Cellular-connected unmanned aerial vehicles (UAVs) have gained increasing
attention due to their potential to enhance conventional UAV capabilities by
leveraging existing cellular infrastructure for reliable communications between
UAVs and base stations. They have been used for various applications, including
weather forecasting and search and rescue operations. However, under extreme
weather conditions such as rainfall, it is challenging for the trajectory
design of cellular UAVs, due to weak coverage regions in the sky, limitations
of UAV flying time, and signal attenuation caused by raindrops. To this end,
this paper proposes a physics-based trajectory design approach for
cellular-connected UAVs in rainy environments. A physics-based electromagnetic
simulator is utilized to take into account detailed environment information and
the impact of rain on radio wave propagation. The trajectory optimization
problem is formulated to jointly consider UAV flying time and
signal-to-interference ratio, and is solved through a Markov decision process
using deep reinforcement learning algorithms based on multi-step learning and
double Q-learning. Optimal UAV trajectories are compared in examples with
homogeneous atmosphere medium and rain medium. Additionally, a thorough study
of varying weather conditions on trajectory design is provided, and the impact
of weight coefficients in the problem formulation is discussed. The proposed
approach has demonstrated great potential for UAV trajectory design under rainy
weather conditions.

在极端天气条件下，本研究提出了一种基于物理的轨迹设计方法，利用基于物理的电磁模拟器考虑详细的环境信息和降雨对无线电波传播的影响，并通过深度强化学习算法基于多步学习和双 Q-learning 解决联合考虑无人机飞行时间和信干比的轨迹优化问题。在均匀大气和雨背景下比较了最优无人机轨迹，还对不同天气条件对轨迹设计的影响进行了彻底研究，并讨论了问题制定中的权重系数的影响。该方法在雨天气条件下展示了优秀的无人机轨迹设计潜力。