Addressing the Scalability of Network Digital Twins: A Network Sampling Approach

With the increasing complexity of mobile communication networks like 5G/6G networks, characterized by the diversity of network devices, technologies, and applications, advanced network management solutions are needed to ensure real-time network optimization with risk-free decision making operations (e.g., safe network reconfiguration). To achieve this objective, a Network Digital Twin (NDT) paradigm represents an attractive perspective, enabling the manipulation of the virtual counterpart of a real communication network. Nonetheless, generating a Digital Twin of a complex network, comprising thousands of heterogeneous devices and highly dynamic network characteristics (resource usage, network topology, link quality, etc.) poses a serious scalability problem. This paper aims at addressing the scalability problem for the generation of a Digital Twin of a complex network like a 5G/6G network. In particular, the paper proposes a sampling approach in conjunction with a structured network information representation, as well as zoom-in/out operations to enable a modular generation of the NDT.