As digital twins (DTs) to physical communication systems, network simulators can aid the design and deployment of communication networks. However, time-consuming simulations must be run for every new set of network configurations. Learnable digital twins (LDTs), in contrast, can be trained offline to emulate simulation outcomes and serve as a more efficient alternative to simulation-based DTs at runtime. In this work, we propose GLANCE, a communication LDT that learns from the simulator ns-3. It can evaluate network key performance indicators (KPIs) and assist in network management with exceptional efficiency. Leveraging graph learning, we exploit network data characteristics and devise a specialized architecture to embed sequential and topological features of traffic flows within the network. In addition, multi-task learning (MTL) and transfer learning (TL) are leveraged to enhance GLANCE's generalizability to unseen inputs and efficacy across different tasks. Beyond end-to-end KPI prediction, GLANCE can be deployed within an optimization framework for network management. It serves as an efficient or differentiable evaluator in optimizing network configurations such as traffic loads and flow destinations. Through numerical experiments and benchmarking, we verify the effectiveness of the proposed LDT architecture, demonstrate its robust generalization to various inputs, and showcase its efficacy in network management applications.

翻译：作为物理通信系统的数字孪生，网络仿真器能够辅助通信网络的设计与部署。然而，针对每一组新的网络配置都需要运行耗时的仿真。相比之下，可学习数字孪生能够通过离线训练来模拟仿真结果，并在运行时作为基于仿真的数字孪生的一种更高效替代方案。本文提出GLANCE，一种从仿真器ns-3中学习的通信可学习数字孪生。它能够以极高的效率评估网络关键性能指标并辅助网络管理。我们利用图学习技术，充分挖掘网络数据特征，并设计了一种专用架构来嵌入网络内流量流的序列特征与拓扑特征。此外，通过采用多任务学习与迁移学习，增强了GLANCE对未见输入的泛化能力及其在不同任务间的效能。除了端到端的关键性能指标预测，GLANCE还可部署于网络管理的优化框架中。在优化诸如流量负载与流目的地等网络配置时，它能作为高效或可微分的评估器。通过数值实验与基准测试，我们验证了所提出的可学习数字孪生架构的有效性，展示了其对各类输入的稳健泛化能力，并证明了其在网络管理应用中的高效性。