Authors | G. Caso, K. Kousias, Ö. Alay, A. Brunström and M. Neri |
Title | NB-IoT Random Access: Data-driven Analysis and ML-based Enhancements |
Afilliation | Communication Systems, Machine Learning |
Project(s) | Department of Mobile Systems and Analytics |
Status | Published |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | IEEE Internet of Things Journal |
Volume | 8 |
Issue | 14 |
Pagination | 11384-11399 |
Date Published | 01/2021 |
Publisher | IEEE |
Keywords | Cellular Internet of Things, Empirical Analysis, massive Machine Type Communications, Narrowband Internet of Things, Random Access |
Abstract | In the context of massive Machine Type Communications (mMTC), the Narrowband Internet of Things (NB-IoT) technology is envisioned to efficiently and reliably deal with massive device connectivity. Hence, it relies on a tailored Random Access (RA) procedure, for which theoretical and empirical analyses are needed for a better understanding and further improvements. This paper presents the first data-driven analysis of NB-IoT RA, exploiting a large scale measurement campaign. We show how the RA procedure and performance are affected by network deployment, radio coverage, and operators’ configurations, thus complementing simulation-based investigations, mostly focused on massive connectivity aspects. Comparison with the performance requirements reveals the need for procedure enhancements. Hence, we propose a Machine Learning (ML) approach, and show that RA outcomes are predictable with good accuracy by observing radio conditions. We embed the outcome prediction in a RA enhanced scheme, and show that optimized configurations enable a power consumption reduction of at least 50%. We also make our dataset available for further exploration, toward the discovery of new insights and research perspectives. |
Notes | Supplementary Materials, Results, and Dataset available at https://mosaic-simulamet.com/nbiot-randomaccess/ |
URL | https://ieeexplore.ieee.org/document/9324758 |
DOI | 10.1109/JIOT.2021.3051755 |
Citation Key | 27661 |