An information geometric approach to channel identification

The semi-blind MIMO channel identification problem is modelled as a stochastic maximum likelihood estimation problem and an iterative method, called information geometric identification (IGID), for channel identification and tracking is presented. The method is developed based on the results from information geometry; specifically, the alternating projections theorem first proved by I. Csiszar and G. Tusnady (see Statistics and Decisions, Suppl. Issue, no.1, p.205-37, 1984). It is demonstrated that the proposed method has similar performance compared to a recently reported method based on the expectation maximization (EM) algorithm (Aldana, C.H. and Cioffi, J., IEEE Int. Conf. on Commun., 2001). Since the IGID method has an analytical solution, the proposed algorithm can be implemented much faster, while having a similar performance. The method can be considered as a generalization of all the methods developed based on the EM algorithm.