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研究生:邱曉莛
研究生(外文):Chiu, Hsiao-Ting
論文名稱:應用於無線通訊網路之非正交多重存取的功率控制及無人機的最佳路徑規劃
論文名稱(外文):Optimal Power Control for Non-Orthogonal Multiple Access and UAV Optimal Trajectory Planning in Wireless Communication Networks
指導教授:高榮鴻高榮鴻引用關係
指導教授(外文):Gau, Rung-Hung
口試委員:高榮鴻廖婉君魏宏宇黃仁竑吳卓諭林靖茹
口試委員(外文):Gau, Rung-HungLiao, Wan-JiunWei, Hung-YuHwang, Ren-HungWu, Jwo-YuhLin, Ching-Ju
口試日期:2021-1-8
學位類別:博士
校院名稱:國立交通大學
系所名稱:電信工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:英文
論文頁數:124
中文關鍵詞:非正交多重存取功率控制無人機軌跡最佳化問題凸優化多根天線傳輸
外文關鍵詞:Non-Orthogonal Multiple AccessPower ControlUAV TrajectoryOptimization ProblemConvex OptimizationMIMO Transmission
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隨著裝置的增加,提升網路效率在未來通訊系統中被視為一個必要的議題。其中一個有效提升傳輸效率的技術即為非正交多重存取技術,其允許基地台同時傳送不同資料給多個裝置而不需要多個正交的頻道。此外,行動裝置的廣泛使用也造成了覆蓋範圍的需求。引用中繼站被視為是其中一個可以快速及有效解決覆蓋範圍的方法。我們提出的功率控制演算法將非正交多重存取技術應用於中繼站協助的網路,並且將非正交多重存技術依序應用到基地台及中繼站來提升網路效率。另外,我們也考慮了當基地台裝有多根天線時應用非正交多重存取技術的傳輸效率。為了更進一步的處理離基地台更遠的裝置,我們引進了無人機來當作傳輸媒介,在論文中提出了新的演算法,其能快速並有效率的設計無人機軌跡及模式。此篇論文的貢獻可分為三個部分。第一,我們提出了功率控制結合連續性非正交多重存取的演算法,在中繼站輔助的網路中提升傳輸容量。第二,當考慮多根天線時,我們提出了機會式演算法來設計預編碼以期能更大化傳輸速度。第三,我們採用無人機設備並設計無人機的運行模式和軌跡,處理座落在郊區的裝置,並盡可能的最小化整趟軌跡的任務時間。此外,在第三部分,我們更進一步的將無人機應用於異質性網路,即同時考慮網路中包含靜態裝置以及動態裝置。數值分析及模擬結果顯示提出的演算法都能有效的改善網路容量。
With the rapidly growing number of devices connected to the Internet, enhancing the communication efficiency has been regarded as a necessary issues in the future 5G/6G system. One of the promising technology to increase the communication capacity is non-orthogonal multiple access, which allows base station to transmit data to multiple devices without multiple orthogonal channels. In addition, the increasing number of devices causes the demand of larger transmission range. To improve the transmission range, using relay nodes is considered as a rapid and effective way. We have proposed the power control algorithm which applies non-orthogonal multiple access (NOMA) to the relay-assisted networks. We have implemented the sequential NOMA approach in both base station and relay nodes. Moreover, the transmission from base station which is equipped with multiple antennas has been considered under NOMA technique. To further handle the devices located far away from the base station or to deal with other emergencies, the thesis has also proposed the innovative algorithms which effectively employed unmanned aerial vehicles (UAV) to the wireless network. The contributions of the thesis can be divided into three parts. First, we propose the power control algorithm for the sequential NOMA approach to enhance the transmission rate under the relay-assisted networks. Second, we raise the opportunistic method to design the precoding matrix for NOMA transmission under MIMO networks to increase the capacity. Last, we adopt UAVs and design the UAV trajectory and the mode selection method which can minimizes the completion time so as to deal with the Internet-of-Things (IoT) devices which may be far away from the base stations. In addition, the heterogeneous wireless networks which contain both static and mobile IoT devices is considered in UAV-assisted wireless networks. Analytical results and simulation results show that the proposed algorithms can efficiently improve network throughput.
