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

• Published in: IEEE wireless communications Vol. 25; no. 1; pp. 154 - 160
• Main Authors: Fengxiao Tang, Bomin Mao, Fadlullah, Zubair Md, Kato, Nei, Akashi, Osamu, Inoue, Takeru, Mizutani, Kimihiro
• Format: Journal Article
• Language: English
• Published: IEEE 01-02-2018
• Subjects: Feature extraction; Machine learning; Proposals; Routing protocols; Telecommunication traffic; Traffic control
• ISSN: 1536-1284; 1558-0687
• DOI: 10.1109/MWC.2017.1700244

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.