TELETWIN AI: AN AI-BASED DIGITAL TWIN FOR INTELLIGENT TELECOMMUNICATION NETWORK MONITORING AND FAULT PREDICTION

Abstract

The growing complexity of telecommunication networks makes monitoring and fault detection increasingly difficult, while traditional rule-based methods rarely identify problems before they affect users. This paper proposes TeleTwin AI, an artificial intelligence-based digital twin approach for intelligent network monitoring and predictive analysis. The system builds a virtual representation of the network, analyzes parameters such as latency, packet loss, bandwidth utilization and signal quality, and applies machine learning to detect anomalies and predict failures. In a synthetic simulation, Random Forest predicted faults about 15 minutes ahead (F1 = 0.98).