An Adaptive Access Class Barring Scheme for Handling Massive M2M Communications in LTE-A

In the near future, a massive number of machine-to-machine communication devices will provide with ubiquitous information and services, but such a high number of devices can cause severe congestion in relaying networks. This is the case of LTE-A networks, in which the random access channel suffers from congestion whenever a bulk of user equipments (UEs) attempt to access the cellular base stations in a highly synchronized manner. Under these conditions, the access class barring (ACB) scheme can effectively reduce congestion in exchange for a longer access delay. Therefore, the access delay can be greatly affected if the configuration parameters of ACB are not correctly adapted to the traffic intensity. In this paper, we present a novel adaptive ACB scheme that can be directly implemented in the LTE-A system. In the proposed scheme, the configuration parameters are updated by means of an adaptive filter algorithm; namely the least-mean-square algorithm. Results show that our adaptive ACB scheme sharply enhances the access of UEs during periods of high congestion; i.e., the access delay can be reduced up to a 50 percent when compared to other ACB schemes. In addition, the access of UEs under normal operating conditions is not affected.