An information geometric approach to ML estimation with incomplete data: Application to semiblind MIMO channel identification

Abstract

In this paper, we cast the stochastic maximum-likelihood estimation of parameters with incomplete data in an information geometric framework. In this vein, we develop the information geometric identification (IGID) algorithm. The algorithm consists of iterative alternating projections on two sets of probability distributions (PDs); i.e., likelihood PDs and data empirical distributions. A Gaussian assumption on the source distribution permits a closed-form low-complexity solution for these projections. The method is applicable to a wide range of problems; however, in this paper, the emphasis is on semiblind identification of unknown parameters in a multiple-input multiple-output (MIMO) commun..