Adaptive Energy-Aware Scheduling and Clustering for Machine-Type Communications in LTE/5G: Modeling and Algorithmic Framework

Abstract

MTC's exponential growth in LTE and 5G networks brings challenge in massive device connectivity, energy efficiency, and QoS requirements. Scheduling and clustering are thus inadequate for MTC devices considering their traffic patterns and stringent energy requirements. This paper considers an adaptive energy-aware scheduling and clustering solution for LTE/5G environments. The solution clusters devices and schedules them dynamically to minimize energy while guaranteeing data reliability and latency. The solution mathematically models the trade-offs among throughput, energy efficiency, and system capacity for dense MTC scenarios. In the algorithm, clusters are adaptively formed based on proximity, traffic type, and channel conditions and then scheduled based on priorities to ensure fairness and scalability.Energy consumption and collision probability in the framework, by experimentation, are considerably less than they are in the conventional method, whereas spectral efficiency and network lifetime are enhanced. This energy conservation solution is wonderful for ultra-dense deployments, where load balancing is critical. In a nutshell, this is a great robust and scalable approach toward implementing sustainable energy-aware MTC operations in the next-generation LTE/5G networks.