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Quantifying NB-IoT Performance in 5G Use-Cases With Mixture of Regular and Stochastic Traffic

Authors: Pavel Masek; Dmitri Moltchanov; Martin Stusek; Radek Mozny; Yevgeni Koucheryavy; Jiri Hosek;

Quantifying NB-IoT Performance in 5G Use-Cases With Mixture of Regular and Stochastic Traffic

Abstract

The increasing demand for power distribution systems in terms of control with nearly immediate response requires deploying a new type of user equipment (UE) that demands permanent connectivity. In NB-IoT systems, the traffic generated by such UEs may constitute a large part of the overall load. In this paper, we first propose a detailed two-dimensional Markov chain model to capture the system's behavior with the mixture of conventional stochastic and regular traffic types. To provide a computationally efficient solution, we then apply the state aggregation technique to reduce it to a one-dimensional model and develop approximations and associated numerical algorithms for assessing the mean delay when transmitting the considered traffic. Our results show that a single NB-IoT cell remains stable for up to 72x10^4 conventional UEs and 9x10^3 UEs demanding permanent connectivity. The presence of the latter UEs type has a linear effect on their delay, but affects conventional UEs more drastically. A delay bound of 10s specified in ITU-R M.2410 is met for the conventional UEs, even under a high number of permanently connected UEs 10^3. However, the delay on the side of the latter UEs is violated even for 100 permanently connected UEs requiring redesigning the NB-IoT channel access mechanism or expanding resources.

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Keywords

5G Mobile Networks, 330, mMTC, Narrowband IoT, Delay Performance, Mixture of Traffic Types, 213

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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