Analyzing effect of noise on LMS-type approaches to blind estimation of simo channels: Robustness issue

An analysis of the noise effect on the convergence characteristic of the least-mean-squares (LMS) type adaptive algorithms for blind channel identification is presented. It is shown that the adaptive blind algorithms misconverge in the presence of noise. A novel technique for ameliorating such misconvergence characteristic, using a frequency domain energy constraint in the adaptation rule, is proposed. Experimental results demonstrate that the robustness of the blind adaptive algorithms can be significantly improved using such constraints.