Error whitening non-parametric maximum likelihood channel estimator

The presence of cochannel interference has been a major hindrance in improving the performance of present day communication systems. In this paper we discuss a iterative block based maximum-likelihood algorithm using kernel density estimates to improve channel estimation in presence of cochannel interference. As it is known that the interference is correlated, we first reduce this correlation by using a whitening filter. After whitening, we estimate this unknown whitened likelihood pdf by using kernel density estimator at the receiver. Thereby combining log-likelihood as cost function with whitening filter and kernel density estimate, a robust channel estimator for correlated noise environments is formed. The simulations for cochannel 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 algorithm, which is optimal in the Gaussian noise environments