Modeling and Analysis of mMTC Traffic in 5G Base Stations

Fidan Mehmeti, Thomas F. La Porta

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations

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 non-intensive 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.

Original languageEnglish (US)
Pages (from-to)652-660
Number of pages9
JournalProceedings - IEEE Consumer Communications and Networking Conference, CCNC
DOIs
StatePublished - 2022
Event19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022 - Virtual, Online, United States
Duration: Jan 8 2022Jan 11 2022

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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