LAUSR

How to construct an effective polar decoding scheme has attracted researchers in the field of communication. The belief propagation list (BPL) decoder has performance improvement over the traditional BP decoder but comes with much higher complexity. To solve the issue of high complexity & latency, a low-density parity-check (LDPC) like BP decoder was proposed but it suffered from performance degradation over the original BP decoder. In this letter, a BP-based sparse graph list (BP-SGL) decoder is proposed by leveraging both list decoding scheme and LDPC-like BP decoding algorithm to achieve performance improvement while maintaining low complexity & latency. The key idea of the proposed list generation method is the similarity comparison of decoding graphs. Testing results verify that selecting graphs with large structural differences helps to construct a list with good overall performance. Simulation results show that the proposed scheme is superior to LDPC-like BP, and even outperforms the original BPL and some state-of-the-art (SOTA) BP-based decoding algorithms with significant reduction in complexity & latency.