A low-complexity LDPC belief propagation decoding in high order M-QAM modulation

Belief propagation (BP) algorithm is commonly used in soft decoding of Low Density Parity Check Code (LDPC). However, a significant disadvantage of this method is that the computational complexity is high in high order M-QAM modulation. This paper proposes an optimized algorithm to reduce the posterior probability calculation in soft decoding, which estimates an approximate decision point in the constellation map via channel equalization. Then a contractible constellation decision space is delimited by the approximate decision point and its surrounding points with the minimum European distance. Compared with the conventional soft decoding, simulation results show that the proposed algorithm can effectively reduce the computational complexity of BP decoding with tiny loss of the decoding accuracy. The proposed algorithm reduces nearly 96 percent of the computational complexity of posterior probability in the case of 256QAM modulation.