Information Geometric Approach to Channel Identification: A Comparison with EM-MCMC Algorithm

After reviewing the information geometric channel identification algorithm (IGID) [1], the application of the algorithm for semi-blind identification of the MIMO channel with Gaussian input sources is discussed. The method is developed based on the results from information geometry; specifically, the alternating projections theorem first proved by Csiszar [2] which provides an iterative method for minimizing the distance between two sets of probability distributions. Also, an EM-type identification algorithm (EM-MCM C) for which the necessary expectation computations are performed using Markov-Chain Monte-Carlo (MCMC) method is introduced. The comparative analysis of channel identification using two methods for MIMO systems with lSI-free flat-fading channels are given. It is shown that the IGID method has a similar performance while benefitting from an analytical solution. Thus, complex multidimensional integrations usually necessary in similar EM-type methods are avoided. This characteristic provides very fast computation times relative to previous EM-type algorithms.