Future-Proofing Mobile Networks: A Digital Twin Approach to Multi-Signal Management
This work addresses network management challenges for mobile network operators, but it is incremental as it builds on existing Digital Twin concepts without introducing a new paradigm.
The paper tackled the problem of managing heterogeneous network access technologies in mobile networks by developing a Digital Twin framework, which was tested in a Campus Area Network environment to provide real-time insights into network performance and environmental sensing.
Digital Twins (DTs) are set to become a key enabling technology in future wireless networks, with their use in network management increasing significantly. We developed a DT framework that leverages the heterogeneity of network access technologies as a resource for enhanced network performance and management, enabling smart data handling in the physical network. Tested in a Campus Area Network environment, our framework integrates diverse data sources to provide real-time, holistic insights into network performance and environmental sensing. We also envision that traditional analytics will evolve to rely on emerging AI models, such as Generative AI (GenAI), while leveraging current analytics capabilities. This capacity can simplify analytics processes through advanced ML models, enabling descriptive, diagnostic, predictive, and prescriptive analytics in a unified fashion. Finally, we present specific research opportunities concerning interoperability aspects and envision aligning advancements in DT technology with evolved AI integration.