Abstract
In HetNets (Heterogeneous Networks), each network is allocated with fixed spectrum resource and provides service to its assigned users using specific RAT (Radio Access Technology). Due to the high dynamics of load distribution among different networks, simply optimizing the performance of individual network can hardly meet the demands from the dramatically increasing access devices, the consequent upsurge of data traffic, and dynamic user QoE (Quality-of-Experience). The deployment of smart networks, which are supported by SRA (Smart Resource Allocation) among different networks and CUA (Cognitive User Access) among different users, is deemed a promising solution to these challenges. In this paper, we propose a frame-work to transform HetNets to smart networks by leveraging WBD (Wireless Big Data), CR (Cognitive Radio) and NFV (Network Function Virtualization) techniques. CR and NFV support resource slicing in spectrum, physical layers, and network layers, while WBD is used to design intelligent mechanisms for resource mapping and traffic prediction through powerful AI (Artificial Intelligence) methods. We analyze the characteristics of WBD and review possible AI methods to be utilized in smart networks. In particular, the potential of WBD is revealed through high level view on SRA, which intelligently maps radio and network resources to each network for meeting the dynamic traffic demand, as well as CUA, which allows mobile users to access the best available network with manageable cost, yet achieving target QoS (Quality-of-Service) or QoE.
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This work is supported by the National Natural Science Foundation of China (Nos. 61571100, 61631005).
Yudi Huang was born in 1994. He received the B.E. degree in University of Electronic Science and Technology of China (UESTC) in 2016. He is now a master candidate in communication and information system. His research interests include cognitive radio and statistical machine learning.
Junjie Tan was born in 1994. He received the B.E. degree in University of Electronic Science and Technology of China (UESTC) in 2016. He is now a master candidate in communication and information system. His research inter- ests include cognitive radio and reinforce- ment learning.
Ying-Chang Liang (F11) [corresponding author] is a professor in the University of Electronic Science and Technology of China (UESTC), China, and also a professor in the University of Sydney, Australia. He was a principal scientist and technical advisor in the Institute for Infocomm Research (I2R), Singapore. His research interest lies in the general area of wireless networking and communications, with current focus on applying artificial intelligence, big data analytics and machine learning techniques to wireless network design and optimization. Dr Liang was elected a fellow of the IEEE in December 2010, and was recognized by Thomson Reuters as a highly cited researcher in 2014, 2015 and 2016. He received IEEE ComSocs TAOS Best Paper Award in 2016, IEEE Jack Neubauer Memorial Award in 2014, the First IEEE ComSocs APB Outstanding Paper Award in 2012, and the EURASIP Journal of Wireless Communications and Networking Best Paper Award in 2010. He also received the Institute of Engineers Singapore (IES)s Prestigious Engineering Achievement Award in 2007, and the IEEE Standards Associations Outstanding Contribution Appreciation Award in 2011, for his contributions to the development of IEEE 802.22 standard. Dr Liang is now serving as the chair of IEEE Communications Society Technical Committee on Cognitive Networks, an associate editor of IEEE Transactions on Signal and Information Processing over Network, and an associate editor-in-chief of the World Scientific Journal on Random Matrices: Theory and Applications. He served as founding editor-in-chief of IEEE Journal on Selected Areas in Communications C Cognitive Radio Series, and was the key founder of the new journal IEEE Transactions on Cognitive Communications and Networking. He has been an (associate) editor of IEEE Transactions on Wireless Communications, IEEE Transactions on Vehicular Technology, and IEEE Signal Processing Magazine. Dr Liang was a distinguished lecturer of the IEEE Communications Society and the IEEE Vehicular Technology Society, and has been a member of the board of Governors of the IEEE Asia-Pacific Wireless Communications Symposium since 2009. He served as Technical Program Committee (TPC) Chair of CROWN08 and DySPAN10, Symposium Chair of ICC12 and Globecom12, General Co-Chair of ICCS10 and ICCS14. He serves as TPC Chair and Executive Co-Chair of Globecom17 to be held in Singapore.
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Huang, Y., Tan, J. & Liang, YC. Wireless big data: transforming heterogeneous networks to smart networks. J. Commun. Inf. Netw. 2, 19–32 (2017). https://doi.org/10.1007/s41650-017-0002-1
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DOI: https://doi.org/10.1007/s41650-017-0002-1