Sub-Gaussian Model Based LDPC Decoder for SαS Noise Channels

OFDM communication through warm shallow water acoustic channel (WSWA) is affected by snapping shrimp noise. Due to its impulsive broadband nature, the snapping shrimp noise is able to affect a large number of OFDM sub-carriers at once. The effect on the sub-carriers can be modeled as a symmetric α-stable (SαS) noise vector with dependent components. Although the performance of the conventional LDPC soft decoder is poorer in non-Gaussian SαS noise than in Gaussian noise, the performance can be improved by employing the dependency between the noise components while decoding. The multivariate probability densities and marginals are required in the computation of symbol a posteriori probabilities. For binary codes, the complexity of the algorithm using marginals is O(2N), where d is the number of dependent components and N is the length of the code. Practical implementation of a decoder employing dependency through marginals is infeasible for high number of dependent components. In order to address this problem we develop a lower complexity algorithm using the sub-Gaussian model of SαS vectors.