Blind channel identification using robust subspace estimation

Abstract

The paper introduces a robust approach to subspace based blind channel identification. The technique is based on estimating the noise subspace from the sample sign covariance matrix. The theoretical motivation for the technique is shown under the white Gaussian noise assumption. A simulation study is performed to demonstrate the robust performance of the algorithm both in Gaussian and non-Gaussian noise. The results indicate that when the noise is Gaussian, the proposed method has similar good performance as the standard subspace method. When the noise is heavy-tailed, the proposed method outperforms the conventional subspace technique.