University of Oulu

M. Shehab, A. K. Hagelskjær, A. E. Kalør, P. Popovski and H. Alves, "Traffic Prediction Based Fast Uplink Grant for Massive IoT," 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, London, UK, 2020, pp. 1-6, doi: 10.1109/PIMRC48278.2020.9217258

Traffic prediction based fast uplink grant for massive IoT

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Author: Shehab, Mohammad1; Hagelskjær, Alexander K.2; Kalør, Anders E.2;
Organizations: 1Centre for Wireless Communications (CWC), University of Oulu, Finland
2Department of Electronic Systems, Aalborg University, Denmark
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-02-19


This paper presents a novel framework for traffic prediction of IoT devices activated by binary Markovian events. First, we consider a massive set of IoT devices whose activation events are modeled by an On-Off Markov process with known transition probabilities. Next, we exploit the temporal correlation of the traffic events and apply the forward algorithm in the context of hidden Markov models (HMM) in order to predict the activation likelihood of each IoT device. Finally, we apply the fast uplink grant scheme in order to allocate resources to the IoT devices that have the maximal likelihood for transmission. In order to evaluate the performance of the proposed scheme, we define the regret metric as the number of missed resource allocation opportunities. The proposed fast uplink scheme based on traffic prediction outperforms both conventional random access and time division duplex in terms of regret and efficiency of system usage, while it maintains its superiority over random access in terms of average age of information for massive deployments.

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Series: IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops
ISSN: 2166-9570
ISSN-E: 2166-9589
ISSN-L: 2166-9570
ISBN: 978-1-7281-4490-0
ISBN Print: 978-1-7281-4491-7
Pages: 1 - 6
Article number: 9217258
DOI: 10.1109/PIMRC48278.2020.9217258
Host publication: 31st IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2020
Conference: IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work is partially supported by Academy of Finland 6Genesis Flagship (Grant no. 318927), Aka Project EE-IoT (Grant no. 319008). This work has been in part supported by the European Research Council (ERC) under the Euro-pean Union Horizon 2020 research and innovation program (ERC Consolidator Grant Nr. 648382 WILLOW) and Danish Council for Independent Research (Grant Nr. 8022-00284B SEMIOTIC).
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
319008 (Academy of Finland Funding decision)
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