In smart city applications, huge numbers of devices need to be connected in an autonomous manner. 3rd Generation Partnership Project (3GPP) specifies that Machine Type Communication (MTC) should be used to handle data transmission among a large number of devices. However, the data transmission rates are highly variable, and this brings about a congestion problem. To tackle this problem, the use of Access Class Barring (ACB) is recommended to restrict the number of access attempts allowed in data transmission by utilizing strategic parameters. In this paper, we model the problem of determining the strategic parameters with a reinforcement learning algorithm. In our model, the system evolves to minimize both the collision rate and the access delay. The experimental results show that our scheme improves system performance in terms of the access success rate, the failure rate, the collision rate, and the access delay.

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LTE-A networks provide excellent wireless connectivity for MTC (Machine Type Communication

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between cellular and D2D links should be controlled while guaranteeing QoS (Quality-of-Service) of communication service. In MTC, network congestion due to the mass concurrent access requests generated by MTC devices should be controlled while guaranteeing QoS of traditional cellular communication. In this paper, we provide an overview of resource management mechanisms for D2D communication and MTC. Besides, we investigate the performance of a typical mechanism of congestion control for MTC. EAB (Extended Access Barring) mechanism barring the radio access of the devices according to the their ACs (Access Classes) releases the overload and congestion of RA (Random Access) channel temporarily.