Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Roberto Morabito, Bivek Pandey, Paulius Daubaris, Yasith R Wanigarathna, Sasu Tarkoma

TL;DR

This paper addresses the need to modernize mobile network management by leveraging heterogeneous radio access technologies (RATs) through Digital Twins (DTs) and integrating GenAI-enabled analytics. It proposes a modular DT framework with End-Devices, Signal Handler, Twin Instance Controller, and Network Twin Console, using an MQTT event bus to enable real-time data flow and interoperability across platforms. The authors validate the approach in the UbiKampus Campus Area Network, demonstrating real-time, multi-RAT insights and the potential for AI-driven, descriptive-to-prescriptive analytics to optimize network and environment management. They also discuss the opportunities and challenges of interoperability, standardized data models, and GenAI integration, outlining a roadmap toward scalable, multi-platform DT deployments in future networks.

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.

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

TL;DR

This paper addresses the need to modernize mobile network management by leveraging heterogeneous radio access technologies (RATs) through Digital Twins (DTs) and integrating GenAI-enabled analytics. It proposes a modular DT framework with End-Devices, Signal Handler, Twin Instance Controller, and Network Twin Console, using an MQTT event bus to enable real-time data flow and interoperability across platforms. The authors validate the approach in the UbiKampus Campus Area Network, demonstrating real-time, multi-RAT insights and the potential for AI-driven, descriptive-to-prescriptive analytics to optimize network and environment management. They also discuss the opportunities and challenges of interoperability, standardized data models, and GenAI integration, outlining a roadmap toward scalable, multi-platform DT deployments in future networks.

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.
Paper Structure (8 sections, 4 figures, 1 table)

This paper contains 8 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. The DT Framework Design
  3. Campus Area Network Use Case
  4. The DT Framework in Action
  5. Advanced Analytics with GenAI Integration
  6. Challenges and Opportunities
  7. Conclusions
  8. Acknowledgements

Figures (4)

  • Figure 1: High-level architecture of the proposed system, illustrating the DT components within the squared dotted box, the physical network (End Devices), the Signal Handler, and the event bus for data transfer.
  • Figure 2: Workflow of the Signal Handler and Twin Instance Controller components. Incoming signals from different wireless technologies are processed before being published to the event bus. Once published, a digital representation is created.
  • Figure 3: On top, overview of UbiKampus environment. The top-left image depicts the physical workspace with smart devices and interactive displays, and the top-right image shows the floor plan with monitoring points. On the bottom, the left image shows the network configuration sensed by a single smartphone device, while the right image presents the user interface of the application running at the end-device.
  • Figure 4: Analytics process within the DT framework. This figure illustrates the use of GenAI-powered analytics to perform descriptive, diagnostic, predictive, and prescriptive analysis on multi-modal data from UbiKampus. Applications hosted into the DT stack for network management (e.g., optimizing bandwidth allocation) or smart space environments (e.g., adjusting HVAC systems based on occupancy and air quality) optimization, trigger specific actions on the physical network based on the analytics outcome.