Robust Solutions to Least-Squares Problems with Uncertain Data
SIAM Journal on Matrix Analysis and Applications
Parameter Estimation in the Presence of Bounded Data Uncertainties
SIAM Journal on Matrix Analysis and Applications
An Efficient Algorithm for a Bounded Errors-in-Variables Model
SIAM Journal on Matrix Analysis and Applications
Simulation of Communication Systems: Modeling, Methodology and Techniques
Simulation of Communication Systems: Modeling, Methodology and Techniques
Fundamentals of wireless communication
Fundamentals of wireless communication
Structured least squares problems and robust estimators
IEEE Transactions on Signal Processing
Competitive linear estimation under model uncertainties
IEEE Transactions on Signal Processing
Channel equalization using simplified least mean-fourth algorithm
Digital Signal Processing
Channel estimation and user selection in the MIMO broadcast channel
Digital Signal Processing
Minimum mean squared error equalization using a priori information
IEEE Transactions on Signal Processing
Robust mean-squared error estimation in the presence of model uncertainties
IEEE Transactions on Signal Processing
Minimax MSE-ratio estimation with signal covariance uncertainties
IEEE Transactions on Signal Processing
A competitive minimax approach to robust estimation of random parameters
IEEE Transactions on Signal Processing
Universal Switching Linear Least Squares Prediction
IEEE Transactions on Signal Processing
Robust Turbo Equalization Under Channel Uncertainties
IEEE Transactions on Signal Processing
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We investigate channel equalization problem for time-varying flat fading channels under bounded channel uncertainties. We analyze three robust methods to estimate an unknown signal transmitted through a time-varying flat fading channel. These methods are based on minimizing certain mean-square error criteria that incorporate the channel uncertainties into their problem formulations instead of directly using the inaccurate channel information that is available. We present closed-form solutions to the channel equalization problems for each method and for both zero mean and nonzero mean signals. We illustrate the performances of the equalization methods through simulations.