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.

翻译：网络密度化的后果之一是更经常地交接(HO)。 HO的故障直接影响到服务质量,不可取,特别是在存在严格的延迟性、可靠性和稳健性限制的情况下。在传统网络中,与HO有关的参数通常由网络运营商调整,自动化技术仍然以过去的经验为基础。在本文中,我们提出了一个利用Bayesian Optimi化(BO)优化HO阈值的方法。我们在选择HO的阈值时提出了一个多目标优化问题,该阈值在室内工厂的情景中将HOS降到了太早和太晚,我们使用多目标BO(MOBO)来寻找最佳值。我们的结果显示MOBO以少量样本达到Pareto最佳解决方案,并通过安全地探索新的数据点确保服务持续。