Towards Mobility Management with Multi-Objective Bayesian Optimization

TL;DR

This paper introduces a multi-objective Bayesian Optimization approach to tune handover thresholds in dense networks, aiming to reduce failures and improve service quality in industrial scenarios.

Contribution

It formulates a multi-objective optimization problem for handover thresholds and applies MOBO to efficiently find Pareto optimal solutions with minimal samples.

Findings

MOBO achieves Pareto optimal solutions efficiently.

The approach ensures service continuity through safe exploration.

Optimizes handover timing to reduce failures in factory environments.

Abstract

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.