{"title":"无人机辅助海上低轨卫星通信网络设计与性能分析","authors":"Nilupuli Senadhira;Salman Durrani;Jing Guo;Nan Yang;Xiangyun Zhou","doi":"10.1109/OJCOMS.2025.3571757","DOIUrl":null,"url":null,"abstract":"Monitoring oceanic conditions via maritime Internet of Things (IoT) is vital to the health of the planet. In this regard, providing wireless connectivity to low-end ships, maritime buoys and beacons at sea, which typically lack the technology needed for direct satellite connections, remains a fundamental challenge given the vast ocean expanse and absence of any coverage from on-shore base stations (BSs). To address this challenge, we consider a uncrewed aerial vehicle (UAV)-assisted maritime low Earth orbit (LEO) satellite communication network to provide coverage for low-end maritime users (MUs), such as buoys in remote ocean regions. In this network, we assume that the MUs are distributed across a finite ocean area outside the coverage of onshore BSs. These MUs transmit data to satellites through a swarm of relay UAVs hovering in a finite aerial region, resulting in two communication phases: (i) MU-to-UAV and (ii) UAV-to-satellite. Leveraging stochastic geometry and UAV-centric analysis (which is different from conventional user-centric analysis) in the uplink, we analyze the location-dependent performance and derive an approximate yet accurate expression for the success probability, a key metric characterizing the overall MU-to-UAV-to-satellite network performance. Our numerical results demonstrate that given a set of satellite constellation parameters (e.g., number of satellites, altitude, and beamwidth), the success probability is governed by the interplay between path loss and interference. These results provide theoretical insights for the deployment and planning of integrated maritime-aerial-satellite networks to extend coverage for low-end MUs in remote ocean regions.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4667-4688"},"PeriodicalIF":6.3000,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11007596","citationCount":"0","resultStr":"{\"title\":\"Design and Performance Analysis of UAV-Assisted Maritime-LEO Satellite Communication Networks\",\"authors\":\"Nilupuli Senadhira;Salman Durrani;Jing Guo;Nan Yang;Xiangyun Zhou\",\"doi\":\"10.1109/OJCOMS.2025.3571757\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Monitoring oceanic conditions via maritime Internet of Things (IoT) is vital to the health of the planet. In this regard, providing wireless connectivity to low-end ships, maritime buoys and beacons at sea, which typically lack the technology needed for direct satellite connections, remains a fundamental challenge given the vast ocean expanse and absence of any coverage from on-shore base stations (BSs). To address this challenge, we consider a uncrewed aerial vehicle (UAV)-assisted maritime low Earth orbit (LEO) satellite communication network to provide coverage for low-end maritime users (MUs), such as buoys in remote ocean regions. In this network, we assume that the MUs are distributed across a finite ocean area outside the coverage of onshore BSs. These MUs transmit data to satellites through a swarm of relay UAVs hovering in a finite aerial region, resulting in two communication phases: (i) MU-to-UAV and (ii) UAV-to-satellite. Leveraging stochastic geometry and UAV-centric analysis (which is different from conventional user-centric analysis) in the uplink, we analyze the location-dependent performance and derive an approximate yet accurate expression for the success probability, a key metric characterizing the overall MU-to-UAV-to-satellite network performance. Our numerical results demonstrate that given a set of satellite constellation parameters (e.g., number of satellites, altitude, and beamwidth), the success probability is governed by the interplay between path loss and interference. These results provide theoretical insights for the deployment and planning of integrated maritime-aerial-satellite networks to extend coverage for low-end MUs in remote ocean regions.\",\"PeriodicalId\":33803,\"journal\":{\"name\":\"IEEE Open Journal of the Communications Society\",\"volume\":\"6 \",\"pages\":\"4667-4688\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2025-03-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11007596\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Journal of the Communications Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11007596/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Communications Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/11007596/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Design and Performance Analysis of UAV-Assisted Maritime-LEO Satellite Communication Networks
Monitoring oceanic conditions via maritime Internet of Things (IoT) is vital to the health of the planet. In this regard, providing wireless connectivity to low-end ships, maritime buoys and beacons at sea, which typically lack the technology needed for direct satellite connections, remains a fundamental challenge given the vast ocean expanse and absence of any coverage from on-shore base stations (BSs). To address this challenge, we consider a uncrewed aerial vehicle (UAV)-assisted maritime low Earth orbit (LEO) satellite communication network to provide coverage for low-end maritime users (MUs), such as buoys in remote ocean regions. In this network, we assume that the MUs are distributed across a finite ocean area outside the coverage of onshore BSs. These MUs transmit data to satellites through a swarm of relay UAVs hovering in a finite aerial region, resulting in two communication phases: (i) MU-to-UAV and (ii) UAV-to-satellite. Leveraging stochastic geometry and UAV-centric analysis (which is different from conventional user-centric analysis) in the uplink, we analyze the location-dependent performance and derive an approximate yet accurate expression for the success probability, a key metric characterizing the overall MU-to-UAV-to-satellite network performance. Our numerical results demonstrate that given a set of satellite constellation parameters (e.g., number of satellites, altitude, and beamwidth), the success probability is governed by the interplay between path loss and interference. These results provide theoretical insights for the deployment and planning of integrated maritime-aerial-satellite networks to extend coverage for low-end MUs in remote ocean regions.
期刊介绍:
The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023.
The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include:
Systems and network architecture, control and management
Protocols, software, and middleware
Quality of service, reliability, and security
Modulation, detection, coding, and signaling
Switching and routing
Mobile and portable communications
Terminals and other end-user devices
Networks for content distribution and distributed computing
Communications-based distributed resources control.