隨著非地面網路(Non-Terrestrial Networks) 的出現，使用非正交多重存取技術 (Non-Orthogonal Multiple Access, NOMA) 的無人機(Unmanned Aerial Vehicles, UAV) 和低軌道衛星(Low Earth Orbit, LEO) 成為超越第五代(Beyond Fifth Generation, B5G) 行動通訊的潛在解決方案。增強型行動寬頻(Enhanced Mobile Broad Band, eMBB) 和超高可靠低延遲通訊(Ultra-Reliable and Low-Latency Communications, URLLC) 是新興B5G 行動通訊的兩大核心服務。URLLC 服務需要更低的延遲和更高的可靠性，而eMBB 服務則期望有最大的資料傳輸率。第三代合作夥伴計畫 (3rd Generation Partnership Project, 3GPP) 針對5G 新無線電（New Radio, NR）技術 制定具有彈性的訊框架構來支援不同服務應用的迷你時槽(Mini-Slots)。本論文主要的貢獻是在地面與非地面網路中使用NOMA 技術提出三個資源分配模組。在第一個模組中，我們研究eMBB 和URLLC 的聯合資源分配問題，其目的是針對基地台的下行(Downlink) 服務品質(Quality of Service, QoS）來最大化eMBB 使用者的資料傳輸率並滿足URLLC 使用者的最低預期抵達率(Minimum Expected Achieved Rate, MEAR)。在第二個模組中，為了增強訊號覆蓋範圍和連接性，我們
在多台無人機的非地面網路中使用新穎的訊框架構提出一個蜂巢式卸載(Cellular Off-loading) 方法。在第三個模組中，為了進一步研究物聯網（Internet of Things, IoT）經由無人機做上行(Uplink) 的資料擷取，我們提出一個UAV-LEO 的資源分配輔助架構。最後，我們透過數值分析求出eMBB 與URLLC 使用者的效能指標，根據這些效能指標，我們驗證本論文所提出的方法比起其他基準方法確實具有一定的優越性。

With the emergence of aerial and space access networks, non-orthogonal multiple access (NOMA) enabled unmanned aerial vehicles (UAVs) and low earth orbit (LEO) satellites are potential solutions for beyond fifth-generation (B5G) networks. Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two core services of the emerging B5G systems. URLLC services require lower latency and high reliability, whereas eMBB services expect maximum data rates. Hence, 5G new radio (NR) aims to support heterogeneous services, the 3rd generation partnership project (3GPP) supports a highly flexible frame structure by introducing mixed numerology and mini-slot approaches to support diverse applications. This dissertation is primarily divided into three modules concerning system frameworks and services. In the first module, we study the joint resource allocation problem of eMBB and URLLC schedulers to maximize the minimum expected achieved rate (MEAR) of eMBB users while satisfying URLLC users’ quality of service (QoS) constraints under downlink single terrestrial base station architecture. In the second module, for enhanced coverage and connectivity, a novel framework with cellular offloading under the aid of multiple UAVs is proposed. We explored the resource allocation for the remote Internet of Things (IoTs) uplink data collection scenario under the UAV-LEO aided framework in the third module. Finally, we evaluate the significance of the proposed methodologies under the given system frameworks in comparison to other baseline approaches through numerical simulations, considering user performance indicators.

