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研究生： 陳冠綸
Chen, Kuan-Lun
論文名稱： 基於深度學習算法的超密集低軌衛星網路性能之研究
Performance of Ultra-Dense Low Earth Orbit Satellite Networks Based on Deep Learning Algorithms
指導教授： 陳曉華
Chen, Hsiao-Hwa
學位類別： 碩士
Master
系所名稱： 工學院 - 工程科學系
Department of Engineering Science
論文出版年： 2026
畢業學年度： 114
語文別： 英文
論文頁數： 181
中文關鍵詞： 低軌衛星 、衛星換手 、強化學習 、功率分配
外文關鍵詞： LEO, Satellite Handover, Reinforcement Learning, Power Resource Allocation
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    • 隨著低軌衛星（Low Earth Orbit, LEO）通訊系統的快速發展，超密集衛星星座已成為未來全球行動通訊與寬頻接取的重要基礎架構。然而，在高動態拓撲與大量用戶同時接入的情境下，如何有效進行衛星換手與功率資源配置，仍是一項具挑戰性的問題。頻繁的換手行為不僅會增加系統負擔，也可能導致通訊品質下降，而不當的功率配置則容易引發衛星過載與干擾問題。本論文針對超密集低軌衛星通訊系統，提出一套基於強化學習的衛星換手與功率配置策略，以提升系統整體吞吐量並降低不必要的換手與過載事件。系統模型考量衛星可見角度、用戶與衛星間距離、訊號干擾以及衛星最大服務容量等實際通訊限制，並將其納入強化學習的狀態設計與獎勵函數中。透過合理設計的獎勵機制，同時平衡吞吐量最大化、換手懲罰與過載懲罰，使學習代理能在動態環境中自動學習合適的決策策略。此外，本研究進一步比較多種非正交多重存取（Non-Orthogonal Multiple Access, NOMA）技術，包括 MUST、PDMA 與 SCMA，在相同資源配置條件下的系統效能差異。模擬結果顯示，所提出的強化學習方法能有效改善系統吞吐量並降低換手次數，其中 SCMA 在整體效能表現上優於 PDMA 與 MUST。研究結果驗證了強化學習應用於超密集低軌衛星通訊系統之可行性，並為未來智慧化衛星資源管理與換手策略設計提供重要參考。

    With the rapid development of Low Earth Orbit (LEO) satellite communication systems, ultra-dense satellite constellations have become a key infrastructure for future global mobile communications and broadband access. However, under highly dynamic network topologies and scenarios with massive user access, efficient satellite handover and power resource allocation remain challenging problems. Frequent handover events not only increase system overhead but also degrade communication quality, while improper power allocation may lead to satellite overload and severe interference. In this thesis, a reinforcement learning based satellite handover and power allocation framework is proposed for ultra-dense LEO satellite communication systems, aiming to maximize system throughput while reducing unnecessary handovers and overload events. The system model incorporates practical communication constraints, including satellite visibility angles, user–satellite distances, signal interference, and satellite service capacity limitations, which are integrated into the state representation and reward function design of the reinforcement learning agent. By carefully designing the reward structure, the proposed approach achieves a balance among throughput maximization, handover penalties, and overload penalties, enabling the agent to autonomously learn effective decision-making strategies in dynamic environments. Furthermore, this study evaluates and compares the performance of several Non-Orthogonal Multiple Access (NOMA) schemes, including MUST, PDMA, and SCMA, under identical resource allocation conditions. Simulation results demonstrate that the proposed reinforcement learning–based approach significantly improves system throughput while effectively reducing handover frequency. Among the evaluated NOMA schemes, SCMA achieves the best overall performance, followed by PDMA and MUST. These results confirm the feasibility and effectiveness of applying reinforcement learning to resource management and handover optimization in ultra-dense LEO satellite communication systems, and provide valuable insights for the design of intelligent satellite network control strategies in future wireless networks.

