This work investigates the secrecy performance of a dual-uncrewed aerial vehicle (UAV)-assisted secure ISAC system, and maximizes the average secrecy rate by optimizing user scheduling strategies, time allocation, transmit power, and UAV trajectories.
Abstract
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.
This paper proposes a novel physical-layer security framework for multi-UAV Integrated Sensing and Communication (ISAC) networks operating in adversarial environments. To maximize the sum secrecy rate of legitimate ground users (GUs) while satisfying minimum sensing beampattern-gain constraints for target illumination, we introduce a dynamic role allocation mechanism in which each UAV can switch, on a per-time-slot basis, between an ISAC mode—combining coherent communications with radar sensing—and a dedicated artificial noise (AN) jammer mode. The resulting optimization is cast as a highly coupled Mixed-Integer Non-Linear Program (MINLP) that jointly optimizes binary role indicators, transmit beamforming and sensing covariance matrices, and UAV trajectories. We solve this problem with a tailored Alternating Optimization (AO) algorithm that integrates a penalty-based Convex-Concave Procedure (CCP) for the binary role subproblem, Semidefinite Relaxation (SDR) for the beamforming subproblem, and a trust-region Successive Convex Approximation (SCA) for the trajectory subproblem. Numerical results demonstrate that the proposed dynamic-role architecture consistently outperforms both a static dedicated-jammer scheme and a fully optimized all-ISAC embedded-AN benchmark, confirming that its secrecy advantage arises from adaptive spatial-functional specialization rather than from artificial-noise transmission alone. Furthermore, we characterize the fundamental tradeoff between secrecy performance and stringent sensing beampattern-gain constraints, showing that moderate sensing requirements can be accommodated with no secrecy penalty.
M. M. Selim, Mohamed Rihan, Armin Dekorsy et al.· IEEE Open Journal of the Com...· 0 citations
An unmanned aerial vehicle (UAV)-enabled ISAC system employing rate-splitting multiple access (RSMA) and a joint beamforming and trajectory optimization framework is investigated and results demonstrate that the proposed algorithm significantly improves the achievable system downlink rate.
Shunxuan Wang, Qi Zhu· Italian National Conference...· 0 citations
This paper forms a multi-objective optimization problem aimed at minimizing AoI and energy consumption while maximizing the eavesdropper’s Bit Error Rate by jointly optimizing UAV trajectories, time scheduling, and jamming parameters and develops an efficient iterative algorithm.
Xiujuan Zhang, Yujiao Han, Shiyu Wang et al.· 0 citations
Integrated sensing and communication (ISAC) technology, when deployed on unmanned aerial vehicles (UAVs), enables aerial base stations to simultaneously provide wireless connectivity to ground users and perform environmental sensing through echo signal analysis. However, the broadcast nature of wireless transmission, combined with the line-of-sight (LoS) propagation characteristics of UAVs, increases the risk of passive eavesdropping on transmitted signals during ISAC missions. This paper investigates the joint trajectory design and power allocation (JTDPA) problem for UAV-enabled ISAC systems in environments with multiple mobile ground users and potential eavesdroppers. The proposed approach formulates the optimization problem as a constrained Markov decision process (CMDP), aiming to balance communication rate, secrecy rate, and energy consumption. To address the limitations of existing secure trajectory designs, such as unnecessary energy expenditure and overly conservative avoidance actions, we propose a two-stage (TS) strategy that incorporates the safe twin delayed deep deterministic policy gradient (Safe-TD3) algorithm, referred to as TS-SafeTD3. In the first stage (sensing stage), the UAV navigates toward a user-centric location without communication to enhance initial coverage efficiency, while satisfying the minimum-distance safety constraints with respect to potential eavesdroppers.In the second stage (ISAC stage), Safe-TD3 is employed to jointly optimize both trajectory and power allocation under the same safety constraints to maximize the weighted secrecy rate. Simulation results indicate that the proposed algorithm improves the weighted secrecy rate and energy efficiency under various operational conditions, while maintaining a low violation probability of the safety constraints.
Yu-Jia Chen, Hai-Yan Huang, Ting-Wei Chen et al.· IEEE Transactions on Network...· 0 citations
The rapid proliferation of Uncrewed Aerial Vehicles (UAVs) introduces significant challenges to low-altitude airspace security, particularly from unauthorized intrusions. To address these vulnerabilities, Integrated Sensing and Communication (ISAC) has emerged as a key enabler for anti-UAV systems. However, existing studies focusing on cellular networks with fixed base stations are ill-suited for the continuous movement of target UAVs, thus failing to meet the dual demands of flexible sensing and reliable positioning. To address this, we propose an ISAC-enabled anti-UAV scheme solely based on cooperative UAVs. Specifically, we first derive the optimal transmit power under the constraint of space-air transmission outage probability tolerance. Subsequently, we deduce the sensing Fisher information matrix and Cramér-Rao Bound (CRB) by incorporating the position uncertainty of the target UAV. Then, we formulate a long-term CRB minimization problem to enhance cooperative sensing performance. To tackle this NP-hard problem, we design a robust optimization algorithm that jointly optimizes transmit-receive beamforming, association scheduling, and UAV trajectory, by transforming the structurally complex CRB matrix into a set of semi-definite constraints, and resolving the inherent position uncertainty. Numerical results demonstrate that our proposed algorithm outperforms representative algorithms in terms of sensing accuracy and robustness.
Xiaojie Wang, Lingfei Li, Zhaolong Ning et al.· IEEE Transactions on Wireles...· 1 citation
The integrated sensing, communication, and computing (ISCC) system overcomes the limitations of conventional standalone architectures. Through resource sharing and collaborative design, it dynamically optimizes and jointly enhances communication, sensing, and computing performance, thereby significantly improving overall system efficiency. This work investigates the performance trade-off among secure communication rate, radar estimation rate, and computational energy efficiency in an uncrewed aerial vehicle (UAV)-assisted ISCC system. By jointly optimizing the UAV's three-dimensional (3D) trajectory, beamforming, user scheduling, and computational frequency, three optimization problems are formulated to maximize the average secrecy rate, sensing rate, and computational energy efficiency, respectively, thus establishing the system's performance boundaries under diverse scenarios. On this basis, the trade-off among security, sensing, and computation is further explored with the goal of maximizing the normalized weighted sum of the three performance metrics, which provides a theoretical basis for the performance-coordinated design of aerial ISCC systems.
Hongjiang Lei, Juntian He, Cong Jiang et al.· 0 citations
