Mobility Prediction Based Proactive Dynamic Network Orchestration for Load Balancing With QoS Constraint (OPERA)

Abstract

Load imbalance among small and macro cells is a major challenge that undermines the gains of emerging ultradense heterogeneous networks (HetNets). Existing load balancing (LB) schemes have one common caveat which is operating in reactive mode i.e., cell parameters are tweaked reactively in accordance with the dynamics of cell loads. The inherent reactiveness of these LB schemes hinder in achieving promising quality of experience (QoE) gains from 5G and beyond. To cope with this issue, in this paper we propose a novel proactive load balancing framework “OPERA” empowered by mobility prediction paradigm for future ultra dense networks (UDNs). The pro-activeness of OPERA stems from its novel capability that instead of passively waiting for congestion indicators to be observed and then reacting to them, OPERA predicts future cell loads and then proactively optimizes key antenna parameters and cell individual offsets (CIOs) to preempt congestion before it happens. OPERA also incorporates capacity and coverage constraints and load aware association strategy for ensuring conflict free operation of LB and coverage and capacity optimization (CCO) self-organizing network (SON) functions. Simulation results show that compared to real network deployments settings and published state-of-the-art reactive schemes, OPERA can yield significant gain in terms of fairness in load distribution and percentage of satisfied users. Superior performance of OPERA on several fronts compared to current schemes stems from its following features: 1) It preempts congestion instead of reacting to it; 2) it actuates more parameters than any current LB schemes thereby increasing system level capacity instead of just shifting it among cells; 3) while performing LB OPERA simultaneously maximizes residual capacity while incorporating throughput and coverage constraints; 4) it incorporates a load aware association strategy for ensuring conflict free operation of LB and CCO SON functions; 5) the ahead of time estimation of cell loads allows ample time for heuristics search algorithms to find LB solutions with high gain.