  One of the consequences of network densification is more frequent handovers
(HO). HO failures have a direct impact on the quality of service and are
undesirable, especially in scenarios with strict latency, reliability, and
robustness constraints. In traditional networks, HO-related parameters are
usually tuned by the network operator, and automated techniques are still based
on past experience. In this paper, we propose an approach for optimizing HO
thresholds using Bayesian Optimization (BO). We formulate a multi-objective
optimization problem for selecting the HO thresholds that minimize HOs too
early and too late in indoor factory scenarios, and we use multi-objective BO
(MOBO) for finding the optimal values. Our results show that MOBO reaches
Pareto optimal solutions with few samples and ensures service continuation
through safe exploration of new data points.
