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Deep Learning Based Traffic Prediction in Mobile Network- A Survey
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  • Xing Wang ,
  • Zhendong Wang ,
  • Kexin Yang ,
  • Zhiyan Song ,
  • Junlan Feng ,
  • Lin Zhu ,
  • Chao Deng
Xing Wang
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Zhendong Wang
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Kexin Yang
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Zhiyan Song
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Junlan Feng
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Chao Deng
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Abstract

With broad deployment of 5G network and pro- liferation of mobile devices, mobile network operators are not only facing massive data growth in mobile traffic, and also observing very complex and dynamic usage patterns, which bring challenges to network operation. In this context, network traffic prediction is becoming a key capability for network operation to assure quality of service and drive down the cost. Timely and accurate traffic prediction plays a crucial role in resource allocation, base-station energy saving, network planning and optimization. Recently, deep learning-based models have been widely used in mobile traffic prediction and shown significant performance gains. This survey provides a thorough account of deep learning solutions of mobile traffic prediction, involving representative data, model architectures, and applications. We start by analyzing the available data and categorize them into three major categories, and divide the traffic prediction problem into six subcategories. Then, we describe in detail how deep learning techniques are utilized to capture four crucial aspects of mobile traffic, namely temporal dependencies, spatial dependencies, external factors, and heterogeneity. We further briefly outline the applications based on mobile traffic prediction and summarize the open data and source codes. Finally, the remaining challenges and potential future directions are discussed to provide guidance for follow-up research. This article surveys the literature over the period 2017-2022 on the deep learning- based mobile traffic prediction. To the best of our knowledge, this paper is the first comprehensive survey of deep learning on mobile traffic prediction.