Blind channel equalization using weighted subspace methods

This paper addresses the problems of blind channel estimation and symbol detection with second order statistics methods from the received data. It can be shown that this problem is similar to Direction Of Arrival (DOA) estimation, where many solutions like the MUSIC algorithm or “weighted” techniques (as Deterministic Maximum Likelihood or Weighted Subspace Fitting method) have been developed. In this proposal we extend these techniques to blind channel identification problem in an unified framework known as Subspace Fitting. In this framework the estimated and the received data are “fitting” through the subspaces in a least square sense. Then, in order to solve this problem and estimate the channel, a modified GaussNewton type algorithm is suggested. Simulations are carried out comparing the proposed solutions with a classical signal subspace-based blind channel identification scheme.