Research Project

CAREER: CatFly: Towards Resilience-Native Wireless Networks through Learning, Twinning, and Reconfiguring Co-Design

 

• Project Information

  • Award numbers: NSF CNS-2440756
  • Project period: 07/01/2025 – 06/30/2030
  • Principal investigator: Dr. Yuchen Liu (PI)
  • Graduate students: Xuanhao Luo, Jiayuan Huang

   

• Project Overview

  • The evolution of mobile and wireless networks is being driven by three major technological advances: 1) a move to higher carrier frequencies (e.g. mmWave/THz), 2) the increased density of task-oriented network entities, and 3) the higher level of intelligence. To guarantee the delivery of consistently high-quality services for users with the above technological evolution, network resilience and self-reconfigurabilities are crucial. This necessitates a heightened level of intelligence and timely situational awareness in the face of various challenges like network or access point failures. To tackle these challenges, this project pursues a novel resilience-native network paradigm called CatFly, inspired by the way a cat behaves during a crash landing. For instance, in a long drop, falling cats can survive because they have time to relax and stretch out their legs like flying squirrels, which increases their air-resistance and reduces the shock of impact when they hit the ground. Inspired by this fact, if the network system is co-designed with such analogous ability - 1) predicting disturbances or failures with sufficient time ahead, 2) automating prepared countermeasures, and 3) reconfiguring physical structure to prevent potential losses - it can also become a well-trained ‘‘paratrooper’’ like a cat, increasing self-resilience to sustain performance in disruptive situations. To this end, this project explores hybrid digital-physical (HDP) intelligence that utilizes an evolutional digital replica of the network with sufficient what-if details to uncover multi-dimensional physical reconfigurability.

   

• Publications

  • Z. Li, X. Luo, M. Chen, G. Li, and Y. Liu, Beamforming Feedback-Driven Wireless Positioning: A Transferable Vision Transformer Approach, IEEE Transactions on Mobile Computing (TMC), 2026.

  • M. Hossen, C. Dickerson, O. Ozdemir, et al., E. Tucker, Y. Liu, et al., Collection: UAV-Based Wireless Multi-modal Measurements from AERPAW Autonomous Data Mule Challenge in Digital Twin and Real-World Environments, IEEE Data Descriptions (DATA), 2026.

  • Z. Zhang, Z. Peng, H. Yu, M. Chen, and Y. Liu, Digital Network Twins for Next-Generation Wireless: Creation, Optimization, and Challenges, IEEE Network Magazine (Network), 2025.

  • X. Luo, Z. Li, M. Chen, R. Yu, S. Mao, and Y. Liu, Unified Packet Compression and Model Adaptation for Integrated Sensing and Multi-Modal Communications, IEEE Journal on Selected Areas in Communications (JSAC), 2025.

  • J. Huang, M. Chen, and Y. Liu, Achieving Resilient and Self-Adaptive Topology Configuration in 3D UAV Networks, ACM Transactions on Internet Technology (TOIT), 2025.

  • Z. Zhang, M. Fang, M. Chen, and Y. Liu, On Transferring, Merging, and Splitting Task-Oriented Network Digital Twins, IEEE International Symposium on Mobility Management and Wireless Access (MobiWac), 2025.

  • S. Yang, D. Wei, H. Yu, Z. Yang, Y. Liu, and M. Chen, Contrastive Language-Image Pre-Training Model based Semantic Communication Performance Optimization, IEEE Global Communications Conference (GLOBECOM), 2025.

   

• Tools and Platforms

  • AERPAW-Sionna: A tool provides fastAPI endpoints, a Sionna ray tracing wrapper and controller system that act together to enable high-fidelity channel emulation in the AERPAW testbed. Link

  • 3D-DT: A UAV digital twin for multi-objective optimization and communication task planning. Link

  • NDTOmni: A roadmap to develop a networking tool Leveraging NVIDIA Omniverse for infrastructure planning and radio unit configuration. Link

  • NetEdu: Network visualizer with hands-on scripts and examples for networking education. Link

   

• Education, Outreach and Broader Impacts

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

  • Cultivating Interests among High School Students: PI has annually organized the Summer Residential Camp – Seeing through a Digital World at NCSU, inspiring high-school students to explore the intricate workings of complex wireless systems and simulations within the digital realm.

  • Facilitating Undergraduate Research: Several NCSU and UNC undergraduate students worked in the PI’s lab on topics related to radio access networks and data generation. To name a few: Everett Tucker (NCSU), Rohit Kota (UNC), Maulik Verma (NCSU).

  • Dissemination to Communities of Interest: PI has organized a series of workshops and special tracks at premier conferences: IEEE INFOCOM 2025, IEEE ICC 2025, IEEE GLOBECOM 2025, IEEE VTC 2025, IEEE SPAWC, and IEEE COMPSAC 2025 on Edge Learning, digital twins, generative AI over mobile and wireless networks to share cutting-edge research, foster collaboration, and advance the understanding of AI-native, resilience-native networking technologies.

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