博碩士論文 108523061 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:26 、訪客IP:3.144.172.115
姓名 吳彥陽(Yen-Yang Wu)  查詢紙本館藏   畢業系所 通訊工程學系
論文名稱
(Intelligent Multi-Connectivity Management for Satellite-aided Vehicular Networks)
相關論文
★ 基於馬賽克特性之低失真實體電路佈局保密技術★ 多路徑傳輸控制協定下從無線區域網路到行動網路之無縫換手
★ 感知網路下具預算限制之異質性子頻段分配★ 下行服務品質排程在多天線傳輸環境下的效能評估
★ 多路徑傳輸控制協定下之整合型壅塞及路徑控制★ Opportunistic Scheduling for Multicast over Wireless Networks
★ 適用多用戶多輸出輸入系統之低複雜度比例公平性排程設計★ 利用混合式天線分配之 LTE 異質網路 UE 與 MIMO 模式選擇
★ 基於有限預算標價式拍賣之異質性頻譜分配方法★ 適用於 MTC 裝置 ID 共享情境之排程式分群方法
★ Efficient Two-Way Vertical Handover with Multipath TCP★ 多路徑傳輸控制協定下可亂序傳輸之壅塞及排程控制
★ 移動網路下適用於閘道重置之群體換手機制★ 使用率能小型基地台之拍賣是行動數據分流方法
★ 高速鐵路環境下之通道預測暨比例公平性排程設計★ 用於行動網路效能評估之混合式物聯網流量產生器
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 隨著衛星網路的逐漸崛起,越來越多研究試著將衛星網路融入現存的應用架構中。在這篇文章中,為了強化系統的覆蓋範圍並且在壅塞的車聯網路中保持良好的通道品質,我們試著整合衛星網路以及車連網路形成衛星協助多連結性車聯網架構。衛星協助多連結性車聯網架構乃指車輛能夠選擇要傳輸資料給衛星、基礎設施以及其他車輛。車輛能夠自行選擇使用何種傳輸模式、能量以及子頻道去最大化整體系統的效益。在我們的研究中,我們使用多智能體行為評斷模型 (MAAC) 去估計廣域狀態中的區域狀態。此外,我們使用城市交通模擬系統 (SUMO) 依照現實地圖去產生城市、郊區以及鄉村的車流量。根據研究結果顯示,我們方法能夠增加在鄉村的系統覆蓋範圍以及緩解都市的車用網路壓力。
摘要(英) With satellite network regaining the attention of the public, there are more and more research try to integrate the satellite network in nowadays application structure. In this paper, to enhance the system coverage in vehicle-to-everything (V2X), we expand the transmission target from infrastructures and other vehicles to satellite. The vehicle agent can arrange their transmission modes, power and sub-channel according to the environment to maximize the overall the system utility. However, the vehicle-to-satellite (V2S), vehicle-to-infrastructure (V2I) and vehicle (V2V) have different advantage and spectrum resources in different area.
To maximize the utility of system in such complex environment, we apply the multi-agent-actor-critic attention (MAAC-A) which estimate the global state given partial information with attention mechanism to increase the learning efficiency. With MAAC-A, the vehicles can do the better selection with local information and maximize the utility of system immediately.
Moreover, in our simulation, the data which including the urban, suburban and rural area is generated by Simulation of Urban Mobility (SUMO) on realistic setup. Finally, the result show that the agent has advantageous performance with the proposed scheme without the satellite network.
關鍵字(中) ★ 車聯網
★ 衛星網路
★ 資源分配
★ 多連接網路
關鍵字(英) ★ Vehicles-to-everything(V2X)
★ Satellite network
★ Resource allocation
★ Multi-connectivity network
論文目次 1 Introduction 1
1.1 Background 1
2 Background and Related Works 4
2.1 Multi-RAT Structure and Satellite Access for 5G and Beyond 4
2.2 Multi-Connectivity Structure and Management 5
2.3 Machine Learning for Multi-Agent Systems 6
3 System Model and Problem Formulation 8
3.1 System Model 8
3.2 Communication Model 8
3.3 Satellite Model 9
3.4 Problem Formulation 12
4 Multi-Agent Reinforcement Learning For V2X 13
4.1 POMDP Model 13
4.2 Multi-Agent Extension and State Estimation 14
4.3 Training and Execution Algorithms 15
5 Implementation 17
5.1 Simulation Setup 17
5.2 Performance Evaluation 17
6 Conclusion and Future work 22
Bibliography 23
參考文獻 [1] Hanbyul Seo, Ki-Dong Lee, Shinpei Yasukawa, Ying Peng, and Philippe Sartori. Lte evolution for vehicle-to-everything services. IEEE Communications Magazine, 54(6):22–28, 2016.
[2] Carlos Renato Storck and Fa´tima Duarte-Figueiredo. A survey of 5g technology evolution, standards, and infrastructure associated with vehicle-to-everything com- munications by internet of vehicles. IEEE Access, 8:117593–117614, 2020.
[3] F. Lisi, G. Losquadro, A. Tortorelli, A. Ornatelli, and M. Donsante. Multi- connectivity in 5g terrestrial-satellite networks: the 5g-allstar solution, 2020.
[4] Subramanya Chandrashekar, Andreas Maeder, Cinzia Sartori, Thomas Ho¨hne, Benny Vejlgaard, and Devaki Chandramouli. 5g multi-rat multi-connectivity ar- chitecture. In 2016 IEEE International Conference on Communications Workshops (ICC), pages 180–186, 2016.
