Priority-Based Massive Random Access of M2M Communications in LTE Networks: Throughput Analysis and optimization

Abstract

The machine-to-machine (M2M) communication is a technology that contains massive number of machine type devices (MTDs) and different kinds of applications, with which it is imperative to improve the access efficiency and provide priority-based service. To address this issue, a priority-based analytical framework is proposed in this paper to optimize the network throughput under diverse throughput requirements for M2M communications. Specifically, MTDs are divided into multiple groups according to their applications. The access behavior of each MTD is characterized by a double-queue model. Based on this model, the network throughput is derived as an explicit function of the number of groups and the parameters of each group including the number of MTDs, the aggregate packet arrival rate, the access class barring (ACB) factor and the uniform backoff (UB) window size. To satisfy the diverse service requirements of different applications, a constraint is imposed to ensure a target throughput ratio among groups. The maximum network throughput is then derived under the throughput ratio constraint. Simulation results verify that with the optimal tuning of backoff parameters, the network can achieve the optimal throughput and meet the diverse throughput requirements between groups at the same time irrespective of the number of MTDs in the network.