Hang Liu
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Publications

For the latest publications, please see my Google Scholar Profile.

Book Chapter

  1. H. Liu, X. Yuan, and Y.-J. A. Zhang, “PHY-Layer design challenges in reconfigurable intelligent surface aided 6G wireless networks”, in 6G Mobile Wireless Networks, edited by Y. Wu, S. Singh, T. Taleb, A. Roy, H.S. Dhillon, M.R. Kanagarathinam, and A. De, Springer, 2021.

Preprints and Working Papers

  1. H. Liu, A. Scaglione, and S. Peisert. Differentially private distribution release of Gaussian mixture models via KL-divergence minimization, submitted to IEEE Transactions on Information Forensics and Security, 2025. [ArXiv]

  2. H. Liu and A. Scaglione. Shuffled Linear Regression via Spectral Matching, under revisions with IEEE Transactions on Signal Processing. [ArXiv]

  3. A. Campbell, H. Liu, L. Woldemariam, and A. Scaglione. Compressed and Sparse Models for Decentralized Learning. [ArXiv]

  4. L. Woldemariam, H. Liu, and A. Scaglione. A multi-level sparse representation scheme for vector source coding, under revisions with IEEE Transactions on Signal Processing.

  5. C. Zhong, H. Liu, and X. Yuan. A unified variational message passing framework for generic and personalized federated learning.

Journal Papers

  1. J. Ouyang, Y. Liu, and H. Liu. A two-timescale approach for wireless federated learning with parameter freezing and power control, IEEE Transactions on Mobile Computing, Early Access. [Link]

  2. H. Liu, A. Scaglione, and S. Peisert. Graph-signal-to-graph matching for network de-anonymization attacks, IEEE Transactions on Information Forensics and Security, vol. 19, pp. 10043-10057, 2024. [Link]

  3. H. Liu, A. Scaglione, and H.-T. Wai. Blind graph matching using graph signals, IEEE Transactions on Signal Processing, vol. 72, pp. 1766-1781, 2024. [Link]

  4. H. Liu, J. Yan, and Y.-J. A. Zhang. Differentially private over-the-air federated learning over MIMO fading channels, IEEE Transactions on Wireless Communications, vol. 23, no. 8, pp. 8232-8247, Aug. 2024. [Link]

  5. H. Liu, Z. Lin, X. Yuan, and Y.-J. A. Zhang. Reconfigurable intelligent surface empowered over-the-air federated edge learning, IEEE Wireless Communications, vol. 30, no. 6, pp. 111-118, Dec. 2023. [Link]

  6. Z. Lin, H. Liu*, and Y.-J. A. Zhang. CFLIT: Coexisting federated learning and information transfer, IEEE Transactions on Wireless Communications, vol. 22, no. 11, pp. 8436-8453, Nov. 2023. [Link][Code]

  7. Z. Lin, H. Liu*, and Y.-J. A. Zhang. Relay-assisted cooperative federated learning, IEEE Transactions on Wireless Communications, vol. 21, no. 9, pp. 7148–7164, Sept. 2022. [Link][Code]

  8. H. Liu, X. Yuan, and Y.-J. A. Zhang. CSIT-free model aggregation for federated edge learning via reconfigurable intelligent surface, IEEE Wireless Communications Letters, vol. 20, no. 11, pp. 7595-7609, Nov. 2021. [Link]

  9. H. Liu, X. Yuan, and Y.-J. A. Zhang. Reconfigurable intelligent surface enabled federated learning: A unified communication-learning design approach, IEEE Transactions on Wireless Communications, vol. 20, no. 11, pp. 7595-7609, Nov. 2021. [Link][Code]

  10. X. Yuan, Y.-J. A. Zhang, Y. Shi, W. Yan, and H. Liu. Reconfigurable-intelligent-surface empowered 6G wireless communications: Challenges and opportunities, IEEE Wireless Communications, vol. 28, no. 2, pp. 136-143, Apr. 2021. (ESI Highly Cited Paper; IEEE ComSoc Best Readings in RIS) [Link][Code]