    摘要 v Abstract vii Acknowledgements ix Table of Contents xi List of Tables xv List of Figures xvii List of Abbreviations xxxiii List of Symbols xxxvii Dedication xxxix Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 A Brief Survey on Related Works 6 Chapter 2 Overview of Low Earth Satellite Networks Techniques & Artificial Intelligence 15 2.1 Introduction to the Ultra-Dense LEO Satellite Networks 16 2.2 Overview of HO Process 17 2.3 LEO Handover Scheme 18 2.3.1 Handover Strategy Based on Graph Theory 18 2.3.2 Handover Strategy Based on Game Theory 19 2.3.3 Handover Strategy Based on Software Defined Networking 20 2.4 Conditional Handover 21 2.5 Reinforcement Learning 23 2.5.1 Machine Learning & Deep Learning 24 2.5.2 Supervised Learning 28 2.5.3 Unsupervised Learning 30 2.5.4 Reinforcement Learning 31 2.6 Literature Discussion 34 2.7 Actor-Critic Algorithm 37 2.7.1 Policy-Based and Value-Based 39 2.8 Multi-User of Low Earth Orbit Satellite Handover 40 2.8.1 Frequent Handovers 42 2.8.2 Channel Allocation 43 2.8.3 Multi-User of Low Earth Orbit Satellite Handover with Power Allocation 45 2.8.4 Multi-User of Low Earth Orbit Satellite Handover with Artificial Intelligence 47 2.8.5 Comparison between the First-Generation and Second-Generation Low Earth Orbit (LEO) Satellites 48 2.9 Power-Domain NOMA 51 2.9.1 Power-Domain NOMA 51 2.9.2 Successive Interference Cancellation (SIC) 54 Chapter 3 Assumptions and Definitions 57 3.1 Network Scenario 57 3.1.1 Single-Satellite NOMA User Separation 58 3.1.2 Multi-Satellite NOMA User Separation 59 3.1.3 Satellite-Initiated Handover Assumption Based on SpaceX Starlink Architecture 60 3.2 AI-Based LEO Handover Framework 62 Chapter 4 Performance Analysis of LEO Handover Based on Artificial Intelligence 65 4.1 Satellite Position and Coordinate Systems 65 4.2 LEO Satellite MUST System Model 71 4.2.1 System Description 71 4.2.2 Successive Interference Cancellation (SIC) 72 4.3 PDMA System Model 73 4.3.1 System Description 74 4.4 SCMA System Model 75 4.5 Neural Network Propagation 78 4.5.1 Environment 79 4.5.2 State Space 80 4.5.3 Action Space 81 4.5.4 Reward Function 82 Chapter 5 Performance Comparison of Low Earth Orbit Satellite Handover 85 5.1 Major Parameters of Simulations 86 5.2 Performance Comparison 86 5.2.1 Reward Function and Weight Design 88 5.2.2 Performance Comparison of LEO Network Based on DQN 90 Chapter 6 Conclusions and Future Works 105 6.1 Conclusions 105 6.2 Future Work 106 References 107 Appendix A Neural Network 121 A.1 Neural Network Propagation 121 A.1.1 Environment 122 A.1.2 State Space 123 A.1.3 Action Space 124 A.1.4 Reward Function 124 A.1.5 DQN Architecture and Training Procedure 126 A.1.6 Forward Propagation 127 A.1.7 Backward Propagation and Training 128 Appendix B ECI to ECEF Transformation 133 B.1 Julian Date and ECI to ECEF Transformation 133 B.1.1 Origin and Purpose of Julian Date 133 B.1.2 Transformation Steps 134 Appendix C SCMA Sparse Code 135 C.1 SCMA Codebook for 6 Users, 4 Dimensions 136 C.2 Message Passing Algorithm (MPA) Detection 137 C.2.1 Variable Node to Factor Node Update 139 C.2.2 Factor Node to Variable Node Update 140 C.2.3 Convergence Check 140 C.2.4 Final Decision 141

    [1] J. G. Puente, “The emergence of commercial digital satellite commu- nications,” IEEE Commun. Mag., vol. 48, no. 7, pp. 16–20, Jul. 2010.
    [2] V. Weerackody and L. Gonzalez, “Mobile small aperture satellite ter- minals for mili- tary communications,” IEEE Commun. Mag., vol. 45, no. 10, pp. 70–75, Oct. 2007.
    [3] T.D.Cola,D.Tarchi,andA.Vanelli-Coralli,“Futuretrendsinbroadbandsatellitecom- munications: Information centric networks and enabling technologies,” Int. J. Satellite Commun. Netw., vol. 33, no. 5, pp. 473–490, 2015.
    [4] A. Guidotti et al., “Architectures and key technical challenges for 5G systems incor- porating satellites,” IEEE Trans. Veh. Technol., vol. 68, no. 3, pp. 2624–2639, Mar. 2019.
    [5] O. Kodheli, A. Guidotti, and A. Vanelli-Coralli, “Integration of satel- lites in 5G through LEO constellations,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), 2017, pp. 1–6.
    [6] D.Bogdan-Martin,MeasuringDigitalDevelopment:FactsandFigures2021,ITU,Geneva, Switzerland, 2021.
    [7] G.Araniti,A.Iera,S.Pizzi,andF.Rinaldi,“Toward6Gnon-terrestrialnetworks,”IEEE Netw., vol. 36, no. 1, pp. 113–120, Feb. 2022.
    [8] M. M. Azari et al., “Evolution of non-terrestrial networks from 5G to 6G: A survey,” IEEE Commun. Surveys Tuts., vol. 24, no. 4, pp. 2633–2672, 4th Quart., 2022.
    [9] I. Ali, N. Al-Dhahir, and J. Hershey, “Doppler characterization for LEO satel
    [10] T. Darwish, G. Kurt, H. Yanikomeroglu, G. Lamontagne, and M. Bellemare, “Loca- tion management in IP-based future LEO satellite networks: A review,” Jun. 2021, arXiv:2101.08336.
