無人機在空中拍攝影片時，由於其飛行路徑上的通道品質會隨時間改變，所以導致無人機在飛行時的平均訊號雜訊干擾比值(Signal to Interference plus Noise Ratio, SINR)與資料傳輸率無法維持其影音播放的品質，因此本論文設計一個動態調整無人機飛行路徑以提升通道品質(Dynamic Trajectory Adjustment for UAV Channel Quality, DTA)的機制，此機制分為兩個部分，第一部分是將開放式無線電存取網路(Open Radio Access Network, O-RAN)的訊號範圍切割為數個立方體空間(Cubicles, CB)，我們根據一個CB的長寬高將一個無線電單元(Radio Unit, RU)的訊號範圍進行切割，因為無人機的經緯度與高度必須轉換為地面的3D座標，所以我們設計一個經緯度與3D座標的轉換(Transformation of Latitude/ Longitude and 3D Axis, TLLA)。此機制的第二部分會先計算每一個CB的中心點的接收功率，由於每一個CB可能包含雜訊與干擾值，所以SINR的計算也要考慮每一個CB的雜訊與干擾值。我們將每一個CB的SINR轉換為通道品質係數(Channel Quality Index, CQI)，在得到所有CB的CQI後，所有CB的CQI會加總並取其平均，接著本論文提出的動態調整通道品質演算法(Dynamic Adjustment of UAV Channel Quality, DACQ)比較無人機周圍的CB的CQI，如果無人機周圍的CB的CQI相同時，演算法會繼續比較每一個CB的干擾值並找出最小干擾值的CB，地面監控中心會派遣無人機飛到這個相鄰的CB。為了驗證本論文提出的動態調整無人機飛行路徑的優越性，我們設計兩種干擾程度(輕度與重度)與不同的CB數量，由實驗結果中，我們發現如果CB的數量增加，因為干擾被限制在某些CB內，所以平均的SINR得到顯著的提升，另外我們設計無人機在四種不同飛行路徑下比較其通道品質(平均SINR)與資料傳輸率(Throughput)，由實驗結果可以證明無人機使用DACQ機制確實有助於通道品質與資料傳輸率的提升。

Employing Unmanned Aerial Vehicle (UAV) shooting video in the air, the channel quality along its trajectory undergoes temporal changes, leading to fluctuations in the UAV's average Signal to Interference plus Noise Ratio (SINR) and throughput during flight, thereby affecting the quality of video playback. To address this challenge, we proposed a novel mechanism called Dynamic Trajectory Adjustment for UAV Channel Quality (DTA), which enhances channel quality by dynamically adjusting the UAV's trajectory during flight. The mechanism comprises two modules. In the first module, the signal range is divided into multiple discrete cubicles (CB) based on size. Given that Latitude, Longitude, and Altitude must be converted to 3D coordinates at ground level, we introduce a formula named Transformation of Latitude/ Longitude and 3D axis (TLLA). In the second module, we determine the received power of each CB with a central point. As each CB may contain noise and interference, calculating SINR requires accounting for their values. Subsequently, we convert the SINR of each CB to a Channel Quality Index (CQI) and compute the average CQI for all CBs. The proposed algorithm, Dynamic Adjustment of UAV Channel Quality (DACQ), compares the CQI of CBs surrounding the UAV. If CBs have the same CQI, we compare the interference values of surrounding CBs and dispatch the UAV to the one with the lowest value. To assess our proposed mechanism, we consider two interference levels (light and severe) and vary CB quantities. Simulation results show that increasing CB numbers under both interference levels notably enhances average SINR, as interference is confined to specific CBs. Additionally, we evaluate UAV channel quality (average SINR) and throughput across four trajectories, demonstrating that implementing the DACQ algorithm in UAV operations effectively enhances channel quality and throughput.

論文審定書 i
致謝 ii
摘要 iii
Abstract iv
目錄 v
圖目錄 vii
表目錄 viii
第一章 導論 1
1.1 研究動機 1
1.2 研究方法 1
1.3 章節介紹 2
第二章 無人機的通道品質 3
2.1 經緯度與3D座標 3
2.1.1 無人機的經緯度 3
2.1.2 3D空間的座標 6
2.2 無人機的通道品質 7
2.2.1 通道衰退的特性 7
2.2.2 雜訊與干擾 10
2.2.3 資料傳輸率 10
2.3 相關研究 11
第三章 無人機通道品質的提升 16
3.1 經緯度與3D空間的轉換 16
3.1.1 3D空間的切割 16
3.1.2 無人機3D空間座標的計算 23
3.1.3 無人機經緯度座標的計算 27
3.2 SINR的計算 28
3.3 DACQ演算法 30
第四章 模擬結果與分析 34
4.1 模擬拓樸 34
4.2 立方體空間與通道品質 35
4.2.1 建立立方體空間 35
4.2.2 調整無人機的通道品質 41
4.2.3 挑選較佳品質的立方體空間 44
4.3 結果與分析 47
4.3.1 環境設定 47
4.3.2 結果的分析 48
第五章 結論與未來工作 62
5.1 結論 62
5.2 遭遇困難 63
5.3 未來工作 63
Reference 64
附錄一 3D座標與經緯度的轉換範例 68
附錄二 CB的SINR的計算範例 77
Acronyms 84
Index 86

[1]Space, M. S. C. G. J. P. Office, and L. ARINC Engineering Services, NAVSTAR Global Positioning System: Interface Specification : Navstar GPS Space Segment/navigation User Interfaces: GPS Joint Program Office, 2006.
