Channel Order Estimation in Cyclostationarity Based Blind Channel Equalization

Communication systems require fast and efficient adaptive channel equalization algorithms. In cyclostationarity based channel equalization, channel order is estimated by decomposing range space of an "overmodeled" data covariance matrix into signal and noise subspaces. These methods are robust with respect to signal to noise ratio, but are computationally intensive leading to slow convergence for large channel order. This paper proposes a method of estimating channel order based on gap between signal and noise subspaces; by taking into account the difference between consecutive eigen values