ZHANG, Jun
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Selected publications by topic

Data Science, AI

Machine learning

  1. X. Li, J. Zhang, “Context-aware communication for multi-agent reinforcement learning,” International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Auckland, New Zealand, May 2024. (Acceptance Rate: 25%) [Paper]

  2. S. Gao, C. Zhou, and J. Zhang, “Generalized relation modeling for transformer tracking,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, Jun. 2023. (Acceptance Rate: 25.78%) [Paper] [GitHub]

  3. X. Wang, X. Li, J. Shao, and J. Zhang, “AC2C: Adaptively controlled two-hop communication for multi-agent reinforcement learning,” International Conference on Autonomous Agents and Multiagent Systems (AAMAS), London, United Kingdom, May-June 2023. (Acceptance Rate: 23.3%)

  4. B. Li*, Y. Shen*, J. Yang, Y. Wang, J. Ren, T. Che, J. Zhang, and Z. Liu, “Sparse Mixture-of-Experts are Domain Generalizable Learners,” International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 2023. (Oral presentation) (Acceptance Rate: 31.8%) [Paper] [GitHub]

  5. Y. Shen, Y. Wu, Y. Zhang, C. Shan, J. Zhang, K. B. Letaief, and D. Li, “How powerful is graph convolution for recommendation?,” ACM International Conference on Information and Knowledge Management (CIKM), virtual conference, Nov. 2021. [Paper] (spotlight presentation) (Acceptance Rate: 21.7%)

  6. Y. Shen, Y. Xue, J. Zhang, K. B. Letaief, and V. Lau, “Complete Dictionary Learning via ell_p-norm Maximization,” Conference on Uncertainty in Artificial Intelligence (UAI) 2020, Toronto, Canada, Aug. 2020. [Paper]

Federated Learning

  1. Z. Li, Y. Sun, J. Shao, Y. Mao, J. Wang, and J. Zhang, “Feature matching data synthesis for non-IID federated learning,” IEEE Trans. Mobile Computing, to appear. [Paper]

  2. Y. Sun, Y. Mao, and J. Zhang, “MimiC: Combating client dropouts in federated learning by mimicking central updates,” IEEE Trans. Mobile Computing, to appear. [Paper]

  3. T. Zhou, J. Zhang, and D. Tsang, “FedFA: Federated learning with feature anchors to align feature and classifier for heterogeneous data,” IEEE Trans. Mobile Computing, to appear. [Paper]

  4. J. Shao, F. Wu, and J. Zhang, “Selective knowledge sharing for privacy-preserving federated distillation without a good teacher,” Nature Communications, January 2024, DOI: 10.1038/s41467-023-44383-9. [Paper]

  5. L. Liu, J. Zhang, S.H. Song, and K. B. Letaief, “Hierarchical federated learning with quantization: Convergence analysis and system design,” IEEE Trans. Wireless Commun., vol. 11, no. 1, pp. 2-18, Jan. 2023. [Paper]

  6. J. Shao, Y. Sun, S. Li, and J. Zhang, “DReS-FL: Dropout-resilient secure federated learning for non-IID clients via secret data sharing,” Advances in neural information processing systems (NeurIPS), New Orleans, LA, Nov-Dec 2022. [Paper] (Acceptance Rate: 25.6%)

Big Data Analytics Systems

  1. Y. Yu, W. Wang, R. Huang, J. Zhang, and K. B. Letaief, “Achieving load-balanced, redundancy-free cluster caching with selective partition,” IEEE Trans. Parallel Distrib. Syst., vol. 31, no. 2, pp. 439–454, Feb. 2020.

  2. Y. Yu, W. Wang, J. Zhang, and K. B. Letaief, “LACS: Load-aware cache sharing with isolation guarantee,” in Proc. IEEE Int. Conf. Distrib. Comput. Syst. (ICDCS), Dallas, TX, Jul. 2019. (Acceptance Rate: 19.6%)

  3. Y. Yu, R. Huang, W. Wang, J. Zhang, and K. B. Letaief, “SP-Cache: Load-balanced, Redundancy-free Cluster Caching with Selective Partition,” in Proc. IEEE/ACM International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18), Dallas, TX, November, 2018. (Acceptance Rate: 19%) [Paper] [GitHub]

