論文

査読有り
2018年2月1日

On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control

IEEE Wireless Communications
  • Fengxiao Tang
  • ,
  • Bomin Mao
  • ,
  • Zubair Md. Fadlullah
  • ,
  • Nei Kato
  • ,
  • Osamu Akashi
  • ,
  • Takeru Inoue
  • ,
  • Kimihiro Mizutani

25
1
開始ページ
154
終了ページ
160
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1109/MWC.2017.1700244
出版者・発行元
Institute of Electrical and Electronics Engineers Inc.

Recently, deep learning has appeared as a breakthrough machine learning technique for various areas in computer science as well as other disciplines. However, the application of deep learning for network traffic control in wireless/heterogeneous networks is a relatively new area. With the evolution of wireless networks, efficient network traffic control such as routing methodology in the wireless backbone network appears as a key challenge. This is because the conventional routing protocols do not learn from their previous experiences regarding network abnormalities such as congestion and so forth. Therefore, an intelligent network traffic control method is essential to avoid this problem. In this article, we address this issue and propose a new, real-time deep learning based intelligent network traffic control method, exploiting deep Convolutional Neural Networks (deep CNNs) with uniquely characterized inputs and outputs to represent the considered Wireless Mesh Network (WMN) backbone. Simulation results demonstrate that our proposal achieves significantly lower average delay and packet loss rate compared to those observed with the existing routing methods. We particularly focus on our proposed method's independence from existing routing protocols, which makes it a potential candidate to remove routing protocol(s) from future wired/ wireless networks.

リンク情報
DOI
https://doi.org/10.1109/MWC.2017.1700244
DBLP
https://dblp.uni-trier.de/rec/journals/wc/TangMFKAIM18
URL
http://dblp.uni-trier.de/db/journals/wc/wc25.html#journals/wc/TangMFKAIM18
ID情報
  • DOI : 10.1109/MWC.2017.1700244
  • ISSN : 1536-1284
  • DBLP ID : journals/wc/TangMFKAIM18
  • SCOPUS ID : 85032734210

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