# Trends, Advancements and Challenges in Intelligent Optimization in Satellite Communication

arXiv: https://arxiv.org/abs/2410.03674 · HTML: https://mlsanity.com/paper/2410.03674/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 85 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Satellite Communications in the New Space Era: A Survey and Future Challenges](https://mlsanity.com/paper/2002.08811/) (2020)
- [CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds](https://mlsanity.com/paper/2007.07978/) (2020)
- [Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling](https://mlsanity.com/paper/2110.06787/) (2021)
- [Deep Learning Aided Routing for Space-Air-Ground Integrated Networks Relying on Real Satellite, Flight, and Shipping Data](https://mlsanity.com/paper/2110.15138/) (2021)
- [Satellite Navigation for the Age of Autonomy](https://mlsanity.com/paper/2005.09144/) (2020)

## Abstract (arXiv)

Efficient satellite communications play an enormously important role in all of our daily lives. This includes the transmission of data for communication purposes, the operation of IoT applications or the provision of data for ground stations. More and more, AI-based methods are finding their way into these areas. This paper gives an overview of current research in the field of intelligent optimization of satellite communication. For this purpose, a text-mining based literature review was conducted and the identified papers were thematically clustered and analyzed. The identified clusters cover the main topics of routing, resource allocation and, load balancing. Through such a clustering of the literature in overarching topics, a structured analysis of the research papers was enabled, allowing the identification of latest technologies and approaches as well as research needs for intelligent optimization of satellite communication.