Special Session

 

Special Session 1: Artificial Intelligence and Machine Learning for Future Wireless Communications and Networks

 

2019年第19届IEEE通信技术国际会议(ICCT 2019)

西安,中国
Call for Papers

The essential technical challenges of wireless communications and networks are unbalanced distribution between traffic and resources; Furthermore, the recent traffic (VR and AR) appears more features corresponding to the conventional traffic services of voice and multimedia. Meanwhile, the multiple types of resources provided by different types of networks are limited, in particular, the radio spectrum, energy, computing resources, and so on.  The sharp rise of mobile traffic demands in wireless communication introduce big challenges to network infrastructures. 5G terrestrial wireless networks have evolved into the Internet of Things paradigm, in which different terrestrial wireless networks will be integrated and millions of objects will be connected through them. In addition, satellite networks support more connections from the space, which can not support by the terrestrial networks. Terrestrial wireless networks and satellite networks will be integrated into satellite-terrestrial networks to provide ubiquitous coverage, massive connectivity, and enhanced capacity. The unmanned areial vehicle (UAV) has some good attributes of mobility and flexibility, which make wider applications to the civil and military domains, including several typical applications of UAV to emergency communications, real-time monitoring and intelligence reconnaissance. Wireless network function virtualization (NFV) and software defined network (SDN) also are becoming emerging network paradigms.

Though future wireless communication and networks networks offer many advantages, the topology of which becomes more and more complicated with high dynamics. These make the efficient resource allocation in them more and more difficult. Challenges always come with opportunities. Recently, artificial intelligence and machine learning has been widely envisaged as an effective tool to deal with intelligent physical, MAC, and network layer design, and topology control and resource allocation in wireless communications and networks. This intends to bring researchers on machine learning for future wireless communication and networks together, and facilitating interdisciplinary exchanges on machine learning applications. Topics of interest include, but are not limited to:


Organizers:
Xianfu Chen, VTT Technical Research Centre of Finland, Finland, xianfu.chen@vtt.fi
Mehdi Bennis,University of Oulu, Finland, Mehdi.Bennis@oulu.fi
Celimuge Wu, The University of Electro-Communications, Japan, celimuge@uec.ac.jp
Chungang Yang, Xidian University, China, chgyang2010@163.com