Congestion Control Through Dynamic Access Class Barring for Bursty MTC Traffic in Future Cellular Networks

The traditional role of cellular networks is centered on human-to-human (H2H) communication, such as voice and data services, therefore, the necessary amendment is mandatory to host the Internet-of-Things (IoT) through machine-to-machine (M2M) communications. This is particularly critical due to the proliferation of the machine-type devices (MTD), which would generate huge traffic in future, which happens to be bursty in nature. The 3GPP has proposed the use of access class barring (ACB) to avoid congestion at each Base Station (BS), using which, each MTD can postpone its request for a random access channel (RACH) with a specific probability value. In this work, we have proposed a dynamic ACB approach in which value of ACB factor is adaptively change according to the traffic load conditions during each time slot. In order to make this idea work practically, the total number of active MTDs are estimated only using the information available at the eNodeB. Simulation results show that our proposed scheme delivers performance very close to the optimum conditions.