A scheme of estimating mobile traffic data without coarse-grained process using conditional SR-GAN
A scheme of estimating mobile traffic data without coarse-grained process using conditional SR-GAN
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DOI：
10.1587/comex.2021etl0017
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发表时间：
2021-01-01
影响因子：
0.3
通讯作者：
Mizutani, Kimihiro
中科院分区：
文献类型：
--
作者：
Tokunaga, Tomoki;Mizutani, Kimihiro
近年来，已经提出了用于生成大量移动的业务数据的方案。在最先进的方案中，生成对抗网络（GANs）用于将大量流量数据转换为粗粒度表示，并从粗粒度数据生成原始流量数据。然而，为了生成原始的交通数据，必须保存粗粒度的数据，这需要浪费存储成本。本文提出了一种使用条件SR-GAN生成移动的流量数据而无需粗粒度处理的方案。在使用真实的移动的交通数据的评估中，我们提出的方案不仅比传统方案减少了25%以上的存储成本，而且可以以94%的准确率生成原始的移动的交通数据。
In recent years, a scheme for generating a large amount of mobile traffic data has been proposed. In the state-of-the-art of the schemes, Generative Adversarial Networks (GANs) is used to transform a large amount of traffic data into a coarse-grained representation and to generate the original traffic data from the coarse-grained data. However, in order to generate the original traffic data, the coarse-grained data must be preserved and it takes waste storage cost. In this paper, we propose a scheme for generating the mobile traffic data without requiring a coarse-grained process by using Conditional SR-GAN. In evaluation using real mobile traffic data, our proposed scheme not only does reduce the storage cost by more than 25% compared to the traditional scheme, but also can generate the original mobile traffic data with 94% accuracy.