Extended Access Barring for Handling Massive Machine Type Communication (mMTC) Deployments

Authors

  • Luis Tello-Oquendo Universidad Nacional de Chimborazo, Ecuador
  • José Ramón Vidal Universitat Politècnica de València, España
  • Vicent Pla Universitat Politècnica de València, España
  • Jorge Martinez-Bauset Universitat Politècnica de València, España

DOI:

https://doi.org/10.37135/unach.ns.001.02.04

Keywords:

Cellular networks, Extended access barring (EAB), Internet of things, Performance analysis, Random access

Abstract

Massive machine type communication (mMTC) has presented a promising moment to generate powerful and ubiquitous connections that face plenty of new challenges. Cellular networks are the potential solution owing to their extensive infrastructure deployment, reliability, security, and efficiency. In cellular-based mMTC networks, the random access channel is used to establish the connection between MTC devices and base stations (eNBs), where the scalable and efficient connectivity for a tremendous number of devices is the primary challenge. To deal with this, the Third Generation Partnership Project (3GPP) has suggested the extended access barring (EAB) as a mechanism for congestion control. The eNBs activate or deactivate EAB using a congestion coefficient. In this paper, an approach to implementing the congestion coefficient is presented so that EAB can operate thus handling congestion episodes in mMTC scenarios. Moreover, the performance of EAB is examined under different MTC traffic loads and paging cycle configurations concerning network key performance indicators (KPIs). Numerical results demonstrate the effectiveness of the proposed method to detect congestion episodes. Also, it is shown that increasing the value of the paging cycle configuration influence on the network behavior under EAB.

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References

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Additional Files

Published

2018-12-12

Issue

Section

Research Articles and Reviews

How to Cite

Extended Access Barring for Handling Massive Machine Type Communication (mMTC) Deployments. (2018). Novasinergia, ISSN 2631-2654, 1(2), 38-44. https://doi.org/10.37135/unach.ns.001.02.04