An Information Geometric Approach to Channel Identification

In this paper, 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 Csiszar [1]. It is demonstrated that the proposed method has similar performance compared to a recently reported method based on the expectation maximization (EM) algorithm [2]. Since the IGID method has an analytical solution, the proposed algorithm can be implemented much faster while while having a similar performance. The method can be considered as a generalization of all the methods developed based on the EM algorithm.