Fine-Grained Bit-Flipping Decoding for LDPC Codes
Yuxing Chen, Hangxuan Cui, Jun Ting Lin, Zhongfeng Wang · IEEE Transactions on Circuits & Systems II Express Briefs · 2020
This brief presents a novel class of hard-decision algorithms for decoding low density parity check codes. The new algorithms, named fine-grained bit-flipping (FBF) algorithms, employ a detailed classification of each bit, by introducing the XOR value of its estimated and received value as a subdividing criterion. The fine-grained classification allows the algorithms to strengthen the information utilization during each iteration. Simulation results show that the FBF algorithms can achieve up to 5 times better decoding performance than the state-of-the-art bit-flipping algorithms over the binary symmetric channel. Additionally, a well-optimized hardware architecture is developed for implementing FBF algorithms. Compared to other decoders, implementation results demonstrate that the FBF decoders achieve higher throughput and area efficiency.