AI 中文总结
针对动态发射机配置下无线电环境重建的挑战，提出CKM赋能的低复杂度方法，无需新测量即可高效更新无线电地图，性能优于CS、Kriging和U-Net且鲁棒性强。
AI 中文摘要
在发射机配置动态变化的场景下，准确且及时的无线电环境重建具有重要意义但也面临挑战。传统方法如压缩感知（CS）、克里金法（Kriging）或U-Net，在发射机位置或辐射方向图改变时，通常需要环境测量和重建开销来更新无线电环境。本文提出一种新颖的信道知识图谱（Channel Knowledge Map, CKM）赋能的动态无线电环境重建方法，用于高效更新无线电地图。具体而言，最新提出的CKM可存储与发射机侧辐射特性解耦的可复用路径级传播知识。当发射机位置和辐射方向图已知时，可利用CKM生成轻量的前向无线电地图，无需进行新的目标地图测量。仿真结果表明，所提方法在重建精度上优于CS、Kriging和U-Net，且在动态发射机配置下表现出强鲁棒性，证明其在动态无线网络中实现灵活高效无线电环境重建的潜力。
英文摘要
Accurate and timely radio environment reconstruction is important but challenging under particularly dynamic transmitter configurations. The conventional methods such as compressed sensing (CS), Kriging method or U-Net typically require environment measurements and reconstruction overhead for radio environment updating as the transmitter locations or radiation patterns change. In this paper, we propose a novel channel knowledge map (CKM)-enabled dynamic radio environment reconstruction method for efficient radio map updating. Specifically, the recently proposed CKM can store reusable path-level propagation knowledge that is decoupled from the transmitter-side radiation characteristics. We can leverage CKM for lightweight forward radio map generation as the transmitter locations and radiation patterns are known, without requiring new target-map measurements. Simulation results show that the proposed method outperforms CS, Kriging, and U-Net in reconstruction accuracy and exhibits strong robustness performance under dynamic transmitter configurations, which demonstrates the potential of the proposed method for flexible and efficient radio environment reconstruction in dynamic wireless networks.
