On the performance of semi-blind subspace-based channel estimation

This paper is devoted to the analysis of a "semi-blind" estimation framewor k in which the standard least-squares estimator (based on a known training sequence) is enhanced by using the statistical structure of the observation s. More specifically, we consider the case of a general time-division multi ple access (TDMA) frame-based receiver equipped with multiple sensor and re strict our attention to second-order based subspace methods that are suitab le for most standard communication applications due to their moderate compu tational cost. The semi-blind channel estimator is obtained as a regularize d least-squares solution where a blind subspace criterion plays the role of the regularization constraint. The main contribution of the paper consists of showing by asymptotic analysis hen; to optimally tune the balance betwe en the blind criterion and the least-squares fit, depending on the design p arameters of the system. Simulations show that the proposed solutions are r obust and significantly improve the efficiency of the equalization.