University of Oulu

D. E. Ruíz-Guirola, O. L. A. López, S. Montejo-Sánchez, R. D. Souza and M. Bennis, "Performance Analysis of ML-Based MTC Traffic Pattern Predictors," in IEEE Wireless Communications Letters, vol. 12, no. 7, pp. 1144-1148, July 2023, doi: 10.1109/LWC.2023.3264273.

Performance analysis of ML-based MTC traffic pattern predictors

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Author: Ruíz-Guirola, David E.1; López, Onel L. A.1; Montejo-Sánchez, Samuel2;
Organizations: 1Centre for Wireless Communications, University of Oulu, Oulu, Finland
2Programa Institucional de Fomento a la Investigación, Desarrollo e Innovación, Universidad Tecnológica Metropolitana, Santiago, Chile
3Department of Electrical Engineering, Federal University of Santa Catarina, Florianópolis, Brazil
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2023
Publish Date: 2023-09-07


Prolonging the lifetime of massive machine-type communication (MTC) networks is key to realizing a sustainable digitized society. Great energy savings can be achieved by accurately predicting MTC traffic followed by properly designed resource allocation mechanisms. However, selecting the proper MTC traffic predictor is not straightforward and depends on accuracy/complexity trade-offs and the specific MTC applications and network characteristics. Remarkably, the related state-of-the-art literature still lacks such debates. Herein, we assess the performance of several machine learning (ML) methods to predict Poisson and quasi-periodic MTC traffic in terms of accuracy and computational cost. Results show that the temporal convolutional network (TCN) outperforms the long-short term memory (LSTM), the gated recurrent units (GRU), and the recurrent neural network (RNN), in that order. For Poisson traffic, the accuracy gap between the predictors is larger than under quasi-periodic traffic. Finally, we show that running a TCN predictor is around three times more costly than other methods, while the training/inference time is the greatest/least.

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Series: IEEE wireless communications letters
ISSN: 2162-2337
ISSN-E: 2162-2345
ISSN-L: 2162-2337
Volume: 12
Issue: 7
Pages: 1144 - 1148
DOI: 10.1109/lwc.2023.3264273
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
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