University of Oulu

T. Senevirathna, B. Thennakoon, T. Sankalpa, C. Senevirathna, S. Ali and N. Rajatheva, "Event-Driven Source Traffic Prediction in Machine-Type Communications Using LSTM Networks," GLOBECOM 2020 - 2020 IEEE Global Communications Conference, Taipei, Taiwan, 2020, pp. 1-6, doi: 10.1109/GLOBECOM42002.2020.9322417

Event-driven source traffic prediction in machine-type communications using LSTM networks

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Author: Senevirathna, Thulitha1; Thennakoon, Bathiya1; Sankalpa, Tharindu1;
Organizations: 1Department of Electrical and Information Engineering, University of Ruhuna, Galle, Sri Lanka
2Center for Wireless Communications (CWC), University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.1 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-02-15


Source traffic prediction is one of the main challenges of enabling predictive resource allocation in machine-type communications (MTC). In this paper, a long short-term memory (LSTM) based deep learning approach is proposed for event-driven source traffic prediction. The source traffic prediction problem can be formulated as a sequence generation task where the main focus is predicting the transmission states of machine-type devices (MTDs) based on their past transmission data. This is done by restructuring the transmission data in a way that the LSTM network can identify the causal relationship between the devices. Knowledge of such a causal relationship can enable event-driven traffic prediction. The performance of the proposed approach is studied using data regarding events from MTDs with different ranges of entropy. Our model outperforms existing baseline solutions in saving resources and accuracy with a margin of around 9%. Reduction in random access (RA) requests by our model is also analyzed to demonstrate the low amount of signaling required as a result of our proposed LSTM based source traffic prediction approach.

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Series: IEEE Global Communications Conference
ISSN: 2334-0983
ISSN-E: 2576-6813
ISSN-L: 2334-0983
ISBN: 978-1-7281-8298-8
ISBN Print: 978-1-7281-8299-5
Pages: 1 - 6
DOI: 10.1109/GLOBECOM42002.2020.9322417
Host publication: GLOBECOM 2020 - 2020 IEEE Global Communications Conference
Conference: IEEE Global Communications Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This Research work is in part funded by the Department of Electrical and Information Engineering, University of Ruhuna and the Telecommunications Regulatory Commission of Sri Lanka (TRCSL). This work was also supported in part by the Academy of Finland 6Genesis Flagship under grant 318927.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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