标题： 时间辅助波束成形和感知增强型无人机通信中的轨迹预测 显示英文标题

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

摘要： 在高速通信不断演变的背景下，从传统的基于导频的方法转向以感知为导向的方法（SOA）预计将加速发展。 本文深入探讨了一个创新的集成感知与通信（ISAC）框架的开发，该框架专门针对波束成形和轨迹预测过程进行设计。 本研究的核心是探索一种无人机（UAV）启用的通信系统，该系统无缝集成了ISAC技术。 这种集成突显了感知和通信能力之间的协同作用。 所提出的系统首先在以感知为重点的阶段部署全向波束，随后过渡到定向波束以实现精确的目标跟踪。 此过程结合了扩展卡尔曼滤波（EKF）方法，用于准确估计和预测目标状态。 引入了一种新颖的帧结构，利用历史感知数据在实时中优化后续时隙的波束成形，我们称这种策略为“时间辅助”波束成形。 为了改进“时间辅助”波束成形技术，我们采用逐次凸逼近（SCA）与迭代秩最小化（IRM）相结合的方法，得到了高质量的次优解。 与传统基于导频的系统相比，我们的方法在多目标场景中提高了156%，在单目标场景中提高了136%。 显示英文摘要

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.