Collaborative Research: NeTS: Small: Digital Network Twins: Mapping Next Generation Wireless into Digital Reality

Project Information

  • Award numbers: NFS CNS-2312139
  • Project period: 10/1/2023 – 9/30/2026
  • Principal investigator: Dr. Mingzhe Chen and Dr. Yuchen Liu
  • Graduate students: Hanzhi Yu

Project Overview

Next-generation (NextG) wireless networks provide users with customized, instant services, especially for bandwidth-hungry and latency-sensitive applications. Despite the significant advantages of NextG wireless networks (e.g., 5G/6G and millimeter-wave / Tera Hertz), realizing them faces several key deployment and evaluation challenges: 1) how to speed up the deployment of novel yet complex NextG network technologies; and 2) how to provide flexible testbed facilities with high availability. In this regard, there is an urgent need for a virtual solution that could create a digital model to replicate as accurately as possible the NextG network ecosystem and help tackle the above obstacles before the full realization of a real system. To this end, this project explores methodologies to run faithful digital network twins that replicate the physical NextG networks, and then to build and optimize the twins over the actual networks while considering communication, computing, and networking resource constraints. The built network twins provide an overarching architecture involving the whole life cycle of physical networks, serving the critical application of innovative technologies such as network planning, construction, optimization, and predictive evaluation, and improving the automation and intelligence level of the wireless networks. This transformative research provides a holistic framework for the implementation and optimization of digital network twins, thus catalyzing the deployment and operation of future network systems with major societal impact.

This proposed research lays the foundations of digital network twins by developing a novel framework that merges tools from machine learning, communication theory, and distributed optimization to advance the networking technologies in: 1) novel mapping approaches that integrate data-driven modeling, ray-tracing analysis, wireless channel derivation, and regression-based predictions to map NextG wireless networks into digital network twins and then to evolve the mapped twins adaptively; 2) new digital network twin management and optimization framework that combines graph neural networks, distributed learning, and reinforcement learning, to allow distributed devices in a physical network to first independently determine their mapping methods and resource utilization, and then collaboratively maximize the digital network twin performance over actual network environments; 3) design of the twinning platform and evaluation methodology based on simulation and experiments to demonstrate the fidelity, efficacy, and optimality of the built network twins. The project provides a rich environment and virtualized platform that facilitate educating and training students at multiple levels.


  • M. Chen and S. Cui, "Communication Efficient Federated Learning for Wireless Networks", Springer International Publishing, Singapore, 2023.
  • H. Yu, Y. Liu, and M. Chen, “Complex-Valued Neural Network Based Federated Learning for Multi-User Indoor Positioning Performance Optimization”, IEEE Internet of Things Journal, to appear, 2024. (Available: ArXiv)
  • Z. Zhang, M. Chen, Z. Yang, and Y. Liu, "Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning", in Proc. IFIP/IEEE Networking, Thessaloniki, Greece, June 2024.
  • Z. Li, X. Luo, M. Chen, C. Xu, and Y. Liu, "Context-Aware Beam Management via Online Probing in Combinatorial Multi-Armed Bandits", in Proc. IEEE International Conference on Communications (ICC), Wireless Communications Symposium, Denver, CO, USA, June 2024.
  • H. Yu, M. Chen, Z. Yang, and Y. Liu, "Complex Neural Networks for Indoor Positioning with Complex-Valued Channel State Information", in Proc. IEEE Global Communications Conference (GLOBECOM), Selected Areas in Communications: Integrated Sensing and Communication, Kuala Lumpur, Malaysia, December 2023.

Outreach and Broader Impacts

  • The introduction of using neural networks for digital network twin optimization has been added in the graduate class ECE 553/653 Neural Networks at University of Miami.
  • One female Ph.D. student has been recruited to work on the related research at University of Miami.
  • Parts of the research results have been presented in IEEE GLOBECOM 2023.