論文審定書. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Thesis Validation Letter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Abstract . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
List of Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xi
Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Chapter 2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1 5G/B5G Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Non-terrestrial Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.1 UAV-aided Cellular Offloading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.2 UAV-LEO Assisted Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Chapter 3 eMBB and URLLC Resource Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.1 System Model and Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.1.1 URLLC Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.1.2 NOMA Superposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.1.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 Solution Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2.1 eMBB Resource Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2.2 URLLC Resource Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.2.3 Evaluating Matching URLLC and eMBB User Pairs . . . . . . . . . . . . . . . . . . . 28
3.2.4 Convergence and Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Chapter 4 Multiple-UAV aided Terrestrial Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.1 System Model and Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.1.1 URLLC Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.1.2 Propagation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.1.3 NOMA Superposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.1.4 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2 Solution Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.2.1 User Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.2.2 Resource Allocation for eMBB Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.2.3 Resource Allocation for URLLC Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2.4 Convergence and Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
Chapter 5 UAV-LEO aided Terrestrial Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.1 System Model and Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.1.1 Propagation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.1.2 IoT-UAV Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.1.3 UAV-LEO Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.1.4 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2 Solution Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.2.1 UAV Deployment Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.2.2 Uplink Resource Optimization in IoT-UAV Framework . . . . . . . . . . . . . . . . 82
5.2.3 Uplink Resource Optimization in UAV-LEO Framework . . . . . . . . . . . . . . . 86
5.2.4 Convergence and Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Chapter 6 Conclusion and Future Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .95
6.1 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .95
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .98
Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
List of Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

[1] M. Alsenwi, S. R. Pandey, Y. K. Tun, K. T. Kim, and C. S. Hong, “A chance
constrained based formulation for dynamic multiplexing of eMBB-URLLC traffics in 5G new radio,” in 2019 International Conference on Information Networking (ICOIN). IEEE, 2019, pp. 108–113.
[2] Study on physical layer enhancements for NR ultra-reliable and low latency case (URLLC), The 3rd Generation Partnership Project (3GPP), 3GPP, document TR38.824, Mar. 2019.
[3] Physical channels and signals for 5G-NR, 3GPP, document TS 38.211, Aug. 2018.
[4] Downlink Multiplexing of eMBB and uRLLC Transmission, 3GPP TSG RAN WG1 NR Ad-Hoc Meeting, R1-1700374, Jan. 2017.
[5] S. R. Islam, M. Zeng, O. A. Dobre, and K. Kwak, “Resource allocation for downlink NOMA systems: Key techniques and open issues,” IEEE Wireless Communications, vol. 25, no. 2, pp. 40–47, 2018.
[6] S. M. A. Kazmi, N. H. Tran, T. M. Ho, A. Manzoor, D. Niyato, and C. S. Hong,
“Coordinated device-to-device communication with non-orthogonal multiple access in future wireless cellular networks,” IEEE Access, vol. 6, pp. 39 860–39 875, 2018.
[7] L. Zhu, J. Zhang, Z. Xiao, X. Cao, and D. O. Wu, “Optimal user pairing for downlink non-orthogonal multiple access (NOMA),” IEEE Wireless Communications Letters, vol. 8, no. 2, pp. 328–331, 2019.
[8] Q. Wu, J. Xu, Y. Zeng, D. W. K. Ng, N. Al-Dhahir, R. Schober, and A. L. Swindlehurst, “A comprehensive overview on 5G-and-beyond networks with UAVs: From
communications to sensing and intelligence,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 10, pp. 2912–2945, 2021.
[9] Y. Zeng, R. Zhang, and T. J. Lim, “Wireless communications with unmanned aerial vehicles: Opportunities and challenges,” IEEE Communications magazine, vol. 54, no. 5, pp. 36–42, 2016.
[10] R. Zhong, X. Liu, Y. Liu, and Y. Chen, “Multi-agent reinforcement learning in NOMA-aided UAV networks for cellular offloading,” IEEE Transactions on Wireless Communications, vol. 21, no. 3, pp. 1498–1512, 2021.
[11] Y. Liu, Z. Qin, Y. Cai, Y. Gao, G. Y. Li, and A. Nallanathan, “UAV communications based on non-orthogonal multiple access,” IEEE Wireless Communications, vol. 26, no. 1, pp. 52–57, 2019.
[12] N. Bhushan, J. Li, D. Malladi, R. Gilmore, D. Brenner, A. Damnjanovic, R. T. Sukhavasi, C. Patel, and S. Geirhofer, “Network densification: the dominant theme for wireless evolution into 5G,” IEEE Communications Magazine, vol. 52, no. 2, pp. 82–89, 2014.
[13] X. Liu, J. Wang, N. Zhao, Y. Chen, S. Zhang, Z. Ding, and F. R. Yu, “Placement and power allocation for NOMA-UAV networks,” IEEE Wireless Communications Letters, vol. 8, no. 3, pp. 965–968, 2019.
[14] W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” IEEE network, vol. 34, no. 3, pp. 134–142, 2019.
