This paper presents a learning approach to predict air-to-ground communication channel strength to support the communication-relay mission using the unmanned aerial vehicle (UAV) in complex urban environments. The knowledge of the air-to-ground communication channel quality between the UAV and ground nodes is essential for optimal relay trajectory planning. However, because of the obstruction by buildings and interferences in the urban environment, modeling and predicting the communication channel strength is a challenging task. We address this issue by leveraging the Gaussian process (GP) method to learn the communication shadow fading in a given environment and then employing the optimization-based relay trajectory planning by using learned communication properties. The key advantage of this learning method over fixed communication model based approaches is that it can keep refining channel prediction and trajectory planning as more channel measurement data are obtained. Two schemes incorporating GP-based channel prediction into trajectory planning are proposed. Monte Carlo simulations demonstrate the performance gain and robustness of the proposed approaches over the existing methods.

中文翻译：

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城市环境中通信中继无人机的基于高斯过程的信道预测

本文提出了一种预测空对地通信信道强度的学习方法，以支持在复杂城市环境中使用无人机 (UAV) 执行的通信中继任务。了解无人机和地面节点之间的空对地通信信道质量对于最佳中继轨迹规划至关重要。然而，由于建筑物的阻碍和城市环境中的干扰，对通信信道强度进行建模和预测是一项具有挑战性的任务。我们通过利用高斯过程 (GP) 方法来学习给定环境中的通信阴影衰落，然后通过使用学习的通信特性采用基于优化的中继轨迹规划来解决这个问题。这种学习方法相对于基于固定通信模型的方法的主要优势在于，随着获得更多的信道测量数据，它可以不断改进信道预测和轨迹规划。提出了两种将基于 GP 的信道预测纳入轨迹规划的方案。蒙特卡罗模拟证明了所提出的方法相对于现有方法的性能增益和鲁棒性。