分组马尔可夫叠加传输的神经网络译码

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

Abstract

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.

Key words: BMST, GA lower bound, NN, sliding window decoding

CLC Number:

TN92

Qianfan WANG,Sheng BI,Zengzhe CHEN,Li CHEN,Xiao MA. Neural network decoding of the block Markov superposition transmission[J]. Journal on Communications, 2020, 41(9): 202-209.

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References 18

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译码方案	加法/次	乘法/次	比较操作/次
方案3 NN（none）	112	112	15
ML译码方案	0	96	15

Neural network decoding of the block Markov superposition transmission

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