# Deep Learning Aided Routing for Space-Air-Ground Integrated Networks Relying on Real Satellite, Flight, and Shipping Data

arXiv: https://arxiv.org/abs/2110.15138 · HTML: https://mlsanity.com/paper/2110.15138/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 28 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence](https://mlsanity.com/paper/1808.01053/) (2018)
- [Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning](https://mlsanity.com/paper/2001.00784/) (2020)
- [Enabling 5G on the Ocean: A Hybrid Satellite-UAV-Terrestrial Network Solution](https://mlsanity.com/paper/2006.00826/) (2020)
- [Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges](https://mlsanity.com/paper/1903.11814/) (2019)
- [Aeronautical Ad Hoc Networking for the Internet-Above-The-Clouds](https://mlsanity.com/paper/1905.07486/) (2019)

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

- [Continual Deep Reinforcement Learning for Decentralized Satellite Routing](https://mlsanity.com/paper/2405.12308/) (2024)
- [Multi-Agent Deep Reinforcement Learning for Distributed Satellite Routing](https://mlsanity.com/paper/2402.17666/) (2024)
- [Trends, Advancements and Challenges in Intelligent Optimization in Satellite Communication](https://mlsanity.com/paper/2410.03674/) (2024)

## Abstract (arXiv)

Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks. With the growth of transcontinental air traffic, the promising concept of aeronautical ad hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-to-ground and multi-hop air-to-air links. In this article, we conceive space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earth-orbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-, ground- and sea-layer. To meet heterogeneous service requirements, and accommodate the time-varying and self-organizing nature of SAGINs, we propose a deep learning (DL) aided multi-objective routing algorithm, which exploits the quasi-predictable network topology and operates in a distributed manner. Our simulation results based on real satellite, flight, and shipping data in the North Atlantic region show that the integrated network enhances the coverage quality by reducing the end-to-end (E2E) delay and by boosting the E2E throughput as well as improving the path-lifetime. The results demonstrate that our DL-aided multi-objective routing algorithm is capable of achieving near Pareto-optimal performance.