Blind identification of multipath channels: A parametric subspace approach

In this paper, blind identification of single-input multiple-output (SIMO) systems using second-order statistics (SOS) only is considered, Using the a ssumption of a specular multipath channel, we investigate a parametric vari ant of the so-called subspace method. Nonparametric subspace-based methods require a precise estimation of the model order; overestimation of the mode l order leads to inconsistent channel estimates. We show that the parametri c subspace method gives consistent channel estimates when only an upper bou nd of the channel order is known. A new algorithm, which exploits parametri c information on the channel structure, is presented. A statistical perform ance analysis of the proposed parametric subspace criterion is presented; l imited Monte Carlo experiments show that the proposed algorithm is second-o rder optimal for a large class of channels.