发表机构
Royal Military College of Canada（加拿大皇家军事学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出VIT-UAVCom系统，联合设计无人机轨迹与通信性能，开发含LS、BS等的优化框架，能效较K-means基准提升144%，BS计算时间较LS减少约50%，用于动态GPS拒止环境的非地面网络通信。
AI 中文摘要
本文提出了一种部署在动态GPS拒止无线环境中的非地面网络的高能可靠通信系统，该系统由视觉-惯性跟踪辅助无人机通信（VIT-UAVCom）系统支撑。据我们所知，这是首个利用无人机机载相机和IMU传感器开展无人机辅助通信的研究。我们考虑了完整的VIT-UAVCom系统，该系统纳入了关键设计参数，同时明确考虑了系统噪声和残余跟踪不准确性。基于此框架，我们构建了一个优化问题，用于联合设计无人机轨迹和通信性能，以提升推进能效、降低中断概率并增强物理层安全。随后，我们开发了专用解决方案框架，用于在动态场景下高效计算近最优轨迹和通信控制动作。此外，为实现实时部署，我们提出并评估了三种优化器，即线性搜索（LS）、二分搜索（BS）和遗传搜索。数值结果表明，我们提出的VIT-UAVCom框架在能耗方面显著优于K-means基准，同时保持了稳健的保密性能和可靠的用户覆盖。具体而言，与基准相比，所提框架将能效提升了144%；有趣的是，我们的结果还显示，与LS相比，BS将计算时间减少了约50%。
英文摘要
In this paper, we propose an energy-efficient and reliable communication system for non-terrestrial networks deployed in dynamic GPS-denied wireless environments, enabled by a Vision--Inertial Tracking-Assisted UAV Communication (VIT-UAVCom) system. To the best of our knowledge, this is the first work to exploit onboard UAV cameras and IMU sensors for UAV-assisted communications. We consider a complete VIT-UAVCom system that incorporates the key design parameters while explicitly accounting for system noise and residual tracking inaccuracies. Building on this framework, we formulate an optimization problem for jointly designing the UAV trajectory and communication performance to improve propulsion energy efficiency, reduce outage probability, and enhance physical-layer security. We then develop a dedicated solution framework to efficiently compute near-optimal trajectory and communication control actions in dynamic scenarios. Furthermore, to enable real-time implementation, we propose and evaluate three optimizers, namely linear search (LS), binary search (BS), and genetic search. Our numerical results demonstrate that our proposed VIT-UAVCom framework significantly outperforms the K-means benchmark in terms of energy consumption while maintaining robust secrecy performance and reliable user coverage. Specifically, our proposed framework improves the energy efficiency by 144% compared to the benchmark. Interestingly, our results also show that, compared with the LS, the BS reduces the computational time by approximately 50%.
