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Joint SB/NMS and Genetic Optimization for High Performance LDPC Decoding

In recent years, many communication standards have adopted the LDPC (Low-Density Parity Check) code due to its excellent error correction performance, but it also induces some problems. For example, the use of 5G in mobile phones dramatically reduces battery life. Therefore, it is particularly significant for LDPC code to design a decoding algorithm that does not increase hardware implementation complexity but has superior error correction performance and iteration speed faster than traditional decoding algorithms. Inspired by several network structures in artificial intelligence, bias is used to improve the performance and robustness of the network. In order to enhance decoding algorithm robustness and accuracy, a method is shown by changing the offset in OMSA to the bias and combining NMSA in the near-zero range in this paper. It significantly avoids the log-likelihood ratio information loss when less than the offset value and diminishes too quickly or sluggishly. A Segmented Bias/Normalize Min-Sum(SB/NMS) LDPC decoding algorithm is proposed with a layered structure. This algorithm can avoid the impact of the problems mentioned above and significantly improve the performance and iteration speed of the decoding process. Compared with the BLER (resp. BER) performance the traditional BP, NMS and OMS algorithm achieves a gain of 0.04, 0.11, and 0.15dB (resp. 0.035dB, 0.06, and 0.13dB). Meanwhile, the minimum SNR requirements of BLER =0.001 can be reduced by 0.02 dB compared with the BP algorithm. The parameters of SN/NMSA can be pre-processed on the computer, so there will be no additional increase in the overhead in resources and get better performance while getting faster iteration speed, while greatly guaranteeing the reliability and effectiveness of data transmission.