The Intelligent Network sysTem Laboratory

The Intelligent Network sysTem (INT) lab focuses on cutting-edge network system research, including wireless networks, edge computing, autonomous driving, and mobile metaverse.
CC* Integration-Large: Husker-Net: Open Nebraska End-to-End Wireless Edge Networks [Webpage]
  • Funding Source: NSF OAC 2321699
  • Total Budget: $891,000
  • Role: Principal Investigator
  • Duration: Oct. 2023 – Sept. 2025
  • Abstract: This project outlines a novel open end-to-end cellular edge network (Husker-Net) by designing, deploying, and operating private 5G network over a light-licensed CBRS spectrum in multiple UNL campuses. Husker-Net is featured with ultra-low operating cost with open-source modules, flexible deployment with both wired and wireless backhaul (e.g., LEO in mid of Nebraska), and zero-touch management with automatic model-free algorithms.
  • CNS Core: Medium: Field-Nets: Field-to-Edge Connectivity for Joint Communication and Sensing in Next-Generation Intelligent Agricultural Networks [Webpage]
  • Funding Source: NSF CNS 2212050
  • Total Budget: $1,000,000
  • Role: Co-Principal Investigator
  • Duration: Oct. 2022 – Sept. 2025
  • Abstract: In this project, an interdisciplinary team of experts in millimeter-wave communications, metamaterial and metasurface-inspired antenna array design, dynamic spectrum access, and radio access networks in collaboration with experts in agricultural robotics and sensor-based plant phenotyping aim to provide connectivity to rural farm fields and increase national competence to bring new technologies to rural America rapidly. More details in here
  • Real-World Machine Learning in Mobile Network Slicing
  • Funding Source: Nebraska EPSCoR FIRST Award
  • Total Budget: $25,000
  • Role: Sole Principal Investigator
  • Duration: Dec. 2022 – Nov. 2023
  • Abstract: This project outlines a novel framework that resolves the simulation-to-reality discrepancy for intelligent and autonomous mobile network slicing with new-designed real-world machine learning techniques. We will investigate the research thrust of simulator augmentation with real-to-sim learning, policy adaptation with sim-to-real transfer, and online network management with safe policy learning.
  • Open Source