[5] Hao Ye and Geoffrey Ye Li. Deep reinforcement learning based distributed resource allocation for v2v broadcasting. In 2018 14th International Wireless Communica- tions Mobile Computing Conference (IWCMC), pages 440–445, 2018.
[6] Hao Ye, Geoffrey Ye Li, and Biing-Hwang Fred Juang. Deep reinforcement learning based resource allocation for v2v communications. IEEE Transactions on Vehicular Technology, 68(4):3163–3173, 2019.
[7] Le Liang, Hao Ye, and Geoffrey Ye Li. Toward intelligent vehicular networks: A machine learning framework. IEEE Internet of Things Journal, 6(1):124–135, 2019.
[8] Liang Wang, Hao Ye, Le Liang, and Geoffrey Ye Li. Learn to compress csi and allocate resources in vehicular networks. IEEE Transactions on Communications, 68(6):3640–3653, 2020.


[9] Le Liang, Hao Ye, and Geoffrey Ye Li. Spectrum sharing in vehicular networks based on multi-agent reinforcement learning. IEEE Journal on Selected Areas in Communications, 37(10):2282–2292, 2019.
[10] Helin Yang, Xianzhong Xie, and Michel Kadoch. Intelligent resource management based on reinforcement learning for ultra-reliable and low-latency iov communi- cation networks. IEEE Transactions on Vehicular Technology, 68(5):4157–4169, 2019.
[11] Min Zhao, Yifei Wei, Mei Song, and Guo Da. Power control for d2d communi- cation using multi-agent reinforcement learning. In 2018 IEEE/CIC International Conference on Communications in China (ICCC), pages 563–567, 2018.
[12] Zheng Li, Caili Guo, and Yidi Xuan. A multi-agent deep reinforcement learning based spectrum allocation framework for d2d communications. In 2019 IEEE Global Communications Conference (GLOBECOM), pages 1–6, 2019.
[13] Dohyun Kwon and Joongheon Kim. Multi-agent deep reinforcement learning for cooperative connected vehicles. In 2019 IEEE Global Communications Conference (GLOBECOM), pages 1–6, 2019.
[14] Rose E. Wang, Michael Everett, and Jonathan P. How. R-maddpg for partially ob- servable environments and limited communication, 2020.
[15] Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P. How, and John Vian. Deep decentralized multi-task multi-agent reinforcement learning under par- tial observability, 2017.
[16] Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, and Shimon White- son. Learning to communicate to solve riddles with deep distributed recurrent q- networks, 2016.
[17] Olga Galinina, Alexander Pyattaev, Sergey Andreev, Mischa Dohler, and Yevgeni Koucheryavy. 5g multi-rat lte-wifi ultra-dense small cells: Performance dynam- ics, architecture, and trends. IEEE Journal on Selected Areas in Communications, 33(6):1224–1240, 2015.
[18] Peng Wang, Jiaxin Zhang, Xing Zhang, Zhi Yan, Barry G. Evans, and Wenbo Wang. Convergence of satellite and terrestrial networks: A comprehensive survey. IEEE Access, 8:5550–5588, 2020.


[19] Gaofeng Cui, Yating Long, Lexi Xu, and Weidong Wang. Joint offloading and re- source allocation for satellite assisted vehicle-to-vehicle communication. IEEE Sys- tems Journal, pages 1–12, 2020.
[20] Janne Janhunen, Johanna Ketonen, Ari Hulkkonen, Juha Ylitalo, Antti Roivainen, and Markku Juntti. Satellite uplink transmission with terrestrial network interfer- ence. In 2015 IEEE Global Communications Conference (GLOBECOM), pages 1–6, 2015.
[21] Vincent Deslandes, Je´roˆme Tronc, and Andre´-Luc Beylot. Analysis of interference issues in integrated satellite and terrestrial mobile systems. In 2010 5th Advanced Satellite Multimedia Systems Conference and the 11th Signal Processing for Space Communications Workshop, pages 256–261, 2010.
[22] Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath. A brief survey of deep reinforcement learning. CoRR, abs/1708.05866, 2017.
[23] Jianqing Fan, Zhaoran Wang, Yuchen Xie, and Zhuoran Yang. A theoretical analysis of deep q-learning. In Alexandre M. Bayen, Ali Jadbabaie, George Pappas, Pablo A. Parrilo, Benjamin Recht, Claire Tomlin, and Melanie Zeilinger, editors, Proceed- ings of the 2nd Conference on Learning for Dynamics and Control, volume 120 of Proceedings of Machine Learning Research, pages 486–489, The Cloud, 10–11 Jun 2020. PMLR.
[24] John N. Tsitsiklis Vijay R. Konda. Actor-critic algorithms. Advances in neural information processing systems, page 1008–1014, 2000.
[25] 3GPP. Technical specification group radio access network; solutions for nr to support non-terrestrial networks (ntn). 2019-12.
[26] C. Kourogiorgas, D. Tarchi, A. Ugolini, P. D. Arapoglou, A. D. Panagopoulos,
G. Colavolpe, and A. Vanelli Coralli. System capacity evaluation of dvb-s2x based medium earth orbit satellite network operating at ka band. In 2016 8th Advanced Satellite Multimedia Systems Conference and the 14th Signal Processing for Space Communications Workshop (ASMS/SPSC), pages 1–8, 2016.
指導教授 黃志煒(Chi-Wei Huang) 審核日期 2021-10-28
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明