A Comprehensive Evaluation of Generating a Mobile Traffic Data Scheme without a Coarse-Grained Process Using CSR-GAN

Tomoki Tokunaga,Kimihiro Mizutani

doi:10.3390/s22051930

Abstract

Large-scale mobile traffic data analysis is important for efficiently planning mobile base station deployment plans and public transportation plans. However, the storage costs of preserving mobile traffic data are becoming much higher as traffic increases enormously population density of target areas. To solve this problem, schemes to generate a large amount of mobile traffic data have been proposed. In the state-of-the-art of the schemes, generative adversarial networks (GANs) are used to transform a large amount of traffic data into a coarse-grained representation and generate the original traffic data from the coarse-grained data. However, the scheme still involves a storage cost, since the coarse-grained data must be preserved in order to generate the original traffic data. In this paper, we propose a scheme to generate the mobile traffic data by using conditional-super-resolution GAN (CSR-GAN) without requiring a coarse-grained process. Through experiments using two real traffic data, we assessed the accuracy and the amount of storage data needed. The results show that the proposed scheme, CSR-GAN, can reduce the storage cost by up to 45% compared to the traditional scheme, and can generate the original mobile traffic data with 94% accuracy. We also conducted experiments by changing the architecture of CSR-GAN, and the results show an optimal relationship between the amount of traffic data and the model size.

Highlights

Mobile traffic data have increased rapidly in recent years due to the dramatic spread of mobile devices and online services
The dataset we used for CSR-generative adversarial networks (GANs) evaluation consisted of real mobile traffic data collected from Telecom Italia’s Big Data Challenge [25]
Our proposed scheme consists of generative adversarial networks (GANs; conditional-super-resolution GAN (CSR-GAN)), combining conditional GAN (C-GAN) and super-resolution GAN (SR-GAN)

Summary

Introduction

Mobile traffic data have increased rapidly in recent years due to the dramatic spread of mobile devices and online services. According to Ericsson research, the amount of monthly mobile traffic data is expected to reach 49 EB per month by the end of 2020 and 237 EB by 2026 [1]. To handle a large amount of future mobile traffic data, it is important to analyze the trends of mobile traffic patterns in order to deploy mobile devices and online services on a large scale in urban areas [2–5]. The storage cost of mobile traffic data is becoming much higher, since it increases enormously with the size and population density of a target area. In order to solve these problems, schemes to estimate a large amount of traffic data have been proposed. With the recent advances in neural network technology, many schemes using deep neural networks have been widely proposed

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Results

Discussion

Conclusion