Iterative Soft Decoding of Reed-Solomon Codes Based on Deep Learning
Wei Zhang, Shuming Zou, Yanyan Liu
In this letter, a deep learning based iterative soft decision decoding algorithm for Reed-Solomon codes is proposed. This algorithm takes advantage of deep neural network and Stochastic Shifting based Iterative Decoding (SSID). By assigning weights to every edge in the Tanner graph and changing the architecture of SSID method, the proposed neural network decoder achieves better decoding performance. Compared with BM decoding, HDD-LCC and traditional SSID, simulation results show that for RS (15, 11), this algorithm provides coding gain up to 1.5dB, 1.1dB and 0.5dB, respectively when FER = 10-2.
IEEE Communications Letters•May 5, 2020•13 citations