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arXiv · 2002.07601

ADMM-based Decoder for Binary Linear Codes Aided by Deep Learning

Yi Wei·Ming-Min Zhao·Min-Jian Zhao·Ming Lei
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Abstract

Inspired by the recent advances in deep learning (DL), this work presents a deep neural network aided decoding algorithm for binary linear codes. Based on the concept of deep unfolding, we design a decoding network by unfolding the alternating direction method of multipliers (ADMM)-penalized decoder. In addition, we propose two improved versions of the proposed network. The first one transforms the penalty parameter into a set of iteration-dependent ones, and the second one adopts a specially designed penalty function, which is based on a piecewise linear function with adjustable slopes. Numerical results show that the resulting DL-aided decoders outperform the original ADMM-penalized decoder for various low density parity check (LDPC) codes with similar computational complexity.

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BibTeXRIS

Yi Wei, Ming-Min Zhao, Min-Jian Zhao, Ming Lei. 2020-02-14. ADMM-based Decoder for Binary Linear Codes Aided by Deep Learning. https://arxiv.org/abs/2002.07601

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