Blind Estimation Of An Approximated Likelihood Ratio In Impulsive Environment

Robust communication is necessary for many wireless applications. Making a decision at the receiver requires an evaluation of the likelihood. However, in impulsive noise, the traditional Gaussian-based receiver exhibits a very significant performance loss. This paper proposes to approximate the likelihood ratio in a binary transmission with a function adapted to impulsive noise conditions but also efficient when noise is purely Gaussian. We introduce a blind estimation of the two parameters defining the approximation and evaluate its performance when used as the inputs of the belief propagation decoder. Our proposal allows us not only to achieve performance close to the optimal decoding but also to have a simple implementation and to adapt to different environment, impulsive or not, independently of the underlying statistical noise model, without the need of a training sequence.