[2]U. Iqbal, J. Georgy, W. F. Abdelfatah, M. J. Korenberg, and A. Noureldin, “Pseudoranges Error Correction in Partial GPS Outages for a Nonlinear Tightly Coupled Integrated System,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1510-1525, Sep. 2013.
[3]Z. Gan, H. Xu, Y. He, W. Cao, and G. Chen, "Autonomous Landing Point Retrieval Algorithm for UAVs Based on 3D Environment Perception," in 2021 IEEE 7th International Conference on Virtual Reality (ICVR), pp. 104-108, May 2021.
[4]K. Wang, R. Zhang, L. Wu, Z. Zhong, L. He, J. Liu, and X. Pang, "Path Loss Measurement and Modeling for Low-Altitude UAV Access Channels," in 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), pp. 1-5, Sep. 2017.
[5]Z. Huang, J. Rodríguez-Piñeiro, T. Domínguez-Bolaño, X. Cai, and X. Yin, “Empirical Dynamic Modeling for Low-Altitude UAV Propagation Channels,” IEEE Transactions on Wireless Communications, vol. 20, no. 8, pp. 5171-5185, Aug. 2021.
[6]T. W. Kang, J. H. Kim, N. W. Kang, and J. S. Kang, “A Thermal Noise Measurement System for Noise Temperature Standards in W-Band,” IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 6, pp. 1741-1747, Jun. 2015.
[7]I. Hadj-Kacem, H. Braham, and S. B. Jemaa, “SINR and Rate Distributions for Downlink Cellular Networks,” IEEE Transactions on Wireless Communications, vol. 19, no. 7, pp. 4604-4616, Jul. 2020.
[8]C. E. Shannon, “A Mathematical Theory of Communication,” The Bell System Technical Journal, vol. 27, no. 3, pp. 379-423, Jul. 1948.
[9]Y. Ruan, Y. Zhang, Y. Li, R. Zhang, and R. Hang, “An Adaptive Channel Division MAC Protocol for High Dynamic UAV Networks,” IEEE Sensors Journal, vol. 20, no. 16, pp. 9528-9539, Aug. 2020.
[10]X. Huang, A. Liu, H. Zhou, K. Yu, W. Wang, and X. Shen, “FMAC: A Self-Adaptive MAC Protocol for Flocking of Flying Ad Hoc Network,” IEEE Internet of Things Journal, vol. 8, no. 1, pp. 610-625, Jan. 2021.
[11]X. Li, F. Hu, J. Qi, and S. Kumar, “Systematic Medium Access Control in Hierarchical Airborne Networks With Multibeam and Single-Beam Antennas,” IEEE Transactions on Aerospace and Electronic Systems, vol. 55, no. 2, pp. 706-717, Oct. 2019.
[12]Y. Zou, Z. Wei, Y. Cui, X. Liu, and Z. Feng, “UD-MAC: Delay Tolerant Multiple Access Control Protocol for Unmanned Aerial Vehicle Networks,” IEEE Sensors Journal, vol. 23, no. 19, pp. 23653-23663, Oct. 2023.
[13]H. Baek and J. Lim, “Time Mirroring Based CSMA/CA for Improving Performance of UAV-Relay Network System,” IEEE Systems Journal, vol. 13, no. 4, pp. 4478-4481, Dec. 2019.
[14]B. Fan, L. Jiang, Y. Chen, Y. Zhang, and Y. Wu, “UAV Assisted Traffic Offloading in Air Ground Integrated Networks With Mixed User Traffic,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 12601-12611, Aug. 2022.
[15]M. A. Ali, Y. Zeng, and A. Jamalipour, “Software-Defined Coexisting UAV and WiFi: Delay-Oriented Traffic Offloading and UAV Placement,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 6, pp. 988-998, Jun. 2020.
