research-article

Predicting handoffs in 3G networks

Authors:

Srinivasan Seshan,

Alexander VarshavskyAuthors Info & Claims

ACM SIGOPS Operating Systems Review, Volume 45, Issue 3

Pages 65 - 70

https://doi.org/10.1145/2094091.2094109

Published: 11 January 2012 Publication History

Abstract

Consumers all over the world are increasingly using their smartphones on the go and expect consistent, high quality connectivity at all times. A key network primitive that enables continuous connectivity in cellular networks is handoff. Although handoffs are necessary for mobile devices to maintain connectivity, they can also cause short-term disruptions in application performance. Thus, applications could benefit from the ability to predict impending handoffs with reasonable accuracy, and modify their behavior to counter the performance degradation that accompanies handoffs. In this paper, we study whether attributes relating to the cellular network conditions measured at handsets can accurately predict handoffs. In particular, we develop a machine learning framework to predict handoffs in the near future. An evaluation on handoff traces from a large US cellular carrier shows that our approach can achieve 80% accuracy - 27% better than a naive predictor.

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Cited By

Üstebay DChuai J(2019)Hierarchical Bayesian Modelling for Wireless Cellular NetworksProceedings of the 2019 Workshop on Network Meets AI & ML10.1145/3341216.3342217(76-82)Online publication date: 14-Aug-2019
https://dl.acm.org/doi/10.1145/3341216.3342217
Elkourdi MMazin AGitlin R(2019)Performance Analysis for Virtual-Cell Based CoMP 5G Networks Using Deep Recurrent Neural Nets2019 Wireless Telecommunications Symposium (WTS)10.1109/WTS.2019.8715542(1-6)Online publication date: Apr-2019
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Wickramasuriya DPerumalla CDavaslioglu KGitlin R(2017)Base station prediction and proactive mobility management in virtual cells using recurrent neural networks2017 IEEE 18th Wireless and Microwave Technology Conference (WAMICON)10.1109/WAMICON.2017.7930254(1-6)Online publication date: Apr-2017
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Index Terms

Predicting handoffs in 3G networks
1. Networks
  1. Network services

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Published In

cover image ACM SIGOPS Operating Systems Review

ACM SIGOPS Operating Systems Review Volume 45, Issue 3

December 2011

94 pages

ISSN:0163-5980

DOI:10.1145/2094091

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Copyright © 2012 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 January 2012

Published in SIGOPS Volume 45, Issue 3

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Cited By

Üstebay DChuai J(2019)Hierarchical Bayesian Modelling for Wireless Cellular NetworksProceedings of the 2019 Workshop on Network Meets AI & ML10.1145/3341216.3342217(76-82)Online publication date: 14-Aug-2019
https://dl.acm.org/doi/10.1145/3341216.3342217
Elkourdi MMazin AGitlin R(2019)Performance Analysis for Virtual-Cell Based CoMP 5G Networks Using Deep Recurrent Neural Nets2019 Wireless Telecommunications Symposium (WTS)10.1109/WTS.2019.8715542(1-6)Online publication date: Apr-2019
https://doi.org/10.1109/WTS.2019.8715542
Wickramasuriya DPerumalla CDavaslioglu KGitlin R(2017)Base station prediction and proactive mobility management in virtual cells using recurrent neural networks2017 IEEE 18th Wireless and Microwave Technology Conference (WAMICON)10.1109/WAMICON.2017.7930254(1-6)Online publication date: Apr-2017
https://doi.org/10.1109/WAMICON.2017.7930254
Li YDeng HLi JPeng CLu S(2016)Instability in Distributed Mobility ManagementACM SIGMETRICS Performance Evaluation Review10.1145/2964791.290145744:1(261-272)Online publication date: 14-Jun-2016
https://dl.acm.org/doi/10.1145/2964791.2901457
Li YDeng HLi JPeng CLu SAlouf SJean-Marie AHegde NProutière A(2016)Instability in Distributed Mobility ManagementProceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science10.1145/2896377.2901457(261-272)Online publication date: 14-Jun-2016
https://dl.acm.org/doi/10.1145/2896377.2901457
Tu GLi YPeng CLi CLu S(2016)Detecting problematic control-plane protocol interactions in mobile networksIEEE/ACM Transactions on Networking10.1109/TNET.2015.240433624:2(1209-1222)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1109/TNET.2015.2404336
Theera-Ampornpunt NMangla TBagchi SPanta RJoshi KAmmar MZegura E(2016)TANGO: Toward a More Reliable Mobile Streaming through Cooperation between Cellular Network and Mobile Devices2016 IEEE 35th Symposium on Reliable Distributed Systems (SRDS)10.1109/SRDS.2016.047(297-306)Online publication date: Sep-2016
https://doi.org/10.1109/SRDS.2016.047
Lee HWicke MKusy BGnawali OGuibas L(2015)Predictive Data Delivery to Mobile Users Through Mobility Learning in Wireless Sensor NetworksIEEE Transactions on Vehicular Technology10.1109/TVT.2014.238823764:12(5831-5849)Online publication date: Dec-2015
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Nadembega AHafid ATaleb T(2015)A Destination and Mobility Path Prediction Scheme for Mobile NetworksIEEE Transactions on Vehicular Technology10.1109/TVT.2014.234526364:6(2577-2590)Online publication date: Jun-2015
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Nadembega AHafid ATaleb T(2015)Mobility-Prediction-Aware Bandwidth Reservation Scheme for Mobile NetworksIEEE Transactions on Vehicular Technology10.1109/TVT.2014.234525564:6(2561-2576)Online publication date: Jun-2015
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