Machine Learning and Deep Learning powered satellite communications: Enabling technologies,applications, open challenges,and future research directions |
| |
Authors: | Arindam Bhattacharyya Shvetha M Nambiar Ritwik Ojha Amogh Gyaneshwar Utkarsh Chadha Kathiravan Srinivasan |
| |
Affiliation: | 1. School of Electronics Engineering, Vellore Institute of Technology, Vellore, India;2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India;3. Faculty of Applied Sciences and Engineering, University of Toronto, Toronto, Ontario, Canada |
| |
Abstract: | The recent wave of creating an interconnected world through satellites has renewed interest in satellite communications. Private and government-funded space agencies are making advancements in the creation of satellite constellations, and the introduction of 5G has brought a new focus to a fully connected world. Satellites are the proposed solutions for establishing high throughput and low latency links to remote, hard-to-reach areas. This has caused the injection of many satellites in Earth's orbit, which has caused many discrepancies. There is a need to establish highly adaptive and flexible satellite systems to overcome this. Machine Learning (ML) and Deep Learning (DL) have gained much popularity when it comes to communication systems. This review extensively provides insight into ML and DL's utilization in satellite communications. This review covers how satellite communication subsystems and other satellite system applications can be implemented through Artificial Intelligence (AI) and the ongoing open challenges and future directions. |
| |
Keywords: | Deep Learning Indoor Positioning and Indoor Navigation Machine Learning Network Intrusion Detection System satellite communications satellite IoT |
|
|