Analytical Framework for Access Class Barring in Machine Type Communication

Abstract

Access class barring (ACB) is regarded as an efficient and practically implementable method to reduce the traffic overload in cellular networks. In this paper, we present a unified analytical framework to analyze the performance of the fixed ACB scheme for a simple random access procedure (i.e., one-shot transmission model) in machine type communication (MTC) over cellular networks. We derive the exact expressions for the probability of a machine's packet being served by the base station (BS), the average number of machine type devices (MTDs) successfully served by the BS per second and the noncollision slot access probability. We verify the accuracy of the derived expressions by comparison with simulations. Based on the analytical expressions, we then maximize the probability of a MTD's packet being served and obtain the sub-optimal probability factor value for the fixed ACB in closed-form. Our results confirm that, the use of ACB scheme is important for scenarios with high MTD packet arrival rate, which is relevant for massive MTC. The proposed framework allows fine tuning and accurate prediction of the MTC performance with ACB.