Zhengming Zhang
Engineering · Purdue University West Lafayette
Publications
24
Citations
513
Est. group size
—
Recurring co-author estimate
Active years
8
Publishing since 2018
Zhengming Zhang works on wireless communication systems, focusing on how machine learning and deep learning can improve technologies like massive MIMO (using many antennas to boost wireless capacity), millimeter-wave (mmWave) communication, and network resource management. Much of the work applies techniques such as federated learning, self-supervised learning, and neural networks to problems like channel estimation, beam selection, and CSI (channel state information) feedback, which are key to making next-generation (5G/6G) wireless networks faster and more efficient. The research also touches on privacy-preserving machine learning and digital twin simulations for network optimization.
Publication output was low and sporadic from 2017-2021 but increased notably starting in 2022, with a sustained higher output through 2024 before appearing to taper in 2025.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Deep Convolutional Neural Network Enhanced by Transfer Learning for Accurate Distance Detection Using Magnetic Field Distribution Images
SSRN Electronic Journal · 2025
- A Recursive Discretization Compression Framework Combined with Selective State Space Model for Massive MIMO CSI Feedback
2025
- Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing
IEEE Transactions on Communications · 2024
- Access Point Selection and Beamforming Design for Cell-Free Network: From Fractional Programming to GNN
IEEE Transactions on Wireless Communications · 2024
- Deep Reciprocity Calibration for TDD mmWave Massive MIMO Systems Toward 6G
IEEE Transactions on Wireless Communications · 2024
- A Denoising Diffusion Probabilistic Model-Based Digital Twinning of ISAC MIMO Channel
IEEE Internet of Things Journal · 2024
- An Effective Network With Discrete Latent Representation Designed for Massive MIMO CSI Feedback
IEEE Communications Letters · 2024
- Uncertainty Differences in Computing Hierarchical Pedestrian Behaviors
Proceedings of the Human Factors and Ergonomics Society Annual Meeting · 2024
- A Self-Supervised Learning-Based Channel Estimation for IRS-Aided Communication Without Ground Truth
IEEE Transactions on Wireless Communications · 2023
- Federated Learning in Heterogeneous Networks With Unreliable Communication
IEEE Transactions on Wireless Communications · 2023
- AIRA-DA: Adversarial Image Reconstruction Alignments for Unsupervised Domain Adaptive Object Detection
IEEE Robotics and Automation Letters · 2023
- TrEP: Transformer-Based Evidential Prediction for Pedestrian Intention with Uncertainty
Proceedings of the AAAI Conference on Artificial Intelligence · 2023
- Meta-Learning for Beam Prediction in a Dual-Band Communication System
IEEE Transactions on Communications · 2022
- Learning-Based Resource Allocation in Heterogeneous Ultradense Network
IEEE Internet of Things Journal · 2022
- Backdoor Federated Learning-Based mmWave Beam Selection
IEEE Transactions on Communications · 2022
- IEEE Transactions on Wireless Communications×5
- IEEE Transactions on Communications×5
- IEEE Wireless Communications Letters×3
- IEEE Transactions on Vehicular Technology×2
- IEEE Internet of Things Journal×2
- Yahia Shabara
Engineering · The Ohio State University
- Jingqi Huang
Engineering · Purdue University West Lafayette
- David J. Love
Engineering · Purdue University West Lafayette
- An-An Lu
Engineering · The Ohio State University
- Hang Li
Engineering · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile