Bipartite Belief Propagation Polar Decoding With Bit-Flipping
Zihao Gong, Yifei Shen, Houren Ji, Wenqing Song, Zaichen Zhang, Xiaohu You, Chuan Zhang
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For the scenarios with high throughput requirements, the belief propagation (BP) decoding is one of the most promising decoding strategies for polar codes. By pruning the redundant variable nodes (VNs) and check nodes (CNs) in the original factor graph, the graph is condensed to a sparse bipartite graph which is similar to the graph for low-density parity-check (LDPC) codes. In this paper, we introduce the bit flipping scheme into the LDPC-like BP (L-BP) decoding and propose two methods to identify the error-prone VNs. By additional decoding attempts, the L-BP flip (L-BPF) decoding improves the error-rate performance with a similar average complexity for high $E_b/N_0$ values. The simulation results show that the L-BPF decoding achieves $0.25$ dB gain compared with the L-BP decoding.