{"title":"Altitude-Based Dynamics Modulation and Power Analysis in LEO Satellites","authors":"Shahid Ali, Bingli Jiao","doi":"10.3103/S0146411624701190","DOIUrl":null,"url":null,"abstract":"<p>Efficient signal modulation schemes are crucial for optimizing communication performance in low Earth orbit (LEO) satellite systems. Traditionally, uniform modulation methods are applied across the satellite constellation, regardless of variations in satellite distances from ground stations. However, this approach leads to inefficient signal power utilization, particularly for satellites at higher altitudes. This paper proposes a novel solution to this issue by implementing dynamic modulation strategies tailored to specific altitude ranges of LEO satellites. Through dynamic modulation, we utilize binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) for satellites positioned farther from earth, while higher-order constellations are used for satellites in closer proximity. This altitude-dependent modulation scheme aims to improve power efficiency throughout the LEO satellite network. Our results demonstrate a significant enhancement in power efficiency across the LEO satellite system compared to the conventional uniform modulation scheme. The adaptability of this dynamic modulation approach to satellite altitude variations allows for more judicious utilization of signal power, thereby maximizing communication quality and network reliability. We validate the effectiveness of the proposed method through extensive simulations.</p>","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 6","pages":"735 - 743"},"PeriodicalIF":0.6000,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.3103/S0146411624701190","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Efficient signal modulation schemes are crucial for optimizing communication performance in low Earth orbit (LEO) satellite systems. Traditionally, uniform modulation methods are applied across the satellite constellation, regardless of variations in satellite distances from ground stations. However, this approach leads to inefficient signal power utilization, particularly for satellites at higher altitudes. This paper proposes a novel solution to this issue by implementing dynamic modulation strategies tailored to specific altitude ranges of LEO satellites. Through dynamic modulation, we utilize binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) for satellites positioned farther from earth, while higher-order constellations are used for satellites in closer proximity. This altitude-dependent modulation scheme aims to improve power efficiency throughout the LEO satellite network. Our results demonstrate a significant enhancement in power efficiency across the LEO satellite system compared to the conventional uniform modulation scheme. The adaptability of this dynamic modulation approach to satellite altitude variations allows for more judicious utilization of signal power, thereby maximizing communication quality and network reliability. We validate the effectiveness of the proposed method through extensive simulations.
期刊介绍:
Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision