Towards Mobility Management with Multi-Objective Bayesian Optimization

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 u...

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Bibliographic Details
Published in:arXiv.org 2023-01
Main Authors: de Carvalho Rodrigues, Eloise, Rial, Alvaro Valcarce, Geraci, Giovanni
Format: Article
Language:English
Subjects:
Bayesian analysis
Data points
Densification
Mobility management
Multiple objective analysis
Network latency
Network reliability
Pareto optimization
Thresholds
Online Access:Get full text
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Summary: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.
ISSN:2331-8422