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Dynamic Adaptive Access Barring Scheme For Heavily Congested M2M Networks

The massive deployment of Machine-to-machine (M2M) communications may overwhelm the cellular network by imposing strong constraints on the Radio Access Network (RAN). As the base station cannot accurately get the exact number of M2M arrivals, it cannot really predict the overload status. Consequently, a better estimation of this number would efficiently help to overcome the risk of congestion. In this paper, we proposed a novel fluid model for M2M communications, which allows gaining an enhanced understanding of the dynamics of such systems. The provided analysis of the model was used to devise a new method to estimate accurately the number of M2M devices. We proposed, then, a novel implementation of the ACB process, which dynamically computes the ACB factor according to the network's overload conditions while includes a corrective action adapting the controller action based on the mismatch existing between the computed and the targeted mean load. The simulation results show that the proposed algorithms allow improving considerably the estimation of the number of M2M devices' arrivals, while outperforming existing techniques.