The Interplay of AI and Digital Twin: Bridging the Gap between Data-Driven and Model-Driven Approaches

TL;DR

This paper explores how Digital Twins and AI can synergistically enhance future wireless networks, unifying model-driven and data-driven approaches to enable reliable, efficient, and low-latency 6G systems.

Contribution

It reveals the fundamental role of Digital Twins in bridging AI and model/data-driven methods, highlighting their mutual benefits for next-generation networks.

Findings

Digital Twins unify model-driven and data-driven approaches.

DTs enable synthetic data generation for AI training.

Theoretical foundations are crucial for reliable DT implementation.

Abstract

The evolution of network virtualization and native artificial intelligence (AI) paradigms have conceptualized the vision of future wireless networks as a comprehensive entity operating in whole over a digital platform, with smart interaction with the physical domain, paving the way for the blooming of the Digital Twin (DT) concept. The recent interest in the DT networks is fueled by the emergence of novel wireless technologies and use-cases, that exacerbate the level of complexity to orchestrate the network and to manage its resources. Driven by AI, the key principle of the DT is to create a virtual twin for the physical entities and network dynamics, where the virtual twin will be leveraged to generate synthetic data and offer an on-demand platform for AI model training. Despite the common understanding that AI is the seed for DT, we anticipate that the DT and AI will be enablers for each other, in a way that overcome their limitations and complement each other benefits. In this article, we dig into the fundamentals of DT, where we reveal the role of DT in unifying model-driven and data-driven approaches, and explore the opportunities offered by DT in order to achieve the optimistic vision of 6G networks. We further unfold the essential role of the theoretical underpinnings in unlocking further opportunities by AI, and hence, we unveil their pivotal impact on the realization of reliable, efficient, and low-latency DT.