[15] N. Cheng, W. Quan, W. Shi, H. Wu, Q. Ye, H. Zhou, W. Zhuang, X. Shen, and B. Bai, “A comprehensive simulation platform for space-air-ground integrated network,” IEEE Wireless Communications, vol. 27, no. 1, pp. 178–185, 2020.
[16] Z. Zhang, Y. Xiao, Z. Ma, M. Xiao, Z. Ding, X. Lei, G. K. Karagiannidis, and P. Fan, “6G wireless networks: Vision, requirements, architecture, and key technologies,” IEEE vehicular technology magazine, vol. 14, no. 3, pp. 28–41, 2019.
[17] J. Jiao, S. Wu, R. Lu, and Q. Zhang, “Massive access in space-based internet of things: Challenges, opportunities, and future directions,” IEEE Wireless Communications, vol. 28, no. 5, pp. 118–125, 2021.
[18] M. Sheng, D. Zhou, R. Liu, Y. Wang, and J. Li, “Resource mobility in space information networks: Opportunities, challenges, and approaches,” IEEE Network, vol. 33, no. 1, pp. 128–135, 2018.
[19] Z. Jia, M. Sheng, J. Li, D. Niyato, and Z. Han, “LEO-satellite-assisted UAV: Joint trajectory and data collection for internet of remote things in 6G aerial access networks,” IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9814–9826, 2020.
[20] M. Mozaffari, W. Saad, M. Bennis, Y.-H. Nam, and M. Debbah, “A tutorial on UAVs for wireless networks: Applications, challenges, and open problems,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2334–2360, 2019.
[21] J.-H. Lee, J. Park, M. Bennis, and Y.-C. Ko, “Integrating LEO satellites and multi-UAV reinforcement learning for hybrid FSO/RF non-terrestrial networks,” IEEE Transactions on Vehicular Technology, vol. 72, no. 3, pp. 3647–3662, 2022.
[22] Y. Zeng, Q. Wu, and R. Zhang, “Accessing from the sky: A tutorial on UAV communications for 5G and beyond,” Proceedings of the IEEE, vol. 107, no. 12, pp. 2327–2375, 2019.
[23] Y. Prathyusha and T.-L. Sheu, “A user pairing technique for mixed traffic types in downlink NOMA,” in 2020 International Computer Symposium (ICS). IEEE, 2020, pp. 13–18.
[24] Y. Prathyusha and T. L. Sheu, “Coordinated resource allocations for eMBB and URLLC in 5G communication networks,” IEEE Transactions on Vehicular Technology, vol. 71, no. 8, pp. 8717–8728, 2022.
[25] E. Dosti, M. Shehab, H. Alves, and M. Latva-aho, “On the performance of nonorthogonal multiple access in the finite blocklength regime,” Ad Hoc Networks,
vol. 84, pp. 148–157, Mar. 2019.
[26] X. Sun, S. Yan, N. Yang, Z. Ding, C. Shen, and Z. Zhong, “Downlink NOMA transmission for low-latency short-packet communications,” in 2018 IEEE International
Conference on Communications Workshops (ICC Workshops). IEEE, 2018, pp. 1–6.
[27] Y. Prathyusha and T.-L. Sheu, “Cellular offloading of eMBB and URLLC services in multiple UAV-aided communication networks,” in 2022 6th European Conference on Electrical Engineering & Computer Science (ELECS). IEEE, 2022, pp. 115–120.
[28] Y. Prathyusha and T. L. Sheu, “Resource allocations for coexisting eMBB and URLLC services in multi-UAV aided communication networks for cellular offloading,” IEEE Transactions on Vehicular Technology, 2023.
[29] Y. Prathyusha and T.-L. Sheu, “A data collection scheme for IoT using NOMA techniques in UAV-LEO 6G networks,” in 2023 2nd International Conference on 6G
Networking (6GNet). IEEE, 2023, pp. 1–4.
[30] P. Korrai, E. Lagunas, S. K. Sharma, S. Chatzinotas, A. Bandi, and B. Ottersten, “A RAN resource slicing mechanism for multiplexing of eMBB and URLLC services in OFDMA based 5G wireless networks,” IEEE Access, vol. 8, pp. 45 674–45 688, 2020.
