On Intelligent Traffic Control for Large-Scale Heterogeneous Networks: A Value Matrix-Based Deep Learning Approach

Zubair Md. Fadlullah, Fengxiao Tang, Bomin Mao, Jiajia Liu, Nei Kato

研究成果: Article査読

18 被引用数 (Scopus)

抄録

Recently, deep learning has emerged as an attractive technique to intelligently control network traffic. However, the contemporary researches only focused on small-/medium-scale networks, since the computational complexity of deep learning based traffic control algorithm significantly increases with the network size. In this paper, we address this issue and envision a reward-based deep learning structure, which jointly employs deep convolutional neural network (CNN) and a deep belief network (DBN) to predict the traffic load value matrix and construct the final action matrix, respectively. In our proposal, the deep CNN is used to construct the award prediction network, while the deep DBN constructs the action decision network. Thus, the final action space is simplified to a next destination action matrix, and the computational complexity is substantially reduced. Computer-based simulation results demonstrate that our proposal is able to achieve an improved performance in the large-scale network in terms of the packets loss rate and throughput in contrast with those in the conventional routing method.

本文言語English
論文番号8489985
ページ(範囲)2479-2482
ページ数4
ジャーナルIEEE Communications Letters
22
12
DOI
出版ステータスPublished - 2018 12

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

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