Robust IRS-Aided Secure Integrated Sensing and Communication With Artificial Noise

Abstract

Integrated sensing and communication (ISAC) can concurrently enhance the hardware utilization and spectrum efficiency. However, due to the signal multiplexing and the openness of wireless channels, ISAC faces increased difficulty to prevent information eavesdropping. In this paper, we propose a robust secure beamforming scheme for the IRS-aided ISAC with imperfect channel state information (CSI), where a device-to-device (D2D) pair is utilized to disrupt the eavesdropping target. Based on a statistical CSI error model, an optimization problem is formulated to minimize the transmit power by jointly optimizing the transmit beamforming and IRS phase shifts, subject to the constraints on the secrecy rate, the D2D communication rate, and the echo signal-to-noise ratio. Due to the robust probabilistic constraints and the coupled variables, the formulated problem is difficult to tackle directly. To address this, we first utilize the Bernstein-type inequality to convert the probabilistic constraints into linear matrix inequality forms. Then, it is decomposed into two subproblems, and an alternating optimization algorithm based on the semi-definite relaxation is developed to solve them iteratively until convergence. Specifically, the first subproblem optimizes the transmit beamforming, and then the IRS phase-shift design is solved based on the obtained variables. Simulation results verify the effectiveness and robustness of the proposed scheme for secure ISAC.