Adaptive blind channel identification under unit-norm constraint

Blind adaptive channel identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling, leading to the so-called, second order statistics techniques. An adaptive blind channel identification technique based on an off-line least-squares approach has been proposed but this method assuming noise-free case. The method resorts to an adaptive filter with a linear constraint. In this paper, a new approach is proposed that is based on eigenvalue decomposition. Indeed, the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the received signals contain the channel impulse response. And we present an adaptive algorithm to solve this problem. The performance of the proposed technique is evaluated over a real measured channel and is compared to existing algorithms.