How does machine learning empower digital twin technology to facilitate autonomous networks?

Digital twin technology is gaining momentum across the entire lifecycle of communication networks—from lab testing to real-world deployment. Industry bodies and standardization organizations such as ITU, O-RAN Alliance, and 3GPP have all recognized its potential.

What’s becoming increasingly clear is that machine learning (ML) will play a central role in enabling digital twin capabilities. At the same time, ML models themselves can benefit from digital twins, particularly through the generation of synthetic training data. This reciprocal relationship is opening new opportunities in network intelligence and automation.

In this panel, academic and industry experts from the 6G-TWIN project will explore how ML-enabled digital twins can address key challenges in communication networks. The discussion will also showcase the diverse forms digital twins may take in next-generation systems.

Our speakers:

• Chris Murphy, VIAVI Solutions
• Ayat Zaki Hindi, Luxembourg Institute of Science and Technology (LIST)
• Paola Andrea Soto Arenas, IMEC / Universiteit Antwerpen
• German Castellanos, Accelleran
• Ali Mokh, Ericsson

🗓️ Panel session: Wednesday, May 28 at 2:00 PM

About IEEE ICMLCN 2025

The IEEE International Conference on Machine Learning in Communications and Networking (ICMLCN) brings together leading researchers from the fields of ML and communication/networking. The conference promotes both foundational and applied research, exploring how ML can be used to design, analyze, and enhance communication systems and protocols. It also addresses the growing need to support ML services through advanced network design. Topics span across all communication domains, including wireless, optical, molecular, the Internet, and WLAN systems.

🔗 Register here: icmlcn2025.ieee-icmlcn.org/registration