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Network-Aided Intelligent Traffic Steering in 5G Mobile Networks

Dae-Young Kim1, Seokhoon Kim2, *

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: email.

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

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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APA Style
Kim, D., Kim, S. (2020). Network-aided intelligent traffic steering in 5G mobile networks. Computers, Materials & Continua, 65(1), 243-261. https://doi.org/10.32604/cmc.2020.011253
Vancouver Style
Kim D, Kim S. Network-aided intelligent traffic steering in 5G mobile networks. Comput Mater Contin. 2020;65(1):243-261 https://doi.org/10.32604/cmc.2020.011253
IEEE Style
D. Kim and S. Kim, "Network-Aided Intelligent Traffic Steering in 5G Mobile Networks," Comput. Mater. Contin., vol. 65, no. 1, pp. 243-261. 2020. https://doi.org/10.32604/cmc.2020.011253

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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