Scattering Random-Access Intensity in LTE Machine-to-Machine (M2M) Communications

Abstract

Supporting a large number of devices in LTE Machine-to-Machine (M2M) communications is a big challenge. The large number of devices will try to access radio resource in a short period of time which may result in severe congestion to the Radio Access Network (RAN). Hence, how to scatter the devices, which are always trying to access to a same eNBs (evolved Node B), to other eNBs has become an important issue. In other words, balancing the random access intensity among eNBs will reduce the congestion of an RAN and improve the network utilization. Nevertheless, how to select an eNB to attach to during the initial access in a way that will balance overall access intensity in the network is a key challenge. Unfortunately, from the perspective of eNB, an eNB is unaware of how many devices will access to the eNB. Thus, an eNB cannot release the information about the access intensity in advance. In this paper, therefore, we propose four effective eNB selection algorithms to estimate the access intensity of an eNB. Simulation results show that the proposed algorithms perform better than conventional approach.