Realtime Mobile Bandwidth Prediction Using LSTM Neural Network

Lifan Mei, Runchen Hu, Houwei Cao, Yong Liu, Zifa Han, Feng Li, Jin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationPassive and Active Measurement - 20th International Conference, PAM 2019, Proceedings
EditorsMarinho Barcellos, David Choffnes
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783030159856
StatePublished - 2019
Event20th International Conference on Passive and Active Measurement, PAM 2019 - Puerto Varas, Chile
Duration: Mar 27 2019Mar 29 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11419 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Passive and Active Measurement, PAM 2019
CityPuerto Varas


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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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