Blind MIMO-AR system identification and source separation with finite-alphabet

Authors:
Tirza Routtenberg;Joseph Tabrikian
Affiliations:
Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel;Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Venue:
IEEE Transactions on Signal Processing
Year:
2010

Citing 13
Cited 2

Fundamentals of statistical signal processing: estimation theory

Fundamentals of statistical signal processing: estimation theory
Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
Blind estimation of finite alphabet digital signals using eigenspace-based beamforming techniques

Signal Processing
Second order blind equalization in multiple input multiple output FIR systems: a weighted least squares approach

ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05
Multiuser channel estimation from higher-order statistical matrix pencil

EURASIP Journal on Applied Signal Processing
MIMO-AR system identification and blind source separation for GMM-distributed sources

IEEE Transactions on Signal Processing
Blind source separation of signals with known alphabets using ε-approximation algorithms

IEEE Transactions on Signal Processing
A blind source separation technique using second-order statistics

IEEE Transactions on Signal Processing
Parameter estimation for autoregressive Gaussian-mixture processes: the EMAX algorithm

IEEE Transactions on Signal Processing
Prediction error method for second-order blind identification

IEEE Transactions on Signal Processing
Multi-input multi-output fading channel tracking and equalizationusing Kalman estimation

IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources

IEEE Transactions on Signal Processing
The minimum description length principle in coding and modeling

IEEE Transactions on Information Theory

A bayesian approach to blind separation of mixed discrete sources by gibbs sampling

UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
Blind separation of non-stationary sources using continuous density hidden Markov models

Digital Signal Processing

Quantified Score

Hi-index	35.68

Visualization

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.