[31] J. Zhang, X. Xu, K. Zhang, B. Zhang, X. Tao, and P. Zhang, “Machine learning based flexible transmission time interval scheduling for eMBB and uRLLC coexistence scenario,” IEEE Access, vol. 7, pp. 65 811–65 820, 2019.
[32] Technical Specification Group Radio Access Network; Study on NR positioning support, The 3rd Generation Partnership Project (3GPP), 3GPP TR 38.855, Mar. 2019.
[33] M.-J. Youssef, J. Farah, C. A. Nour, and C. Douillard, “Resource allocation in NOMA systems for centralized and distributed antennas with mixed traffic usin matching theory,” IEEE Transactions on Communications, vol. 68, no. 1, pp. 414– 428, 2020.
[34] M. Alsenwi, N. H. Tran, M. Bennis, S. R. Pandey, A. K. Bairagi, and C. S. Hong, “Intelligent resource slicing for eMBB and URLLC coexistence in 5G and beyond: A
deep reinforcement learning based approach,” IEEE Transactions on Wireless Communications, vol. 20, no. 7, pp. 4585–4600, 2021.
[35] A. Karimi, K. I. Pedersen, N. H. Mahmood, G. Pocovi, and P. Mogensen, “Efficient low complexity packet scheduling algorithm for mixed URLLC and eMBB traffic in 5G,” in 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring). IEEE, 2019, pp. 1–6.
[36] A. Pradhan and S. Das, “Joint preference metric for efficient resource allocation in co-existence of eMBB and URLLC,” in 2020 International Conference on Communication Systems & Networks (COMSNETS). IEEE, 2020, pp. 897–899.
[37] Y. Huang, S. Li, C. Li, Y. T. Hou, and W. Lou, “A deep-reinforcement-learningbased approach to dynamic eMBB/URLLC multiplexing in 5G NR,” IEEE Internet
of Things Journal, vol. 7, no. 7, pp. 6439–6456, 2020.
[38] N. U. Ginige, K. S. Manosha, N. Rajatheva, and M. Latva-aho, “Admission control in 5G networks for the coexistence of eMBB-URLLC users,” in 2020 IEEE 91st
Vehicular Technology Conference (VTC2020-Spring). IEEE, 2020, pp. 1–6.
[39] M. Al-Ali, E. Yaacoub, and A. Mohamed, “Dynamic resource allocation of eMBBuRLLC traffic in 5G new radio,” in 2020 IEEE International Conference on Advanced
Networks and Telecommunications Systems (ANTS). IEEE, 2020, pp. 1–6.
[40] A. K. Bairagi, M. S. Munir, M. Alsenwi, N. H. Tran, S. S. Alshamrani, M. Masud, Z. Han, and C. S. Hong, “Coexistence mechanism between eMBB and uRLLC in 5G wireless networks,” IEEE Transactions on Communications, vol. 69, no. 3, pp. 1736–1749, 2020.
[41] H. Yin, L. Zhang, and S. Roy, “Multiplexing URLLC traffic within eMBB services in 5G NR: Fair scheduling,” IEEE Transactions on Communications, vol. 69, no. 2, pp. 1080–1093, 2020.
[42] A. Anand, G. De Veciana, and S. Shakkottai, “Joint scheduling of URLLC and eMBB traffic in 5G wireless networks,” IEEE/ACM Transactions on Networking, vol. 28, no. 2, pp. 477–490, 2020.
[43] A. Manzoor, S. A. Kazmi, S. R. Pandey, and C. S. Hong, “Contract-based scheduling of URLLC packets in incumbent EMBB traffic,” IEEE Access, vol. 8, pp. 167 516– 167 526, 2020.
[44] W. Liang, Z. Ding, Y. Li, and L. Song, “User pairing for downlink non-orthogonal multiple access networks using matching algorithm,” IEEE Transactions on Communications, vol. 65, no. 12, pp. 5319–5332, 2017.
