What-if Analysis Framework for Digital Twins in 6G Wireless Network Management

This study explores implementing a digital twin network (DTN) for efficient 6 G wireless network management, aligning with the fault, configuration, accounting, performance, and security (FCAPS) 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. We introduce a robust 'What-if Analysis' module, utilizing conditional tabular generative adversarial network for synthetic data generation to mimic various network scenarios. These scenarios assess four network performance metrics: throughput, latency, packet loss, and coverage. Our findings demonstrate the efficiency of the proposed what-if analysis framework in managing complex network conditions, highlighting the importance of the scenario-maker and the impact of twinning intervals on network performance.