The study explores implementing a digital twin network (DTN) architecture for efficient 6G wireless network management, aligning with the FCAPS (Fault, Configuration, Accounting, Performance, and Security) model. The DTN architecture comprises the Physical Twin Layer, implemented using NS-3, and the Service Layer, featuring machine learning and reinforcement learning for optimizing carrier sensitivity threshold and transmit power control in wireless networks.
The researchers introduce a robust "What-if Analysis" module in the Digital Twin Layer, utilizing CTGAN for synthetic data generation to mimic various network scenarios. These scenarios assess four key network performance metrics: throughput, latency, packet loss, and coverage. The findings demonstrate the efficiency of the proposed what-if analysis framework in managing complex network conditions, highlighting the importance of the scenario-maker step and the impact of twinning intervals on network performance.
The study also examines the performance of different Service Layer approaches, including running services independently or sequentially, and the impact on the effectiveness score (ξ) across various scenarios. The results show that considering a diverse range of scenarios in the what-if analysis improves the chances of selecting optimal network configurations, aligning with FCAPS objectives in 5G/6G networks. Additionally, the twinning interval is identified as a critical parameter that must be tuned according to the specific network topology to maintain optimal performance.
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