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

• URL: http://arxiv.org/abs/2002.07601v1
• Date: Fri, 14 Feb 2020 03:32:14 GMT
• Title: ADMM-based Decoder for Binary Linear Codes Aided by Deep Learning
• Authors: Yi Wei, Ming-Min Zhao, Min-Jian Zhao, and Ming Lei
• Abstract summary: 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. Numerical results show that the resulting DL-aided decoders outperform the original ADMM-penalized decoder.
• Score: 40.25456611849273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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