加入收藏夹
联系我们
关于本站
个人主页
西电导航
西安电子科技大学
    当前位置:首页>>学术讲座
On Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence
时间:2018-10-16 15:33    点击:   所属单位:通信工程学院
讲座名称 On Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence
讲座时间 2018-10-22 09:30:00
讲座地点 北校区老科技楼一楼报告厅
讲座人 Nei Kato
讲座人介绍 Nei Kato is a full professor (Deputy Dean) with Graduate School of Information Sciences(GSIS) and the Director of Research Organization of Electrical Communication(ROEC), Tohoku University, Japan. He has been engaged in research on computer networking, wireless mobile communications, satellite communications, ad hoc & sensor & mesh networks, smart grid, IoT, Big Data, and pattern recognition. He has published more than 400 papers in prestigious peer-reviewed journals and conferences. He is the Vice-President (Member & Global Activities) of IEEE Communications Society(2018-2019), the Editor-in-Chief of IEEE Transactions on Vehicular Technology(2017-), and the Chair of IEEE Communications Society Sendai Chapter. He served as the Editor-in-Chief of IEEE Network Magazine (2015-2017), a Member-at-Large on the Board of Governors, IEEE Communications Society(2014-2016), a Vice Chair of Fellow Committee of IEEE Computer Society(2016), and a member of IEEE Communications Society Award Committee (2015-2017). He has also served as the Chair of Satellite and Space Communications Technical Committee (2010-2012) and Ad Hoc & Sensor Networks Technical Committee (2014-2015) of IEEE Communications Society. His awards include Minoru Ishida Foundation Research Encouragement Prize(2003), Distinguished Contributions to Satellite Communications Award from the IEEE Communications Society, Satellite and Space Communications Technical Committee(2005), the FUNAI information Science Award(2007), the TELCOM System Technology Award from Foundation for Electrical Communications Diffusion(2008), the IEICE Network System Research Award(2009), the IEICE Satellite Communications Research Award(2011), the KDDI Foundation Excellent Research Award(2012), IEICE Communications Society Distinguished Service Award(2012), IEICE Communications Society Best Paper Award(2012), Distinguished Contributions to Disaster-resilient Networks R&D Award from Ministry of Internal Affairs and Communications, Japan(2014), Outstanding Service and Leadership Recognition Award 2016 from IEEE Communications Society Ad Hoc & Sensor Networks Technical Committee, Radio Achievements Award from Ministry of Internal Affairs and Communications, Japan (2016), IEEE Communications Society Asia-Pacific Outstanding Paper Award(2017), Prize for Science and Technology from the Minister of Education, Culture, Sports, Science and Technology, Japan(2018), Award from Tohoku Bureau of Telecommunications, Ministry of Internal Affairs and Communications, Japan(2018), and Best Paper Awards from IEEE ICC/GLOBECOM/WCNC/VTC. Nei Kato is a Distinguished Lecturer of IEEE Communications Society and Vehicular Technology Society. He is a fellow of IEEE and IEICE.

讲座内容 It is widely acknowledged that the development of traditional terrestrial communication technologies is unable to provide all users with fair and high quality services due to the scarce network resources and limited coverage areas. To complement the terrestrial connection, particularly for users in rural, disaster-stricken, and/or other difficult-to-serve areas, satellites, unmanned aerial vehicles (UAV), and balloons have been utilized to relay the communication signals. Recently, the Space-Air-Ground Integrated Networks, referred to as SAGINs, were proposed to improve the users’ Quality of Experience (QoE). However, compared with existing networks such as ad hoc networks and cellular networks, the SAGINs are much more complex due to the various characteristics of the three network segments. In order to improve the network performance of SAGINs, researchers are confronting many unprecedented challenges. In this keynote, I will demonstrate how the Artificial Intelligence (AI) technique, in particular deep learning, can be harnessed to optimize the SAGINs. As the AI technique has shown its predominant advantages in many applications, in the keynote, I will analyze several main challenges of SAGINs and explain how these problems can be solved by AI. I will also consider the satellite traffic balancing as an example and present a deep learning based method to improve the traffic control performance. I will show how the state-of-the-art deep learning techniques such as deep convolutional neural networks can be exploited to uniquely characterize the inputs and outputs of the SAGINs. I will also focus on issues such as datasets needed to train such deep learning architectures that are essential to acquire accurate training and prediction.

转载请注明出处:西安电子科技大学学术信息网
如果您有学术信息或学术动态,欢迎投稿。我们将在第一时间确认并收录,投稿邮箱: meeting@xidian.edu.cn
Copyright © 2011-2018 西安电子科技大学 
开发维护:电子工程学院网络信息中心  管理员:meeting@xidian.edu.cn 站长统计: