Estimation of an approximated likelihood ratio for iterative decoding in impulsive environment

This paper deals with the robustness of soft iterative decoders in impulsive interference modeled by Middleton class A distribution, epsilon-contaminated Gaussian noise or a sum of a Gaussian thermal noise and an impulsive alpha-stable noise. The inputs of belief propagation decoder should be the log likelihood ratios of the received symbols. But in case of impulsive interference, their computation are highly non-linear and relies on the knowledge of the noise probability distribution. Besides, two of the most often used models-Middleton and alpha-stable noises-give complex analytical expression, for instance through infinite series. In a previous work, we proposed an easily computable approximation of the log likelihood ratio in an impulsive environment. Even with this simplification, performance stays close to the one obtained using the true probability density function. In this paper, we investigate the robustness of our approximation against different interference models and its parameters' estimation method.