Reduced-complexity Deep Neural Network-aided Channel Code Decoder: A Case Study for BCH Decoder

Abstract

Error-correcting codes are very important in modern communication systems. In this paper, we investigate efficient reduced-complexity deep neural network (DNN)-aided channel decoders. Specifically, we leverage DNN training to obtain individual scaling parameters for normalized min-sum algorithms, thereby leading to much faster convergence for the same target bit error rate (BER). Also, we propose to compress the DNN-aided channel decoders via weight sharing. A case study on DNN-aided BCH decoders is investigated. Simulation results and hardware complexity analysis show that our method can reduce 2.59 times of memory cost than non-compressed DNN-aided BCH decoders. Meanwhile, compared to the conventional BCH decoders, our method can improve convergence rate by 6 times with similar decoding performance.