An Adaptive Belief Propagation Difference-map Iterative Decoding Algorithm with Low Complexity

In this paper, an adaptive belief difference-map propagation algorithm with low complexity is proposed for short and middle length LDPC regular codes by modifying message update rules and transforming factor graph. To improve decoding performance, a new selective belief propagation difference-map message update rule is introduced by borrowing the difference-map strategy for variable node messages oscillation, and the normalized factor is adjusted adaptively. Meanwhile, the computational complexity exponential in the degree of check node is decreased into linear in degree by opening the check node. The simulation results illustrate that the proposed algorithm has better performance and lower complexity than other iterative decoding algorithms based on the modified factor graphs. Compared to the LLR-BP, it better performance at high Eb/N0 and the computational complexity is apparently downgraded at low Eb/N0.