    [11] B.Di,H.Zhang,L.Song,Y.Li,andG.Y.Li,“Ultra-denseLEO:Integratingterrestrial- satellite networks into 5G and beyond for data offloading,” IEEE Trans. Wireless Com- mun., vol. 18, no. 1, pp. 47–62, Jan. 2019
    [12] R.Deng,B.Di,H.Zhang,andL.Song,“Ultra-denseLEOsatelliteconstellationdesign for global coverage in terrestrial-satellite networks,” in Proc. IEEE Glob. Commun. Conf., 2020, pp. 1–6.
    [13] T. Pultarova, “Mega-constellations: Will they bridge [space communications],” Eng. Technol., vol. 13, no. 1, pp. 66–69, 2018.
    [14] Y. Su, Y. Liu, Y. Zhou, J. Yuan, H. Cao, and J. Shi, “Broadband leo satellite communi- cations: Architectures and key technologies,” IEEE Wireless Commun., vol. 26, no. 2, pp. 55–61, Apr. 2019.
    [15] O. Kodheli, E. Lagunas, N. Maturo, S. K. Sharma, and G. Goussetis, “Satel- lite com- munications in the new space era: A survey and future challenges,” IEEE Commun. Surv. Tut., vol. 23, no. 1, pp. 70–109, Jan.–Mar. 2020.
    [16] I.DelPortillo,B.G.Cameron,andE.F.Crawley,“Atechnicalcomparisonofthreelow earth orbit satellite constellation systems to provide global broadband,” Acta Astronau- tica, vol. 159, pp. 123–135, 2019.
    [17] Q. Zou and L. Zhu, ”Dynamic Channel Allocation Strategy of Satellite Communi- cation Systems Based on Grey Prediction,” 2019 International Symposium on Net- works, Computers and Communications (ISNCC), Istanbul, Turkey, 2019, pp. 1-5, doi: 10.1109/ISNCC.2019.8909122. keywords: Satellites;Handover;Predictive mod- els;Mathematical model;Channel allocation;Satellite communication;satellite commu- nication system;grey model;LEO;channel allocation;dynamic,
    [18] A. Ivanov, M. Stoliarenko, S. Kruglik, S. Novichkov and A. Savinov, ”Dy- namic Resource Allocation in LEO Satellite,” 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC), Tangier, Morocco, 2019, pp. 930-935, doi: 10.1109/IWCMC.2019.8766756. keywords: Satellite broadcasting;Resource management;Low earth orbit satellites;Time-frequency analy- sis;Shape;Earth;Handover;Resource Allocation;LEO Satellite;Hybrid Beamforming,
    [19] F. Rinaldi, S. Pizzi, A. Molinaro, A. Iera, and G. Araniti, “Cooperative resource allo- cation in integrated terrestrial/non-terrestrial 5G and beyond networks,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2020, pp. 1–6.
    [20] J. Ye, G. Pan, and M.-S. Alouini, “Earth rotation-aware non-stationary satellite com- munication systems: Modeling and analysis,” IEEE Trans. Wireless Commun., vol. 20, no. 9, pp. 5942–5956, Sep. 2021.
    [21] S. Ji, M. Sheng, D. Zhou, W. Bai, Q. Cao, and J. Li, “Flexible and distributed mobility management for integrated terrestrial-satellite net- works: Challenges, architectures, and approaches,” IEEE Netw., vol. 35, no. 4, pp. 73–81, Jul. 2021.
    [22] S. Zhang, A. Liu, and X. Liang, “A multi-objective satellite handover strategy based on entropy in LEO satellite communications,” in Proc. IEEE 6th Int. Conf. Comput. Commun. (ICCC), Dec. 2020, pp. 1–6.
    [23] N. Pachler, I. del Portillo, E. F. Crawley, and B. G. Cameron, “An updated comparison of four low earth orbit satellite constellation systems to provide global broadband,” in Proc. IEEE Int. Conf. Commun. Workshops, 2021, pp. 1–7.
    [24] B.D.Tapley,B.E.Schutz,andG.H.Born,StatisticalOrbitDetermination.Burlington, MA: Elsevier Academic Press, 2004.
    [25] M. Werner, A. Jahn, E. Lutz, and A. Bottcher, “Analysis of system parameters for LEO/ICO-satellite communication networks,” IEEE J. Sel. Areas Commun., vol. 13, no. 2, pp. 371–381, Feb. 1995.
    [26] M. Richharia, “Satellite constellations,” in Mobile Satellite Commu- nications: Princi- ples and Trends. Hoboken, NJ, USA: Wiley, 2013, pp. 37–87.
    [27] D. Beste, “Design of satellite constellations for optimal continuous coverage,” IEEE Trans. Aerosp. Electron. Syst., vol. AES-14, no. 3, pp. 466–473, May 1978.
