Neural network decoding of the block Markov superposition transmission

Qianfan WANG; Sheng BI; Zengzhe CHEN; Li CHEN; Xiao MA

doi:10.11959/j.issn.1000-436x.2020158

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Neural network decoding of the block Markov superposition transmission

Correspondences | 更新时间：2024-06-05

- Neural network decoding of the block Markov superposition transmission
- Journal on Communications Vol. 41, Issue 9, Pages: 202-209(2020)
- 作者机构：
  
  1. 中山大学电子与通信工程学院，广东广州 510006
  2. 中山大学数据科学与计算机学院，广东广州 510006
  3. 中山大学广东省信息安全重点实验室，广东广州 510006
  4. 中山大学电子与信息工程学院，广东广州 5100063
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61971454);The National Natural Science Foundation of China(61671486);The Basic and Applied Basic Research Foundation of Guangdong(2020A1515010687);The Natural Science Foundation of Guangdong Province(2016A030308008)
- DOI：10.11959/j.issn.1000-436x.2020158
  CLC： TN92
- Online First：2020-09，
  
  Published：25 September 2020
- 稿件说明：
移动端阅览
Qianfan WANG, Sheng BI, Zengzhe CHEN, et al. Neural network decoding of the block Markov superposition transmission[J]. Journal on Communications, 2020, 41(9): 202-209.
DOI：

Qianfan WANG, Sheng BI, Zengzhe CHEN, et al. Neural network decoding of the block Markov superposition transmission[J]. Journal on Communications, 2020, 41(9): 202-209. DOI： 10.11959/j.issn.1000-436x.2020158.

摘要

研究了分组马尔可夫叠加传输的神经网络（NN）译码方案。利用NN，实现了不同网络结构、数据表征形式的基本码译码器。在此基础上，将所实现的基本码译码器嵌入迭代译码机制中，提出了基于NN的分组马尔可夫叠加传输的滑窗译码算法，并分析了其对应的性能下界。所提出的译码算法提供了一种将NN运用到长码译码的解决思路，即用NN替代译码中的部分模块。仿真结果表明，利用NN实现的基本码译码器可以达到最大似然译码性能。基于NN的分组马尔可夫叠加传输的滑窗译码算法性能在中高信噪比区域与对应精灵辅助下界贴合，获得了额外的编码增益。

Abstract

A neural network (NN)-based decoding algorithm of block Markov superposition transmission (BMST) was researched.The decoders of the basic code with different network structures and representations of training data were implemented using NN.Integrating the NN-based decoder of the basic code in an iterative manner

a sliding window decoding algorithm was presented.To analyze the bit error rate (BER) performance

the genie-aided (GA) lower bounds were presented.The NN-based decoding algorithm of the BMST provides a possible way to apply NN to decode long codes.That means the part of the conventional decoder could be replaced by the NN.Numerical results show that the NN-based decoder of basic code can achieve the BER performance of the maximum likelihood (ML) decoder.For the BMST codes

BER performance of the NN-based decoding algorithm matches well with the GA lower bound and exhibits an extra coding gain.

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references

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