Optimal and Robust AN-aided Precoding Design for Cognitive MIMOME Wiretap Channels

摘要

In this paper, we investigate a cognitive radio network (CRN) for a multiple-input multiple-output multiple-eavesdropper (MIMOME) secrecy channel with artificial noise. The cognitive transmitter aims to send confidential messages to its receiver in the presence of multiple eavesdroppers. While the artificial noise (AN) approach or masked precoding is applied to provide the information secrecy, the target of this paper is to maximize the achievable secrecy rate with the constraint of the total transmit power, as well as the interference temperature limits (ITL) for the primary user. The original optimization problems are non-convex and challenging, which is efficiently solved via a two-layer decomposition approach. The inner layer problem can be efficiently handled by solving a sequence of semi-definite problems, and the outer layer problem can be recast as a single-variable optimization problem, which is tackled by one-dimensional search. We also generalize the framework to an imperfect CSI case where a worst-case robust secrecy rate maximization (SRM) formulation is considered. Finally, simulation results are provided to validate the secrecy performance of the proposed methods.