What-if Analysis Framework for Digital Twins in 6G Wireless Network Management
Elif Ak, Berk Canberk, Vishal Sharma, Octavia A. Dobre, Trung Q. Duong
TL;DR
This work addresses proactive 6G network management under the FCAPS framework by embedding a three-layer What-if Analysis Framework into a Digital Twin Network (DTN). It couples an NS-3-based Physical Twin Layer with a Microsoft Azure Digital Twins-enabled Digital Twin Layer and a Service Layer containing CST-optimizing Neural Networks and TPC-optimizing Reinforcement Learning. The What-if Analysis leverages CTGAN-generated synthetic data to explore four KPIs—throughput, latency, packet loss, and coverage—and computes composite scores to guide configuration choices, with findings highlighting the value of scenario diversity and appropriately chosen twinning intervals. The results suggest CTGAN-enabled scenario generation as a practical method to predict network behavior and support proactive, FCAPS-aligned management in 6G environments.
Abstract
This study explores implementing a digital twin network (DTN) for efficient 6G 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 (CTGAN) 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 step and the impact of twinning intervals on network performance.