UAV Fluid-Antenna Channel Acquisition under Intra-Scan Channel Aging

📅 2026-09-06
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🤖 AI Summary
本文解决了无人机流体天线系统中因时序扫描导致的信道老化问题,通过动态估计、协方差信息增益和老化折扣方法优化探测位置和时间。
📝 Abstract
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--Lo\`eve 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.
Problem

Research questions and friction points this paper is trying to address.

UAV
Fluid-Antenna Systems
Channel Aging
Information-Freshness Tradeoff
Sequential Sounding
Innovation

Methods, ideas, or system contributions that make the work stand out.

dynamic Karhunen-Loève estimator
covariance information gain
age-discount identity
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Yuanhui Wu
College of Artificial Intelligence, Nanjing University of Information Science and Technology, China
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Hao Jiang
School of Cyber Science and Engineering, Southeast University, China
Liang Wu
Liang Wu
National Mobile Communications Research Laboratory, Southeast University, China; Purple Mountain Laboratories, Nanjing, China
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Zaichen Zhang
National Mobile Communications Research Laboratory, Southeast University, China; Purple Mountain Laboratories, Nanjing, China