Channel identification and tracking using alternating projections

In this paper an iterative method for semi-blind MIMO channel identification and tracking is presented. The method is based on results from information geometry; specifically, the alternating projections theorem first proved by Csiszar, which provides a rigorous iterative method for stochastic maximum likelihood estimation. It is demonstrated that the proposed method has similar performance compared to a recently reported method based on the expectation maximization (EM) algorithm. In addition to having a complete analytical solution, the proposed algorithm avoids the complex multidimensional integrations usually found necessary in similar EM-type methods. The result is a much faster implementation.