Multi-objective Hybrid Scheduler enabling Efficient Resource Management for 5G UDN

Scheduling algorithms allocate radio resources to optimize network efficiency with respect to some competing performance metrics. Common schedulers include the Round Robin (RR), Proportional Fair (PF) and the Best Channel Quality Indicator (Best CQI) schedulers, as well as their different variants, modified or hybrid versions. RR promotes fairness, Best CQI targets increased throughput while PF guarantees a balance in the fairness and throughput objectives of the network. In this paper, we propose a multi-objective hybrid scheduler (MOHS), a hybrid of RR and Best CQI schedulers, that adapts the scheduling policy by toggling between RR and Best CQI schedulers to optimize system performance. The results show that the proposed scheduler provides higher spectral efficiency, energy efficiency and area network capacity than RR and PF, as well as significantly higher fairness than the Best CQI scheduler. By improving multiple system objectives, MOHS enables efficient radio resource allocation that optimizes the network efficiency for ultra-dense 5G networks and beyond.