    [28] C.-J.Wang,“StructuralpropertiesofalowEarthorbitsatelliteconstellation–theWalker delta network,” in Proc. MILCOM-IEEE Mil. Commun. Conf., Boston, MA, USA, Oct. 1993, pp. 968–972.
    [29] D. Mortari and M. P. Wilkins, “Flower constellation set theory. Part I: Compatibility and phasing,” IEEE Trans. Aerosp. Electron. Syst., vol. 44, no. 3, pp. 953–962, Jul. 2008.
    [30] M. P. Wilkins and D. Mortari, “Flower constellation set theory part II: Secondary paths and equivalency,” IEEE Trans. Aerosp. Electron. Syst., vol. 44, no. 3, pp. 964–976, Jul. 2008.
    [31] J. Jiang, S. Yan, and M. Peng, “Regional LEO satellite constellation design based on user requirements,” in Proc. IEEE/CIC Int. Conf. Commun. China (ICCC), Beijing, China, Aug. 2018, pp. 855–860.
    [32] A. Al-Hourani, “A tractable approach for predicting pass duration in dense satellite networks,” IEEE Commun. Lett., vol. 25, no. 8, pp. 2698–2702, Aug. 2021.
    [33] N.Cheng,W.C.Xu,W.S.Shi,Y.Zhou,N.Lu,H.B.Zhou,andX.M.Shen,“Airgroundinte- grated mobile edge networks: Architecture, challenges, and opportunities,” IEEE Com- mun. Mag. , vol. 56, no. 8, pp. 26–32, Aug. 2018.
    [34] T. Wei, W. Feng, Y. F. Chen, C. X. Wang, N. Ge, and J. H. Lu, “Hybrid satellite- terrestrial communication networks for the maritime internet of things: Key tech- nologies, opportunities, and challenges,” IEEE Internet Things J., vol. 8, no. 11, pp. 8910–8934, Jun. 2021.
    [35] L. Feng, Y. Liu, L. Wu, Z. Zhang and J. Dang, ”A Satellite Handover Strategy Based on MIMO Technology in LEO Satellite Networks,” in IEEE Communications Letters, vol. 24, no. 7, pp. 1505-1509, July 2020, doi: 10.1109/LCOMM.2020.2988043.
    [36] J. Zhang, S. Zhu, H. Bai and C. Li, ”Optimization Strategy to Solve Transmission Interruption Caused by Satellite-Ground Link Switching,” in IEEE Access, vol. 8, pp. 32975-32988, 2020, doi: 10.1109/ACCESS.2020.2973698.
    [37] S. Z. Chen, Y. C. Liang, S. H. Sun, S. L. Kang, W. C. Cheng, and M. G.Peng, “Vision, requirements, and technology trend of 6G: How to tackle the challenges of system coverage, capacity, user data-rate and movement speed,” IEEE Wireless Commun., vol. 27, no. 2, pp. 218–228, Apr. 2020.
    [38] J. Li, K. Xue, J. Liu and Y. Zhang, ”A User-Centric Handover Scheme for Ultra-Dense LEO Satellite Networks,” in IEEE Wireless Communications Letters, vol. 9, no. 11, pp. 1904-1908, Nov. 2020, doi: 10.1109/LWC.2020.3007818.
    [39] T. Ma, H. B. Zhou, B. Qian, N. Cheng, X. M. Shen, X. Chen, and B. Bai, “ UAV-LEO integrated backbone: A ubiquitous data collection approach for B5G internet of remote things networks,” IEEE J. Sel. Areas Commun., vol. 39, no. 11, pp. 3491–3505, Nov. 2021.
    [40] J.J.Liu,Y.P.Shi,Z.M.Fadlullah,andN.Kato,“Space-air-groundintegratednetwork: A survey,” IEEE Commun. Surv. Tut., vol. 20, no. 4, pp. 2714–2741, May 2018.
    [41] S. Z. Chen, S. H. Sun, and S. L. Kang, “System integration of terrestrial mobile com- munication and satellite communication — the trends, challenges and key technologies in B5G and 6G,” China Commun., vol. 17, no. 12, pp. 156–171, Dec. 2020.
    [42] O. Kodheli, E. Lagunas, N. Maturo, S. K. Sharma, B. Shankar, J. F. M.Montoya, J. C. M. Duncan, D. Spano, S. Chatzinotas, S. Kisseleff, J. Querol, L. Lei, T. X. Vu, and G. Goussetis, “Satellite communications in the new space era: A survey and future challenges,” IEEE Commun. Surv. Tut., vol. 23, no. 1, pp. 70–109, Feb. 2021.
    [43] J. P. Choi and C. Joo, “ Challenges for efficient and seamless space- terrestrial hetero- geneous networks,” IEEE Commun. Mag. , vol. 53, no. 5, pp. 156–162, May 2015.
