发表机构
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无人机频谱测绘中测量稀疏和频率不完整的问题，提出基于深度展开在线张量分解的主动框架，实现快速地图更新与不确定性引导的感知，显著提升重建精度并大幅降低误差。
AI 中文摘要
频谱测绘对于低空网络中频谱感知资源管理至关重要，其中无人机（UAV）收集频谱测量数据以重建功率谱密度（PSD）图。然而，有限的能量和感知带宽使得测量在空间上稀疏且在频率上不完整。为了高效地收集这些测量数据，无人机根据最新地图及其不确定性主动规划下一步行动，这需要快速的在线重建。因此，我们提出了一种主动在线频谱测绘框架。我们首先开发了在线深度展开张量分解（ODU-TD）以实现快速地图更新。随后，一组重建器用于估计不确定性，以选择信息丰富的感知目标，并且基于学习的策略在能量预算下确定无人机移动和感知带宽。实验表明，在稀疏的空间和频谱观测下，ODU-TD相比在线张量分解实现了约27倍的加速，并在所比较的重建器中取得了最低的归一化均方误差（NMSE），同时所提出的框架与代表性基线相比将NMSE降低了至少55%。
英文摘要
Spectrum cartography is crucial for spectrum-aware resource management in low-altitude networks, where uncrewed aerial vehicles (UAVs) collect spectrum measurements to reconstruct power spectral density (PSD) maps. However, limited energy and sensing bandwidth make measurements sparse in space and incomplete in frequency. To collect these measurements efficiently, the UAV actively plans its next move based on the latest map and its uncertainty, which requires rapid online reconstruction. We therefore propose an active online spectrum cartography framework. We first develop online deep-unfolded tensor decomposition (ODU-TD) for rapid map updates. An ensemble of reconstructors then estimates uncertainty to select informative sensing targets, and a learning-based policy determines the UAV movement and sensing bandwidth under the energy budget. Experiments show that ODU-TD achieves an approximately 27-fold speedup over online tensor decomposition and the lowest normalized mean square error (NMSE) among the compared reconstructors under sparse spatial and spectral observations, and the proposed framework reduces the NMSE by at least 55% compared with the representative baselines.
