Abstract
We study the robust transceiver optimization in multiple-input multiple-output (MIMO) systems aiming at minimizing transmit power under probabilistic quality-of-service (QoS) requirements. Owing to the unknown distributed interference, the channel estimation error can be arbitrary distributed. Under this situation, the QoS requirements should account for the worst-case channel estimation error distribution. While directly finding the worst-case distribution is challenging, two methods are proposed to solve the robust transceiver design problem. One is based on the Chebyshev inequality, the other is based on a novel duality method. Simulation results show that the QoS requirement is satisfied by both proposed algorithms. Furthermore, among the two proposed methods, the duality method shows a superior performance in transmit power, while the Chebyshev method demonstrates a lower computational complexity.
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