Secrecy Rate Maximization of Cognitive MIMOME System

The physical layer security of MIMOME Cognitive Radio Networks (CRN) with respect of the secrecy rate maximization is studied. We consider the optimization problem of maximizing the system security rate on the security strategy of adding artificial noise into the transmitting signal of the Secondary User Transmitter (SU-Tx). The optimization problem is non-convex. In this paper, a two-layer optimization algorithm is proposed. The inner layer problem can be efficiently handled by solving a sequence of semi-definite problems, and the outer layer problem can be converted to a single-variable optimization problem, which can be tackled by one-dimensional search. Furthermore, the algorithm is extended to the case of imperfect Channel State Information (CSI), and a worst case based robust artificial noise approach is designed to ensure that all the channel constraints can be satisfied within ellipsoid-bounded uncertainty regions. The simulation results verify the effectiveness of the algorithm in both perfect and imperfect CSI, especially compared with the zero-forcing artificial noise precoding optimization algorithm, which obtains the higher security rate.