Robust transmit beamforming based on probabilistic constraint

Transmit beamforming is a powerful technique for enhancing performance of wireless communication systems. Most existing transmit beamforming techniques require perfect channel state information at the transmitter (CSIT), which is typically not available in practice. In such situations, the design should take into account errors in the channel estimates, so that the beamformers are less sensitive to these errors. Two robust approaches are widely used. The stochastic approach optimizes the average performance of the system and assumes that the statistics, such as mean and covariance, of the errors are known. The maximin approach assumes that the errors belong to a worst-case uncertainty region and optimizes the worst-case system performance. This type of design usually leads to conservative results as the worst-case conditions may occur at a very low probability. In this paper, we propose a more flexible approach that optimizes the average beamforming performance and takes the extreme (but rare) conditions into account proportionally. Simulation results show that the proposed beamformer offers higher robustness against errors in CSIT than serval state-of-the-art beamformers.