On Intelligent Traffic Control for Large-Scale Heterogeneous Networks: A Value Matrix-Based Deep Learning Approach
On Intelligent Traffic Control for Large-Scale Heterogeneous Networks: A Value Matrix-Based Deep Learning Approach
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DOI：
10.1109/lcomm.2018.2875431
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发表时间：
2018-12-01
期刊：
影响因子：
--
通讯作者：
Kato, Nei
中科院分区：
文献类型：
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作者：
Fadlullah, Zubair Md.;Tang, Fengxiao;Kato, Nei
最近，深度学习已经成为智能控制网络流量的一种有吸引力的技术。然而，目前的研究仅集中在中小规模网络上，因为基于深度学习的流量控制算法的计算复杂度随着网络规模的增加而显著增加。在本文中，我们解决了这个问题，并设想了一种基于奖励的深度学习结构，该结构联合使用深度卷积神经网络（CNN）和深度信念网络（DBN）来预测流量负载值矩阵并分别构建最终动作矩阵。在我们的提议中，深度CNN用于构建奖励预测网络，而深度DBN用于构建行动决策网络。因此，最终动作空间被简化为下一个目的动作矩阵，并且计算复杂度大大降低。基于计算机的仿真结果表明，我们的建议是能够实现在大规模的网络中的数据包丢失率和吞吐量相比，在传统的路由方法的性能有所改善。
Recently, deep learning has emerged as an attractive technique to intelligently control network traffic. However, the contemporary researches only focused on small-/mediumscale 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.