    [44] M. De Sanctis, E. Cianca, G. Araniti, I. Bisio, and R. Prasad, “Satellite communica- tions supporting internet of remote things,” IEEE Internet Things J., vol. 3, no. 1, pp. 113–123, Feb. 2016.
    [45] C. Zhang, C. X. Jiang, L. L. Kuang, J. Jin, Y. Z. He, and Z. Han, “Spatial spectrum sharing for satellite and terrestrial communication networks,” IEEE Trans. Aerospace and Electronic Systems , vol. 55, no. 3, pp. 1075–1089, Jun. 2019.
    [46] D.C.Nguyen,M.Ding,P.N.Pathirana,A.Seneviratne,J.Li,D.Niyato,O.Dobre,and H. V. Poor, “ 6G Internet of things: A comprehensive survey,” IEEE Internet Things J. , vol. 9, no. 1, pp. 359– 383, Jan. 2022.
    [47] Y. Li, W. Zhou and S. Zhou, ”Forecast Based Handover in an Extensible Multi-Layer LEO Mobile Satellite System,” in IEEE Access, vol. 8, pp. 42768-42783, 2020, doi: 10.1109/ACCESS.2020.2977831.
    [48] F. Wang, D. Jiang, Z. Wang, J. Chen and T. Q. S. Quek, ”Seamless Handover in LEO Based Non-Terrestrial Networks: Service Continuity and Optimization,” in IEEE Transactions on Communications, vol. 71, no. 2, pp. 1008-1023, Feb. 2023, doi: 10.1109/TCOMM.2022.3229014.
    [49] Y. Lee and J. P. Choi, “ Connectivity analysis of mega-constellation satellite networks with optical intersatellite links,” IEEE Trans. Aerosp. Electron. Syst., vol. 57, no. 6, pp. 4213–4226, Dec. 2021.
    [50] Z. Q. Lai, Q. Wu, H. W. Li, M. Y. Lv, and J. P. Wu, “OrbitCast: Exploiting mega- constellations for low-latency earth observation,” in Proc. 29th IEEE Int. Conf. Network Protocols, Dallas, TX, USA, 2021, pp. 1–12.
    [51] Z. Wu, F. Jin, J. Luo, Y. Fu, J. Shan and G. Hu, ”A Graph-Based Satellite Han- dover Framework for LEO Satellite Communication Networks,” in IEEE Commu- nications Letters, vol. 20, no. 8, pp. 1547-1550, Aug. 2016, doi: 10.1109/L- COMM.2016.2569099.
    [52] B. Yang, Y. Wu, X. Chu and G. Song, ”Seamless Handover in Software-Defined Satel- lite Networking,” in IEEE Communications Letters, vol. 20, no. 9, pp. 1768-1771, Sept. 2016, doi: 10.1109/LCOMM.2016.2585482.
    [53] H. Xu, D. Li, M. Liu, G. Han, W. Huang and C. Xu, ”QoE-Driven Intelligent Handover for User-Centric Mobile Satellite Networks,” in IEEE Transactions on Vehicular Tech- nology, vol. 69, no. 9, pp. 10127-10139, Sept. 2020, doi: 10.1109/TVT.2020.3000908.
    [54] K. Xue, W. Meng, H. Zhou, D. S. L. Wei, and M. Guizani, “A lightweight and secure group key based handover authentication protocol for the software-defined space infor- mation network,” IEEE Trans. Wireless Commun., vol. 19, no. 6, pp. 3673–3684, Jun. 2020.
    [55] S. He, T. Wang and S. Wang, ”Load-Aware Satellite Handover Strategy Based on Multi-Agent Reinforcement Learning,” GLOBECOM 2020 - 2020 IEEE Global Communications Conference, Taipei, Taiwan, 2020, pp. 1-6, doi: 10.1109/GLOBE- COM42002.2020.9322449.
    [56] P. Zuo, C. Wang, Z. Wei, Z. Li, H. Zhao and H. Jiang, ”Deep Reinforcement Learning Based Load Balancing Routing for LEO Satellite Network,” 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), Helsinki, Finland, 2022, pp. 1-6, doi: 10.1109/VTC2022-Spring54318.2022.9860582.
    [57] Y. Cao, S. -Y. Lien and Y. -C. Liang, ”Deep Reinforcement Learning For Multi-User Access Control in Non-Terrestrial Networks,” in IEEE Transactions on Communications, vol. 69, no. 3, pp. 1605-1619, March 2021, doi: 10.1109/T- COMM.2020.3041347.
    [58] Z. Li, Z. Xie and X. Liang, ”Dynamic Channel Reservation Strategy Based on DQN Algorithm for Multi-Service LEO Satellite Communication System,” in IEEE Wireless Communications Letters, vol. 10, no. 4, pp. 770-774, April 2021, doi: 10.1109/LWC.2020.3043073.
