Machine Learning and Deep Learning powered satellite communications: Enabling technologies, applications, open challenges, and future research directions
{"title":"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","doi":"10.1002/sat.1482","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>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.</p>\n </div>","PeriodicalId":50289,"journal":{"name":"International Journal of Satellite Communications and Networking","volume":"41 6","pages":"539-588"},"PeriodicalIF":0.9000,"publicationDate":"2023-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Satellite Communications and Networking","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/sat.1482","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
引用次数: 2
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.
期刊介绍:
The journal covers all aspects of the theory, practice and operation of satellite systems and networks. Papers must address some aspect of satellite systems or their applications. Topics covered include:
-Satellite communication and broadcast systems-
Satellite navigation and positioning systems-
Satellite networks and networking-
Hybrid systems-
Equipment-earth stations/terminals, payloads, launchers and components-
Description of new systems, operations and trials-
Planning and operations-
Performance analysis-
Interoperability-
Propagation and interference-
Enabling technologies-coding/modulation/signal processing, etc.-
Mobile/Broadcast/Navigation/fixed services-
Service provision, marketing, economics and business aspects-
Standards and regulation-
Network protocols