Chinese Abstract i
English Abstract iii
Acknowledgements v
Contents vi
List of Tables x
List of Figures xi
Symbols xiii
1 Introduction 1
2 Sequential NOMA in Relay-Assisted Cooperative Wireless Networks 4
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 System Model and Problem Formulation . . . . . . . . . . . . 11
2.4 The Geometric Eight-Point Algorithm . . . . . . . . . . . . . . 15
2.5 Simulation Setup and Results . . . . . . . . . . . . . . . . . . . 24
2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3 Opportunistic Precoding Matrix Design for Non-Separable Wireless MIMO-NOMA Networks 29
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3 System Model and Problem Formulation . . . . . . . . . . . . 34
3.4 Opportunistic Matrix Precoding for Non-Separable Channels 37
3.4.1 The first algorithm component for C22 (Q1, Q2) ≤ C12 (Q1, Q2) . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.4.2 The second algorithm component for C12 (Q1, Q2) ≤ C22 (Q1, Q2) . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.5 User Pairing and Scheduling . . . . . . . . . . . . . . . . . . . . 52
3.6 Simulation Setup and Results . . . . . . . . . . . . . . . . . . . 53
3.6.1 The opportunistic matrix precoding algorithm . . . . . 54
3.6.2 User pairing and fairness . . . . . . . . . . . . . . . . . 59
3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.8 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.8.1 Proof of Theorem 1 . . . . . . . . . . . . . . . . . . . . 62
3.8.2 Proof of Theorem 2 . . . . . . . . . . . . . . . . . . . . 64
3.8.3 Proof of Theorem 4 . . . . . . . . . . . . . . . . . . . . 64
4 Completion Time Minimization for UAV-Assisted Communication Networks with Static and Mobile Devices 66
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.2 System Model and Problem Formulation . . . . . . . . . . . . 73
4.3 Optimal Trajectory Planning for a Static IoT Device . . . . . 77
4.3.1 Baseline center turning point . . . . . . . . . . . . . . . 80
4.3.2 The type-1 optimal turning point . . . . . . . . . . . . 81
4.3.3 The communicating-while-moving mode and the type-
1 planning algorithm . . . . . . . . . . . . . . . . . . . . 83
4.3.4 Obtaining point F in O(1) time . . . . . . . . . . . . . 86
4.3.5 The type-2 optimal turning point and the type-2 planning
algorithm . . . . . . . . . . . . . . . . . . . . . . . 87
4.3.6 The dynamic location-dependent planning algorithm . 88
4.4 Choosing an Optimal Pair of Data Rates . . . . . . . . . . . . 91
4.5 UAV Trajectory for Static and Mobile IoT Devices . . . . . . 92
4.5.1 TSP-based visiting order . . . . . . . . . . . . . . . . . 93
4.5.2 The communicating-after-locking mode for mobile IoT devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.5.3 Serving multiple ground IoT devices . . . . . . . . . . . 94
4.6 Simulation Setup and Results . . . . . . . . . . . . . . . . . . . 96
4.6.1 Analysis of optimal turning points . . . . . . . . . . . . 97
4.6.2 Simulation results of optimal trajectory planning for a
static IoT device . . . . . . . . . . . . . . . . . . . . . . 99
4.6.3 Simulation results of UAV trajectory for static IoT
devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.6.4 Simulation results of UAV trajectory for static and mobile IoT devices . . . . . . . . . . . . . . . . . . . . . 108
4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
4.8 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
4.8.1 Proof of Theorem 1 . . . . . . . . . . . . . . . . . . . . 111
4.8.2 Proof of Theorem 2 . . . . . . . . . . . . . . . . . . . . 112
4.8.3 Proof of Theorem 3 . . . . . . . . . . . . . . . . . . . . 114
5 Conclusion 115
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