Covert ISAC: Fundamentals, Mathematics, and Challenges

With the rapid development of wireless networks, the explosive growth of devices has imposed tremendous pressure on the spectrum. Integrated sensing and communication (ISAC) has emerged as a promising solution to improve the spectral efficiency by combining these two functionalities. Nevertheless, the potential eavesdropping of sensing targets poses significant security threats for ISAC. This article explores the covert ISAC to tackle this problem by hiding the communication. Specifically, we first begin with the principles of covert ISAC and the design trade-off among covertness, communication and sensing, and summarize some enhancing techniques. Then, we discuss the mathematics to achieve the optimal design trade-off. Numerical optimization methods are demonstrated, and the potentials of intelligent optimization methods to support covert ISAC are further explored. On this basis, we investigate a covert ISAC system, where a deep reinforcement learning scheme is proposed to maximize the covert transmission rate while ensuring effective sensing. Through the case study, the results demonstrate that the proposed scheme can effectively improve the communication performance while guaranteeing the covertness and sensing accuracy. Finally, some challenges and opportunities are pointed out for the future research in this direction.