Abstract
Machine-to-Machine (M2M) communication in the Long Term Evolution (LTE) network has recently grown exponentially as the volume of connected devices has increased rapidly in the last decade. M2M traffic can be understood via certain parameters in terms of packet length, packet generation frequency, delay, and data rate requirements, and it typically flows in the uplink direction. Primarily, the LTE network design is optimized for Human-to-Human (H2H) communication. As a result, designing uplink scheduling in LTE networks is fraught with difficulties which restrict the use of potential capacity. In response to the preceding methodologies, focusing on the QCI priority degrades resource utilization and cell throughput. Therefore, a scheduling mechanism needs to optimize the system performance with priority support to use LTE in M2M communication. This paper highlights existing flaws in the optimization process and proposes a scalable priority-based resource allocation scheme for M2M communication in the LTE/LTE-Advance network. The proposed scheme for resource allocation strikes a balance between resource utilization and application priority support. According to the results, the proposed scheduling algorithm outperforms the standard algorithms concerning resource sharing fairness, average resource utilization, QCI priority support, and delay budget violation.
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•This article provides a scalable priority-based radio resource allocation scheme.•Scalable priority minimizes the trade-off between resource utilization and priority.•Scheme also minimizes the starvation for the devices.