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Realtime Mobile Bandwidth Prediction Using LSTM Neural Network

  • Lifan MeiEmail author
  • Runchen Hu
  • Houwei Cao
  • Yong Liu
  • Zifa Han
  • Feng Li
  • Jin Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11419)

Abstract

With the popularity of mobile access Internet and the higher bandwidth demand of mobile applications, user Quality of Experience (QoE) is particularly important. For bandwidth and delay sensitive applications, such as Video on Demand (VoD), Realtime Video Call, Games, etc., if the future bandwidth can be estimated in advance, it will greatly improve the user QoE. In this paper, we study realtime mobile bandwidth prediction in various mobile networking scenarios, such as subway and bus rides along different routes. The main method used is Long Short Term Memory (LSTM) recurrent neural network. In specific scenarios, LSTM achieves significant accuracy improvements over the state-of-the-art prediction algorithms, such as Recursive Least Squares (RLS). We further analyze the bandwidth patterns in different mobility scenarios using Multi-Scale Entropy (MSE) and discuss its connections to the achieved accuracy.

Keywords

Bandwidth prediction Long Short Term Memory Multi-Scale Entropy Bandwidth measurement 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lifan Mei
    • 1
    Email author
  • Runchen Hu
    • 1
  • Houwei Cao
    • 2
  • Yong Liu
    • 1
  • Zifa Han
    • 3
  • Feng Li
    • 3
  • Jin Li
    • 3
  1. 1.ECENew York UniversityNew York CityUSA
  2. 2.CSNew York Institute of TechnologyNew York CityUSA
  3. 3.Huawei TechnologiesNanjingChina

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