University of Oulu

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

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

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Author: Ruíz-Guirola, David E.1; Rodríguez-López, Carlos A.2; Montejo-Sánchez, Samuel3;
Organizations: 1Centre for Wireless Communications University of Oulu, Finland
2Department of Electronics and Telecommunications, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, Cuba
3Programa Institucional de Fomento a la Investigación, Desarrollo e Innovación, Universidad Tecnológica Metropolitana, Santiago, Chile
4Federal University of Santa Catarina, Florianópolis, SC, Brazil
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 3.1 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-08-02


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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Series: IEEE internet of things journal
ISSN: 2372-2541
ISSN-E: 2327-4662
ISSN-L: 2327-4662
Volume: In press
DOI: 10.1109/JIOT.2022.3181889
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work has been partially supported in Chile by ANID FONDECYT Iniciación No. 11200659, FONDEQUIP-EQM180180, and Collaborative Research Activities between PIDi/UTEM and FIE/UCLV, in Brazil by CNPq (402378/2021-0, 305021/2021-4), Print CAPES-UFSC “Automation 4.0”, and RNP/MCTIC (Grant 01245.010604/2020-14) 6G Mobile Communications Systems, and in Finland by 6Genesis Flagship (Grant no. 318927) and Tekniikan Edistämissäätiön.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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