通信与感知并行：空地协同的主动在线频谱测绘
Sensing While Communicating: Active Online Spectrum Cartography via Air-Ground Cooperation
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中文总结 AI 辅助
针对低空网络中无人机能量与传输受限问题，提出空地协同主动在线频谱测绘框架，通过深度展开张量分解与位深选择实现近27倍加速，并显著降低误差与中断率。
中文摘要 AI 辅助
频谱测绘对于低空网络中频谱感知的资源管理至关重要，其中无人机（UAV）收集测量数据以重建功率谱密度（PSD）无线电地图。然而，无人机有限的能量预算限制了采样密度和机载计算能力，同时复杂的城市遮挡阻碍了测量数据向远程站点的可靠传输。为应对这些挑战，我们提出了一种空地协同的主动在线频谱测绘框架。通过自适应带宽分配，无人机利用不确定性主动收集PSD测量数据，并同时将其传输给移动无人地面车辆（UGV），而UGV维持可靠的空地链路以更新地图。具体而言，我们首先开发了一种在线深度展开张量分解方法，以加速在线地图更新。然后，我们推导了一个依赖于位深度的插值误差模型，以指导量化位深度的选择，从而在开销和精度之间取得平衡。最后，我们利用不确定性信息分别协调无人机和UGV进行主动采样和链路维护。大量实验表明，所提出的重建方法相比在线张量分解实现了近27倍的加速。在城市场景中，所提出的框架优于代表性基线，将归一化均方误差（NMSE）和中断率均降低了超过23%，并将数据包服务完成率提高了超过4%。
英文摘要
Spectrum cartography is crucial for spectrum-aware resource management in low-altitude networks, where uncrewed aerial vehicles (UAVs) collect measurements to reconstruct power spectral density (PSD) radio maps. However, limited UAV energy budgets constrain both sampling density and onboard computation, while complex urban blockages hinder reliable measurement transmission to a remote station. To address these challenges, we propose an air-ground cooperative framework for active online spectrum cartography. Through adaptive bandwidth allocation, the UAV actively collects PSD measurements using uncertainty and simultaneously transmits them to a mobile uncrewed ground vehicle (UGV), while the UGV maintains a reliable air-ground link for map updates. Specifically, we first develop an online deep-unfolded tensor decomposition method to accelerate online map updates. We then derive a bit-depth-dependent interpolation-error model to guide the selection of quantization bit depths to balance overhead and accuracy. Finally, we use uncertainty information to coordinate the UAV and UGV for active sampling and link maintenance, respectively. Extensive experiments show that the proposed reconstruction method achieves a nearly 27-fold speedup over online tensor decomposition. In urban scenes, the proposed framework outperforms the representative baselines, reducing both normalized mean squared error (NMSE) and outage ratio by more than 23% each and increasing the packet-service completion ratio by over 4%.
