Adaptive Bayesian decision feedback equaliser for alpha-stable noise environments

Abstract

In some communication systems the channel noise is known to be non-Gaussian due, largely, to impulsive phenomena. The performance of signal processing algorithms designed under the Gaussian assumption may degrade seriously in such environments. In this paper we investigate the problem of adaptive channel equalisation in an impulsive noise environment. The impulsive interfering noise is modelled as an α-stable process. We first derive the optimum Bayesian decision feedback equaliser and present a novel analytical framework for the evaluation of systems in infinite variance environments. A family of generalised adaptive channel identification algorithms for this infinite variance noise environment is also presented. The combination of a Bayesian equaliser and a channel estimator operating as an adaptive channel equaliser is experimentally studied and its performance is compared with that of a traditional system designed under the Gaussian assumption. The experimental data suggest that the proposed combination of equaliser and channel estimator outperforms the traditionally designed adaptive equaliser in terms of error probability. We finally provide some useful approximations concerning the practical implementation of an α-stable adaptive equaliser.