Research Article
A Convolutional Neural Network Decoder for Convolutional Codes
@INPROCEEDINGS{10.1007/978-3-030-41117-6_10, author={Zhengyu Zhang and Dongping Yao and Lei Xiong and Bo Ai and Shuo Guo}, title={A Convolutional Neural Network Decoder for Convolutional Codes}, proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part II}, proceedings_a={CHINACOM PART 2}, year={2020}, month={2}, keywords={Deep learning Convolutional code Viterbi decoding algorithm Neural network}, doi={10.1007/978-3-030-41117-6_10} }- Zhengyu Zhang
Dongping Yao
Lei Xiong
Bo Ai
Shuo Guo
Year: 2020
A Convolutional Neural Network Decoder for Convolutional Codes
CHINACOM PART 2
Springer
DOI: 10.1007/978-3-030-41117-6_10
Abstract
The convolutional neural network (CNN) decoder for general convolutional decoding is proposed. The parameters of CNN are determined by the initial state of each input block and the constraint relationship between adjacent bits is extracted by the convolutional layer as the constraint features. Then CNN decoder realizes decoding process through the extracted constraint feature instead of codewords directly. The result shows that, without changing the structure of decoder, the decoding performance of CNN decoder on different convolutional codes is equivalent to Viterbi soft decoding algorithm. Compared with Viterbi decoding, the larger constraint length or the lower SNR, the greater gain can be obtained in CNN decoder. Besides, we consider CNN trained by the two kinds of training sets in order to further investigate the potential and limitations of CNN decoder with respect to decoding performance, analysing the advantages and factors of these two kinds of training sets.
