of the non-list version under the same code length. Hence, it reduces the intrinsic loss of performance given by the use of NNDs and allows the application of NN-based decoding to longer codewords.
With the development of communication technology, it is urgent to design channel decoding algorithms to maximize the high throughput and minimize the latency. As the capacity achieving polar codes are adopted by 5-th generation wireless system (5G), it is required to design efficient the decoding algorithms for polar codes with high performance and low complexity. Deep neural network (DNN)-based algorithms which have shown superiority in other fields, can obtain better performance using multi-scale belief propagation (BP) algorithm, whereas the decoding complexity needs to be decreased. We propose a residual neural network based belief propagation (ResNet- BP) algorithm to solve the inherent drawbacks in the existing methods, by reducing the number of variables in the network and reducing the complexity in terms of multiplication. Simulation results demonstrate that the proposed method can achieve the approximating performance of recurrent neural network-based belief propagation (RNN-BP) with a lower number of iterations.