Estimation Based Adaptive ACB Scheme for M2M Communications

It is a critical challenge to support massive access of devices in a short period in machine-to-machine (M2M) communications. In this paper, we propose a estimation based adaptive Access Class Barring (ACB) scheme to improve the scalability of M2M networks. First, we propose a simple estimation scheme to estimate the network loads. Then we obtain two functions which are the relationship of ACB's barring factor (the parameter of access probability) and network loads to maximize the system throughput. According to the estimation and the obtained functions, eNB changes ACB barring factor dynamically to adapt to the network load conditions. At the same time, we summarize the boundedness of the adaptive scheme. Finally, simulation results illustrate that our scheme can increase the system throughput and reduce access delay obviously.