【百家大讲堂】第240期：空天地一体化车联网资源管理中的增强学习
讲座题目：空天地一体化车联网资源管理中的增强学习
Reinforcement Learning for Resource Management in Space-Air-Ground (SAG) Integrated Vehicular Networks
报 告 人：沈学民（教授、加拿大工程院院士）
时 间：2019年9月28日(周六)14:30-16:00
地 点：中关村校区信息科学实验楼205会议室
主办单位：研究生院、信息与电子学院
报名方式：登录mk体育在线（中国）微信企业号---第二课堂---课程报名中选择“【百家大讲堂】第240期：空天地一体化车联网资源管理中的增强学习”
【主讲人简介】
沈学民是加拿大滑铁卢大学电气与计算机工程系教授，兼任研究生教学委员会委员。沈博士的研究重点是无线资源管理、无线网络安全、智能电网、车联网和传感器网络。他是 IEEE IoT J 的总主编。他担任 Mobihoc'15 的总主席，IEEE Globecom 16、IEEE INFOCOM'14、IEEE VTC'10、IEEE ICC'10 、IEEE Globecom'07 的技术委员会主席、IEEE 通信协会无线通信技术委员会主席。沈博士是当选的IEEE 通信协会出版分会的副主席，IEEE通信协会杰出讲师评选委员会委员，以及IEEE通信协会会士评审委员会委员。沈博士于2006年获得优秀研究生监督奖，2003年获得加拿大安大略省总理研究卓越奖（PREA）。沈博士是加拿大安大略省注册专业工程师、IEEE会士、加拿大工程学会院士、加拿大工程院院士、加拿大皇家学会院士和 IEEE 车辆技术协会和通信协会的杰出讲师。
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.
【讲座信息】
空-天-地一体化车联网是一种非常多样化的车联网，其可在任何地方、任何环境条件、和任何时间突发的事件下同时保证超可靠性的低延迟通信并提供高带宽流量。另一方面，要同时有效地管理和分配地面网络、空中网络和空间（卫星）资源面临巨大挑战，因为它们在延迟、吞吐量和覆盖范围方面具有异构访问特性。此外，车辆的高机动性和实时决策要求进一步使问题难以解决。在本次报告中，我们提出在空-天-地一体化车联网中使用强化学习进行资源管理，从而实现自适应访问控制、按需无人机部署和无人机轨迹设计的无模型、快速决策。我们还将展示我们的空天地模拟器和一些演示结果。
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.