MIMO transceiver design via majorization theory
Foundations and Trends in Communications and Information Theory
IEEE Transactions on Communications
Stochastic MV-PURE estimator: robust reduced-rank estimator for stochastic linear model
IEEE Transactions on Signal Processing
Robust transceiver optimization in downlink multiuser MIMO systems
IEEE Transactions on Signal Processing
Robust QoS-constrained optimization of downlink multiuser MISO systems
IEEE Transactions on Signal Processing
On multicast beamforming for minimum outage
IEEE Transactions on Wireless Communications
Quasi-convex designs of max-min linear BC precoding with outage QoS constraints
ISWCS'09 Proceedings of the 6th international conference on Symposium on Wireless Communication Systems
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Traditional multiuser receiver algorithms developed for multiple-input-multiple-output (MIMO) wireless systems are based on the assumption that the channel state information (CSI) is precisely known at the receiver. However, in practical situations, the exact CSI may be unavailable because of channel estimation errors and/or outdated training. In this paper, we address the problem of robustness of multiuser MIMO receivers against imperfect CSI and propose a new linear technique that guarantees the robustness against CSI errors with a certain selected probability. The proposed receivers are formulated as probabilistically constrained stochastic optimization problems. Provided that the CSI mismatch is Gaussian, each of these problems is shown to be convex and to have a unique solution. The fact that the CSI mismatch is Gaussian also enables to convert the original stochastic problems to a more tractable deterministic form and to solve them using the second-order cone programming approach. Numerical simulations illustrate an improved robustness of the proposed receivers against CSI errors and validate their better flexibility as compared with the robust multiuser MIMO receivers based on the worst case designs