Intelligent Cooperative Covert ISAC via Fluid Antennas: A GAN-Optimized Framework

Abstract

The dual functionality of Integrated sensing and communications (ISAC) signals introduces significant security challenges, including information leakage to unauthorized users and heightened vulnerability to detection by malicious adversaries. This paper addresses these issues by leveraging the extremely large fluid antenna system (FAS) to enable covert and secure communication while simultaneously sensing multiple radar targets in a cooperative millimeter-wave (mmWave) ISAC system. To ensure security and covertness, we formulate a joint optimization problem for the analog beamforming, digital precoder, FAS port-activation matrix, and radar-sensing signal covariance matrix. Our goal is to maximize the minimum secrecy rate and Willie's detection error probability (DEP), subject to a minimum sensing signal-to-interference-plus-noise ratio (SINR) constraint. This problem is highly challenging due to three main factors: (i) it is a multi-objective optimization problem; (ii) the lack of an analytical expression for the DEP makes a tractable problem formulation difficult; and (iii) the problem is non-convex, stemming from the combinatorial nature of the activation matrix design, coupled variables, and the non-convex objective functions. To overcome these challenges, we propose a novel joint design framework based on a generative adversarial network (GAN). This framework employs a discriminator network to emulate the optimal detector at Willie and a generator network to optimize the system parameters. Adversarial training is then used to optimize the system design against this optimal detector. Numerical simulations demonstrate the potential of FAS in enhancing both communication security and sensing performance, and confirm the superior performance of the proposed GAN-based joint optimization framework.