Dr. Xiaofeng Liu | Wireless Communication | Best Researcher Award
Dr. Xiaofeng Liu | Lecture | Yancheng Teachers University | China
Dr. Xiaofeng Liu is a dedicated researcher and lecturer in Artificial Intelligence with a strong background in wireless communications, machine learning, and statistical inference. His research primarily focuses on developing advanced algorithms for massive MIMO systems, channel estimation, and machine learning-driven communication models. Dr. Liu has significantly contributed to the integration of statistical learning frameworks in communication system design, particularly through innovations like correlated hybrid message passing and generative diffusion models for channel estimation. His collaborative work with experts from leading research laboratories has produced high-impact publications in IEEE journals, reflecting both theoretical advancement and practical application in intelligent communication systems. His inventive contributions are further evident in several granted Chinese invention patents related to MIMO positioning, channel modeling, and beamspace communications. Dr. Liu’s research achievements are widely recognized, with his publications indexed in Scopus and Google Scholar, accumulating over 135 citations, an h-index of 6, and an i10-index of 5. His scholarly record demonstrates consistent contributions to next-generation wireless communication technologies, bridging the gap between deep learning models and complex signal processing challenges.
Publication Profile
Google Scholar | ORCID
Featured Publications
Liu, X., Gong, X., & Fu, X. (2025). Activity detection and channel estimation based on correlated hybrid message passing for grant-free massive random access. Entropy.
Fu, X., Gong, X., Liu, X., Sun, R., Shen, Q., & Gao, X. (2025). Beamspace multi-ACB for mMTC in massive MIMO system. IEEE Transactions on Vehicular Technology.
Gong, X., Liu, X., Lu, A. A., Gao, X., Xia, X. G., Wang, C. X., & You, X. (2025). Digital twin of channel: Diffusion model for sensing-assisted statistical channel state information generation. IEEE Transactions on Wireless Communications.
Gong, X., Lu, A. A., Fu, X., Liu, X., Gao, X., & Xia, X. G. (2023). Semisupervised representation contrastive learning for massive MIMO fingerprint positioning. IEEE Internet of Things Journal.
Liu, X., Wang, W., Gong, X., Fu, X., Gao, X., & Xia, X. G. (2023). Structured hybrid message passing based channel estimation for massive MIMO-OFDM systems. IEEE Transactions on Vehicular Technology.