@Article{cmc.2020.011253,
AUTHOR = {Dae-Young Kim, Seokhoon Kim},
TITLE = {Network-Aided Intelligent Traffic Steering in 5G Mobile  Networks},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {243--261},
URL = {http://www.techscience.com/cmc/v65n1/39564},
ISSN = {1546-2226},
ABSTRACT = {Recently, the fifth generation (5G) of mobile networks has been deployed and 
various ranges of mobile services have been provided. The 5G mobile network supports 
improved mobile broadband, ultra-low latency and densely deployed massive devices. It 
allows multiple radio access technologies and interworks them for services. 5G mobile 
systems employ traffic steering techniques to efficiently use multiple radio access 
technologies. However, conventional traffic steering techniques do not consider dynamic 
network conditions efficiently. In this paper, we propose a network aided traffic steering 
technique in 5G mobile network architecture. 5G mobile systems monitor network 
conditions and learn with network data. Through a machine learning algorithm such as a 
feed-forward neural network, it recognizes dynamic network conditions and then 
performs traffic steering. The proposed scheme controls traffic for multiple radio access 
according to the ratio of measured throughput. Thus, it can be expected to improve traffic 
steering efficiency. The performance of the proposed traffic steering scheme is evaluated 
using extensive computer simulations.},
DOI = {10.32604/cmc.2020.011253}
}