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.

翻译：本研究探索构建数字孪生网络（DTN）以实现6G无线网络的高效管理，并与故障、配置、计费、性能及安全（FCAPS）模型相契合。该DTN架构由基于NS-3实现的物理孪生层和服务层组成，其中服务层采用机器学习和强化学习技术，用于优化无线网络中的载波灵敏度阈值与发射功率控制。我们引入了一个鲁棒的"假设分析"模块，利用条件表格生成对抗网络（CTGAN）生成合成数据以模拟各类网络场景。这些场景评估了四项网络性能指标：吞吐量、时延、丢包率及覆盖范围。研究结果表明，所提出的假设分析框架在处理复杂网络条件方面具有高效性，同时凸显了场景生成步骤的重要性以及孪生间隔对网络性能的影响。