AuthorsG. Caso, K. Kousias, Ö. Alay, A. Brunström and M. Neri
TitleNB-IoT Random Access: Data-driven Analysis and ML-based Enhancements
AfilliationCommunication Systems, Machine Learning
Project(s)Department of Mobile Systems and Analytics
Publication TypeJournal Article
Year of Publication2021
JournalIEEE Internet of Things Journal
Date Published01/2021
Publisher IEEE
KeywordsCellular Internet of Things, Empirical Analysis, massive Machine Type Communications, Narrowband Internet of Things, Random Access

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.


Supplementary Materials, Results, and Dataset available at 

Citation Key27661