Future-Proofing Mobile Networks: A Digital Twin Approach to Multi-Signal Management

TL;DR

This paper presents a digital twin framework for future mobile networks that enhances management by integrating heterogeneous data sources, leveraging AI, and enabling real-time insights for improved performance and environmental sensing.

Contribution

The paper introduces a novel digital twin framework that utilizes network heterogeneity and AI integration to improve management and analytics in wireless networks.

Findings

Framework tested in Campus Area Network environment

Real-time holistic network insights achieved

Integration of AI models for advanced analytics

Abstract

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.