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

摘要

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.