    [59] L. Shi, F. Yang, W. Wu, A. Sun, Y. Sun and T. Sun, ”Load Balancing and Remain- ing Visible Time based Handover Algorithm for LEO Satellite Network,” 2022 IEEE 8th International Conference on Computer and Communications (ICCC), Chengdu, China, 2022, pp. 391-395, doi: 10.1109/ICCC56324.2022.10065821. keywords: Mea- surement;Satellite constellations;Satellites;Low earth orbit satellites;Quality of ser- vice;Handover;Load management;LEO;Handover;Multi-attributes;CNR
    [60] Y. Liu, X. Tang, Y. Zhou, J. Shi, M. Qian and S. Li, ”Channel Reservation based Load Aware Handover for LEO Satellite Communications,” 2022 IEEE 95th Vehic- ular Technology Conference: (VTC2022-Spring), Helsinki, Finland, 2022, pp. 1-5, doi: 10.1109/VTC2022-Spring54318.2022.9860956. keywords: Vehicular and wire- less technologies;Satellites;Simulation;Low earth orbit satellites;Focusing;Quality of service;Handover;handover;mobility management;LEO satellite;load fairness;channel reservation,
    [61] R. Deng, B. Di, H. Zhang, L. Kuang and L. Song, ”Ultra-Dense LEO Satel- lite Constellations: How Many LEO Satellites Do We Need?,” in IEEE Transac- tions on Wireless Communications, vol. 20, no. 8, pp. 4843-4857, Aug. 2021, doi: 10.1109/TWC.2021.3062658.
    [62] S. Zhang, A. Liu, C. Han, X. Ding and X. Liang, ”A Network-Flows-Based Satellite Handover Strategy for LEO Satellite Networks,” in IEEE Wireless Communications Letters, vol. 10, no. 12, pp. 2669-2673, Dec. 2021, doi: 10.1109/LWC.2021.3111680.
    [63] B. Di, H. Zhang, L. Song, Y. Li and G. Y. Li, ”Ultra-Dense LEO: Integrating Terrestrial-Satellite Networks Into 5G and Beyond for Data Offloading,” in IEEE Transactions on Wireless Communications, vol. 18, no. 1, pp. 47-62, Jan. 2019, doi: 10.1109/TWC.2018.2875980.
    [64] H. Jiang, H. Wang, Y. Hu and J. Wu, ”Dynamic User Association in Scalable Ultra Dense LEO Satellite Networks,” in IEEE Transactions on Vehicular Technology, vol. 71, no. 8, pp. 8891-8905, Aug. 2022, doi: 10.1109/TVT.2022.3174575.
    [65] Y.Wang,M.Sheng,J.Li,X.Wang,R.Liu,andD.Zhou,“Dynamiccontactplandesign in broadband satellite networks with varying contact capacity,” IEEE Commun. Lett., vol. 20, no. 12, pp. 2410–2413, Dec. 2016.
    [66] J. A. Fraire, P. Madoery, and J. M. Finochietto, “On the design of fair contact plans in predictable delay tolerant networks,” in Proc. IEEE WiSEE, Baltimore, MD, USA, Nov. 2013, pp. 1–7.
    [67] D. Zhou, M. Sheng, J. Li, C. Xu, R. Liu, and Y. Wang, “Toward high throughput con- tact plan design in resource-limited small satellite networks,” in Proc. IEEE PIMRC, Valencia, Spain, Sep. 2016, pp. 1–6.
    [68] D. Zhou, M. Sheng, R. Liu, Y. Wang, and J. Li, “Channel aware mission scheduling in broadband data relay satellite networks,” IEEE J. Sel. Areas Commun., vol. 36, no. 5, pp. 1052–1064, May 2018.
    [69] D. Zhou, M. Sheng, X. Wang, C. Xu, R. Liu, and J. Li, “Mission aware contact plan design in resource-limited small satellite networks,” IEEE Trans. Commun., vol. 65, no. 6, pp. 2451–2466, Jun. 2017.
    [70] L. He, J. Li, M. Sheng, R. Liu, K. Guo, and D. Zhou, “Dynamic scheduling of hybrid tasks with time windows in data relay satellite networks,” IEEE Trans. Veh. Technol., vol. 68, no. 5, pp. 4989–5004, May 2019.
    [71] X.Zhai,X.Niu,H.Tang,L.Wu,andY.Shen,“Robustsatelliteschedulingapproachfor dynamic emergency tasks,” Math. Probl. Eng., vol. 2015, Dec. 2015, Art. no. 482923.
    [72] D. Zhou, M. Sheng, B. Li, J. Li, and Z. Han, “Distributionally robust planning for data delivery in distributed satellite cluster network,” IEEE Trans. Wireless Commun., vol. 18, no. 7, pp. 3642–3657, Jul. 2019.