[16]C. C. Lai, Bhola, A. H. Tsai, and L. C. Wang, “Adaptive and Fair Deployment Approach to Balance Offload Traffic in Multi-UAV Cellular Networks,” IEEE Transactions on Vehicular Technology, vol. 72, no. 3, pp. 3724-3738, Mar. 2023.
[17]Z. Liao, Y. Ma, J. Huang, J. Wang, and J. Wang, “HOTSPOT: A UAV-Assisted Dynamic Mobility-Aware Offloading for Mobile-Edge Computing in 3-D Space,” IEEE Internet of Things Journal, vol. 8, no. 13, pp. 10940-10952, Jul. 2021.
[18]M. A. Ali and A. Jamalipour, “UAV-Aided Cellular Operation by User Offloading,” IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9855-9864, Jun. 2021.
[19]N. Tafintsev, D. Moltchanov, S. Andreev, S. p. Yeh, N. Himayat, Y. Koucheryavy, and M. Valkama, “Handling Spontaneous Traffic Variations in 5G+ via Offloading Onto mmWave-Capable UAV “Bridges”,” IEEE Transactions on Vehicular Technology, vol. 69, no. 9, pp. 10070-10084, Sep. 2020.
[20]F. Cheng, S. Zhang, Z. Li, Y. Chen, N. Zhao, F. R. Yu, and V. C. M. Leung, “UAV Trajectory Optimization for Data Offloading at the Edge of Multiple Cells,” IEEE Transactions on Vehicular Technology, vol. 67, no. 7, pp. 6732-6736, Jul. 2018.
[21]L. Wang, Q. Zhou, and Y. Shen, “Computation Efficiency Maximization for UAV-Assisted Relaying and MEC Networks in Urban Environment,” IEEE Transactions on Green Communications and Networking, vol. 7, no. 2, pp. 565-578, Jun. 2023.
[22]X. Fan, H. Zhou, K. Sun, X. Chen, and N. Wang, “Channel Assignment and Power Allocation Utilizing NOMA in Long-Distance UAV Wireless Communication,” IEEE Transactions on Vehicular Technology, vol. 72, no. 10, pp. 12970-12982, Oct. 2023.
[23]C. Guo, C. Guo, S. Zhang, and Z. Ding, “UAV-Enabled NOMA Networks Analysis With Selective Incremental Relaying and Imperfect CSI,” IEEE Transactions on Vehicular Technology, vol. 69, no. 12, pp. 16276-16281, Dec. 2020.
[24]S. Farrag, E. Maher, A. El-Mahdy, and F. Dressler, “Performance Analysis of UAV Assisted Mobile Communications in THz Channel,” IEEE Access, vol. 9, pp. 160104-160115, Dec. 2021.
[25]M. T. Dabiri and M. Hasna, “3D Uplink Channel Modeling of UAV-Based mmWave Fronthaul Links for Future Small Cell Networks,” IEEE Transactions on Vehicular Technology, vol. 72, no. 2, pp. 1400-1413, Feb. 2023.
[26]C. Ge, R. Zhang, D. Zhai, Y. Jiang, and B. Li, “UAV-Correlated MIMO Channels: 3-D Geometrical-Based Polarized Model and Capacity Analysis,” IEEE Internet of Things Journal, vol. 10, no. 2, pp. 1446-1460, Jan. 2023.
 
[27]X. Yan, Y. Lin, H. C. Wu, Q. Wang, and S. Zhu, “Novel Robust Dynamic Distributed Drone-Deployment Strategy for Channel-Capacity Optimization for 3-D UAV-Aided Ad Hoc Networks,” IEEE Internet of Things Journal, vol. 10, no. 12, pp. 10190-10206, Jun. 2023.
[28]R. Chen, J. Chen, H. Wang, X. Tong, Y. Xu, N. Qi, and Y. Xu, “Joint Channel Access and Power Control Optimization in Large-Scale UAV Networks: A Hierarchical Mean Field Game Approach,” IEEE Transactions on Vehicular Technology, vol. 72, no. 2, pp. 1982-1996, Feb. 2023.
[29]L. Li, T. H. Chang, and S. Cai, “UAV Positioning and Power Control for Two-Way Wireless Relaying,” IEEE Transactions on Wireless Communications, vol. 19, no. 2, pp. 1008-1024, Feb. 2020.
[30]C. Zhan and Y. Zeng, “Energy-Efficient Data Uploading for Cellular-Connected UAV Systems,” IEEE Transactions on Wireless Communications, vol. 19, no. 11, pp. 7279-7292, Nov. 2020.
[31]NCERT, Practical Work in Geography Part - I Textbook for Class - XI, pp. 27-30, India, 2014.