Structured spatio-temporal sample covariance matrix enhancement with application to blind channel estimation in cyclic prefix systems

Multichannel aspect allows the introduction of blind channel estimation techniques. Most existing such techniques for frequency-selective channels are quite complex. In this paper, we consider the blind channel estimation problem for Single Input Multi Output (SIMO) cyclic prefix (CP) systems. We have shown before [1] that blind channel estimation becomes computationally much more attractive and more straight forward to analyze in terms of performance in CP systems. Inspired by the iterative sample covariance matrix (SCM) structure enhacement techniques of Cadzow and others [2], we propose here an algorithm to structure the sample block circulant covariance matrix by enforcing two essential properties: rank and FIR structure. These two properties are exhibited by the true covariance matrix in the case of FIR SIMO channels with spatially white noise and CP transmission. The proposed enhancement procedure leads to an interesting enhanced SCM, even for the single CP symbol case. A simulation study for some classical channel estimators that depend on the SCM (with and without structuring) is presented, indicating that structuring allows for considerable performance gain in terms of the channel normalized mean square error (NMSE) over a wide SNR range.