To address the issue of larger errors in the normalized minimum sum decoding algorithm compared to the belief propagation decoding algorithm,an adaptive minimum sum decoding algorithm is proposed.By calculating the hard decision values of the current iteration posterior probability and the previous iteration posterior probability,the normalization factor and offset factor are dynamically adjusted to make the improved algorithm closer to the belief propagation decoding algorithm.Based on this,a hierarchical scheduling strategy is applied to propose a hierarchical adaptive minimum sum decoding algorithm,which improves the convergence speed of the decoding algorithm.Simulation results show that when the bit error rate is 10-6,the proposed decoding algorithm has a gain of 0.25 dB of bit error performance compared to the hierarchical normalized minimum sum decoding algorithm.Compared with the hierarchical belief propagation decoding algorithm,the proposed decoding performance has a similar performance,with only one increase in iteration times sum better convergence performance.
关键词
低密度奇偶校验码/分层自适应最小和译码算法/归一化因子/偏移因子
Key words
low-density parity check(LDPC)code/hierarchical adaptive minimum sum decoding algorithm/normalization factor/offset factor
维普
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