CALL FOR INTERNATIONAL WORKSHOP MACHINE LEARNING FOR SATELLITE-TERRESTRIAL NETWORKS
来源: 桂冠/
南京邮电大学
105
0
0
2018-09-04

http://psats.eu/call-workshop4/


Terrestrial wireless networks have evolved into the Internet of Things (IoTs) paradigm, in which different terrestrial wireless networks will be integrated and millions of objects will be connected. In addition, satellite networks support more connections from the space, which cannot be supported by terrestrial wireless networks. Terrestrial wireless networks and satellite networks will be integrated into satellite-terrestrial networks to provide ubiquitous coverage, massive connectivity, and enhanced capacity. Though satellite-terrestrial networks offer many advantages over terrestrial wireless networks, the topology of which becomes much more complicated with high dynamics. These make the efficient resource allocation extremely difficult. This issue calls for intelligent operations and control of satellite-terrestrial networks. Recently, machine learning has been widely recognized as an effective tool to deal with intelligent topology control and resource allocation in wireless communications and networks. This half-day workshop intends to bring researchers on machine learning for satellite-terrestrial networks together, and facilitate interdisciplinary exchanges on machine learning applications. Topics of interest include, but are not limited to:

 

  • Machine learning for wireless sensor network deployment

  • Machine learning for unmanned aerial vehicle planning and management

  • Machine learning for satellite-terrestrial network architectures and protocols

  • Machine learning for interference and resource management in satellite-terrestrial networks

  • Machine learning for spectrum access and sharing in satellite-terrestrial networks

  • Machine learning for hybrid access in satellite-terrestrial networks

  • Machine learning for energy harvesting in satellite-terrestrial networks

  • Machine learning for physical layer security in satellite-terrestrial networks

  • Machine learning for theoretical analysis and design of satellite-terrestrial networks

Important dates

Full paper submission: October 15, 2018

Acceptance notification: November 15, 2018

Camera-ready submission: December 1, 2018

 

Workshop organizers

Jun Cai, University of Manitoba, Canada (jun.cai@umanitoba.ca)

Hongbin Chen, Guilin University of Electronic Technology, China (chbscut@guet.edu.cn)

Guan Gui, Nanjing University of Posts and Telecommunications, China (guiguan@njupt.edu.cn)

Bin Li, Beijing University of Posts and Telecommunications, China (Binli@bupt.edu.cn)

Jie Xu, Guangdong University of Technology, China (jiexu@gdut.edu.cn)

Chungang Yang, Xidian University, China (cgyang@xidian.edu.cn)

Youping Zhao, Beijing Jiaotong University, China (yozhao@bjtu.edu.cn)



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