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。

基于深度强化学习的雨天环境中面向蜂窝连接的无人机物理轨迹设计

Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy  Environments Based on Deep Reinforcement Learning

Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be
integrated into future cellular networks as new aerial mobile users. Providing
cellular connectivity to UAVs will enable a myriad of applications ranging from
online video streaming to medical delivery. However, to enable a reliable
wireless connectivity for the UAVs as well as a secure operation, various
challenges need to be addressed such as interference management, mobility
management and handover, cyber-physical attacks, and authentication. In this
paper, the goal is to expose the wireless and security challenges that arise in
the context of UAV-based delivery systems, UAV-based real-time multimedia
streaming, and UAV-enabled intelligent transportation systems. To address such
challenges, artificial neural network (ANN) based solution schemes are
introduced. The introduced approaches enable the UAVs to adaptively exploit the
wireless system resources while guaranteeing a secure operation, in real-time.
Preliminary simulation results show the benefits of the introduced solutions
for each of the aforementioned cellular-connected UAV application use case.

本文介绍了在细胞连接的无人机领域中所涉及到的无线连接和安全挑战，提出了基于人工神经网络的方案来适应性地利用无线系统资源并在保证实时安全的情况下解决挑战。