无人机流体天线在扫描内信道老化下的信道获取
UAV Fluid-Antenna Channel Acquisition under Intra-Scan Channel Aging
- College of Artificial Intelligence, Nanjing University of Information Science and Technology（南京信息工程大学人工智能学院）
- School of Cyber Science and Engineering, Southeast University（东南大学网络空间安全学院）
- National Mobile Communications Research Laboratory, Southeast University（东南大学移动通信国家重点实验室）
- Purple Mountain Laboratories, Nanjing（紫金山实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对无人机流体天线系统中顺序探测导致信道老化的问题，提出联合优化探测位置与停止时机的方法，通过动态估计器与协方差感知策略平衡信息与新鲜度，仿真验证了其在高移动性下的有效性。
AI中文摘要：
无人机流体天线系统（FASs）中的顺序探测提供了额外的空间信息，但会延迟传输，导致较早的信道观测老化。本文通过联合确定探测位置和停止时机，在块状硬件约束下解决由此产生的信息-新鲜度权衡。动态Karhunen–Loève估计器将异步测量与传输状态对齐，协方差信息增益选择可行的探测位置，年龄折扣恒等式与局部多一槽条件刻画了信息与新鲜度之间的协方差级平衡。成对仿真表明，时间对齐恢复了静态堆叠所损失的大部分低尾可靠性，协方差感知探测在评估的策略中数值上排名最高，且在最高测试移动性下，优化的一槽探测在统计上与两槽替代方案具有竞争力。在评估的调度族内，移动性增加将竞争操作区域转向更短的扫描，支持联合探测位置和探测持续时间设计。
英文摘要：
Sequential sounding in UAV fluid-antenna systems (FASs) provides additional spatial information but delays transmission, causing earlier channel observations to age. This paper addresses the resulting information--freshness tradeoff by jointly determining where to probe and when to stop under blockwise hardware constraints. A dynamic Karhunen--Loève estimator aligns asynchronous measurements with the transmission state, covariance information gain selects feasible probe locations, and an age-discount identity with a local one-more-slot condition characterizes the covariance-level balance between information and freshness. Paired simulations show that temporal alignment recovers most of the lower-tail reliability lost by static stacking, covariance-aware probing ranks highest numerically among the evaluated policies, and optimized one-slot sounding becomes statistically competitive with two-slot alternatives at the highest tested mobility. Within the evaluated schedule family, increasing mobility shifts the competitive operating region toward shorter scans, supporting joint probe-placement and sounding-duration design.
