One of the promising technologies which is being regarded in the future 5G standard long Term Evolution (LTE) Release 13 is Machine-to-Machine or M2M communications. It represents a key component of Internet of Things (IoT) and smart spaces. One of the most difficult challenges to support M2M communications is their huge number compared to available radio resources. Hence, to deal with random access channel (RACH) overload and to better control the access in LTE-A Networks, the 3GPP proposed the Access Class Barring (ACB) factor concept. In this paper, we proposed an algorithm to improve this method based on adaptive multiple ACB factors, according to M2M traffic category with respect to their access delay budget and drop rate (QoS parameters). Simulations were done for testing the effectiveness of our MACB scheme and show that using multiples ACB factors and dynamically adjusting them to the congestion level in addition to traffic type category increases the access probability and reduces overload. The obtained results were then compared to the basic ACB method and show that the access probability increases especially for high priority traffic.
['Nawel Zangar', 'Sami Gharbi', 'Marwen Abdennebi']
Service differentiation strategy based on MACB factor for M2M Communications in LTE-A Networks