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This page is updated to Mar. 2026

Overview

  • My full publication list can be seen here: [Google Scholar].

  • Citations: 52978; H-index: 107

  • Thompson-Reuters (Clarivate) highly cited researcher

Books and Special Issues:

  • 1. Z.-Q. Luo, J.-S. Pang and D. Ralph, Mathematical Programs with Equilibrium Constraints, Cambridge University Press, 400 pages, 1996.

  • 2. Z.-Q. Luo and J.-S. Pang (Guest Editors), Error Bounds and Their Applications in Mathematical Programming, Mathematical Programming, Series B, 2000.

  • 3. M. Chiang, S. Low, Z.-Q. Luo, N. Shroff and W. Yu (Guest Editors), Special issue of IEEE Journal of Selected Areas of Communications on 'Nonlinear Optimization of Communication Systems’, 2006.

  • 4. Z.-Q. Luo, M. Gastpar, J. Liu and A. Swami (Guest Editors), Special issue of IEEE Signal Processing Magazine on 'Distributed Signal Processing for Sensor Networks’, 2006.

Recent Publications

Journal Publications:

  • 1. Y. Qiu, C. Huang, Y. Xue, Z. Jiang, Q. Shi, D. Zhang and Z.-Q. Luo, “Relaxation-Free Min-k-Partition for PCI Assignment in 5G Networks,” IEEE Transactions on Signal Processing, 2025.

  • 2. J. Wang, F. Yin, T. Ding, T.-H. Chang, Z.-Q. Luo, Q. Yan, “ Learning to Gridize: Segment Physical World by Wireless Communication Channel,” in arXiv preprint arXiv:2507.15386, 2025.

  • 3. D. Rybin, Y. Zhang, Z.-Q. Luo, “ XX^t Can Be Faster,” in arXiv preprint arXiv:2505.09814, 2025.

  • 4. Y. Zhang, D. Rybin, Z.-Q. Luo, “ Finite horizon optimization: Framework and applications,” in arXiv preprint arXiv:2412.21068, 2024.

  • 5. K. Li, Y. Li, L. Cheng and Z.-Q. Luo, “Enhancing Multi-Stream Beamforming Through CQIs for 5G NR FDD Massive MIMO Communications: A Tuning-Free Scheme,” in IEEE Transactions on Wireless Communications, vol. 23, no. 11, pp. 17508-17521, Nov. 2024.

  • 6. H. Liang, and Z.-Q. Luo. “Bridging distributional and risk-sensitive reinforcement learning with provable regret bounds.” Journal of Machine Learning Research 25.221 (2024): 1-56.

  • 7. S. Zhang, X. Ning, X. Zheng, Q. Shi, T.-H. Chang and Z.-Q. Luo, “A Physics-Based and Data-Driven Approach for Localized Statistical Channel Modeling,” in IEEE Transactions on Wireless Communications, vol. 23, no. 6, pp. 5409-5424, June 2024.

  • 8. K. Li, Y. Li, L. Cheng, Q. Shi and Z.-Q. Luo, “Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO Systems With Limited Feedback,” in IEEE Transactions on Signal Processing, vol. 72, pp. 1032-1048, 2024.

  • 9. Q. Wu, B. Zheng, C. You, L. Zhu, K. Shen, X. Shao, W. Mei, B. Di, H. Zhang, E. Basar, L. Song, M. D. Renzo, Z.-Q. Luo, Rui Zhang, “Intelligent Surfaces Empowered Wireless Network: Recent Advances and the Road to 6G,” in Proceedings of the IEEE, 2024.

  • 10. X. Zhao, S. Lu, Q. Shi and Z.-Q. Luo, “Rethinking WMMSE: Can Its Complexity Scale Linearly With the Number of BS Antennas?” in IEEE Transactions on Signal Processing, vol. 71, pp. 433-446, 2023.

  • 11. Y.-B. Zhao and Z.-Q. Luo, “Dynamic Orthogonal Matching Pursuit for Sparse Data Reconstruction,” in IEEE Open Journal of Signal Processing, vol. 4, pp. 242-256, 2023.

  • 12. Y. Zhao, Z.-Q. Luo, Improved RIP-based bounds for guaranteed performance of two compressed sensing algorithms. Sci. China Math. 66, 1123–1140 (2023).

  • 13. Z.-Q. Luo, X. Zheng, D. López-Pérez, Q. Yan, X. Chen, N. Wang, Q. Shi, T.-H. Chang, A. Garcia-Rodriguez, “SRCON: A Data-Driven Network Performance Simulator for Real-World Wireless Networks,” in IEEE Communications Magazine, vol. 61, no. 6, pp. 96-102, June 2023.

