Detection of Gaussian constellations in MIMO systems under imperfect CSI

Authors:
Ido Nevat;Gareth W. Peters;Jinhong Yuan
Affiliations:
School of Electrical Engineering and Telecommunications, University of NSW, Sydney, Australia;School of Mathematics and Statistics, University of NSW, Sydney, Australia and CSIRO, Mathematical and Information Sciences, Sydney, Australia;School of Electrical Engineering and Telecommunications, University of NSW, Sydney, Australia
Venue:
IEEE Transactions on Communications
Year:
2010

Citing 7
Cited 0

An Introduction to Numerical Methods: A MATLAB Approach

An Introduction to Numerical Methods: A MATLAB Approach
On Limits of Wireless Communications in a Fading Environment when UsingMultiple Antennas

Wireless Personal Communications: An International Journal
Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals

IEEE Transactions on Signal Processing
Linear Regression With Gaussian Model Uncertainty: Algorithms and Bounds

IEEE Transactions on Signal Processing
Modulation and coding for linear Gaussian channels

IEEE Transactions on Information Theory
Multilevel codes: theoretical concepts and practical design rules

IEEE Transactions on Information Theory
Closest point search in lattices

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper considers the problem of Gaussian symbols detection in MIMO systems in the presence of channel estimation errors. Under this framework we develop a computationally efficient approximations of the MAP detector. The new detectors are based on a relaxation of the discrete nature of the digital constellation and on the channel estimation error statistics. This leads to a non-convex program that is solved efficiently via a hidden convexity minimization approach. Additionally, we show that using a Bayesian EM approach, comparable BER performance to that of the MAP detector can be achieved. Next we extend the detection scheme to the case where the noise variance is unknown. We present a modified Bayesian EM approach with annealed Gibbs sampling to perform joint noise variance estimation and symbols detection. Simulation results in a random MIMO system show that the proposed algorithm outperforms the linear MMSE receiver in terms of BER.