Research Project

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

 

• Project Information

  • Award numbers: NSF CNS-2312138, CNS-2312139
  • Project period: 10/1/2023 – 9/30/2026
  • Principal investigator: Dr. Yuchen Liu (lead PI, NCSU), Dr. Mingzhe Chen (UM)
  • Graduate students: Zifan Zhang (NCSU), Zhizhen Li (NCSU), Hanzhi Yu (UM)

   

• 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.

   

• Publications

  • Z. Li, X. Luo, M. Chen, C. Xu, S. Mao, and Y. Liu, ‘‘Contextual Combinatorial Beam Management via Online Probing for Multiple Access mmWave Wireless Networks’’, IEEE Journal on Selected Areas in Communications (JSAC) [J], to appear, 2025.

  • M. Mushi, Y. Liu, S. Sreenivasa, O. Ozdemir, I. Guvenc, M. Sichitiu, R. Dutta, and R. Gyurek, ‘‘Open RAN Testbeds with Controlled Air Mobility’’, Elsevier Computer Communications (CC) [J], to appear, 2025.

  • H. Yu, Y. Liu, and M. Chen, “Joint Communication and Synchronization Performance Optimization in Digital Twin Enabled Networks”, Proc. of IEEE Global Communications Conference (GLOBECOM), to appear, 2024.

  • W. Ding, Z. Yang, M. Chen, Y. Liu, M. Shikh-Bahaei, ‘‘Joint Vehicle Connection and Beamforming Optimization in Digital Twin Assisted Integrated Sensing and Communication Vehicular Networks’’, IEEE Internet of Things Journal (IoT-J) [J], 2024.

  • Z. Li, M. Chen, G. Li, X. Lin, and Y. Liu, ‘‘Map-Driven mmWave Link Quality Prediction with Spatial-Temporal Mobility Awareness’’, IEEE Transactions on Mobile Computing (TMC) [J], 2024.

  • Z. Zhang, M. Fang, M. Chen, G. Li, X. Lin, and Y. Liu, ‘‘Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks’’, IEEE Internet of Things Journal (IoT-J) [J], 2024.

  • Z. Zhang, Y. Liu, Z. Peng, M. Chen, D. Xu, and S. Cui, ‘‘Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks’’ [J], IEEE Journal on Selected Areas in Communications (JSAC) [J], to appear, 2024.

  • X. Zhang, J. Zhang, K. Chow, J. Chen, Y. Mao, M. Rahouti, X. Li, Y. Liu, and W Wei, “Visualizing the Shadows: Unveiling Data Poisoning Behaviors in Federated Learning”, IEEE International Conference on Distributed Computing Systems (ICDCS), Demo paper, 2024.

  • Z. Zhang, M. Chen, Z. Yang, and Y. Liu, “Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning”, IFIP/IEEE Networking, 2024.

  • B. Chatterjee, S. Chaudhari, Z. Li, Y. Liu, and R. Dutta, “Wireless Signal Source Localization by Unmanned Aerial Vehicle using AERPAW Digital Twin and Testbed”, IFIP/IEEE Networking, in the Workshop on Thought experiments, data and reproducibility for networking and FutureG research (SLICES), 2024.

  • H. Yu, Y. Liu, and M. Chen, “Complex-Valued Neural Network Based Federated Learning for Multi-User Indoor Positioning Performance Optimization” [J], IEEE Internet of Things Journal (IoT-J), 2024

  • X. Luo, Z. Li, Z. Peng, D. Xu, and Y. Liu, “RM-Gen: Conditional Diffusion Model-Based Radio Map Generation for Wireless Networks”, IFIP/IEEE Networking, 2024.

  • Z. Li, X. Luo, M. Chen, C. Xu, and Y. Liu, “Context-Aware Beam Management via Online Probing in Combinatorial Multi-Armed Bandits”, IEEE International Conference on Communications (ICC), 2024.

  • H. Yu, M. Chen, Z. Yang, and Y. Liu, “Complex Neural Networks for Indoor Positioning with Complex-Valued Channel State Information”, IEEE Global Communications Conference (GLOBECOM), 2023.

  • M. Chen and S. Cui, “Communication Efficient Federated Learning for Wireless Networks”, Springer International Publishing, Singapore, 2023.

   

• Tools and Platforms

  • The repository for the development of digital twin in next-generation wireless network (mmWave, 5G/6G) based on ns-3 and ray tracer. Link

  • Joint vertical-horizontal mapper for the creation of decentralized digital network twins. Link Paper

  • Ray-tracing software for mapping signal propagation profiles. Link

  • Network visualizer with hands-on scripts and examples for digital twin education: Link

  • The platform for the development of digital twin in 3D wireless networks for signal source localization based on NSF AERPAW testbed and Virtual Development Environment. Link (to be announced)

   

• Education, Outreach and Broader Impacts

  • Integrating Research & Teaching: The introduction of digital network twin has been added in the newly developed course CSC/ECE Advanced NextG Network Design at both Department of Computer Science and Department of Electrical and Computer Engineering, North Carolina State University. 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.

  • Cultivating Interests among High School Students: PI has regularly organized the Summer Residential Camp – Seeing through a Digital World, inspiring high-school students to explore the intricate workings of complex network systems and simulations within the digital realm. Several rising senior high school students from Green Level and Enloe High Schools in Cary, NC also worked in the PI’s lab on topics related to digital network twins. To name a few: Rohit Kota, Maulik Verma, and Jaden Mu.

  • Facilitating Undergraduate Research: Several NCSU undergraduate students worked in the PI’s lab on topics related to radio access networks and data generation. To name a few: Jordan Miller, Nitesh Kanamarlapudi, and Krushi Bandam.

  • Dissemination to Communities of Interest: PI has co-organized The 48th IEEE COMPSAC Workshop on Digital Twins for the Metaverse and IEEE ICC Workshop on Edge Learning over 5G Mobile Networks and Beyond to share cutting-edge research, foster collaboration, and advance the understanding of digital twins and edge learning technologies.

Back to Homepage