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.

Related papers

• Threshold Selection for Iterative Decoding of $(v,w)$-regular Binary Codes [84.0257274213152]
  Iterative bit flipping decoders are an efficient choice for sparse $(v,w)$-regular codes. We propose concrete criteria for threshold determination, backed by a closed form model.
  arXiv  Detail & Related papers  (2025-01-23T17:38:22Z)
• GNN-based Auto-Encoder for Short Linear Block Codes: A DRL Approach [43.17241175857862]
  It integrates deep reinforcement learning (DRL) and graph neural networks (GNN) in code design. An edge-weighted GNN (EW-GNN) decoder is proposed, which operates on the Tanner graph with an iterative message-passing structure. An iterative joint training of the DRL-based code designer and the EW-GNN decoder is performed to optimize the end-end encoding and decoding process.
  arXiv  Detail & Related papers  (2024-12-03T00:25:14Z)
• Learning Linear Block Error Correction Codes [62.25533750469467]
  We propose for the first time a unified encoder-decoder training of binary linear block codes. We also propose a novel Transformer model in which the self-attention masking is performed in a differentiable fashion for the efficient backpropagation of the code gradient.
  arXiv  Detail & Related papers  (2024-05-07T06:47:12Z)
• Graph Neural Networks for Enhanced Decoding of Quantum LDPC Codes [6.175503577352742]
  We propose a differentiable iterative decoder for quantum low-density parity-check (LDPC) codes. The proposed algorithm is composed of classical belief propagation (BP) decoding stages and intermediate graph neural network (GNN) layers.
  arXiv  Detail & Related papers  (2023-10-26T19:56:25Z)
• Machine Learning-Aided Efficient Decoding of Reed-Muller Subcodes [59.55193427277134]
  Reed-Muller (RM) codes achieve the capacity of general binary-input memoryless symmetric channels. RM codes only admit limited sets of rates. Efficient decoders are available for RM codes at finite lengths.
  arXiv  Detail & Related papers  (2023-01-16T04:11:14Z)
• A Scalable Graph Neural Network Decoder for Short Block Codes [49.25571364253986]
  We propose a novel decoding algorithm for short block codes based on an edge-weighted graph neural network (EW-GNN) The EW-GNN decoder operates on the Tanner graph with an iterative message-passing structure. We show that the EW-GNN decoder outperforms the BP and deep-learning-based BP methods in terms of the decoding error rate.
  arXiv  Detail & Related papers  (2022-11-13T17:13:12Z)
• A PDD Decoder for Binary Linear Codes With Neural Check Polytope Projection [43.97522161614078]
  We propose a PDD algorithm to address the fundamental polytope based maximum likelihood (ML) decoding problem. We also propose to integrate machine learning techniques into the most time-consuming part of the PDD decoding algorithm. We present a specially designed neural CPP (N CPP) algorithm to decrease the decoding latency.
  arXiv  Detail & Related papers  (2020-06-11T07:57:15Z)
• Pruning Neural Belief Propagation Decoders [77.237958592189]
  We introduce a method to tailor an overcomplete parity-check matrix to (neural) BP decoding using machine learning. We achieve performance within 0.27 dB and 1.5 dB of the ML performance while reducing the complexity of the decoder.
  arXiv  Detail & Related papers  (2020-01-21T12:05:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.