Augmenting wireless networks with Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, offers a promising avenue for providing reliable, cost-effective, and on-demand wireless services to desired areas. However, existing UAV communication and trajectory schemes are inefficient as they assume limited drone mobility and static transmission power. Furthermore, they tend to rely upon convex approximations to highly non-linear functions and fail to adopt a combination of heuristic and convex methods. This paper considers a Multi-UAV system where UAV-mounted mobile base stations serve users on the ground. An iterative approach using block gradient descent is used to jointly optimize user scheduling, UAV trajectories, and transmission power for maximizing throughput over all users. Subsequently, an innovative technique for initial trajectory predictions was developed using a K-means clustering algorithm for partitioning users into subgroups and a genetic algorithm for initializing shortest flight paths within clusters. Finally, convex optimization solvers such as MATLAB's Fmincon are used for fine-tuning parameters. Extensive simulation and optimization results demonstrate a 33.57%, 87.4%, and 53.2% increase in system throughput for the 1, 2, and 3 UAV scenarios respectively when compared to existing trajectory and communication design schemes. Furthermore, the K-means and genetic algorithm reveal additional improvements in throughput by around 15%. Our results note diminished increases in throughput for increases in UAV trajectory period as the period approaches higher values. Further research into joint adoption of convex and non-convex schemes as well as consideration of environment-dependent channel models would allow for a faster and more optimal deployment of UAVs.
翻译:增强无人驾驶飞行器(UAVs)的无线网络,通常称为无人驾驶飞行器(UAVs),这为向理想地区提供可靠、具有成本效益和按需的无线服务提供了一个充满希望的渠道。然而,现有的UAV通信和轨迹计划效率低下,因为它们承担了无人驾驶无人驾驶飞行器的有限机动性和静态传输能力。此外,它们往往依赖高非线性功能的曲线近似和高度非线性功能,并且没有采用超光速和混凝土方法的组合。本文审议了无人驾驶飞行器上载移动基站为地面用户服务的多UAVAV系统。使用块梯度下降的迭代方法,共同优化用户的时间安排、UAVAV轨迹和传输能力,以便最大限度地增加所有用户的吞吐量。随后,开发了最初轨迹预测的创新技术,使用K-手段组合算法将用户分成分组和基因算法来启动最短的飞行轨迹。最后,UATLAB和Fmincon等顶端移动移动基站的优化模型将用于优化参数。广泛模拟和优化模拟结果显示,在15-57-57%的联合设计周期内,将不断递增增压,通过KAVAVAVL4和不断增长的周期内, 和不断递增压的周期内,通过不断增压的频率增加。