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

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number8489985
Pages (from-to)2479-2482
Number of pages4
JournalIEEE Communications Letters
Volume22
Issue number12
DOIs
Publication statusPublished - 2018 Dec

Keywords

  • Deep learning
  • convolutional neural network (CNN)
  • deep belief network (DBN)
  • non-supervised learning
  • packets forwarding
  • routing protocol

ASJC Scopus subject areas

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

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