A Decoding Method For Modulo Operations-Based Fountain Codes Using the Accelerated Hopfield Neural Network
Zaihui Deng, Xiaojun Tong, Liangcai Gan
Available Online September 2016.
- https://doi.org/10.2991/iccia-16.2016.2How to use a DOI?
- Fountain codes; Modulo operation; Decoding; Chinese remainder theorem; Neural network.
- 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.
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 SP - 6 EP - 11 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 -