Probabilistic Constraint Robust Transceiver Design for MIMO Interference Channel Networks

—In this paper, we investigate the robust transceiver design for Multi-Input Multi-Output (MIMO) Interference Channel (IC) networks with imperfect Channel State Information (CSI). With the assumption of Gaussian CSI uncertainty, a probabilistic constraint robust transceiver design problem is formulated by maximizing the average received signal while constraining the probability of large interference plus noise, both in downlink and uplink. To solve the formulated design problem, the probabilistic constraints are first transformed as Linear Matrix Inequalities (LMIs) using Markov’s inequality, and a semidefinite relaxation (SDR) technique is then applied to further recast the design problem as convex semidefinite programming (SDP) problem, which can be solved efficiently. An iterative algorithm based on alternative optimizing is proposed for the probabilistic constraint robust design. Simulation results verify that the proposed probabilistic constraint based robust transceiver design can provide robustness against Gaussian CSI errors.