  11. H. Liu, X. Yuan, and Y.-J. A. Zhang. Matrix-calibration-based cascaded channel estimation for reconfigurable intelligent surface assisted multiuser MIMO, IEEE Journal on Selected Areas in Communications, 38(11):2621–2636, Nov. 2020. (ESI Highly Cited Paper) [Link][Code]

  12. H. Liu, X. Yuan, and Y.-J. A. Zhang. Statistical beamforming for FDD downlink massive MIMO via spatial information extraction and beam selection, IEEE Transactions on Wireless Communications, 19(7):4617–4631, Jul. 2020. [Link]

  13. X. Kuai, X. Yuan, W. Yan, H. Liu, and Y.-J. A. Zhang. Double-sparsity learning based channel-and-signal estimation in massive MIMO with generalized spatial modulation, IEEE Transactions on Communications, 68(5):2863–2877, May 2020. [Link]

  14. H. Liu, X. Yuan, and Y.-J. A. Zhang. Super-resolution blind channel-and-signal estimation for massive MIMO with one-dimensional antenna array, IEEE Transactions on Signal Processing, 67(17):4433–4448, Sep. 2019. [Link]

Conference Papers

  1. A. Campbell, H. Liu, A. Scaglione, and Tong Wu. A federated learning approach for graph convolutional neural networks, In IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), July 2024. (Invited paper)

  2. H. Liu, A. Scaglione, and S. Peisert. Privacy leakage in graph signal to graph matching problems. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 9371-9375, Apr. 2024.

  3. L. Woldemariam, H. Liu, and A. Scaglione. Low-complexity vector source coding for discrete long sequences with unknown distributions. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8991-8995, Apr. 2024.

  4. J. Ouyang, Y. Liu, and H. Liu. Two-Timescale Energy Optimization for Wireless Federated Learning. In IEEE Conference on Computer Communications Workshops (INFOCOM Workshops), pages 1–6, 2024.

  5. H. Liu, A. Scaglione, and H.-T. Wai. Solutions of the graph matching problem using graph signals. In IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pages 266-270, Dec. 2023. (Invited paper)

  6. H. Liu, J. Yan, and Y.-J. A. Zhang. On the privacy leakage of over-the-air federated learning over MIMO fading channels. In IEEE Global Communications Conference (GLOBECOM), pages 5274-5279, Dec. 2023, .

  7. Z. Lin, H. Liu, and Y.-J. A. Zhang. Relay-assisted over-the-air federated learning. In IEEE Global Communications Conference (GLOBECOM), Dec. 2021.

  8. Z.-Q. He, H. Liu, X. Yuan, Y.-J. A. Zhang, and Y.-C. Liang. Semi-blind channel estimation for RIS-aided massive MIMO: A trilinear AMP approach. In IEEE International Symposium on Information Theory (ISIT), pages 2822-2827, June 2021.

  9. H. Liu, X. Yuan, and Y.-J. A. Zhang. Joint communication-learning design for RIS-assisted federated learning. In IEEE International Conference on Communications (ICC), pages 1-6, June 2021.

  10. H. Liu, X. Yuan, and Y.-J. A. Zhang. Message-passing based channel estimation for reconfigurable intelligent surface assisted MIMO. In IEEE International Symposium on Information Theory (ISIT), pages 2983–2988, June 2020.

  11. H. Liu, X. Yuan, and Y.-J. A. Zhang. Beam-selection-based statistical beamforming for FDD massive MIMO: Exploiting spatial reciprocity. In IEEE Global Communications Conference (GLOBECOM), pages 1–6, Dec. 2019.

  12. X. Kuai, X. Yuan, W. Yan, H. Liu, and Y.-J. A. Zhang. Sparsity learning based blind signal detection for massive MIMO with generalized spatial modulation. In IEEE/CIC International Conference on Communications in China (ICCC), pages 64–69, Aug. 2019.

  13. H. Liu, X. Yuan, and Y.-J. A. Zhang. Message-passing based blind signal detection for massive MIMO with general antenna arrays. In IEEE International Conference on Communications (ICC), pages 1–7, May 2019.