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dc.contributor.authorWang, Li-Chunen_US
dc.contributor.authorLai, Chuan-Chien_US
dc.contributor.authorShuai, Hong-Hanen_US
dc.contributor.authorLin, Hsin-Piaoen_US
dc.contributor.authorLi, Chi-Yuen_US
dc.contributor.authorCheng, Teng-Huen_US
dc.contributor.authorChen, Chiun-Hsunen_US
dc.date.accessioned2020-10-05T02:01:29Z-
dc.date.available2020-10-05T02:01:29Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-1-7281-0960-2en_US
dc.identifier.issn2166-0069en_US
dc.identifier.urihttp://hdl.handle.net/11536/155260-
dc.description.abstractFuture mobile communication networks require an Aerial Base Station (ABS) with fast mobility and long-term hovering capabilities. At present, unmanned aerial vehicles (UAV) or drones do not have long flight times and are mainly used for monitoring, surveillance, and image post-processing. On the other hand, the traditional airship is too large and not easy to take off and land. Therefore, we propose to develop an "Artificial Intelligence (AI) Drone-Cruiser" base station that can help 5G mobile communication systems and beyond quickly recover the network after a disaster and handle the instant communications by the flash crowd. The drone-cruiser base station can overcome the communications problem for three types of flash crowds, such as in stadiums, parades, and large plaza so that an appropriate number of aerial base stations can be accurately deployed to meet large and dynamic traffic demands. Artificial intelligence can solve these problems by analyzing the collected data, and then adjust the system parameters in the framework of Self-Organizing Network (SON) to achieve the goals of self-configuration, self-optimization, and self-healing. With the help of AI technologies, 5G networks can become more intelligent. This paper aims to provide a new type of service, On-Demand Aerial Base Station as a Service. This work needs to overcome the following live technical challenges: innovative design of drone-cruisers for the long-time hovering, crowd estimation and prediction, rapid 3D wireless channel learning and modeling, 3D placement of aerial base stations and the integration of WiFi front-haul and millimeter wave/WiGig back-haul networks.en_US
dc.language.isoen_USen_US
dc.subjectAerial Base Stationen_US
dc.subjectDrone-Cruiseren_US
dc.subjectArtificial Intelligenceen_US
dc.subjectSelf-Organizing Networken_US
dc.subject3D Placementen_US
dc.subjectFlying Access Pointen_US
dc.titleCommunications and Networking Technologies for Intelligent Drone Cruisersen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2019 IEEE GLOBECOM WORKSHOPS (GC WKSHPS)en_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department機械工程學系zh_TW
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department電機工程學系zh_TW
dc.contributor.departmentDepartment of Mechanical Engineeringen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000554832400237en_US
dc.citation.woscount0en_US
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