STAR-RIS-Enabled Secure Dual-Functional Radar-Communications: Joint Waveform and Reflective Beamforming Optimization - Wang, Chao; Wang, Cheng-Cai; Li, Zan; Ng, Derrick Wing Kwan; Wong, Kai-Kit; Al-Dhahir, Naofal; Niyato, Dusit (IEEE transactions on information forensics and security, 2023)
Considering a simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-aided dual-functional radar-communications (DFRC) system, this paper proposes a symbol-level precoding-based scheme for concurrent securing confidential information transmission and performing target sensing, where the public signals intended for multiple unclassified users are exploited to deceive the multiple potential malicious radar targets. Specifically, the STAR-RIS-aided DFRC system design is formulated as a joint optimization problem that determines the transmission waveform signal, the transmission and reflection coefficients of STAR-RIS. The objective is to maximize the average received radar sensing power subject to the quality-of-service constraints for multiple communication users, the security constraint for multiple potential eavesdroppers, as well as various practical waveform design restrictions. However, the formulated problem is challenging to handle due to its nonconvexity. Furthermore, the high dimensionality of the optimization variables also renders existing optimization algorithms inefficient. To address these issues, we propose a distance-majorization induced low-complexity algorithm to obtain an efficient solution, which converts the nonconvex joint design problem into a sequence of subproblems that can be solved in closed-form, relieving the required high computational burden of the conventional approaches, e.g., the interior point method. Simulation results confirm the effectiveness of the STAR-RIS in improving the DFRC performance. Besides, by comparing with the state-of-the-art alternating direction method of multipliers (ADMM) algorithm, simulation results validate the efficiency of our proposed optimization algorithm and show that it enjoys excellent scalability for different number of T-R elements equipped at the STAR-RIS.