Load-Aware Handover Optimization in Heterogeneous Networks: A Multi-Objective Learning Approach

In heterogeneous networks (HetNets), the dense deployment of base stations (BSs) often leads to severe signal interference. This interference causes radio link failures (RLFs) and ping-pong handovers (PPs), undermining the connectivity of user equipments (UEs). To address these problems, mobility robustness optimization (MRO) can be an effective solution. However, MRO operation without considering load distribution can overload specific BSs. This can increase signal interference and lower channel quality in high-load areas, degrading MRO performance. To mitigate these issues, we propose a load-aware MRO framework using multi-objective reinforcement learning. In the proposed framework, agents use two separate objective functions to learn the individual impacts of adjusting handover control parameters on both preventing occurrences of RLFs/PPs and distributing load. Through multi-objective learning, our framework minimizes the occurrences of RLFs/PPs while flexibly distributing load across BSs. This prevents additional RLFs/PPs and channel quality degradation caused by load concentration. Simulation results show that the proposed algorithm reduces the occurrence rates of RLFs and PPs by up to 27% and 52%, respectively.