# Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond

arXiv: https://arxiv.org/abs/1903.05289 · HTML: https://mlsanity.com/paper/1903.05289/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2019.

## Built on by (most cited first, 28 of 28)

- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs](https://mlsanity.com/paper/2506.08045/) (2025)
- [Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control](https://mlsanity.com/paper/2210.00945/) (2022)
- [Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning](https://mlsanity.com/paper/2104.04477/) (2021)
- [Learning-Based UAV Trajectory Optimization with Collision Avoidance and Connectivity Constraints](https://mlsanity.com/paper/2104.06256/) (2021)
- [Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2505.03230/) (2025)
- [Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks](https://mlsanity.com/paper/2209.07367/) (2022)
- [Efficient UAV Trajectory-Planning using Economic Reinforcement Learning](https://mlsanity.com/paper/2103.02676/) (2021)
- [Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks](https://mlsanity.com/paper/2104.10403/) (2021)
- [Energy-Efficient Cellular-Connected UAV Swarm Control Optimization](https://mlsanity.com/paper/2303.10398/) (2023)
- [Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy Environments Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2309.00017/) (2023)
- [Towards Scalable Wireless Federated Learning: Challenges and Solutions](https://mlsanity.com/paper/2310.05076/) (2023)
- [Spatial Deep Learning for Site-Specific Movement Optimization of Aerial Base Stations](https://mlsanity.com/paper/2312.10490/) (2023)
- [ARDDQN: Attention Recurrent Double Deep Q-Network for UAV Coverage Path Planning and Data Harvesting](https://mlsanity.com/paper/2405.11013/) (2024)
- [3D Dynamic Radio Map Prediction Using Vision Transformers for Low-Altitude Wireless Networks](https://mlsanity.com/paper/2511.19019/) (2025)
- [Expert Routing for Communication-Efficient MoE via Finite Expert Banks](https://mlsanity.com/paper/2605.05278/) (2026)
- [Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Team of Aerial Robots](https://mlsanity.com/paper/2107.04924/) (2021)
- [Autonomous Resource Management in Construction Companies Using Deep Reinforcement Learning Based on IoT](https://mlsanity.com/paper/2208.08087/) (2022)
- [Handover and SINR-Aware Path Optimization in 5G-UAV mmWave Communication using DRL](https://mlsanity.com/paper/2504.02688/) (2025)
- [GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication](https://mlsanity.com/paper/2505.17530/) (2025)
- [Integrated Communication and Control for Energy-Efficient UAV Swarms: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2509.23905/) (2025)
- [PIKAN: Physics-Inspired Kolmogorov-Arnold Networks for Explainable UAV Channel Modelling](https://mlsanity.com/paper/2510.06355/) (2025)
- [QoS-Aware Hierarchical Reinforcement Learning for Joint Link Selection and Trajectory Optimization in SAGIN-Supported UAV Mobility Management](https://mlsanity.com/paper/2512.15119/) (2025)
- [Arm2Air: Cross-Embodiment Skeleton Transfer for 3D Relay Formation](https://mlsanity.com/paper/2607.27627/) (2026)
- [WONDER: A Radio World Model-based Negotiation Framework for Multi-Agent UAV Coverage Optimization](https://mlsanity.com/paper/2608.16955/) (2026)
- [The Operable Pareto Front: Distilling Offline Search into Run-Time Control for Multi-Objective UAV Edge-Computing Scheduling](https://mlsanity.com/paper/2609.17992/) (2026)
- [Joint Movement and Compression Ratio Design for Mobile Embodied AI Networks (MEAN)](https://mlsanity.com/paper/2610.02334/) (2026)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) have found numerous applications and are expected to bring fertile business opportunities in the next decade. Among various enabling technologies for UAVs, wireless communication is essential and has drawn significantly growing attention in recent years. Compared to the conventional terrestrial communications, UAVs' communications face new challenges due to their high altitude above the ground and great flexibility of movement in the three-dimensional (3D) space. Several critical issues arise, including the line-of-sight (LoS) dominant UAV-ground channels and resultant strong aerial-terrestrial network interference, the distinct communication quality of service (QoS) requirements for UAV control messages versus payload data, the stringent constraints imposed by the size, weight and power (SWAP) limitations of UAVs, as well as the exploitation of the new design degree of freedom (DoF) brought by the highly controllable 3D UAV mobility. In this paper, we give a tutorial overview of the recent advances in UAV communications to address the above issues, with an emphasis on how to integrate UAVs into the forthcoming fifth-generation (5G) and future cellular networks. In particular, we partition our discussions into two promising research and application frameworks of UAV communications, namely UAV-assisted wireless communications and cellular-connected UAVs,where UAVs serve as aerial communication platforms and users, respectively. Furthermore, we point out promising directions for future research and investigation.