Deep Learning Algorithms for BCH Decoding in Satellite Communication

Abstract

Deep learning is widely used in various fields due to the advancement of algorithms, the enrichment of high-efficiency databases, and the increase in computing power. Especially in the satellite communication, the learning and parallel computing capabilities of neural networks make them ideal for decoding. Many researchers have recently applied deep learning neural networks to decode high-density parity check (HDPC) codes (such as BCH and RS code), improving the decoder’s performance. This review aims to provide general insights on applying neural network decoders to satellite communications. Due to the neural network’s learning ability, the neural network-based decoder can be trained to change the weights, thereby reducing the influence of non-white noise in satellite communications, such as the influence between the satellite and the terrestrial network and the mutual interference within the satellites. To compensate for non-white noise, shortest circles in Tanner graph and unreliable information, a decoder system model for satellite communication constructed by three neural networks is presented.

A new efficient decoder of linear block codes based on ensemble learning models: case of boosting

“…Alaoui et al [18] have studied the efficiency of their decoders over a Rayleigh channel. Ruan [19] present general insights on applying neural network decoders to satellite communications. Chen and Ye [20] proposed a neural decoder.…”