Modified BP Decoding Algorithms Combined with GA for Low-Density Parity Check Codes
Zerong Deng, Xingcheng Liu, Man Teng · 2008
In this paper we propose algorithms which combine Belief-propagation (BP) decoding algorithm with genetic algorithm (GA). The main idea is to improve the performance of traditional BP decoding algorithm by efficiently using the belief of variable nodes passing to check nodes. By considering the belief as genes, the proposed algorithms mainly employ the mutation operation in GA, and change the genes¿ amplitudes (either increase or decrease) when updating the check nodes¿ information. First, we explore that the marriage of the GA and BP decoding algorithm is reasonable. Three algorithms are proposed accordingly. Proposed algorithm I mutates chosen genes by amplitude enhancement, while proposed algorithm II by amplitude reduction. Finally, proposed algorithm III is a mixture of the former two. From computer simulation results, we show that for short and middle length LDPC codes, our proposed algorithms can improve both BER (bit error rate) and FER (frame error rate) performance over traditional BP decoding algorithm, with slight modification of traditional BP decoding algorithm and little increase of computation complexity.