Digital Twin for Advanced Network Planning: Tackling Interference

Abstract

Operational data in next-generation networks offers a valuable resource for Mobile Network Operators to autonomously manage their systems and predict potential network issues. Machine Learning and Digital Twin can be applied to gain important insights for intelligent decision-making. This paper proposes a framework for Radio Frequency planning and failure detection using Digital Twin reducing the level of manual intervention. In this study, we propose a methodology for analyzing Radio Frequency issues as external interference employing clustering techniques in operational networks, and later incorporating this in the planning process. Simulation results demonstrate that the architecture proposed can improve planning operations through a data-aided anomaly detection strategy.