# UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operations

arXiv: https://arxiv.org/abs/2202.10338 · HTML: https://mlsanity.com/paper/2202.10338/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges](https://mlsanity.com/paper/1805.00881/) (2018)
- [UAV Communications for 5G and Beyond: Recent Advances and Future Trends](https://mlsanity.com/paper/1901.06637/) (2019)
- [Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges](https://mlsanity.com/paper/1809.01752/) (2018)
- [Reinforcement Learning in Multiple-UAV Networks: Deployment and Movement Design](https://mlsanity.com/paper/1904.05242/) (2019)
- [On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks](https://mlsanity.com/paper/1804.08415/) (2018)
- [Drone Small Cells in the Clouds: Design, Deployment and Performance Analysis](https://mlsanity.com/paper/1509.01655/) (2015)
- [Backhaul-aware Robust 3D Drone Placement in 5G+ Wireless Networks](https://mlsanity.com/paper/1702.08395/) (2017)
- [The New Frontier in RAN Heterogeneity: Multi-tier Drone-Cells](https://mlsanity.com/paper/1604.00381/) (2016)
- [Trajectory Optimization of Flying Energy Sources using Q-Learning to Recharge Hotspot UAVs](https://mlsanity.com/paper/2003.12258/) (2020)
- [Deployment of Drone Base Stations for Cellular Communication Without Apriori User Distribution Information](https://mlsanity.com/paper/1809.07033/) (2018)

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

- [Adaptive Sampling for Automated Post-Disaster Rapid Damage Assessment via Level-Set Cost-Aware Bayesian Optimization](https://mlsanity.com/paper/2608.02868/) (2026)

## Abstract (arXiv)

Because of disaster, terrestrial base stations (TBS) would be partly crashed. Some user equipments (UE) would be unserved. Deploying unmanned aerial vehicles (UAV) as aerial base stations is a method to cover UEs quickly. But existing methods solely refer to the coverage of UAVs. In those scenarios, they focus on the deployment of UAVs in the post-disaster area where all TBSs do not work any longer. There is limited research about the combination of available TBSs and UAVs. We propose the method to deploy UAVs cooperating with available TBSs as aerial base stations. And improve the coverage by reinforcement learning. Besides, in the experiments, we cluster UEs with balanced iterative reducing and clustering using hierarchies (BIRCH) at first. Finally, achieve base stations' better coverage to UEs through Q-learning.