An Efficient Design for Robust Downlink Power Control Using Worst-case Performance Optimization

Abstract

Downlink power control and beamforming designs in wireless system have been a recent research focus. To achieve reliable and efficient designs, good estimation of wireless channel knowledge is desired. However, the presence of uncertain channel knowledge due to constant changing radio environment will cause performance degradation in system designs. Thus the mismatches between the actual and presumed channel state information (CSI) may frequently occur in practical situations. Robust power control and beamforming were introduced considering the channel uncertainty. In this thesis, a new robust downlink power control solution based on worst-case performance optimization is developed. Our approach explicitly models uncertainties in the downlink channel correlation (DCC) matrices, uses worst-case performance optimization and guarantees that the quality of service (QoS) constraints are satisfied for all users using minimum amount of power. An iterative algorithm to find the optimum power allocation is proposed. The key in the iteration is the step to solve an originally non-convex problem to obtain worst-case uncertainty matrices. When the uncertainty is small enough to guarantee that the DCC matrices are positive semidefinite, we obtain a closed-form solution of this problem. When the uncertainty is large, we transform this intractable problem into a convex problem. Simulation results show that our proposed robust downlink power control using the approach of worst-case performance optimization converges in a few iterations and reduces the transmission power effectively under imperfect knowledge of the channel condition.