@article {27661,
	title = {NB-IoT Random Access: Data-driven Analysis and ML-based Enhancements},
	journal = {IEEE Internet of Things Journal},
	volume = {8},
	year = {2021},
	note = {Supplementary Materials, Results, and Dataset available at\&nbsp;https://mosaic-simulamet.com/nbiot-randomaccess/\&nbsp;},
	month = {01/2021},
	pages = {11384-11399},
	publisher = { IEEE},
	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{\textquoteright} 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.},
	keywords = {Cellular Internet of Things, Empirical Analysis, massive Machine Type Communications, Narrowband Internet of Things, Random Access},
	doi = {10.1109/JIOT.2021.3051755},
	url = {https://ieeexplore.ieee.org/document/9324758},
	author = {Caso, Giuseppe and Kousias, Konstantinos and Alay, {\"O}zg{\"u} and Brunstr{\"o}m, Anna and Neri, Marco}
}
