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

arXiv: https://arxiv.org/abs/2309.00017 · HTML: https://mlsanity.com/paper/2309.00017/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 53 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 18 of 18)

- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges](https://mlsanity.com/paper/1602.03602/) (2016)
- [Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks](https://mlsanity.com/paper/1705.02723/) (2017)
- [Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond](https://mlsanity.com/paper/1903.05289/) (2019)
- [Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.07574/) (2020)
- [Radio Map Based 3D Path Planning for Cellular-Connected UAV](https://mlsanity.com/paper/1912.00021/) (2019)
- [Survey of Important Issues in UAV Communication Networks](https://mlsanity.com/paper/1603.08462/) (2016)
- [Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges](https://mlsanity.com/paper/1809.01752/) (2018)
- [Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management](https://mlsanity.com/paper/1801.05500/) (2018)
- [A Survey of Channel Modeling for UAV Communications](https://mlsanity.com/paper/1801.07359/) (2018)
- [Cellular UAV-to-X Communications: Design and Optimization for Multi-UAV Networks](https://mlsanity.com/paper/1801.05000/) (2018)
- [Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective](https://mlsanity.com/paper/1805.07182/) (2018)
- [Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs](https://mlsanity.com/paper/1804.05348/) (2018)
- [Energy Efficient UAV Communication with Energy Harvesting](https://mlsanity.com/paper/1911.00351/) (2019)
- [UAV-to-UAV Communications in Cellular Networks](https://mlsanity.com/paper/1912.07534/) (2019)
- [Trajectory Planning of Cellular-Connected UAV for Communication-assisted Radar Sensing](https://mlsanity.com/paper/2207.13238/) (2022)
- [A Double Q-Learning Approach for Navigation of Aerial Vehicles with Connectivity Constraint](https://mlsanity.com/paper/2002.10563/) (2020)

## Built on by (most cited first, 1 of 1)

- [Intelligent Optimization of Wireless Access Point Deployment for Communication-Based Train Control Systems Using Deep Reinforcement Learning](https://mlsanity.com/paper/2509.24819/) (2025)

## Abstract (arXiv)

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.