论文标题
航空光谱测量：具有自动无人机的无线电图估计
Aerial Spectrum Surveying: Radio Map Estimation with Autonomous UAVs
论文作者
论文摘要
无线电图成为一种流行的手段，可以将下一代无线通信具有情境意识。特别是，预计无线电图将在无人驾驶汽车（UAV）通信中发挥核心作用，因为它们可用于确定无人机以前从未有过的空间位置的干扰或通道增益。无线电图估计的现有方法利用无法控制位置的传感器收集的测量。相比之下，本文提出了一种计划，其中无人机沿着轨迹收集测量。该轨迹旨在在短时间操作中获得目标无线电图的准确估计。路线规划算法依靠地图不确定性指标来收集更具信息性的位置的测量值。开发了一种在线贝叶斯学习算法，以更新地图估计和不确定性度量，每次收集新的测量时，可以实时操作。
Radio maps are emerging as a popular means to endow next-generation wireless communications with situational awareness. In particular, radio maps are expected to play a central role in unmanned aerial vehicle (UAV) communications since they can be used to determine interference or channel gain at a spatial location where a UAV has not been before. Existing methods for radio map estimation utilize measurements collected by sensors whose locations cannot be controlled. In contrast, this paper proposes a scheme in which a UAV collects measurements along a trajectory. This trajectory is designed to obtain accurate estimates of the target radio map in a short time operation. The route planning algorithm relies on a map uncertainty metric to collect measurements at those locations where they are more informative. An online Bayesian learning algorithm is developed to update the map estimate and uncertainty metric every time a new measurement is collected, which enables real-time operation.
