Modeling and Analysis of mMTC Traffic in 5G Base Stations

TL;DR

This paper models the traffic patterns of massive machine-type communications in 5G networks, deriving inter-arrival time distributions and analyzing how user numbers affect traffic variability at base stations.

Contribution

It introduces a generalized traffic model for mMTC in 5G, deriving inter-arrival time distributions and validating results with real traces.

Findings

Increasing mMTC users linearly increases traffic variability.

Traffic generation rate has less impact on variability.

Model helps in efficient network resource planning.

Abstract

Massive Machine-Type Communications (mMTC) are one of the three types of services that should be supported by 5G networks. These are distinguished by the need to serve a large number of devices which are characterized by nonintensive traffic and low energy consumption. While the sporadic nature of the mMTC traffic does not pose an exertion to efficient network operation, multiplexing the traffic from a large number of these devices within the cell certainly does. Therefore, planning carefully the network resources for this traffic is of paramount importance. To do this, the statistics of the traffic pattern that arrives at the base station should be known. To this end, in this paper, we derive the distribution of the inter-arrival times of the traffic at the base station from a general number of mMTC users within the cell, assuming a generic distribution of the traffic pattern by individual users. We validate our results on traces. Results show that adding more mMTC users in the cell increases the variability of the traffic pattern at the base station almost linearly, which is not the case with increasing the traffic generation rates.