This letter addresses the scenario where uncrewed aerial vehicles (UAVs) perform covert transmission in three-dimensional (3D) space against ground Willie (GW). Aiming to maximize the minimum throughput among ground users (GUs) while ensuring fairness, we first derive the UAV transmit power subject to covertness constraints under GW position uncertainty. Building upon the successive-hover-and-fly (SHF) structure, we incorporate the UAV’s vertical dimension to transform the original 3D continuous trajectory into a finite set of connected discrete waypoints. Accordingly, a joint trajectory design and power allocation problem is formulated based on the transformed trajectory. Ultimately, a successive convex approximation technique is leveraged to convexify the problem, which is then efficiently solved via an iterative algorithm based on the ellipsoid method. Simulation results demonstrate that the proposed scheme significantly outperforms traditional benchmarks.
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
— Unmanned Aerial Vehicles (UAVs) have emerged as flexible relay platforms capable of enhancing wireless connectivity in beyond-5G and 6G networks. This paper investigates the joint optimization of UAV trajectory and power allocation to maximize end-to-end throughput under practical mobility and power constraints. The problem is highly non-convex due to the strong coupling between trajectory variables and transmission power. To address this challenge, we develop a penalty-based metaheuristic framework that incorporates a constraint-handling mechanism into the Bat Algorithm (BAT). Simulation results show that the proposed BAT-based approach achieves significant throughput improvement, efficient power allocation, and fast convergence compared with baseline convex optimization and heuristic schemes. These findings highlight the potential of BAT for reliable and energy-efficient UAV-assisted communication in future wireless networks.
Pham Thi Quynh Trang· Journal of Communications· 0 citations
This study investigates the optimization of three-dimensional (3D) trajectory planning and resource allocation in unmanned aerial vehicle (UAV)-enabled wireless networks with no-fly zones (NFZs) using a deep learning framework. The objective is to maximize the minimum average spectral efficiency (SE) among mobile users served by multiple UAVs while addressing key challenges, including interference from concurrent UAV transmissions, collision avoidance, and NFZ constraints. A realistic probabilistic channel model is considered, where the likelihood of a line-of-sight (LoS) condition is modeled as a function of the elevation angle in the air-to-ground (A2G) link. To solve the formulated optimization problem, a novel deep learning framework with specialized deep neural network (DNN) structures is developed. This framework jointly optimizes 3D UAV trajectory planning and resource allocation, employing an unsupervised learning-based training approach that eliminates the need for labeled data. Performance evaluations demonstrate that the proposed scheme effectively accounts for the probabilistic channel model and co-channel interference while accounting for collision avoidance and NFZ-related constraints. Moreover, it outperforms baseline methods by achieving a higher minimum average SE with real-time computational efficiency, making it practical for UAV-assisted wireless networks.
This paper studies covert communications in an uncrewed aerial vehicle (UAV)-assisted interweave cognitive radio network (ICRN). Specifically, a secondary user (UAV) opportunistically utilizes the idle spectrum resource of a primary user (Warden) to covertly transmit short-packets to ground receivers (GRs), while Warden tries to detect the existence of the UAV covert communications. To ensure fairness of covert communications, this paper maximizes the minimum covert throughput from UAV to GRs, which can be formulated as an optimization problem with the constraints of covertness, UAV trajectory, power and user-association. We further simplify the optimization problem by determining the optimal transmission power of UAV. To solve this optimization problem, we derive the optimal transmission power of UAV and user-association index as analytical expressions of UAV’s positions, respectively. We also define UAV trajectory as a successive hovering-and-flying structure. Based on these results, we employ a successive convex approximation method to obtain the maximum value of the minimum covert throughput. The numerical results are presented to illustrate the impact of system parameters on the minimum covert throughput.
Riyu Wang, Bin Yang, Shikai Shen et al.· IEEE Transactions on Cogniti...· 0 citations
This letter considers a secure multi-uncrewed aerial vehicle (UAV) enabled over-the-air computation (AirComp) system, where multiple UAVs cooperatively transmit data to a ground fusion center (FC) via AirComp under eavesdropping threats. To achieve reliable and secure aggregation, we jointly optimize the UAV transmit power, FC denoising factor, and UAV trajectories to minimize the mean square error (MSE) at the FC while enforcing the eavesdropper (EVE) MSE, UAV power, and mobility constraints. The formulated problem is non-convex due to coupled variables and mobility constraints. An efficient algorithm combining alternating optimization and successive convex approximation (SCA) is proposed to obtain a stationary solution. Numerical results verify the effectiveness of the proposed scheme and its superiority over benchmark schemes.
Jianping Yao, Yuxia Gong, Sunan Wang et al.· IEEE Wireless Communications...· 0 citations
With the advancement of wireless communication technology, the demand for data transmission speed continues to increase. This paper investigates an unmanned aerial vehicle–assisted nonorthogonal multiple access network integrating simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), in which a cascaded channel model is developed for base station–to–ground user equipment and base station–to–low Earth orbit satellite links. A joint optimization framework is proposed to maximize the system traversal total rate (STTR) through dynamic coordination of STAR-RIS energy splitting ratios and unmanned aerial vehicle 3D trajectory. The analysis quantifies STTR variations with STAR-RIS element counts, channel states, and ground user equipment communication requirements. Simulations validate the framework's effectiveness, demonstrating 64.2% STTR improvement over a system with no optimization and underscoring its potential in next-generation wireless communications.
C. Deng, D. Qing, Zhi Li et al.· International Journal of Inf...· 0 citations