[45] H. Wu, Z. Wei, Y. Hou, N. Zhang, and X. Tao, “Cell-edge user offloading via flying uav in non-uniform heterogeneous cellular networks,” IEEE Transactions on Wireless Communications, vol. 19, no. 4, pp. 2411–2426, 2020.
[46] J. Liu, Y. Shi, Z. M. Fadlullah, and N. Kato, “Space-air-ground integrated network: A survey,” IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2714–2741, 2018.
[47] J. Lyu, Y. Zeng, and R. Zhang, “UAV-aided offloading for cellular hotspot,” IEEE Transactions on Wireless Communications, vol. 17, no. 6, pp. 3988–4001, 2018.
[48] Z. Hu, Z. Zheng, L. Song, T. Wang, and X. Li, “UAV offloading: Spectrum trading contract design for UAV-assisted cellular networks,” IEEE Transactions on Wireless Communications, vol. 17, no. 9, pp. 6093–6107, 2018.
[49] R. I. Bor-Yaliniz, A. El-Keyi, and H. Yanikomeroglu, “Efficient 3-D placement of an aerial base station in next generation cellular networks,” in 2016 IEEE international conference on communications (ICC). IEEE, 2016, pp. 1–5.
[50] Y. Sun, D. Xu, D. W. K. Ng, L. Dai, and R. Schober, “Optimal 3D-trajectory design and resource allocation for solar-powered UAV communication systems,” IEEE Transactions on Communications, vol. 67, no. 6, pp. 4281–4298, 2019.
[51] 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, 2023.
[52] Y. Li, W. Liang, W. Xu, Z. Xu, X. Jia, Y. Xu, and H. Kan, “Data collection maximization in IoT-sensor networks via an energy-constrained UAV,” IEEE Transactions on Mobile Computing, vol. 22, no. 1, pp. 159–174, 2021.
[53] N. H. Chu, D. T. Hoang, D. N. Nguyen, N. Van Huynh, and E. Dutkiewicz, “Joint speed control and energy replenishment optimization for UAV-assisted IoT data collection with deep reinforcement transfer learning,” IEEE Internet of Things Journal, vol. 10, no. 7, pp. 5778–5793, 2022.
[54] Z. Wei, M. Zhu, N. Zhang, L. Wang, Y. Zou, Z. Meng, H. Wu, and Z. Feng, “UAVassisted data collection for internet of things: A survey,” IEEE Internet of Things
Journal, vol. 9, no. 17, pp. 15 460–15 483, 2022.
[55] Y. Sun, D. Xu, D. W. K. Ng, L. Dai, and R. Schober, “Optimal 3D-trajectory design and resource allocation for solar-powered UAV communication systems,” IEEE Transactions on Communications, vol. 67, no. 6, pp. 4281–4298, 2019.
[56] S. Zhang, J. Yang, H. Zhang, and L. Song, “Dual trajectory optimization for a cooperative internet of UAVs,” IEEE Communications Letters, vol. 23, no. 6, pp. 1093– 1096, 2019.
[57] M. Chen, M. Mozaffari, W. Saad, C. Yin, M. Debbah, and C. S. Hong, “Caching in the sky: Proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 5, pp. 1046–1061, 2017.
[58] X. Xu, H. Zhao, H. Yao, and S. Wang, “A blockchain-enabled energy-efficient data collection system for UAV-assisted IoT,” IEEE Internet of Things Journal, vol. 8, no. 4, pp. 2431–2443, 2020.
[59] R. Chen, Y. Sun, L. Liang, and W. Cheng, “Joint power allocation and placement scheme for UAV-assisted IoT with QoS guarantee,” IEEE Transactions on Vehicular Technology, vol. 71, no. 1, pp. 1066–1071, 2021.
[60] X. Li, W. Feng, Y. Chen, C.-X. Wang, and N. Ge, “Maritime coverage enhancement using UAVs coordinated with hybrid satellite-terrestrial networks,” IEEE Transactions on Communications, vol. 68, no. 4, pp. 2355–2369, 2020.
[61] Y. Wang, Z. Li, Y. Chen, M. Liu, X. Lyu, X. Hou, and J. Wang, “Joint resource allocation and UAV trajectory optimization for space–air-ground internet of remote things networks,” IEEE Systems Journal, vol. 15, no. 4, pp. 4745–4755, 2020.
