Resource sharing between M2M and H2H traffic under time-controlled scheduling scheme in LTE networks

Abstract

Machine-to-Machine (M2M) communication is becoming a commonly used terminology due to the idea of Internet of Things (IoT). M2M communication has many areas of application, such as in; medical, transport, environmental monitoring, smart grids among others. As the field of its application expands, the number of M2M devices is expected to grow exponentially in the next few years. Long Term Evolution (LTE) has been identified as one of the suitable wireless communication technologies for M2M communication. Incorporating M2M communication on top of regular Human-to-Human (H2H) communication in LTE is a challenging task due to the expected increase in the number of M2M devices coupled with the unique characteristics of M2M traffic. Therefore, the current scheduling and resource allocation techniques among others being used in LTE need to be refined to efficiently accommodate M2M communication. A scheduling scheme called fixed Access Grant Time Interval (AGTI) time-controlled scheduling scheme was proposed for scheduling M2M traffic in LTE. Resource sharing and utilization under this scheme is inefficient due to fixed AGTI assignment which results into fixed nature of resource allocation. In this work, we propose a scheduling scheme called Dynamic AGTI Time-controlled Scheduling Scheme in which the AGTI is dynamically assigned basing on M2M and H2H traffic intensities. We model the proposed scheme using M/G/m/m queuing technique focusing on resource utilization and Quality of Service (QoS) of both M2M and H2H traffic. The analytical results show that, the Dynamic AGTI Time-controlled Scheduling Scheme achieves; better percentage resource utilization as compared to Fixed AGTI Time-controlled Scheduling Scheme while providing optimal blocking probability for both M2M and H2H traffic. However, the monitoring of resource usage and reassignments of AGTI in Dynamic AGTI Time-controlled Scheduling Scheme increases scheduler complexity.