2026· IEEE Open Journal of the Communications Society· Vol 7, pp. 9290-9305· 0 citations· 34 references
TL;DR
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.
Abstract
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.
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.
Hongjiang Lei, Jianshuo Geng, Ki-Hong Park et al.· 1 citation
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
—Unmanned aerial vehicle (UAV) swarm-assisted integrated sensing and communication (ISAC) networks are a crucial technology for providing communication and sensing services in emergency rescue scenarios without base station support. However, the strong coupling between communication and sensing resources in such networks fundamentally limits the communication and sensing performance of ISAC systems. This paper jointly optimizes spectrum allocation, UAV association and deployment to maximize average system throughput while ensuring localization accuracy in such networks, where sensing is realized through localization. We begin by deriving an analytical expression for localization accuracy, which explicitly captures the joint effects of link quality and anchor geometry under shared communication-localization spectrum resources. We then formulate average system throughput maximization as a mixed-integer nonlinear and non-convex optimization problem with the constraints of localization accuracy, sub-channels, UAV association, UAV deployment and signal-to-interference-plus-noise ratio. We further develop an alternating iterative optimization method to solve this complex optimization problem. Within this method, a particle swarm optimization-based method is developed to jointly optimize spectrum allocation and UAV association, and a dueling double deep Q-network-based method is further employed for UAV deployment optimization. Finally, extensive simulation results are presented to validate the efficiency of our optimization method, and also to illustrate how key parameters influence average system throughput and localization accuracy.
Zhuo-Jia Yang, Wei Su, Bin Yang et al.· IEEE Transactions on Mobile...· 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
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
Simulation results demonstrate that the proposed method significantly reduces the time-averaged CRB by over 10%, compared with the ISAC system without UAV assistance, and also achieves a higher sensing accuracy than both the fixed-UAV-trajectory and the maximum-ratio-transmission-based beamforming benchmarks.
Yi Yang, Qianqian Zhang, Huaxia Wang· arXiv.org· 0 citations
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