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【百家大講堂】第240期:空天地一體化車聯網資源管理中的增強學習

編輯: 研究生院 發布日期: 2019-09-18 瀏覽量:

講座題目:空天地一體化車聯網資源管理中的增強學習

Reinforcement Learning for Resource Management in Space-Air-Ground (SAG) Integrated Vehicular Networks

報 告 人:沈學民(教授国民平台app、加拿大工程院院士)

時   間:2019年9月28日(周六)14:30-16:00

地   點:中關村校區信息科學實驗樓205會議室

主辦單位:研究生院国民平台app、信息與電子學院

報名方式:登錄北京理工大學微信企業號---第二課堂---課程報名中選擇“【百家大講堂】第240期:空天地一體化車聯網資源管理中的增強學習”

【主講人簡介】

  沈學民是加拿大滑鐵盧大學電氣與計算機工程系教授国民平台app,兼任研究生教學委員會委員。沈博士的研究重點是無線資源管理、無線網絡安全国民平台app、智能電網、車聯網和傳感器網絡国民平台app。他是 IEEE IoT J 的總主編。他擔任 Mobihoc'15 的總主席,IEEE Globecom 16、IEEE INFOCOM'14国民平台app、IEEE VTC'10、IEEE ICC'10 、IEEE Globecom'07 的技術委員會主席、IEEE 通信協會無線通信技術委員會主席国民平台app。沈博士是當選的IEEE 通信協會出版分會的副主席,IEEE通信協會杰出講師評選委員會委員,以及IEEE通信協會會士評審委員會委員。沈博士于2006年獲得優秀研究生監督獎,2003年獲得加拿大安大略省總理研究卓越獎(PREA)国民平台app。沈博士是加拿大安大略省注冊專業工程師、IEEE會士、加拿大工程學會院士国民平台app、加拿大工程院院士国民平台app、加拿大皇家學會院士和 IEEE 車輛技術協會和通信協會的杰出講師。

 

Xuemin (Sherman) Shen is a University Professor, and Associate Chair for Graduate Study, Department of Electrical and Computer Engineering, University of Waterloo, Canada. Dr. Shen's research focuses on wireless resource management, wireless network security, smart grid and vehicular ad hoc and sensor networks. He is the Editor-in-Chief of IEEE IoT J. He serves as the General Chair for Mobihoc'15, the Technical Program Committee Chair for IEEE Globecom'16, IEEE Infocom'14, IEEE VTC'10, the Symposia Chair for IEEE ICC'10, the Technical Program Committee Chair for IEEE Globecom'07, the Chair for IEEE Communications Society Technical Committee on Wireless Communications. Dr. Shen is an elected IEEE ComSoc Vice President - Publications, the chair of IEEE ComSoc Distinguish Lecturer selection committee, and a member of IEEE ComSoc Fellow evaluation committee. Dr. Shen received the Excellent Graduate Supervision Award in 2006, and the Premier's Research Excellence Award (PREA) in 2003 from the Province of Ontario, Canada. Dr. Shen is a registered Professional Engineer of Ontario, Canada, an IEEE Fellow, an Engineering Institute of Canada Fellow, a Canadian Academy of Engineering Fellow, a Royal Society of Canada Fellow, and a Distinguished Lecturer of IEEE Vehicular Technology Society and Communications Society.

 

【講座信息】

  空-天-地一體化車聯網是一種非常多樣化的車聯網国民平台app,其可在任何地方国民平台app、任何環境條件国民平台app、和任何時間突發的事件下同時保證超可靠性的低延遲通信并提供高帶寬流量。另一方面,要同時有效地管理和分配地面網絡、空中網絡和空間(衛星)資源面臨巨大挑戰,因為它們在延遲、吞吐量和覆蓋范圍方面具有異構訪問特性。此外,車輛的高機動性和實時決策要求進一步使問題難以解決。在本次報告中,我們提出在空-天-地一體化車聯網中使用強化學習進行資源管理,從而實現自適應訪問控制、按需無人機部署和無人機軌跡設計的無模型、快速決策。我們還將展示我們的空天地模擬器和一些演示結果国民平台app。

 

Space-Air-Ground integrated Vehicular Network (SAGVN) is a prominent paradigm to provide an extremely versatile vehicular network that can simultaneously guarantee ultra-reliability low-latency communications (URLLC) and deliver high-bandwidth traffic anywhere, any environment condition, and any event at anytime. However, it is challenging to manage and allocate the terrestrial network, aerial network (UAV), and space (satellite) resources simultaneously and efficiently, as they have heterogeneous access features in terms of delay, throughput, and coverage range. In addition, high vehicle mobility and real-time decision requirement further render the problem intractable. In this talk, we advocate the usage of reinforcement learning for resource management in SAGVN, which can enable model-free and fast decision makings for adaptive access control, on-demand UAV deployment, and UAV trajectory design. We will also show the detail development of our SAG simulator and some demos.

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