Joint Antenna Position and Beamforming Optimization for Movable Antenna-Enabled Secure IRS-ISAC Network

Integrated sensing and communication (ISAC) can effectively improve the spectrum utilization rate and enhance the system performance. This paper focuses on the secure communication problem in an ISAC system, which consists of a base station (BS), a legitimate user, an eavesdropping target, and an intelligent reflecting surfaces (IRS). To further investigate the impact of channel reconfigurability on the security performance of the ISAC system, movable antennas (MA) are employed at the BS instead of traditional fixed antennas. We aim to jointly design the communication transmit beamforming at the BS, the sensing covariance matrix, the phase shifts of the IRS, and the positions of the MAs to ensure secure communication for the user equipment (UE), subject to constraints including the maximum transmit power at the BS, the constant-modulus constraint of the IRS, the minimum spacing requirement between MAs, and the sensing beam gain constraint. To address the non-convex optimization problem, we adopt an alternating optimization algorithm. Specifically, by combining successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques, the problems of designing the transmit beamforming matrix at the BS and the phase shifts of the IRS are transformed into convex optimization problems. Subsequently, a particle swarm optimization (PSO) algorithm is used to optimize the positions of the MA. Simulation results demonstrate the effectiveness of the IRS in improving the system performance and highlight the superiority of thr MA in improving security compared to traditional ISAC systems.