2026· Journal of Communications· 0 citations· 23 references
Abstract
— 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.
Unmanned Aerial Vehicles (UAVs) are promising relay platforms due to their flexible deployment and high probability of line-of-sight (LoS) connectivity. This paper compares three deep reinforcement learning (DRL) algorithms-Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Recurrent PPO with LSTM memory-for joint UAV trajectory and energy optimization in UAV based relay systems. The problem formulated is a non-convex optimization problem that minimizes UAV propulsion energy while satisfying Quality of Service (QoS) and mobility constraints under realistic 3GPP channel conditions. Simulation results show that all methods achieve over 99% QoS satisfaction. SAC exhibits the fastest convergence, whereas the proposed Recurrent PPO achieves the lowest energy consumption (44.72 kJ), reducing energy usage by 5.1% compared with PPO. These results highlight the trade-off between convergence speed and energy efficiency in DRL-based UAV relay optimization.
Aniket Subbanwar, Ojas Joshi, Amit Agarwal· International Conference on...· 0 citations
This paper proposes a joint optimization algorithm for trajectory control and task offloading ratios based on multi-agent deep reinforcement learning. By jointly optimizing the flight trajectories of unmanned aerial vehicles (UAVs), user scheduling strategies, and task offloading ratios, the decoupled coordination of resource allocation and trajectory planning is achieved, thereby minimizing system delay and weighted energy consumption. An enhanced multi-agent proximal policy optimization algorithm, named FMAHPPO, is designed. Compared with existing benchmark algorithms, the FMAHPPO algorithm significantly reduces the total system overhead and effectively improves the energy efficiency and task processing success rate of multi-UAV swarms. This research provides a valuable theoretical foundation and algorithmic support for the collaborative management of edge resources in future space-air-ground integrated networks (SAGIN).
Simulation results validate the effectiveness of the proposed OP approximation, demonstrate the significant outage capacity improvement of the proposed robust optimization schemes over benchmark schemes, and illustrate the superiority of the IWS-based scheme over the CE-based scheme.
Yifan Jiang, Qing-Bin Wu, Hongxun Hui et al.· 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
In temporary emergency communication coverage scenarios where terrestrial communication infrastructure is damaged or lacks sufficient capacity, UAVs equipped with base stations have emerged as an effective solution due to their flexible deployment and rapid response capability. However, in multi-UAV networks, the three-dimensional deployment of UAVs significantly affects air-to-ground link quality, while power allocation further determines the level of system interference and throughput performance. To address this issue, this paper considers a multi-UAV communication system and jointly takes into account user link reliability and service requirement satisfaction, thereby establishing a joint optimization model for QoS-constrained coverage and network throughput. To address the non-convex joint optimization problem, a problem-tailored dual-population cooperative NSGA-II framework, termed IDPC-NSGA-II, is developed. By coupling dual-population evolution, adaptive mutation, uncovered-user-guided local search, and interference-aware repair with the characteristics of multi-UAV emergency communications, the proposed method improves the trade-off between QoS-constrained coverage and network throughput. Simulation results in a representative emergency communication scenario show that the proposed method achieves a favorable trade-off between QoS-constrained coverage and throughput, and outperforms the compared algorithms under the considered network setting.
Guifen Chen, Ruiyang Liu· Digital Signal and Computer...· 0 citations
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.