LTE-advanced random access channel congestion detection method for IoT

Abstract

The Long Term Evolution - LTE - is one of the very last evolutions in mobile communication systems that offer a much wider bandwidth than its predecessors. That is why it is very much in demand for a massive deployment of the Internet of Things (IoT) also called Machine to Machine communication or Machine Type Communication (MTC). With the IoT, the network is subject to recurrent congestion when densely charged which is due to increased uplink solicitation. MTC devices must complete the RACH process to access the network. Collisions occur during this process that leads to the congestion which, in turn, has a negative impact on the quality of service. The Third Generation Partnership Project (3GPP) provided some solutions to alleviate the problem. In this paper we propose a congestion detection method since 3GPP only proposed contention resolution methods. We first determine the interval of use of preambles during which the success rate is the highest. By doing so, we determine the maximal preamble utilization threshold (Rlimit) beyond which quality of service is no more guaranteed. The novelty with this method is that once Rlimit threshold is reached, a contention resolution scheme could be activated and will remain so until the threshold drops below Rlimit. Our method can give better results if applied to contention resolution methods. Moreover it is simple, less complex and easy to implement in the LTE. Moreover, it does not require large investments.