P.-J. Chung, H. Du and J. Gondzio
Abstract
Transmit beamforming (or precoding) is a powerful
technique for enhancing performance of wireless multi-antenna
communication systems. Standard transmit beamformers require
perfect channel state information at the transmitter (CSIT) and
are sensitive to errors in channel estimation. In practice, such
errors are inevitable due to finite feedback resources, quantization
errors and other physical constraints. Hence, robustness
has become a crucial issue recently. Among two popular robust
designs, the stochastic approach exploits channel statistics and
optimizes the average system performance while the maximin
approach considers errors as deterministic and optimizes the
worst case performance. The latter usually leads to a very
conservative design against extreme (but rare) conditions which
may occur at a very low probability. In this work, we propose a
more flexible approach that maximizes the average signal-to-noise
ratio (SNR) and takes the extreme conditions into account using
the probability with which they may occur. Simulation results
show that the proposed beamformer offers higher robustness
against channel estimation errors than several popular transmit
beamformers.
Key words: Robust Transmit Beamforming, Imperfect Channel Information, MIMO Communications, Probabilistic Constraints, Convex Optimization.