# Survey of Important Issues in UAV Communication Networks

arXiv: https://arxiv.org/abs/1603.08462 · HTML: https://mlsanity.com/paper/1603.08462/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2016.

## Built on by (most cited first, 23 of 23)

- [Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges](https://mlsanity.com/paper/2103.04505/) (2021)
- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [Aerial Monocular 3D Object Detection](https://mlsanity.com/paper/2208.03974/) (2022)
- [Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control](https://mlsanity.com/paper/2210.00945/) (2022)
- [Deep Reinforcement Learning for Sim-to-Real Policy Transfer of VTOL-UAVs Offshore Docking Operations](https://mlsanity.com/paper/2406.00887/) (2024)
- [Scalable UAV Multi-Hop Networking via Multi-Agent Reinforcement Learning with Large Language Models](https://mlsanity.com/paper/2505.08448/) (2025)
- [Fully-echoed Q-routing with Simulated Annealing Inference for Flying Adhoc Networks](https://mlsanity.com/paper/2103.12870/) (2021)
- [Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method](https://mlsanity.com/paper/2202.02459/) (2022)
- [Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning](https://mlsanity.com/paper/2306.02990/) (2023)
- [Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach](https://mlsanity.com/paper/2308.06973/) (2023)
- [Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2412.10700/) (2024)
- [Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learning](https://mlsanity.com/paper/2102.02078/) (2021)
- [Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle-based network coverage optimization](https://mlsanity.com/paper/2203.13607/) (2022)
- [Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy Environments Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2309.00017/) (2023)
- [Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defenses](https://mlsanity.com/paper/2502.04201/) (2025)
- [FedBA: Non-IID Federated Learning Framework in UAV Networks](https://mlsanity.com/paper/2210.04699/) (2022)
- [SkyCharge: Deploying Unmanned Aerial Vehicles for Dynamic Load Optimization in Solar Small Cell 5G Networks](https://mlsanity.com/paper/2311.12944/) (2023)
- [A Task-Driven Multi-UAV Coalition Formation Mechanism](https://mlsanity.com/paper/2403.05108/) (2024)
- [Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach](https://mlsanity.com/paper/2410.02827/) (2024)
- [Graph Neural Network Aided Deep Reinforcement Learning for Resource Allocation in Dynamic Terahertz UAV Networks](https://mlsanity.com/paper/2505.04981/) (2025)
- [Timely Information Updating for Mobile Devices Without and With ML Advice](https://mlsanity.com/paper/2512.17381/) (2025)
- [TASER: Task-Aware Spectral Energy Refine for Backdoor Suppression in UAV Swarms Decentralized Federated Learning](https://mlsanity.com/paper/2603.10075/) (2026)
- [Resource-Efficient Distributed Recursive Gaussian Processes](https://mlsanity.com/paper/2609.26979/) (2026)

## Abstract (arXiv)

Unmanned Aerial Vehicles (UAVs) have enormous potential in the public and civil domains. These are particularly useful in applications where human lives would otherwise be endangered. Multi-UAV systems can collaboratively complete missions more efficiently and economically as compared to single UAV systems. However, there are many issues to be resolved before effective use of UAVs can be made to provide stable and reliable context-specific networks. Much of the work carried out in the areas of Mobile Ad Hoc Networks (MANETs), and Vehicular Ad Hoc Networks (VANETs) does not address the unique characteristics of the UAV networks. UAV networks may vary from slow dynamic to dynamic; have intermittent links and fluid topology. While it is believed that ad hoc mesh network would be most suitable for UAV networks yet the architecture of multi-UAV networks has been an understudied area. Software Defined Networking (SDN) could facilitate flexible deployment and management of new services and help reduce cost, increase security and availability in networks. Routing demands of UAV networks go beyond the needs of MANETS and VANETS. Protocols are required that would adapt to high mobility, dynamic topology, intermittent links, power constraints and changing link quality. UAVs may fail and the network may get partitioned making delay and disruption tolerance an important design consideration. Limited life of the node and dynamicity of the network leads to the requirement of seamless handovers where researchers are looking at the work done in the areas of MANETs and VANETs, but the jury is still out. As energy supply on UAVs is limited, protocols in various layers should contribute towards greening of the network. This article surveys the work done towards all of these outstanding issues, relating to this new class of networks, so as to spur further research in these areas.