Temporal-Assisted Beamforming and Trajectory Prediction in Sensing-Enabled UAV Communications

作     者：Zhou, Shengcai Yang, Halvin Xiang, Luping Yang, Kun 

作者机构：University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China University College London Department of Electronic and Electrical Engineering LondonWClE 7JE United Kingdom Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China Nanjing University Suzhou Campus School of Intelligent Software and Engineering Suzhou215163 China 

出 版 物：《IEEE Transactions on Communications》 (IEEE Trans Commun)

年 卷 期：2024年

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0813[工学-建筑学] 0825[工学-航空宇航科学与技术] 

主　　题：Extended Kalman filters 

摘      要：In the evolving landscape of high-speed communication, the shift from traditional pilot-based methods to a Sensing-Oriented Approach (SOA) is anticipated to gain momentum. This paper delves into the development of an innovative Integrated Sensing and Communication (ISAC) framework, specifically tailored for beamforming and trajectory prediction processes. Central to this research is the exploration of an Unmanned Aerial Vehicle (UAV)-enabled communication system, which seamlessly integrates ISAC technology. This integration underscores the synergistic interplay between sensing and communication capabilities. The proposed system initially deploys omnidirectional beams for the sensing-focused phase, subsequently transitioning to directional beams for precise object tracking. This process incorporates an Extended Kalman Filtering (EKF) methodology for the accurate estimation and prediction of object states. A novel frame structure is introduced, employing historical sensing data to optimize beamforming in real-time for subsequent time slots, a strategy we refer to as temporal-assisted beamforming. To refine the temporal-assisted beamforming technique, we employ Successive Convex Approximation (SCA) in tandem with Iterative Rank Minimization (IRM), yielding high-quality suboptimal solutions. Comparative analysis with conventional pilot-based systems reveals that our approach yields a substantial improvement of 156% in multi-object scenarios and 136% in single-object scenarios. © 1972-2012 IEEE.