On Mutual Information-Maximizing Quantized Belief Propagation Decoding of LDPC Codes
Xuan He, Kui Cai, Zhen Xing Mei · 2019
A severe problem for mutual information-maximizing lookup table (MIM-LUT) decoding of low-density parity-check (LDPC) code is the high memory cost for using large tables, while decomposing large tables to small tables deteriorates decoding error performance. In this paper, we propose a systematic method, called mutual information- maximizing quantized belief propagation (MIM-QBP) decoding, to remove the lookup tables used for MIM-LUT decoding. Our method leads to a very practical decoder, namely the MIM-QBP decoder, which can be implemented based only on simple mappings and additions. Simulation results show that the proposed MIM-QBP decoder can outperform the state-of-the-art MIM-LUT decoder. Moreover, the MIM-QBP decoder with only 3 bits per message can outperform the floating-point belief propagation (BP) decoder at high signal-to-noise ratio (SNR) regions with a maximum of 10 iterations.