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Toward accurate energy-efficient cellular network: switching off excessive carriers based on traffic profiling

Published: 04 April 2016 Publication History

Abstract

The carrier based energy saving (CBES) method, i.e., switching off the excessive carriers when traffic load is light, can help network operators obtain significant energy savings in cellular network. In practice, however, due to the lack of corresponding traffic classifications, current CBES method uses same traffic threshold for switching off all the BS carriers, which limits its effectiveness. In this paper, we seek to illuminate that the CBES method could be improved in a more precise way and make cellular network more energy efficient. Based on the collected large volume of 3G data set, we profile the traffic in sector granularity and classify them. With the help of machine learning approach, three traffic classes are derived and for each of them, we articulate corresponding strategy in high-level vision for improving CBES method. The simulation results for energy saving show the feasibility of our strategies.

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Cited By

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  • (2023)Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research AgendaInformatics10.3390/informatics1003007310:3(73)Online publication date: 9-Sep-2023
  • (2017)Traffic Prediction Based Power Saving in Cellular NetworksProceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3139958.3140053(1-10)Online publication date: 7-Nov-2017

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    cover image ACM Conferences
    SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing
    April 2016
    2360 pages
    ISBN:9781450337397
    DOI:10.1145/2851613
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 04 April 2016

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    Author Tags

    1. cellular network
    2. energy efficient
    3. traffic profiling

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    SAC 2016: Symposium on Applied Computing
    April 4 - 8, 2016
    Pisa, Italy

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    SAC '16 Paper Acceptance Rate 252 of 1,047 submissions, 24%;
    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    View all
    • (2023)Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research AgendaInformatics10.3390/informatics1003007310:3(73)Online publication date: 9-Sep-2023
    • (2017)Traffic Prediction Based Power Saving in Cellular NetworksProceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3139958.3140053(1-10)Online publication date: 7-Nov-2017

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