Min–Max Decoding for Non-Binary LDPC Codes

Xinmiao Zhang

Kenta Kasai, Kohichi Sakaniwa

Valentin Savin

Iterative decoding of non-binary LDPC codes is currently performed using either the sum-product or the min-sum algorithms or slightly different versions of them. In this paper, several low-complexity quasi-optimal iterative algorithms are proposed for decoding non-binary codes. The min-max algorithm is one of them and it has the benefit of two possible LLR domain implementations: a standard implementation, whose complexity scales as the square of the Galois field's cardinality and a reduced complexity implementation called selective implementation, which makes the min-max decoding very attractive for practical purposes.

Xuefei Yang, Wei Li, Huang Lian

Jin Xu, Jun Xu, Hai Tian +2 more

Non-binary low density parity check codes can achieve better err-correcting performance than binary LDPC codes when the code length is moderate at the cost of higher decoding complexity. Min-Max decoding algorithm is one of the low-complexity quasi-optimal iterative algorithms. However, a problem of the Min-Max decoding algorithm is iteration inefficient in the case of low SNR. In this paper, an adaptive method is proposed based on the Min-Max decoding algorithm. By comparing the correlation among each iteration output, setting an appropriate threshold to determine whether ahead of the end of iteration, which makes the adaptive Min-Max decoding algorithm very attractive for practical purposes

健太 笠井, Kenta Kasai