A random access channel resources allocation approach to control machine-to-machine communication congestion over LTE-advanced networks

The Internet of Things (IoT) is becoming a reality, and one of the core elements to make this reality come true is machine-to-machine (M2M) communication. With the fast-growing rate in which devices are being deployed, communication among them has become crucial to support the development of IoT applications. The Long-Term Evolution-Advanced (LTE-A) is a potential access network for these M2M devices. However, the LTE-A inherited characteristics from older cellular network standards, which were designed for human-to-human (H2H) and human-to-machine (H2M) communication. Accordingly, supporting M2M communication poses some challenges to LTE-A, with a highlight of the congestion and overload problems in the radio access network (RAN) during the random access channel (RACH) procedure. Such a problem arises due to the large number of devices sending access-request messages to the network and the limited number of resources to satisfy this new demand. In this paper, we introduce a solution to mitigate the impact of M2M communication in the LTE-A network. Precisely, we model how to divide the random access resources into different types of devices as a bankruptcy problem. Then, we propose two solutions: (i) A game theory approach to formulate the bankruptcy problem as a transferable utility game, and (ii) an axiomatic method where some elements are considered for judging the amount of resources each class should receive. The simulation results show that our approaches present improvements in terms of energy efficiency, impact control of M2M over H2H accesses, and priority respect among the different classes of devices.