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The Design and Simulation of Neural Network Encoder in Confidential Communication Field

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Wei Xiao
Wei Xiao
Wei Xiao
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Yong Ai at Wuhan University
Yong Ai
Dun Pu
Dun Pu
Dun Pu
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Zhicheng Dong at Tibet University
Zhicheng Dong
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Abstract and Figures

Both all-connection model and part connection model is simulated, which adopt self-organizing map neural network to generate check bits. Consequently, N source bits and K check bits are composed a complete codeword. In the decoding port, multi-layer perceptron network (MLPN) is utilized to implement decoding function. The specific steps are as follows: (1) Constructing the MLPN according to the size of codeword sets and source bits; (2) Training the MLPN with codeword sets generated by neural network decoder until qualified; (3) Accepting and decoding codeword sets via trained MLPN. Actual tests show that: (1) There exist no evident performance differences between all-connection model and part-connection model; (2) The connection of weight sets is similar to Tanner graph in part-connection model, which reduce the computational complex greatly and remain good performance at the same time. In sum, the method of encoding and decoding has certain market prospect in confidential communication field.
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The Design and Simulation of Neural Network Encoder
in Confidential Communication Field
Wei Xiao
1,2
•Yong Ai
1
•Dun Pu
3
•Zhicheng Dong
2
Published online: 20 February 2018
Springer Science+Business Media, LLC, part of Springer Nature 2018
Abstract Both all-connection model and part connection model is simulated, which adopt
self-organizing map neural network to generate check bits. Consequently, N source bits
and K check bits are composed a complete codeword. In the decoding port, multi-layer
perceptron network (MLPN) is utilized to implement decoding function. The specific steps
are as follows: (1) Constructing the MLPN according to the size of codeword sets and
source bits; (2) Training the MLPN with codeword sets generated by neural network
decoder until qualified; (3) Accepting and decoding codeword sets via trained MLPN.
Actual tests show that: (1) There exist no evident performance differences between all-
connection model and part-connection model; (2) The connection of weight sets is similar
to Tanner graph in part-connection model, which reduce the computational complex
greatly and remain good performance at the same time. In sum, the method of encoding
and decoding has certain market prospect in confidential communication field.
Keywords Neural network encoder All-connection model Part connection
model Self-organizing map (SOM) neural network Multi-layer perceptron
network (MLPN) Tanner graph
&Wei Xiao
xiaowei@utibet.edu.cn
1
Electronic Information School, Wuhan University, Wuhan, Hubei province, People’s Republic of
China
2
School of Engineering, Tibet University, Lhasa, People’s Republic of China
3
School of Science, Tibet University, Lhasa, People’s Republic of China
123
Wireless Pers Commun (2018) 102:3769–3779
https://doi.org/10.1007/s11277-018-5408-z
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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Wei Xiao was born in Wuhan, Hubei province, China, in 1974. He received the B.E. degree from the Electronic School, Wuhan University, Hubei province, China, in 2000, and M.S. degree from Information and Technology School, Ocean University of China, Qingdao, Shandong province, in 2005. He is an Associate Professor in School of Engineering, Tibet University, Lhasa, China. Also, he is a student (Ph.D. degree candidate) in Wuhan University, major in channel encoding and decoding in Free Space Optical Communications (FSO) field.