Deploying UAV Base Stations in Communication Network Using Machine Learning
| dc.contributor.author | Zhong, Xukai | |
| dc.contributor.supervisor | Dong, Xiaodai | |
| dc.date.accessioned | 2019-12-21T23:57:30Z | |
| dc.date.available | 2019-12-21T23:57:30Z | |
| dc.date.copyright | 2019 | en_US |
| dc.date.issued | 2019-12-21 | |
| dc.degree.department | Department of Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Engineering M.Eng. | en_US |
| dc.description.abstract | Today has witnessed a constantly increasing demand for high-quality wireless communications services. Moreover, the quality of service (QoS) requirement of future 5G and beyond cellular networks leads to the possible use of the unmanned aerial vehicle base station (UAV-BS). Deploying UAV-BSs to assist the communications network has become a research direction with great potential. In this project, we focus on the problem of deploying UAV-BSs to provide satisfactory wireless communication services, with the aim that maximizes the total number of covered user equipment subject to user data rate requirements and UAV-BS capacity limit. Then, the report extends to a reinforcement learning based method to adjust the locations of UAVs to maximize the sum data rate of the user equipment (UE). Numerical experiments under practical settings provide supportive evidences to our design. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/11405 | |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Unmanned Aerial Vehicle | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Reinforcement Learning | en_US |
| dc.subject | Optimization | en_US |
| dc.subject | Genetic Algorithm | en_US |
| dc.title | Deploying UAV Base Stations in Communication Network Using Machine Learning | en_US |
| dc.type | project | en_US |