Integrated sensing and communication (ISAC) is a rising technology in the next wireless communication networks, enabling the simultaneous execution of communication and sensing tasks by fully utilizing limited spectrum resources. In this work, we investigate the secrecy performance of a dual-uncrewed aerial vehicle (UAV)-assisted secure ISAC system. Specifically, a base station UAV communicates with users and transmits radar signals to locate potential eavesdroppers, while simultaneously providing information to a jammer UAV to perform jamming tasks. Considering constraints such as maximum UAV velocity, transmit power, propulsion energy, and sensing thresholds, we maximize the average secrecy rate by optimizing user scheduling strategies, time allocation, transmit power, and UAV trajectories. The presence of a non-convex problem, originating from tightly coupled variables, is tackled by an efficient iterative algorithm. In particular, the original optimization problem is decomposed into six subproblems, and non-convex subproblems are transformed into approximately convex forms via successive convex approximation. Then, block coordinate descent techniques are employed to solve all subproblems sequentially. Numerical results demonstrate the convergence and effectiveness of the proposed algorithm.
Hongjiang Lei, Jianshuo Geng, Ki-Hong Park et al.· 0 citations
Integrated Sensing, Communication, and Power Transfer (ISCPT) is a key enabler for sixth-generation networks, enhancing resource utilization to support massive low-power devices. However, existing research has predominantly focused on a single Uncrewed Aerial Vehicle (UAV) in communication and power transfer, lacking the capability to facilitate joint sensing and power transfer of multi-UAVs for moving targets considering estimation errors. To tackle the above challenge, we propose for the first time a multi-UAV-assisted ISCPT algorithm serving both multiple Communication Users (CUs) and Energy Receivers (ERs), featuring a sensing-assisted Wireless Power Transfer (WPT) framework. Specifically, we first derive the Fisher information matrix and Cramér–Rao bound for multi-ER positioning under location uncertainty. Then, we formulate a two-phase optimization problem where sensing directly refines location estimation to achieve robust WPT efficiency maximization. To solve the formulated NP-hard problems, we design a two-phase algorithm by jointly optimizing association scheduling with CUs and ERs, beamforming and trajectory design for UAVs, based on generalized Petersen’s sign-definiteness lemma, Lagrangian relaxation and S-procedure. Numerical results validate that the proposed algorithm achieves a maximum WPT efficiency improvement of 42.3% compared with several representative baseline schemes, demonstrating strong practicality for multi-UAV-assisted ISCPT networks.
Zhaolong Ning, Lingfei Li, Xiaojie Wang et al.· IEEE Journal on Selected Are...· 1 citation