  • 14. W. Pu, Y.-F. Liu and Z.-Q. Luo, “Efficient Estimation of Sensor Biases for the 3-D Asynchronous Multi-Sensor System,” in IEEE Transactions on Signal Processing, vol. 71, pp. 2420-2433, 2023.

  • 15. C. Chen, L. Shen, W. Liu and Z.-Q. Luo, “Efficient-Adam: Communication-Efficient Distributed Adam,” in IEEE Transactions on Signal Processing, vol. 71, pp. 3257-3266, 2023.

  • 16. Y. Zhang, K. Shen, S. Ren, X. Li, X. Chen and Z.-Q. Luo, “Configuring Intelligent Reflecting Surface With Performance Guarantees: Optimal Beamforming,” in IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 5, pp. 967-979, Aug. 2022.

  • 18. J. Zhang and Z.-Q. Luo, “ A global dual error bound and its application to the analysis of linearly constrained nonconvex optimization,” in SIAM Journal on Optimization, vol. 32, no. 3, pp. 2319–2346, 2022.

  • 19. M. Asgarian, G. Mirjalily and Z.-Q. Luo, “Embedding Multicast Service Function Chains in NFV-Enabled Networks,” in IEEE Communications Letters, vol. 25, no. 4, pp. 1264-1268, April 2021.

Conference Papers:

  • 1. Y. Qiu, Y. Xue, A. Wang, Y. Wang, Q. Shi, Z.-Q. Luo, “ROS: A GNN-based Relax-Optimize-and-Sample Framework for Max-k-Cut Problems,” Proceedings of the 42nd International Conference on Machine Learning, PMLR, 2025.

  • 2. Z. Li, T. Xu, Y. Zhang, Z. Lin, Y. Yu, R. Sun, Z.-Q. Luo, “ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models,” Forty-first International Conference on Machine Learning, 2024.

  • 3. H. Liang, Z.-Q. Luo, “Regret Bounds for Risk-Sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures”, Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:1774-1782, 2024.

  • 4. Y. Li, Z.-Q. Luo, Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:559-567, 2024.

  • 5. Y. Li, J. Xu, Z.-Q. Luo, “Efficient and scalable reinforcement learning via Hypermodel,” NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World, 2023.

  • 6. J. Yao, F. Xu, W. Lai, K. Shen, X. Li, X. Chen, Z.-Q. Luo, “Blind Beamforming for Multiple Intelligent Reflecting Surfaces,” ICC 2023 - IEEE International Conference on Communications, Rome, Italy, 2023, pp. 871-876.

  • 7. T. Xu, Z. Li, Y. Yu, Z.-Q. Luo, “Provably Efficient Adversarial Imitation Learning with Unknown Transitions”, Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2367-2378, 2023 (Oral).

  • 8. H. Liang, Z.-Q. Luo, “A Distribution Optimization Framework for Confidence Bounds of Risk Measures”, Proceedings of the 40th International Conference on Machine Learning, PMLR 202:20677-20705, 2023.

  • 9. W.-K. Chen, Y.-F. Liu, R.-J. Zhang, Y.-H. Dai and Z.-Q. Luo, “An Efficient Decomposition Algorithm for Large-Scale Network Slicing,” 2023 IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Shanghai, China, 2023, pp. 171-175.

  • 10. F. Xu, J. Yao, W. Lai, K. Shen, X. Li, X. Chen, Z.-Q. Luo, “Blind Beamforming for Multiple-IRS Assisted Wireless Transmission,” 2023 IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Shanghai, China, 2023, pp. 136-140.

  • 11. K. Li, W. Pu and Z.-Q. Luo, “An Exploration-Estimation Beamforming Scheme for 5GNR FDD Massive MIMO Communications,” 2023 IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Shanghai, China, 2023, pp. 146-150.

  • 12. K. Li, Y. Li, L. Cheng, Q. Shi and Z.-Q. Luo, “Pushing the Limit of Type I Codebook for FDD Massive MIMO Beamforming: A Channel Covariance Reconstruction Approach,” ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada, 2021, pp. 4785-4789.

  • 13. Y. Li, K. Li, L. Cheng, Q. Shi and Z.-Q. Luo, “Digital Twin-Aided Learning to Enable Robust Beamforming: Limited Feedback Meets Deep Generative Models,” 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Lucca, Italy, 2021, pp. 26-30.

  • 14. K. Li, Y. Li, L. Cheng, Q. Shi and Z.-Q. Luo, “Learning Enhanced Beamforming Vector From CQIs in 5G NR FDD Massive MIMO Systems: A Tuning-free Approach,” 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Lucca, Italy, 2021, pp. 21-25.