Optimal Access Class Barring in Machine to Machine Systems with Random Activation Time

Abstract

Machine type communication (MTC) systems are a new paradigm in communication systems where machines talk to each other rather than humans. It is expected that more than twenty billion smart devices are deployed around the globe by 2020. The machines talk to each other and communicate with cloud based MTC servers to monitor and control everything around us. Such ubiquitous sensing and actuating require a communication infrastructure. Cellular networks due to their wide coverage are the best candidate for the communication infrastructure. The 3GPP LTE system is the future cellular network. Access class barring (ACB) is introduced by the standard as a solution to alleviate the congestion at the access layer. It works as a persistent probability for network access at the data link layer. In this paper, we consider an MTC system with several devices using LTE system as communication network. Based on the suggestions of the 3GPP standard, we consider uniform activation of devices within a long interval. This activation pattern results in Poisson arrival traffic in each random access channel. Using this arrival traffic pattern, we obtain the ACB factor which maximizes the throughput in the access link. This factor depends on the traffic parameters. Then, we propose a scheme to estimate the traffic parameters. At the end, we propose an algorithm which takes into account practical considerations. We validate our analytical models through extensive simulations.