    [73] D. Zhou, M. Sheng, J. Luo, R. Liu, J. Li, and Z. Han, “Collaborative data scheduling with joint forward and backward induction in small satellite networks,” IEEE Trans. Commun., vol. 67, no. 5, pp. 3443–3456, May 2019.
    [74] D. Zhou, M. Sheng, Y. Zhu, J. Li, and Z. Han, “Mission QoS and satellite service lifetime tradeoff in remote sensing satellite networks,” IEEE Wireless Commun. Lett., vol. 9, no. 7, pp. 990–994, Jul. 2020.
    [75] A. Masood, T.-V. Nguyen, T. P. Truong, and S. Cho, “Content caching in HAP-assisted multi-UAV networks using hierarchical federated learning,” in Proc. ICTC, vol. 10, Oct. 2021, pp. 1160–1162.
    [76] X. Hu, S. Liu, R. Chen, W. Wang, and C. Wang, “A deep rein- forcement learning- based framework for dynamic resource allocation in multibeam satellite systems,” IEEE Commun. Lett., vol. 22, no. 8, pp. 1612–1615, Aug. 2018.
    [77] F. Li, K.-Y. Lam, X. Liu, J. Wang, K. Zhao, and L. Wang, “Joint pricing and power allocation for multibeam satellite systems with dynamic game model,” IEEE Trans. Veh. Technol., vol. 67, no. 3, pp. 2398–2408, Mar. 2018.
    [78] A. I. Aravanis, M. R. B. Shankar, P.-D. Arapoglou, G. Danoy, P. G. Cottis, and B. Ot- tersten, “Power allocation in multibeam satellite systems: A two-stage multi-objective optimization,” IEEE Trans. Wireless Commun., vol. 14, no. 6, pp. 3171–3182, Jun. 2015.
    [79] F. Li, K.-Y. Lam, X. Liu, J. Wang, K. Zhao, and L. Wang, “Joint pricing and power allocation for multibeam satellite systems with dynamic game model,” IEEE Trans. Veh. Technol., vol. 67, no. 3, pp. 2398–2408, Mar. 2018.
    [80] A. I. Aravanis, M. R. B. Shankar, P.-D. Arapoglou, G. Danoy, P. G. Cottis, and B. Ot- tersten, “Power allocation in multibeam satellite systems: A two-stage multi-objective optimization,” IEEE Trans. Wireless Commun., vol. 14, no. 6, pp. 3171–3182, Jun. 2015.
    [81] Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Kavukcuoglu, K. (2018). IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor- Learner Architectures. arXiv preprint arXiv:1802.01561.
    [82] Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., and Kavukcuoglu, K. Asynchronous methods for deep reinforcement learning. ICML, 2016.
    [83] X. Hu, S. Liu, R. Chen, W. Wang, and C. Wang, “A deep reinforcement learning based framework for dynamic resource allocation in multibeam satellite systems,” IEEE Com- mun. Lett., vol. 22, no. 8, pp. 1612–1615, Aug. 2018.
    [84] Y. Wang, D. Zhou, N. Song, M. Sheng, J. Li, and J. Liu, “Concurrent reconfiguration of resource oriented emergency TT&C mission planning for space information networks,” J. Commun. Inf. Netw., vol. 6, no. 2, pp. 142–152, Jun. 2021.
    [85] P. Zhang, C. Wang, N. Kumar, and L. Liu, “Space-air-ground integrated multi-domain network resource orchestration based on virtual network architecture: A DRL method,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 3, pp. 2798–2808, Mar. 2022.
    [86] N. Cheng et al., “Space/aerial-assisted computing offloading for IoT applications: A learning-based approach,” IEEE J. Sel. Areas Commun., vol. 37, no. 5, pp. 1117–1129, May 2019.
    [87] C. Zhou et al., “Deep reinforcement learning for delay oriented IoT task scheduling in SAGIN,” IEEE Trans. Wireless Commun., vol. 20, no. 2, pp. 911–925, Feb. 2021.
    [88] H. Wu et al., “Resource management in space air ground integrated vehicular networks SDN control and AI algorithm design,” IEEE Wireless Commun., vol. 27, no. 6, pp. 52–60, Dec. 2020.
    [89] D. Zhou, M. Sheng, Y. Wang, J. Li, and Z. Han, “Machine learning based resource allocation in satellite networks supporting Internet of Remote Things,” IEEE Trans. Wireless Commun., vol. 20, no. 10, pp. 6606–6621, Oct. 2021.
    [90] C. Li, H. Zhu, G. Li, Y. Zhang, and G. Li, “Analysis of ULA spacing threshold and capacity reduction in distributed satellite MIMO scenarios,” IEEE Commun. Lett., vol. 25, no. 11, pp. 3669–3673, Nov. 2021.
    [91] C. Li, H. Zhu, J. Cai, J. Hu, G. Li, and G. Li, “Capacity analysis of terrestrial an- tenna array in distributed satellite MIMO communication system,” IEEE Trans. Veh. Technol., vol. 70, no. 5, pp. 4435–4450, May 2021.
