Performance Study and Enhancement of Access Barring for Massive Machine-Type Communications

Abstract

Machine-type communications (MTC) is an emerging technology that boosts the development of the Internet of Things by providing ubiquitous connectivity and services. Cellular networks are an excellent choice for providing such hyper-connectivity thanks to their widely deployed infrastructure, among other features. However, dealing with a large number of connection requests is a primary challenge in the cellular-based MTC. Severe congestion episodes can occur when a large number of devices try to access the network almost simultaneously. Extended access barring (EAB) is a congestion control mechanism for the MTC that has been proposed by the 3GPP. In this paper, we carry out a thorough performance analysis of the EAB and show the limitations of its current specification. To overcome these limitations, we propose the two enhanced EAB schemes: the combined use of the EAB and access class barring, and the introduction of a congestion avoidance backoff after the barring status of a UE is switched to unbarred. It is shown through extensive simulations that our proposed solutions improve the key performance indicators. A high successful access probability can be achieved even in heavily congested scenarios, the access delay is shortened, and, most importantly, the number of required preamble retransmissions is reduced, which results in significant energy savings. Furthermore, we present an accurate congestion estimation method that solely relies on the information available at the base station. We show that this method permits a realistic and effective implementation of the EAB.

Keywords

• Machine-to-machine communications
• performance analysis
• radio access networks
• 5G mobile communication