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Proactive Wake-up Scheduler based on Recurrent Neural Networks

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Details

Original languageEnglish
Title of host publication2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
PublisherIEEE
Number of pages6
ISBN (Electronic)9781728150895
DOIs
Publication statusPublished - Jun 2020
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Communications -
Duration: 7 Jun 202011 Jun 2020

Publication series

NameIEEE International Conference on Communications
Volume2020-June
ISSN (Print)1550-3607

Conference

ConferenceIEEE International Conference on Communications
Period7/06/2011/06/20

Abstract

Recently, wake-up scheme has been proposed to enhance the energy-efficiency of 5G mobile devices and prolong its battery lifetime while reducing the buffering delay. The existing wake-up optimization mechanisms use off-line methods and are tied to specific traffic models. In this paper, a novel concept of wake-up scheduling is introduced to further improve the energy-efficiency of mobile devices and to deal with realistic traffic. The main idea is to use a fixed configuration of the wake-up scheme and adjust the scheduling of the wake-up signals dynamically. For this, a proactive wake-up scheduler is proposed to take online decisions based on traffic prediction. Towards this end, a framework to predict packet arrivals based on recurrent neural networks is developed. Numerical results show that for given delay requirements of video, audio streaming, and mixed traffic flow, the proactive wake-up scheduler reduces the power consumption of the baseline wake-up scheme without scheduler by up to 36%, 28% and 9%, respectively.

Keywords

  • 5G, energy efficiency, LSTM, machine learning, wake-up scheme

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