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Proactive Wake-up Scheduler based on Recurrent Neural Networks

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Standard

Proactive Wake-up Scheduler based on Recurrent Neural Networks. / Rostami, Soheil; Trinh, Hoang Duy; Lagen, Sandraslagen; Costa, Mario; Valkama, Mikko; Dini, Paolo.

2020 IEEE International Conference on Communications, ICC 2020 - Proceedings. IEEE, 2020. (IEEE International Conference on Communications; Vol. 2020-June).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Rostami, S, Trinh, HD, Lagen, S, Costa, M, Valkama, M & Dini, P 2020, Proactive Wake-up Scheduler based on Recurrent Neural Networks. in 2020 IEEE International Conference on Communications, ICC 2020 - Proceedings. IEEE International Conference on Communications, vol. 2020-June, IEEE, IEEE International Conference on Communications, 7/06/20. https://doi.org/10.1109/ICC40277.2020.9148671

APA

Rostami, S., Trinh, H. D., Lagen, S., Costa, M., Valkama, M., & Dini, P. (2020). Proactive Wake-up Scheduler based on Recurrent Neural Networks. In 2020 IEEE International Conference on Communications, ICC 2020 - Proceedings (IEEE International Conference on Communications; Vol. 2020-June). IEEE. https://doi.org/10.1109/ICC40277.2020.9148671

Vancouver

Rostami S, Trinh HD, Lagen S, Costa M, Valkama M, Dini P. Proactive Wake-up Scheduler based on Recurrent Neural Networks. In 2020 IEEE International Conference on Communications, ICC 2020 - Proceedings. IEEE. 2020. (IEEE International Conference on Communications). https://doi.org/10.1109/ICC40277.2020.9148671

Author

Rostami, Soheil ; Trinh, Hoang Duy ; Lagen, Sandraslagen ; Costa, Mario ; Valkama, Mikko ; Dini, Paolo. / Proactive Wake-up Scheduler based on Recurrent Neural Networks. 2020 IEEE International Conference on Communications, ICC 2020 - Proceedings. IEEE, 2020. (IEEE International Conference on Communications).

Bibtex - Download

@inproceedings{92c39d5cc14a4b0cb87b34ab13f117e4,
title = "Proactive Wake-up Scheduler based on Recurrent Neural Networks",
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",
author = "Soheil Rostami and Trinh, {Hoang Duy} and Sandraslagen Lagen and Mario Costa and Mikko Valkama and Paolo Dini",
note = "EXT={"}Rostami, Soheil{"}",
year = "2020",
month = "6",
doi = "10.1109/ICC40277.2020.9148671",
language = "English",
series = "IEEE International Conference on Communications",
publisher = "IEEE",
booktitle = "2020 IEEE International Conference on Communications, ICC 2020 - Proceedings",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Proactive Wake-up Scheduler based on Recurrent Neural Networks

AU - Rostami, Soheil

AU - Trinh, Hoang Duy

AU - Lagen, Sandraslagen

AU - Costa, Mario

AU - Valkama, Mikko

AU - Dini, Paolo

N1 - EXT="Rostami, Soheil"

PY - 2020/6

Y1 - 2020/6

N2 - 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.

AB - 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.

KW - 5G

KW - energy efficiency

KW - LSTM

KW - machine learning

KW - wake-up scheme

U2 - 10.1109/ICC40277.2020.9148671

DO - 10.1109/ICC40277.2020.9148671

M3 - Conference contribution

T3 - IEEE International Conference on Communications

BT - 2020 IEEE International Conference on Communications, ICC 2020 - Proceedings

PB - IEEE

ER -