Artificial Noise Assisted Secure ISAC Beamforming Countering Covert Multiantenna Eavesdropper: A Symbol-Level Precoding Scheme

Abstract

The current security designs in integrated sensing and communication (ISAC) systems frequently rely on prior information about the eavesdropper, such as location and channel state information (CSI). However, for the covert eavesdropper, the absence of prior information significantly degrades security performance. To address this issue, we propose an artificial noise (AN)-assisted symbol-level precoding (SLP) ISAC beamforming method. We project AN into the null space of legitimate users’ channels, utilizing barrage jamming to counter the covert multiantenna eavesdropper. We establish an optimization problem aimed at minimizing the mean square error (mse) of the system’s instantaneous beampattern, subject to constraints such as signal-to-interference-plus-noise ratio (SINR) of the communication user (CU), power budget, constant modulus, and secrecy rate. To solve this optimization problem efficiently, we propose an algorithm framework consisting of the augmented Lagrangian method (ALM)-alternating direction method of multipliers (ADMM)-constant-trace gradient descent (CTGD) algorithm. We analyze the security performance of the system under finite-alphabet constellation inputs. We further derive an asymptotic lower bound for the legitimate users’ mutual information. Simulation results demonstrate that the proposed scheme exhibits excellent communication and sensing performance. Furthermore, the system can achieve secure information transmission even in the presence of a multiantenna eavesdropper without any prior information.