Title
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) communications system. It is shown by simulations that the performance of the algorithm [in terms of both estimation error and bit-error rate (BER)] is similar to that of the expectation-maximization (EM)-based algorithm proposed previously by Aldana et al., but with a substantial improvement in computational speed, especially for large constellations.
Year
DOI
Venue
2007
10.1109/TSP.2007.896091
IEEE Transactions on Signal Processing
Keywords
Field
DocType
semiblind mimo channel identification,incomplete data,bit-error rate,semiblind identification,information geometric identification,information geometric approach,ml estimation,gaussian assumption,stochastic maximum-likelihood estimation,information geometric framework,data empirical distribution,likelihood pds,estimation error,bit error rate,gaussian distribution,probability distribution,geometry,maximum likelihood estimation,information geometry,expectation maximization algorithm,maximum likelihood estimate,stochastic processes,communication system,empirical distribution,expectation maximization
Mathematical optimization,Iterative method,Expectation–maximization algorithm,MIMO,Gaussian,Probability distribution,Estimation theory,System identification,Mathematics,Computational complexity theory
Journal
Volume
Issue
ISSN
55
8
1053-587X
Citations 
PageRank 
References 
6
0.69
19
Authors
4
Name
Order
Citations
PageRank
A. Zia1191.59
James Reilly245743.42
Jonathan H. Manton384371.93
S. Shirani430125.69