# Air-Ground Integrated Mobile Edge Networks: Architecture, Challenges and Opportunities

arXiv: https://arxiv.org/abs/1804.04763 · HTML: https://mlsanity.com/paper/1804.04763/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2018.

## Built on by (most cited first, 3 of 3)

- [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023)
- [EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles](https://mlsanity.com/paper/2101.06056/) (2021)
- [DeepAir: A Multi-Agent Deep Reinforcement Learning Based Scheme for an Unknown User Location Problem](https://mlsanity.com/paper/2408.05712/) (2024)

## Abstract (arXiv)

The ever-increasing mobile data demands have posed significant challenges in the current radio access networks, while the emerging computation-heavy Internet of things (IoT) applications with varied requirements demand more flexibility and resilience from the cloud/edge computing architecture. In this article, to address the issues, we propose a novel air-ground integrated mobile edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and assist the communication, caching, and computing of the edge network. In specific, we present the detailed architecture of AGMEN, and investigate the benefits and application scenarios of drone-cells, and UAV-assisted edge caching and computing. Furthermore, the challenging issues in AGMEN are discussed, and potential research directions are highlighted.