  4. Y. Yu, W. Wang, J. Zhang, Q. Weng, and K. B. Letaief, “OpuS: Fair and efficient cache sharing for in-memory data analytics,” in Proc. IEEE Int. Conf. Distrib. Comput. Syst. (ICDCS), Vienna, Austria, Jul. 2018. (Acceptance Rate: 20%) [Paper]

  5. Y. Yu, W. Wang, J. Zhang, and K. B. Letaief, “LRC: Dependency-aware cache management in data analytics clusters,” in Proc. IEEE INFOCOM 2017, Atlanta, GA, May 2017. (Acceptance Rate: 20.93%) [Paper] [Slides]

Edge Computing and Edge AI

Edge AI

  1. X. Zhang, J. Shao, and J. Zhang, “LDMIC: Learning-based distributed multi-view image coding,” International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 2023. (Acceptance Rate: 31.8%) [Paper]

  2. J. Shao, Y. Mao, and J. Zhang, “Task-oriented communication for multi-device cooperative edge inference,” IEEE Trans. Wireless Commun., vol. 11, no. 1, pp. 73-87, Jan. 2023. [Paper]

  3. J. Shao, Y. Mao, and J. Zhang, “Learning task-oriented communication for edge inference: An information bottleneck approach,” IEEE J. Select. Areas Commun, vol. 40, no. 1, pp. 197-211, Jan. 2022. [Paper] [GitHub]

  4. J. Shao, J. Zhang, “Communication-computation trade-off in resource-constrained edge inference,” IEEE Commun. Mag., vol. 58, no. 12, pp. 20–26, Dec. 2020. [Paper] [GitHub]

  5. Y. Shi, K. Yang, T. Jiang, J. Zhang, and K. B. Letaief, “Communication-efficient edge AI: Algorithms and systems,” IEEE Commun. Surveys Tuts., vol. 22, no. 4, pp. 2167–2191, 4th Quart. 2020. [Paper]

  6. J. Shao, J. Zhang, “BottleNet++: An end-to-end approach for feature compression in device-edge co-inference systems,” in Proc. IEEE Int. Conf. Commun. (ICC) Workshop on Edge Machine Learning for 5G Mobile Networks and Beyond, Dublin, Ireland, Jun. 2020. [Paper] [GitHub]

  7. J. Zhang and K. B. Letaief, “Mobile edge intelligence and computing for the Internet of vehicles,” Proc. IEEE, vol. 108, no. 2, pp. 246–261, Feb. 2020. [Paper]

  8. G. Zhu, D. Liu, Y. Du, C. You, J. Zhang, and K. Huang, “Toward an intelligent edge: Wireless communication meets machine learning,” IEEE Commun. Mag., vol. 58, no. 1, pp. 19–25, Jan. 2020. [Paper]

Mobile Edge Computing

  1. Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge computing: The communication perspective,” IEEE Commun. Surveys Tuts., vol. 19, no. 4, pp. 2322-2358, 4th Quart. 2017. [Paper] (The 2021 Best Survey Paper Award of IEEE Communications Society)

  2. Y. Mao, J. Zhang, S.H. Song, and K. B. Letaief, “Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems,” IEEE Trans. Wireless Commun., vol. 16, no. 9, pp. 5994-6009, Sept. 2017. [Paper]

  3. Y. Mao, J. Zhang, and K. B. Letaief, “Dynamic computation offloading for mobile-edge computing with energy harvesting devices,” IEEE J. Select. Areas Commun. - Series on Green Commun. and Networking, vol. 34, no. 12, pp. 3590-3605, Dec. 2016. [Paper] (The 2019 IEEE Communications Society & Information Theory Society Joint Paper Award)

Mobile Edge Caching

  1. R. Wang, J. Zhang, S.H. Song, and K. B. Letaief, “Exploiting mobility in cache-assisted D2D networks: Performance analysis and optimization,” IEEE Trans. Wireless Commun., vol. 17, no. 8, pp. 5592-5605, Aug. 2018. [Paper]

  2. X. Peng, Y. Shi, J. Zhang, and K. B. Letaief, “Layered group sparse beamforming for cache-enabled wireless networks,” IEEE Trans. Commun., vol. 65, no. 12, pp. 5589-5603, Nov. 2017. [Paper]

  3. J. Liu, B. Bai, J. Zhang, and K. B. Letaief, “Cache placement in Fog-RANs: from centralized to distributed algorithms,” IEEE Trans. Wireless Commun., vol. 16, no. 11, pp. 7039-7051, Nov. 2017. [Paper]

