Open Access

ARTICLE

Network-Aided Intelligent Traffic Steering in 5G Mobile Networks

Dae-Young Kim¹, Seokhoon Kim^{2, *}

1 School of Computer Software, Daegu Catholic University, Geyongsan, 38430, Korea.
2 Department of Computer Software Engineering, Soonchunhyang University, Asan, 31538, Korea.

* Corresponding Author: Seokhoon Kim. Email: .

Computers, Materials & Continua 2020, 65(1), 243-261. https://doi.org/10.32604/cmc.2020.011253

Received 29 April 2020; Accepted 25 May 2020; Issue published 23 July 2020

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.

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Citations