Semi-blind Subspace Techniques for Digital Communication Systems

This paper is devoted to the analysis of a “semi-blind” estimation framework in which the standard input-output (training sequence based) estimation is enhanced by using the statistical structure of the information sequence. More specifically, we consider the case of a general TDMA frame-based receiver equipped with multiple sensors, and restrict our attention to second-order based subspace methods which are suitable for most standard communication applications due to their moderate computational cost. The channel estimator is obtained as a regularized least-squares solution where the blind subspace criterion plays the role of the regularization constraint. The main contribution of the paper consists in showing by asymptotic analysis how to optimally tune the balance between the blind criterion and the least-squares fit depending on the design parameters of the system. Simulations show that the proposed solutions are very effective and significantly improve the efficiency of the equalization.