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.