This work investigates a SAGIN-enabled secure downlink communication system in which UAVs select service links among satellite, aerial, and terrestrial networks while adjusting the positions of the movable antenna (MA) array to fully exploit connectivity and spatial degrees of freedom for improved secrecy communication performance.
Abstract
Space-air-ground integrated networks (SAGINs) can provide ubiquitous and reliable connectivity for unmanned aerial vehicles (UAVs). However, air-to-ground links, which are typically dominated by line-of-sight (LoS) propagation, are vulnerable to passive eavesdropping due to the broadcast nature of wireless channels. To enhance physical-layer security, we investigate a SAGIN-enabled secure downlink communication system in which UAVs select service links among satellite, aerial, and terrestrial networks while adjusting the positions of the movable antenna (MA) array to fully exploit connectivity and spatial degrees of freedom for improved secrecy communication performance. Specifically, we maximize the secrecy energy efficiency (SEE) of a UAV swarm by jointly optimizing the MA positions, UAV trajectories, and link selections, subject to UAV mobility, MA movement, and link connectivity constraints. To reduce the real-time channel state information (CSI) acquisition overhead, we propose a channel knowledge map (CKM)-assisted multi-agent reinforcement learning framework. Specifically, the CKM is first constructed from sparse channel measurements via Kriging interpolation and is then leveraged together with satellite ephemeris information to enable efficient storage and retrieval of CSI. To reduce the action-space dimensionality and computational complexity, we model the MA array using rigid-body kinematics and adjust its position through global rigid-body translation, thereby constructing a low-dimensional hybrid action space for the joint optimization decisions. To align local decisions with system-wide performance under system constraints, we design an individual-team collaborative reward mechanism and introduce action masks to enforce constraints on UAV mobility, collision avoidance, MA regions, and connectivity capacity.
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
Covert Communication (CC) has emerged as a vital paradigm for 6G security, offering protection against eavesdropping without sole reliance on upper-layer encryption. Using their strong mobility and flexible deployment, Unmanned Aerial Vehicles (UAVs) can serve as the ideal platforms for CC. However, UAV mobility and multi-user interference in Non-Orthogonal Multiple Access (NOMA) enabled UAV networks degrade system performance. This paper investigates a robust joint power and trajectory optimization framework designed to secure NOMA-enabled system against an Eavesdropper (Eve) with uncertain locations. To determine the covertness constraint, we first derive the closed-form expressions for the optimal normalized detection threshold at Eve and the minimum total detection error probability. Given the unfair distribution of resources imposed by UAV mobility, we formulate a robust optimization problem with the objective of maximizing the minimum average covert transmission rate to guarantee a baseline quality of service for all users. To further address the impact of UAV mobility on the successive interference cancellation decoding order, we introduce binary variables to dynamically model the strong-weak channel relationships among users. Since the formulated problem is non-convex and intractable, we utilize auxiliary variables and the convex-concave procedure to transform it into a tractable form. To solve this problem, we then propose a joint optimization scheme based on penalty dual decomposition algorithm, which iteratively optimizes trajectory, power, and resource allocation via a dual-loop mechanism. Numerical simulations demonstrate the effectiveness of the proposed joint optimization scheme.
Zhi-Xin Liu, Zhi-Cheng Liu, Yuan-Ai Xie et al.· IEEE Transactions on Communi...· 0 citations
Integrated sensing and communication (ISAC) is a key enabling technology for 6G wireless networks, but its broadcast nature raises a physical-layer security concern when the sensing target can act as a potential eavesdropper. Although intelligent reflecting surfaces (IRSs) can enhance wireless propagation and improve secrecy, existing secure IRS-assisted ISAC designs are mostly limited to fixed deployments and passive phase control, which offer limited spatial adaptability in line-of-sight-dominated low-altitude scenarios. To address this limitation, we investigate an unmanned aerial vehicle (UAV)-mounted six-dimensional movable IRS-assisted secure ISAC system, where the IRS location, orientation, and reflection coefficients are jointly optimized with the BS beamformer to maximize the secrecy rate under communication quality-of-service (QoS), power, unit-modulus, and visibility constraints. The resulting problem is highly non-convex due to the coupled active/passive beamforming variables and the location-and-orientation-dependent (pose-dependent) channel responses. To solve it efficiently, we develop a three-block alternating optimization (AO) framework, in which the active beamformer, IRS pose, and passive reflection vector are updated via linearized ADMM, warm-started particle swarm optimization, and Riemannian gradient descent, respectively. Simulation results show that the proposed design significantly outperforms fixed-location and orientation-only baselines, highlighting the importance of joint translation, rotation, and phase control for secure ISAC.
Cheng-Ye Hong, Botang Shi, R. Zhu et al.· 0 citations
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
Unmanned aerial vehicle (UAV) swarms have become a promising solution to enhance wireless communication in complicated environments. In this paper, we study a UAV swarmassisted secure communication system, where multiple UAVs cooperatively construct an aerial virtual antenna array (AVAA) to deliver confidential information to ground users under the threat of an aerial eavesdropper. we seek to maximize the secrecy energy efficiency (SEE) through the joint optimization of UAV positions and excitation current weights. To handle this highly non-convex problem, we cast it as a Markov decision process (MDP) and propose an energy-penalty based TD3 (EP-TD3) algorithm. In particular, the sum secrecy rate is adopted as the main reward term, while energy consumption and constraint violations are incorporated as penalty terms to guide the learning process toward a better balance of secrecy enhancement and propulsion energy expenditure. Numerical simulation results reveal that the proposed EP-TD3 achieves better overall performance compared with benchmark schemes regarding average sum secrecy rate, total energy consumption, and average SEE.
Chun-Jia Tang, Zhihong Lu, Zhiyu Huang et al.· IEEE International Conferenc...· 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
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