    [92] Q. Liu, X. Li, H. Ji and H. Zhang, ”User Grouping-Based Beam Handover Scheme with Load-Balancing for LEO Satellite Networks,” GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023, pp. 3965-3970, doi: 10.1109/GLOBECOM54140.2023.10436950.
    [93] Q. Liu, X. Li, H. Ji and H. Zhang, ”User Grouping-Based Beam Handover Scheme with Load-Balancing for LEO Satellite Networks,” GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023, pp. 3965-3970, doi: 10.1109/GLOBECOM54140.2023.10436950.
    [94] A. Abdi, W. C. Lau, M. -. Alouini and M. Kaveh, “A new simple model for land mobile satellite channels: First- and second-order statistics,” IEEE Wireless Commun. Lett., vol. 2, no. 3, pp. 519-528, May 2003.
    [95] F. Yang, W. Wu, Y. Gao, Y. Sun, T. Sun and P. Si, ”Multi-Agent Fingerprints- Enhanced Distributed Intelligent Handover Algorithm in LEO Satellite Networks,” in IEEE Transactions on Vehicular Technology, doi: 10.1109/TVT.2024.3412287. keywords: Satellites;Handover;Low earth orbit satellites;Delays;Decision mak- ing;Orbits;Heuristic algorithms;LEO satellite networks;handover;decentralized Markov decision process;multi-agent double deep Q-network;multi-agent fingerprints,
    [96] H. Liu, Y. Wang, P. Li and J. Cheng, ”A Multi-Agent Deep Reinforcement Learning Based Handover Scheme for Mega-Constellation Under Dynamic Propagation Conditions,” in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2024.3407358. keywords: Satellites;Handover;Low earth or- bit satellites;Satellite communications;Planetary orbits;Optimization;Heuristic algorithms;Satellite communication;low Earth orbit (LEO) satellite;mega- constellation;satellite handover;multi-agent deep reinforcement learning;propagation condition
    [97] A. Gannon, B. Schoenholz, J. Downey and J. Clapham, ”Continuous Spacecraft Com- munications via Make-Before-Break Antenna Array Beam Steering,” ICC 2024 - IEEE International Conference on Communications, Denver, CO, USA, 2024, pp. 1837-1842, doi: 10.1109/ICC51166.2024.10622772.
    [98] E. Juan, M. Lauridsen, J. Wigard and P. Mogensen, ”Location-Based Handover Trig- gering for Low-Earth Orbit Satellite Networks,” 2022 IEEE 95th Vehicular Tech- nology Conference: (VTC2022-Spring), Helsinki, Finland, 2022, pp. 1-6, doi: 10.1109/VTC2022-Spring54318.2022.9860992.
    [99] G. Barros et al., ”A Soft-Handover Scheme for LEO Satellite Networks,” 2013 IEEE 78th Vehicular Technology Conference (VTC Fall), Las Vegas, NV, USA, 2013, pp. 1-5, doi: 10.1109/VTCFall.2013.6692061. keywords: Handover;Satellite broadcast- ing;Diversity reception;Quality of service;Low earth orbit satellites
    [100] T. Sun and L. Shi, ”Load Balancing and Carrier-to-Noise Ratio based Handover Al- gorithm for LEO Satellite Network,” 2023 IEEE 11th International Conference on Information, Communication and Networks (ICICN), Xi’an, China, 2023, pp. 207- 211, doi: 10.1109/ICICN59530.2023.10392821. keywords: Satellites;Costs;Heuristic algorithms;Low earth orbit satellites;Handover;Load management;Throughput;LEO- BSs;Handover;Multi-attributes;Carrier-to-Noise Ratio
    [101] H. Chen, G. Nie and H. Tian, ”A Multi-Slot Load Balancing Scheme for LEO Satellite Communication Handover Target Selection,” 2024 IEEE Wireless Communications and Networking Conference (WCNC), Dubai, United Arab Emi- rates, 2024, pp. 1-6, doi: 10.1109/WCNC57260.2024.10570974. keywords: Satel- lites;Simulation;Low earth orbit satellites;Quality of service;Handover;Load manage- ment;Search problems;LEO;QoS;load;multi-slot;handover
    [102] L. Tello-Oquendo, F. Ahmed, C. -H. Lin, S. -C. Lin and M. Lee, ”Enabling LEO Satellite Vertical Handover for Massive 6G IoT Random Access,” ICC 2024 - IEEE International Conference on Communications, Denver, CO, USA, 2024, pp. 4293-4298, doi: 10.1109/ICC51166.2024.10623063. keywords: Satellites;Satellite broadcasting;Low earth orbit satellites;Transportation;Handover;Delays;Internet of Things;Beyond 5G;the internet of things;satellite communications;vertical han- dover;massive random access.

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