Telecom networks scale with growing users and data-intensive applications, generating heavy traffic that causes congestion, reducing throughput, increasing delay, and raising computational costs. Traditional routing protocols act only after performance degradation, making them unsuitable for dynamic traffic and topological changes. Addressing these challenges requires a routing approach that adapts in real time, scales with network growth, operates without disrupting active services, and provides continuous feedback for congestion-aware traffic optimisation. The Network Digital Twin (NDT) addresses these needs by mirroring global network behaviour using Message Passing Neural Networks (MPNNs) through bidirectional communication with the physical network. To align the NDT with physical network behaviour, synthetic traffic is generated with increasing load across topological structures that incrementally scale as routers are added. These topologies are created by graph-generating models such as Erdos-Renyi, Barabasi-Albert, and Watts-Strogatz, customised with vertex degree limitations. The NDT collects performance metrics from routers and links, and MPNNs classify edges based on local vertex and global network behaviours. Based on these classifications, feedback is sent as Policy-Based Routing (PBR) protocol commands to each router, enabling optimal traffic distribution across links of the physical network.

翻译：随着用户数量和数据处理密集型应用的增加，电信网络规模不断扩大，由此产生的高流量负载导致网络拥塞，进而降低吞吐量、增加时延、提升计算成本。传统路由协议仅在性能退化后作出响应，无法适应动态流量及拓扑变化。为应对这些挑战，需采用一种能够实时自适应调整、随网络扩展而扩展、不影响现有服务运行，并为拥塞感知流量优化提供持续反馈的路由策略。网络数字孪生（NDT）通过消息传递神经网络（MPNN）与物理网络建立双向通信，以镜像全局网络行为，从而满足上述需求。为使NDT与物理网络行为对齐，在路由器逐步添加的递增式拓扑结构中，通过生成递增负载的合成流量进行模拟。这些拓扑结构由Erdos-Renyi、Barabasi-Albert和Watts-Strogatz等图生成模型创建，并针对顶点度数进行定制化限制。NDT收集来自路由器和链路的性能指标，MPNN根据局部顶点行为与全局网络行为对边进行分类。基于这些分类结果，向每个路由器发送基于策略的路由（PBR）协议命令作为反馈，从而实现物理网络中链路间的流量最优分配。