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Channel identification and tracking using alternating projections

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3 Author(s)
A. Zia ; Dept. of ECE, McMaster Univ., Hamilton, Ont., Canada ; J. P. Reilly ; S. Shirani

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.

Published in:

Statistical Signal Processing, 2003 IEEE Workshop on

Date of Conference:

28 Sept.-1 Oct. 2003