Proceedings of the 2016 International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2016)

A Decoding Method For Modulo Operations-Based Fountain Codes Using the Accelerated Hopfield Neural Network

Authors
Zaihui Deng, Xiaojun Tong, Liangcai Gan
Corresponding Author
Zaihui Deng
Available Online September 2016.
DOI
https://doi.org/10.2991/iccia-16.2016.2How to use a DOI?
Keywords
Fountain codes; Modulo operation; Decoding; Chinese remainder theorem; Neural network.
Abstract
This paper describes a decoding method using the accelerated Hopfield neural network, in order to address the high complexity of decoding for modulo operations-based fountain codes. The method constructs a neural network model based on a non-linear differential equation, and runs the model after setting an initial value. During the process, the model's output value first rapidly decreases under the effect of the accelerator resistor, slows down near an equilibrium point, and finally regresses to a unique equilibrium point with an arbitrarily small error. The result is half-adjusted to obtain the source data sequence. Simulated tests indicate the method to be valid, and can potentially bring the modulo fountain codes closer to practical application.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2016)
Part of series
Advances in Computer Science Research
Publication Date
September 2016
ISBN
978-94-6252-240-4
ISSN
2352-538X
DOI
https://doi.org/10.2991/iccia-16.2016.2How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zaihui Deng
AU  - Xiaojun Tong
AU  - Liangcai Gan
PY  - 2016/09
DA  - 2016/09
TI  - A Decoding Method For Modulo Operations-Based Fountain Codes Using the Accelerated Hopfield Neural Network
BT  - 2016 International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2016)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-16.2016.2
DO  - https://doi.org/10.2991/iccia-16.2016.2
ID  - Deng2016/09
ER  -