Survey on Aerial Radio Access Networks: Toward a Comprehensive 6G Access Infrastructure
Not on the map. Why?
Abstract (arXiv)
Current network access infrastructures are characterized by heterogeneity, low latency, high throughput, and high computational capability, enabling massive concurrent connections and various services. Unfortunately, this design does not pay significant attention to mobile services in underserved areas. In this context, the use of aerial radio access networks (ARANs) is a promising strategy to complement existing terrestrial communication systems. Involving airborne components such as unmanned aerial vehicles, drones, and satellites, ARANs can quickly establish a flexible access infrastructure on demand. ARANs are expected to support the development of seamless mobile communication systems toward a comprehensive sixth-generation (6G) global access infrastructure. This paper provides an overview of recent studies regarding ARANs in the literature. First, we investigate related work to identify areas for further exploration in terms of recent knowledge advancements and analyses. Second, we define the scope and methodology of this study. Then, we describe ARAN architecture and its fundamental features for the development of 6G networks. In particular, we analyze the system model from several perspectives, including transmission propagation, energy consumption, communication latency, and network mobility. Furthermore, we introduce technologies that enable the success of ARAN implementations in terms of energy replenishment, operational management, and data delivery. Subsequently, we discuss application scenarios envisioned for these technologies. Finally, we highlight ongoing research efforts and trends toward 6G ARANs.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Feb 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 2; 2023: 0; 2024: 2; 2025: 2.
6 papers.
- 2025Diffusion Models for Future Networks and Communications: A Comprehensive Surveycited by 6
- 2022Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Controlcited by 5
- 2022Need of 6G for the Metaverse Realizationcited by 0
- 2024Deep UAV Path Planning with Assured Connectivity in Dense Urban Settingcited by 0
- 2024DeepAir: A Multi-Agent Deep Reinforcement Learning Based Scheme for an Unknown User Location Problemcited by 0
- 2025Integration of TinyML and LargeML: A Survey of 6G and Beyondcited by 0
- 2025Diffusion Models for Future Networks and Communications: A Comprehensive Survey3 Aug 2025
- 2025Integration of TinyML and LargeML: A Survey of 6G and Beyond20 May 2025
- 2024DeepAir: A Multi-Agent Deep Reinforcement Learning Based Scheme for an Unknown User Location Problem11 Aug 2024
- 2024Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting21 Jun 2024
- 2022Need of 6G for the Metaverse Realization28 Dec 2022
- 2022Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control3 Oct 2022