arXiv:2611.13726 · v1

Weighted-AoI-Driven UAV Trajectory and Reporting for Dynamic CKM Tracking

首次提交 2026-11-10 16:29

版本更新 2026-11-10 16:29

雷达观测 2026-11-11 11:04

摘要

Existing channel knowledge map (CKM) construction assumes quasi-static environments, failing when channels rapidly decorrelate. This paper investigates continuous online dynamic CKM tracking over an airborne 2D plane via a UAV fleet. We formulate a long-term error minimization problem under UAV mobility and reporting constraints. This problem, however, is intractable with unobservable errors for unmeasured cells. To tackle this, we propose a metric-driven scheduling approach featuring the Weighted-Age of Information (W-AoI). W-AoI evaluates cells by combining exact temporal staleness with spatial uncertainty predicted by a 2D U-Net. We prove that top-$K$ W-AoI selection maximizes the one-slot error reduction bound. Guided by this guarantee, we develop a lightweight greedy trajectory policy incorporating continuous along-path sensing. Simulations demonstrate our method reduces steady-state error by 3.8 dB$^2$ and 4.8 dB$^2$ over AoI-only and FIFO baselines, closely approaching an idealized oracle.

作者备注：6 pages, 2 figures, 2 tables, submitted to the WCNC conference