Applying nonlinear optimal control strategy for the access management of MTC devices

Machine Type Communications (MTC) come up with substantial revenue growth for Mobile Network Operators (MNO), but they represent at the same time the most important challenge they are facing. In fact, a massive number of MTC devices performs simultaneously the Random Access (RA), which causes severe congestion and reduces the RA success probability. To control the Radio Access Network (RAN) overload and alleviate the congestion between MTC devices, 3GPP developed the Access Class Barring (ACB) procedure that depends on an access probability called the ACB factor. In this paper, we, first, present a simple fluid model of MTC devices' random access. This model is, then, used to derive a novel adaptive regulator of the ACB factor that in contrast with previous existing contributions, which generally rely on heuristics. The main advantages of the proposed approach are twofold. First, the proposal is fully compliant with the standard while it reduces significantly the computation and the signaling overheads. Second, it provides an efficient mean to regulate adaptively the ACB factor as it guarantees having an optimal number of MTC devices accessing concurrently to the RAN. The obtained results based on simulations show clearly the robustness of the proposed approach, and its superiority compared to existing work.