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Energy-efficient wake-up signalling for machine-type devices based on traffic-aware long-short term memory prediction

Ruíz-Guirola, David E.; Rodríguez-López, Carlos A.; Montejo-Sánchez, Samuel; Souza, Richard Demo; López, Onel L. A.; Alves, Hirley (2022-06-09)

 
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URL:
https://doi.org/10.1109/JIOT.2022.3181889

Ruíz-Guirola, David E.
Rodríguez-López, Carlos A.
Montejo-Sánchez, Samuel
Souza, Richard Demo
López, Onel L. A.
Alves, Hirley
Institute of Electrical and Electronics Engineers
09.06.2022

D. E. Ruiz-Guirola, C. A. Rodríguez-López, S. Montejo-Sánchez, R. D. Souza, O. L. A. López and H. Alves, "Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long-Short Term Memory Prediction," in IEEE Internet of Things Journal, 2022, doi: 10.1109/JIOT.2022.3181889

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© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1109/JIOT.2022.3181889
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2022080252544
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Abstract

Reducing energy consumption is a pressing issue in low-power machine-type communication (MTC) networks. In this regard, the Wake-up Signal (WuS) technology, which aims to minimize the energy consumed by the radio interface of the machine-type devices (MTDs), stands as a promising solution. However, state-of-the-art WuS mechanisms use static operational parameters, so they cannot efficiently adapt to the system dynamics. To overcome this, we design a simple but efficient neural network to predict MTC traffic patterns and configure WuS accordingly. Our proposed forecasting WuS (FWuS) leverages an accurate long-short term memory (LSTM)-based traffic prediction that allows extending the sleep time of MTDs by avoiding frequent page monitoring occasions in idle state. Simulation results show the effectiveness of our approach. The traffic prediction errors are shown to be below 4%, being false alarm and miss-detection probabilities respectively below 8.8% and 1.3%. In terms of energy consumption reduction, FWuS can outperform the best benchmark mechanism in up to 32%. Finally, we certify the ability of FWuS to dynamically adapt to traffic density changes, promoting low-power MTC scalability.

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