Real-Time Network Digital Twin Synchronization for Predictive Capacity Planning in Multi-Vendor Private 5G Systems

Abstract

Private 5G networks are increasingly deployed in industrial environments to support mission-critical applications requiring high reliability, low latency, and scalable connectivity. However, capacity planning across multi-vendor infrastructures remains challenging due to heterogeneous equipment, dynamic traffic patterns, and diverse management interfaces. This paper proposes a Real-Time Network Digital Twin Synchronization Framework for predictive capacity planning in multi-vendor private 5G systems. The framework continuously synchronizes the physical network with its digital twin using real-time telemetry, network performance indicators, and user mobility information. Artificial intelligence-based forecasting models analyze synchronized data to predict future traffic demand, identify congestion hotspots, and recommend proactive resource allocation strategies. The digital twin enables safe simulation of network optimization scenarios before deployment in the live environment. Experimental evaluation indicates improved forecasting accuracy, reduced synchronization latency, enhanced spectrum utilization, minimized congestion, and higher Quality of Service, enabling intelligent and proactive operation of enterprise private 5G networks.