Blind MIMO-AR System Identification and Source Separation With Finite-Alphabet

Author

Routtenberg, Tirza ; Tabrikian, Joseph

Author_Institution

Dept. of Electr. & Comput. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel

Volume

58

Issue

3

fYear

2010

fDate

3/1/2010 12:00:00 AM

Firstpage

990

Lastpage

1000

Abstract

In this paper, a new method for system identification and blind source separation in a multiple-input multiple-output (MIMO) system is proposed. The MIMO channel is modeled by a multi-dimensional autoregressive (AR) system. The transmitted signals are assumed to take values from a finite alphabet, modeled by the Gaussian mixture model (GMM) with infinitesimal variances. The expectation-maximization (EM) algorithm for estimation of the MIMO-AR model parameters is derived. The performance of the proposed algorithm in terms of probability of error in signal detection and root mean squared error (RMSE) of the system parameters and system transfer function estimates is evaluated via simulations. It is shown that the obtained probability of error is very close to the probability of error of the optimal algorithm which assumes known channel state information.

Keywords

Gaussian processes; MIMO communication; autoregressive processes; blind source separation; error statistics; expectation-maximisation algorithm; mean square error methods; signal detection; transfer functions; Gaussian mixture model; MIMO channel; blind MIMO-AR system identification; error probability; expectation-maximization algorithm; finite-alphabet; multidimensional autoregressive system; multiple-input multiple-output; root mean squared error; signal detection; source separation; system transfer function estimates; BSS; Blind deconvolution; EM; MIMO system identification; MIMO-AR; convolutive mixtures; finite-alphabet;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/TSP.2009.2036043

Filename

5313940