Non-parametric likelihood based channel estimator for Gaussian mixture noise

Abstract

Extensive work to develop and optimise signal processing for signals that are corrupted by additive Gaussian noise has been done so far mainly because of the central limit theorem and the ease in analytic manipulations. It has been observed that the algorithms designed for Gaussian noise typically perform poor in presence of Gaussian mixture (non-Gaussian) noise. This paper discusses a likelihood based algorithm using kernel density estimates to improve channel estimation over a block in non-Gaussian noise environments. The likelihood pdf is assumed unknown and is estimated by using kernel density estimator at the receiver. A novel technique for channel estimation using a whitening filter for interference limited channels is also proposed in this paper. The performance of the proposed estimator is compared with the Cramér Rao lower bound for associated noise distribution. The simulations for impulsive noise and co-channel interference in presence of Gaussian noise, confirms that a better estimate can be obtained by using the proposed technique as compared to the traditional least-squares-based algorithms in highly non-Gaussian environments.