Adaptive Access Class Barring Method for Machine Generated Communications

Abstract

Cellular network is provisioned to serve traffic demands generated by human being. The random access channel used for nodes to compete for a connection with an eNB is limited. Even though machines generate very small amount of data traffic, the signaling channel of a network becomes overloaded and collisions occur to fail the access if too many MTC (Machine Type Communication) devices attempt to access network. To tackle the issue, 3GPP specifies an access class barring but leaves a specific algorithm as an implementation issue. In this paper, we propose an adaptive access barring method. Generally, an eNB does not know the number of MTC devices in its coverage area. Thus, it is difficult to control the barring factor by predicting the number of MTC devices in a service area of a cell. On the contrary, we control the barring factor based on the prediction of access intensity which can be measured at an eNB. Simulation results show that since the proposed method can manipulate the barring factor autonomously according to the access intensity, it is superior to the original method in terms of the access success probability and the collision probability.