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Huacheng Zeng

Associate Professor
Department of Computer Science and Engineering
College of Engineering
Michigan State University

428 S. Shaw Lane, Room 3115
Engineering Building
East Lansing, MI 48824-1226

Office: EB3144; Phone: (517) 353-3851; Email: hzeng [at] msu.edu

RA positions in machine learning and wireless networking/sensing systems are available in my group. Selected students will receive full support including tuition remission, monthly stipend, and health insurance. If interested, please send me an email with your CV attached.

Biography

Dr. Huacheng Zeng is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University (MSU). He directs the Intelligent Networking and Sensing Systems (INSS) Lab, which focuses on the design, implementation, and optimization of AI-driven wireless communication and sensing systems. Prior to joining MSU, Dr. Zeng was an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Louisville. He also worked as a Senior System Engineer at Marvell Semiconductor, where he contributed to the development of wireless system solutions. He received his Ph.D. in Computer Engineering from Virginia Polytechnic Institute and State University (Virginia Tech). Dr. Zeng is a recipient of the NSF CAREER Award (2019), the Best Paper Award at IEEE SECON (2021), and the Best Student Paper Award at ACM WUWNET (2014). His research interests broadly include AI-empowered networking and sensing systems.

Research Interests

AI for wireless networking
  • 5G, 6G, O-RAN
  • Reinforcement learning
  • Online optimization
  • mmWave and beamforming
  • Backscatter communications
  • Network testing and validation
  • Physical-layer attacks and defense
AI for wireless sensing
  • Integrated sensing and communication (ISAC)
  • Human activity recognition
  • Vital sign detection
  • Localization and tracking
  • RF sensing for healthcare
  • 3D scene reconstruction
  • Remote radar imaging

Recent Research Projects

poster image 

MSU's Private 5G Network
We built the first private 5G network on MSU campus. The network comprises six commercial indoor O-RUs, one commercial outdoor O-RU, and 20+ smartphones. The system is deployed on the third floor of MSU Engineering Building. This 5G network testbed supports both OpenAirInterface and srsRAN stacks. Four NVIDIA GPU A6000 devices have been installed for Near-RT RIC.

 

Measurement of Real-Time Uplink CSI in MSU's Private 5G Network
5G uplink Channel State Information (CSI) include not only MIMO channel matrix coefficients but also Time Advance (TA). Additionally, 5G operates in master-slave mode so the network can proactive hand over a smartphone from one gNB to another. This video shows the CSI measurement at a commercial base station from a smartphone when the user is standing and walking.

 

Facial Expression Reconstruction using 5G mmWave Signal
5G mmWave signals can be used to estimate human facial expressions. The left (blue) image shows the facial expression reconstructed using a depth camera, which serves as the ground-truth label. The right (orange) image shows the facial expression reconstructed solely from 5G mmWave signals using deep learning models.

 

Human Skeleton Reconstruction using Wi-Fi Signal
Radio signals from a commercial Wi-Fi router are used to estimate the skeleton of a walking person using an AI model. The overlaid green skeleton is generated by a camera using an off-the-shelf computer vision algorithm and serves as the ground-truth label for training the AI model. The overlaid red skeleton represents the skeleton estimated solely from the Wi-Fi signals.

 

3D Scene Reconstruction using 5G mmWave Signal
We built a bistatic ISAC device that uses 5G mmWave signals for sensing using RFSoC 4x2 and Sivers EVK. The sampling rate is 1.2288GSPS and the carrier frequency is 60GHz. The device operates in full-duplex mode: it transmits 5G signals for communication while simultaneously receiving the backscattered signals for sensing. As the device moves, it generates radio images of the surrounding scene in real time.

 

Bee Localization and Tracking using Tiny RF Backscatter Tag
A miniature backscatter tag, less than 4 mm in diameter, was designed and fabricated for bee tracking. The tag was attached to the thorax of a bee (see video), while an RF reader was installed outside the beehive. When excited by the RF reader, the tag generates a sub-harmonic response, which is used to localize and track the bee.

Awards and Honors

  • Honorable Mention Recognition, ACM CHI, 2025.

  • Best Paper Award, IEEE SECON 2021

  • NSF CAREER Award, 2019

  • Distinguished Member of IEEE INFOCOM TPC, 2019, 2021, and 2023

  • Best Student Paper Award of ACM WUWNet, 2014

Services

  • General Co-chair, IEEE INFOCOM 2026 Workshop – ISAC-FutureG

  • Workshop Co-chair, ACM MobiHoc 2026

  • Co-chair of EDAS, WiOPT 2026

  • TPC Vice Chair for Information Systems and Publication Co-Chair, IEEE INFOCOM 2025

  • Co-chair of Student Travel Grant, IEEE INFOCOM 2024

  • Co-chair of Student Travel Grant, ACM MobiHoc 2023

  • Co-chair of Poster/Demo Workshop, IEEE INFOCOM 2022, 2023

  • Co-chair OF Publicity, IEEE INFOCOM 2020, 2021

  • Co-chair of Publicity, IEEE WCNC 2021

  • TPC, IEEE INFOCOM 2015–present

  • TPC, ACM MobiHoc 2022, 2023, 2024, 2025, 2026

  • TPC, WiOpt 2021

  • TPC, IEEE/ACM International Symposium on Quality of Service (IWQoS) 2021