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Teletwin Ai: an Ai-based Digital Twin for Intelligent Telecommunication Network Monitoring and Fault Prediction

Year: 2026

Type: article

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).

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.23137054

Authors: Mamatovich Juraev Nurmakhamad, Ziyodakhon Abdusattor qizi Nabiyeva

Institutions: Kurgan State University, Fergana State Technical University

Language: English

Topic: Software System Performance and Reliability

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W7219682252

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