University of Oulu

E. Eldeeb, M. Shehab and H. Alves, "A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory," in IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3889-3898, 1 March1, 2022, doi: 10.1109/JIOT.2021.3101978

A learning-based fast uplink grant for massive IoT via support vector machines and long short-term memory

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Author: Eldeeb, Eslam1; Shehab, Mohammad1; Alves, Hirley1
Organizations: 1Centre for Wireless Communications (CWC), University of Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.4 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-02-04


The current random access (RA) allocation techniques suffer from congestion and high signaling overhead while serving massive machine type communication (mMTC) applications. To this end, 3GPP introduced the need to use fast uplink grant (FUG) allocation in order to reduce latency and increase reliability for smart internet-of-things (IoT) applications with strict QoS constraints. We propose a novel FUG allocation based on support vector machine (SVM), First, MTC devices are prioritized using SVM classifier. Second, LSTM architecture is used for traffic prediction and correction techniques to overcome prediction errors. Both results are used to achieve an efficient resource scheduler in terms of the average latency and total throughput. A Coupled Markov Modulated Poisson Process (CMMPP) traffic model with mixed alarm and regular traffic is applied to compare the proposed FUG allocation to other existing allocation techniques. In addition, an extended traffic model based CMMPP is used to evaluate the proposed algorithm in a more dense network. We test the proposed scheme using real-time measurement data collected from the Numenta Anomaly Benchmark (NAB) database. Our simulation results show the proposed model outperforms the existing RA allocation schemes by achieving the highest throughput and the lowest access delay of the order of 1 ms by achieving prediction accuracy of 98 % when serving the target massive and critical MTC applications with a limited number of resources.

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Series: IEEE internet of things journal
ISSN: 2372-2541
ISSN-E: 2327-4662
ISSN-L: 2327-4662
Volume: 9
Issue: 5
Pages: 3889 - 3898
DOI: 10.1109/JIOT.2021.3101978
Type of Publication: A1 Journal article – refereed
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), FIREMAN (Grant no. 326301)
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
319008 (Academy of Finland Funding decision)
326301 (Academy of Finland Funding decision)
Copyright information: © 2021 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see