# Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networks

arXiv: https://arxiv.org/abs/2202.12683 · HTML: https://mlsanity.com/paper/2202.12683/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 72 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 25 of 25)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Towards an Intelligent Edge: Wireless Communication Meets Machine Learning](https://mlsanity.com/paper/1809.00343/) (2018)
- [Satellite Communications in the New Space Era: A Survey and Future Challenges](https://mlsanity.com/paper/2002.08811/) (2020)
- [Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms](https://mlsanity.com/paper/2002.08196/) (2020)
- [Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues](https://mlsanity.com/paper/1809.08707/) (2018)
- ["Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment](https://mlsanity.com/paper/2101.01524/) (2021)
- [Vehicle Routing Problems for Drone Delivery](https://mlsanity.com/paper/1608.02305/) (2016)
- [The Final Frontier: Deep Learning in Space](https://mlsanity.com/paper/2001.10362/) (2020)
- [Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges](https://mlsanity.com/paper/1809.01752/) (2018)
- [Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory](https://mlsanity.com/paper/2003.04451/) (2020)
- [Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence](https://mlsanity.com/paper/1808.01053/) (2018)
- [Artificial Intelligence for Satellite Communication: A Review](https://mlsanity.com/paper/2101.10899/) (2021)
- [Artificial Intelligence for UAV-enabled Wireless Networks: A Survey](https://mlsanity.com/paper/2009.11522/) (2020)
- [Software Defined Space-Air-Ground Integrated Vehicular Networks: Challenges and Solutions](https://mlsanity.com/paper/1703.02664/) (2017)
- [Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2107.11015/) (2021)
- [Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs](https://mlsanity.com/paper/1804.05348/) (2018)
- [Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks](https://mlsanity.com/paper/2005.12521/) (2020)
- [Machine Learning assisted Handover and Resource Management for Cellular Connected Drones](https://mlsanity.com/paper/2001.07937/) (2020)
- [Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method](https://mlsanity.com/paper/2202.02459/) (2022)
- [Deep Learning For Experimental Hybrid Terrestrial and Satellite Interference Management](https://mlsanity.com/paper/1906.03012/) (2019)
- [Stochastic Geometry-based Analysis of LEO Satellite Communication Systems](https://mlsanity.com/paper/2006.15591/) (2020)
- [SuperCell: A Wide-Area Coverage Solution Using High-Gain, High-Order Sectorized Antennas on Tall Towers](https://mlsanity.com/paper/2012.00161/) (2020)
- [Coverage Analysis for UAV-Assisted Cellular Networks in Rural Areas](https://mlsanity.com/paper/2103.13794/) (2021)

## Abstract (arXiv)

Connectivity in rural areas is one of the main challenges of communication networks. To overcome this challenge, a variety of solutions for different situations are required. Optimizing the current networking paradigms is therefore mandatory. The high costs of infrastructure and the low revenue of cell sites in rural areas compared with urban areas are especially unattractive for telecommunication operators. Therefore, space, air, and ground networks should all be optimized for achieving connectivity in rural areas. We highlight the latest works on rural connectivity, discuss the solutions for terrestrial networks, and study the potential benefits of nonterrestrial networks. Furthermore, we present an overview of artificial intelligence (AI) techniques for improving space, air, and ground networks, hence improving connectivity in rural areas. AI enables intelligent communications and can integrate space, air, and ground networks for rural connectivity. We discuss the rural connectivity challenges and highlight the latest projects and research and the empowerment of networks using AI. Finally, we discuss the potential positive impacts of providing connectivity to rural communities.