Development of adaptive exponential min sum decoding algorithm

This paper presents an optimized min sum (MS) decoding algorithm with low complexity and high decoding performance for LDPC short codes. The MS algorithm has low computational complexity and is simple to deploy. The MS decoding algorithm, while demonstrating a performance gap compared to the belief propagation (BP) and likelihood ratio BP (LLR-BP) decoding algorithms, shows significant potential for optimization. To improve the decoding performance of traditional MS algorithm, secondary external information is introduced into the control node (CNs) update operations of MS algorithm and optimized as adaptive exponential correction factor (AECF). The optimized MS algorithm is named as adaptive exponential exponential MS decoding algorithm (AEMS). The decoding efficiency of the AEMS algorithm for regular, irregular and LDPC codes of the Consultative Committee on Space Data Systems (CCSDS) was extensively tested, then the complexity of the AEMS algorithm was analyzed and compared with other decoding algorithms. The results show that the AEMS algorithm outperforms the offset MS (OMS) and normalized MS (NMS) algorithms in decoding performance, and outperforms the BP algorithm as the signal-to-noise ratio (SNR) gradually increases.

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