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Graduate School of Ajou University
Department of Artificial Intelligence Convergence Network
3. Theses(Master)
Joint Trajectory Optimization and MAC Protocol Design for UAV-Assisted Emergency Networks
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 김재현 | - |
| dc.contributor.author | 강정화 | - |
| dc.date.accessioned | 2025-01-25T01:35:52Z | - |
| dc.date.available | 2025-01-25T01:35:52Z | - |
| dc.date.issued | 2023-02 | - |
| dc.identifier.other | 32474 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/24318 | - |
| dc.description | 학위논문(석사)--AI융합네트워크학과,2023. 2 | - |
| dc.description.tableofcontents | 1 Introduction 1 <br> 1.1 Background and motivation 1 <br> 1.2 Contributions 2 <br> 1.3 Overview 3 <br>2 Related works 4 <br> 2.1 The UAV optimal trajectory and its related works 4 <br> 2.2 The Lyapunov optimization 5 <br> 2.3 Indoor-to-outdoor path loss 6 <br>3 The UAV optimal trajectory in disaster environment 7 <br> 3.1 System model 7 <br> 3.1.1 Indoor-to-outdoor path loss by ITU-R 7 <br> 3.1.2 Indoor-to-outdoor path loss considering the floor penetration 9 <br> 3.2 Adaptive UAV movements frame-work 11 <br> 3.2.1 Initial flight for connection 12 <br> 3.2.2 Searching flight for optimal location 12 <br> 3.2.3 The Lyapunov-based UAV optimal trajectory 17 <br> 3.2.4 Pseudo code and computational complexity 21 <br>4 Performance evaluation 23 <br> 4.1 Simulation environments 23 <br> 4.2 Key performance indicators 23 <br> 4.3 Results and discussion 25 <br>5 Conclusion 47 <br>References 48 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | Joint Trajectory Optimization and MAC Protocol Design for UAV-Assisted Emergency Networks | - |
| dc.title.alternative | UAV 비상 네트워크를 위한 궤적 최적화 및 MAC 프로토콜 설계 | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 대학원 | - |
| dc.contributor.department | 일반대학원 AI융합네트워크학과 | - |
| dc.date.awarded | 2023-02 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | T000000032474 | - |
| dc.identifier.url | https://dcoll.ajou.ac.kr/dcollection/common/orgView/000000032474 | - |
| dc.subject.keyword | Emergency networks | - |
| dc.subject.keyword | Lyapunov optimization | - |
| dc.subject.keyword | MAC protocol | - |
| dc.subject.keyword | Optimal trajectory | - |
| dc.subject.keyword | UAV | - |
| dc.description.alternativeAbstract | In recent years, various researches on covering the shadow areas have been conducted around the world through moving base stations using unmanned aerial vehicles (UAVs). Most recently, communication using UAVs has been in the spotlight for military and civilian users because of its low cost and variety usage. In particular, studies related to aerial base station using UAVs are being actively conducted because of the convenience of operation in local areas such as city or mountainous areas, where communications are unavailable. Although most studies have focused on optimal UAV localization to provide efficient communications for outdoor users, supporting indoor users is also of great importance. Especially, the importance of indoor users increase even more in disaster situation. In a disaster situation, it is important to connect many users at early stage by receiving rescue signals from all users at least once. In addition, it is important to receive a lot of continuously requested rescue status signals of many users to recognize and send the current status to rescue team. Therefore, this thesis proposes optimal trajectory frame-work using Lyapunov optimization for receiving continuous rescue signals of many users. Moreover, we consider initial time for connecting all users. First, the additional information in acknowledgement (ACK) packet is considered for initial user connection in disaster environment. This method guarantee that users are served at least once that all the most important in a disaster environment. Second, the Lyapunov optimization-based UAV trajectory is designed based on the characteristics of trade-off constants. In performance evalution, two building types were considered: standard- and factory-type buildings. In conclusion, the performance evaluation results prove that the proposed Lyapunov optimization-based UAV trajectory achieves desired performance based on the characteristics of trade-off constants. | - |
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