[62] D.-H. Tran, S. Chatzinotas, and B. Ottersten, “Satellite-and cache-assisted UAV: A joint cache placement, resource allocation, and trajectory optimization for 6G aerial networks,” IEEE Open Journal of Vehicular Technology, vol. 3, pp. 40–54, 2022.
[63] Q. Hu, J. Jiao, Y. Wang, S. Wu, R. Lu, and Q. Zhang, “Multitype services coexistence in uplink NOMA for dual-layer LEO satellite constellation,” IEEE Internet of Things Journal, vol. 10, no. 3, pp. 2693–2707, 2022.
[64] R. Ge, D. Bian, K. An, J. Cheng, and H. Zhu, “Performance analysis of cooperative nonorthogonal multiple access scheme in two-layer geo/leo satellite network,” IEEE Systems Journal, vol. 16, no. 2, pp. 2300–2310, 2021.
[65] R. Ge, D. Bian, J. Cheng, K. An, J. Hu, and G. Li, “Joint user pairing and power allocation for NOMA-based GEO and LEO satellite network,” IEEE Access, vol. 9, pp. 93 255–93 266, 2021.
[66] Z. Gao, A. Liu, C. Han, and X. Liang, “Files delivery and share optimization in LEO satellite-terrestrial integrated networks: A NOMA based coalition formation game approach,” IEEE Transactions on Vehicular Technology, vol. 71, no. 1, pp. 831–843, 2021.
[67] C. Gamal, K. An, X. Li, V. G. Menon, G. Ragesh, M. M. Fouda, and B. M. ElHalawany, “Performance of hybrid satellite-UAV NOMA systems,” in ICC 2022-IEEE
International Conference on Communications. IEEE, 2022, pp. 189–194.
[68] Y. Polyanskiy, H. V. Poor, and S. Verdú, “Channel coding rate in the finite blocklength regime,” IEEE Transactions on Information Theory, vol. 56, no. 5, pp. 2307–
2359, 2010.
[69] W. Yang, G. Durisi, T. Koch, and Y. Polyanskiy, “Quasi-static multiple-antenna fading channels at finite blocklength,” IEEE Transactions on Information Theory, vol. 60, no. 7, pp. 4232–4265, 2014.
[70] D. Gale and L. S. Shapley, “College admissions and the stability of marriage,” The American Mathematical Monthly, vol. 69, no. 1, pp. 9–15, 1962.
[71] J. Lee and S. Nam, “Effective area of a receiving antenna in a lossy medium,” IEEE Transactions on Antennas and Propagation, vol. 57, no. 6, pp. 1843–1845, 2009.
[72] 3GPP,“Technical Specification Group Radio Access Network; Study on Enhanced LTE Support for Aerial Vehicles,”3rd Generation Partnership Project (3GPP), Technical Specification (TS) 36.777, 01 2018, version 15.0.0.
[73] J. Zhao, X. Yue, S. Kang, and W. Tang, “Joint effects of imperfect CSI and SIC on NOMA based satellite-terrestrial systems,” IEEE Access, vol. 9, pp. 12 545–12 554, 2021.
[74] A. Raviteja, K. Singh, K. Agrawal, Z. Ding, and C.-P. Li, “FD-NOMA enabled multiuser transmission with backscatter communication,” IEEE Wireless Communications Letters, 2023.
[75] C. Goutte, P. Toft, E. Rostrup, F. Å. Nielsen, and L. K. Hansen, “On clustering fMRI time series,” NeuroImage, vol. 9, no. 3, pp. 298–310, 1999.
[76] J. Cui, Z. Ding, P. Fan, and N. Al-Dhahir, “Unsupervised machine learning-based user clustering in millimeter-wave-NOMA systems,” IEEE Transactions on Wireless Communications, vol. 17, no. 11, pp. 7425–7440, 2018.
[77] R. K. Jain, D.-M. W. Chiu, W. R. Hawe et al., “A quantitative measure of fairness and discrimination,” Eastern Research Laboratory, Digital Equipment Corporation, Hudson, MA, vol. 21, 1984.