Priority-based adaptive access barring for M2M communications in LTE networks using learning automata

Supporting a huge number of machine-to-machine devices with different priorities in Long Term Evolution networks is addressed in this paper. We propose a learning automaton (LA)–based scheme for dynamically allocating random access resources to different classes of machine-to-machine devices according to their priorities and their demands in each cycle. We then use another LA-based scheme to adjust the barring factor for each class to control the possible overload. We show that by appropriate updating procedure for these LAs, the system performance asymptotically converges to the optimal performance in which the evolved node B knows the number of access-attempting devices from each class a priori. Simulation results are provided to show the performance of the proposed scheme in random access resource allocation to defined classes and adjusting the barring factor for each of them.