# Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges

arXiv: https://arxiv.org/abs/1602.03602 · HTML: https://mlsanity.com/paper/1602.03602/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2016.

## Built on by (most cited first, 30 of 43)

- [UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach](https://mlsanity.com/paper/2106.15734/) (2021)
- [Forking Uncertainties: Reliable Prediction and Model Predictive Control with Sequence Models via Conformal Risk Control](https://mlsanity.com/paper/2310.10299/) (2023)
- [UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learning](https://mlsanity.com/paper/2108.00354/) (2021)
- [Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2107.11015/) (2021)
- [Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems](https://mlsanity.com/paper/2101.05206/) (2021)
- [Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control](https://mlsanity.com/paper/2210.00945/) (2022)
- [Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems](https://mlsanity.com/paper/2302.04445/) (2023)
- [AI-based Drone Assisted Human Rescue in Disaster Environments: Challenges and Opportunities](https://mlsanity.com/paper/2406.15875/) (2024)
- [Learning Emergent Random Access Protocol for LEO Satellite Networks](https://mlsanity.com/paper/2112.01765/) (2021)
- [Multi-armed Bandit Learning on a Graph](https://mlsanity.com/paper/2209.09419/) (2022)
- [UAV-assisted Distributed Learning for Environmental Monitoring in Rural Environments](https://mlsanity.com/paper/2407.02693/) (2024)
- [Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learning](https://mlsanity.com/paper/2102.02078/) (2021)
- [Multi-Agent Reinforcement Learning with Action Masking for UAV-enabled Mobile Communications](https://mlsanity.com/paper/2303.16737/) (2023)
- [Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2306.10071/) (2023)
- [EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV](https://mlsanity.com/paper/2403.05422/) (2024)
- [Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks](https://mlsanity.com/paper/2501.14603/) (2025)
- [Optimization for Master-UAV-powered Auxiliary-Aerial-IRS-assisted IoT Networks: An Option-based Multi-agent Hierarchical Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2112.10630/) (2021)
- [Convergence Theory of Generalized Distributed Subgradient Method with Random Quantization](https://mlsanity.com/paper/2207.10969/) (2022)
- [Sum-Rate Maximization of RSMA-based Aerial Communications with Energy Harvesting: A Reinforcement Learning Approach](https://mlsanity.com/paper/2306.12977/) (2023)
- [Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy Environments Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2309.00017/) (2023)
- [Learn for Variation: Efficient AAV Trajectory Learning through a Differentiable Wireless World Model](https://mlsanity.com/paper/2603.18853/) (2026)
- [Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Team of Aerial Robots](https://mlsanity.com/paper/2107.04924/) (2021)
- [Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2108.11012/) (2021)
- [Networking of Internet of UAVs: Challenges and Intelligent Approaches](https://mlsanity.com/paper/2111.07078/) (2021)
- [5G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-based Integrated Access and Backhaul](https://mlsanity.com/paper/2202.02006/) (2022)
- [Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle](https://mlsanity.com/paper/2202.08972/) (2022)
- [SkyCharge: Deploying Unmanned Aerial Vehicles for Dynamic Load Optimization in Solar Small Cell 5G Networks](https://mlsanity.com/paper/2311.12944/) (2023)
- [Top-k Multi-Armed Bandit Learning for Content Dissemination in Swarms of Micro-UAVs](https://mlsanity.com/paper/2404.10845/) (2024)
- [GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Network](https://mlsanity.com/paper/2409.00036/) (2024)
- [Towards Federated Multi-Armed Bandit Learning for Content Dissemination using Swarm of UAVs](https://mlsanity.com/paper/2501.09146/) (2025)

## Abstract (arXiv)

Wireless communication systems that include unmanned aerial vehicles (UAVs) promise to provide cost-effective wireless connectivity for devices without infrastructure coverage. Compared to terrestrial communications or those based on high-altitude platforms (HAPs), on-demand wireless systems with low-altitude UAVs are in general faster to deploy, more flexibly re-configured, and are likely to have better communication channels due to the presence of short-range line-of-sight (LoS) links. However, the utilization of highly mobile and energy-constrained UAVs for wireless communications also introduces many new challenges. In this article, we provide an overview of UAV-aided wireless communications, by introducing the basic networking architecture and main channel characteristics, highlighting the key design considerations as well as the new opportunities to be exploited.