Service-Aware Resource Scheduling for Heterogeneous M2M Communication in 5G Networks

Abstract

Heterogeneous Machine to Machine communication or massive machine type communication in 5G networks presents ‎unique challenges due to diverse device types, service requirements, and scarce radio resources. The work proposes a Service-‎Aware Resource Scheduling (S-RS) scheme to optimize resource allocation while simultaneously considering both resource ‎utilization and cell throughput. S-RS dynamically allocates resource blocks based on a combination of device priority, channel ‎quality, and service type. Simulation results demonstrate optimal improvements compared to proportional fair, priority, and Best-‎CQI methods, achieving optimal resource utilization and cell throughput. Future directions for further research are discussed, ‎including advanced service differentiation. Our findings may lay the groundwork for efficient and adaptable resource allocation for ‎reliable M2M communication in 5G-enabled smart cities or smart homes‎.