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Constructing Radio Maps for UAV Communications via Dynamic Resolution Virtual Obstacle Maps

机译：通过动态分辨率虚拟障碍物地图构建用于无人机通信的无线电地图

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Radio maps that characterize the air-to-ground communication channels play an important role in optimizing unmanned aerial vehicle (UAV) communications. Constructing a radio map is very challenging because a city may have a complicated building and vegetation topology that affects the air-to-ground radio propagation. Existing methods usually require a large amount of measurement data for training. This paper focuses on small sample regime. A learning framework is developed to decompose the radio map into a structural component, which predicts the path loss via constructing a hidden virtual obstacle map, and a non-structural component, which captures the random scattering due to signal reflection and diffraction. It is found that constructing a virtual obstacle map with dynamic resolution improves the learning efficiency. This paper develops a simple grouping method that locally adjusts the map resolution according to the sample density and side information from a 2D street map. Numerical experiments show that the proposed method outperforms existing schemes in small to large sample regimes.
机译：表征空对地通信信道的无线电地图在优化无人机（UAV）通信中起着重要作用。建造无线电地图非常具有挑战性，因为城市可能具有复杂的建筑物和植被拓扑结构，从而影响空对地无线电传播。现有方法通常需要大量的测量数据来进行训练。本文着重于小样本制度。开发了一种学习框架，以将无线电图分解为通过构造隐藏的虚拟障碍物图来预测路径损耗的结构性组件，以及捕获因信号反射和衍射而引起的随机散射的非结构性组件。发现构建具有动态分辨率的虚拟障碍物地图可以提高学习效率。本文开发了一种简单的分组方法，可以根据2D街道地图的样本密度和辅助信息在本地调整地图分辨率。数值实验表明，所提出的方法在小样本到大样本情况下均优于现有方案。

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