# Reinforcement Learning for a Cellular Internet of UAVs: Protocol Design, Trajectory Control, and Resource Management

arXiv: https://arxiv.org/abs/1911.08771 · HTML: https://mlsanity.com/paper/1911.08771/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control](https://mlsanity.com/paper/2103.04666/) (2021)
- [Machine Learning Methods for Management UAV Flocks -- a Survey](https://mlsanity.com/paper/2108.13448/) (2021)
- [Efficient UAV Trajectory-Planning using Economic Reinforcement Learning](https://mlsanity.com/paper/2103.02676/) (2021)
- [Modelling resource allocation in uncertain system environment through deep reinforcement learning](https://mlsanity.com/paper/2106.09461/) (2021)
- [Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks](https://mlsanity.com/paper/2410.22578/) (2024)
- [Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2108.11012/) (2021)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) can be powerful Internet-of-Things components to execute sensing tasks over the next-generation cellular networks, which are generally referred to as the cellular Internet of UAVs. However, due to the high mobility of UAVs and the shadowing in the air-to-ground channels, UAVs operate in an environment with dynamics and uncertainties. Therefore, UAVs need to improve the quality of service (QoS) of sensing and communication without complete information, which makes reinforcement learning suitable to be employed in the cellular Internet of UAVs. In this article, we propose a distributed sense-and-send protocol to coordinate the UAVs for sensing and transmission. Then, we apply reinforcement learning in the cellular Internet of UAVs to solve key problems such as trajectory control and resource management. Finally, we point out several potential future research directions.