    1. Part of this work was presented at ICC 2016, and received a Best Paper Award.

  4. R. Wang, J. Zhang, S.H. Song, and K. B. Letaief, “Mobility-aware caching in D2D networks,” IEEE Trans. Wireless Commun., vol. 16, no. 8, pp. 5001-5015, Aug. 2017. [Paper] [Codes]

  5. R. Wang, X. Peng, J. Zhang, and K. B. Letaief, “Mobility-aware caching for content-centric wireless networks: Modeling and methodology,” IEEE Commun. Mag., vol. 54, no. 8, pp. 77-83, Aug. 2016. [Paper]

  6. X. Peng, J.-C. Shen, J. Zhang, and K. B. Letaief, “Joint data assignment and beamforming for backhaul limited caching networks,” in Proc. IEEE Int. Symp. on Personal Indoor and Mobile Radio Comm. (PIMRC), Washington, DC, Sept. 2014. (Best Paper Award) [Paper])

Wireless Communications and Networking

Learning to Communicate

  1. W. Yu, Y. Shen, H. He, X. Yu, S.H. Song, J. Zhang, and K. B. Letaief, “An adaptive and robust deep learning framework for THz ultra-massive MIMO channel estimation,” IEEE J. Sel. Topics Signal Process., vol. 17, no. 4, pp. 761-776, July 2023. [Paper] [GitHub]

  2. Y. Shi, L. Lian, Y. Shi, Z. Wang, Y. Zhou, L. Fu, L. Bai, J. Zhang, and W. Zhang, “Machine learning for large-scale optimization in 6G wireless networks,” IEEE Commun. Surveys Tuts., vol. 25, no. 4, pp. 2088-2132, 4th Quart. 2023. [Paper]

  3. Y. Ma, Y. Shen, X. Yu, J. Zhang, S.H. Song, and K. B. Letaief, “Learn to communicate with neural calibration: Scalability and generalization,” IEEE Trans. Wireless Commun., vol. 21, no. 11, pp. 9947-9961, Nov. 2022. [Paper]

  4. Y. Shen, J. Zhang, S.H. Song, and K. B. Letaief, “Graph neural networks for wireless communications: From theory to practice,” IEEE Trans. Wireless Commun., vol. 22, no. 5, pp. 3554-3569, May 2023. [Paper] [GitHub]

  5. Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief, “Graph neural networks for scalable radio resource management: architecture design and theoretical analysis,” IEEE J. Select. Areas Commun, vol. 39, no. 1, pp. 101–115, Jan. 2021. [Paper]

  6. Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief, “A graph neural network approach for scalable wireless power control,” in Proc. IEEE GLOBECOM 2019 Workshop on Machine Learning for Wireless Communications, Waikoloa, HI, USA, Dec. 2019. [Paper] [GitHub]

  7. Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief, “LORM: Learning to optimize for resource management in wireless networks with few training samples,” IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 665–679, Jan. 2020. [Paper]

Massive Random Access

  1. X. Bian, Y. Mao, and J. Zhang, “Grant-free massive random access with retransmission: Receiver optimization and performance analysis,” IEEE Trans. Commun., to appear. [Paper]

  2. X. Bian, Y. Mao, and J. Zhang, “Joint activity detection, channel estimation, and data decoding for grant-free massive random access,” IEEE Internet of Things Journal, vol. 10, no. 16, pp. 14042-14057, August 2023. [Paper]

  3. J. Dong, J. Zhang, Y. Shi, and J. Wang, “Faster activity and data detection in massive random access: A multi-armed bandit approach,” IEEE Internet of Things Journal, to appear. [Paper]

  4. Y. Xue, Y. Shen, V. Lau, J. Zhang, and K. B. Letaief, “Blind data detection in massive MIMO via ell_3-norm maximization over the Stiefel manifold,” IEEE Trans. Wireless Commun., vol. 20, no. 2, pp. 1411–1424, Feb. 2021. [Paper]

  5. T. Jiang, Y. Shi, J. Zhang, and K. B. Letaief, “Joint activity detection and channel estimation for IoT networks: phase transition and computation-estimation tradeoff,” IEEE Internet of Things J., vol. 6, no. 4, pp. 6212–6225, Aug. 2019. [Paper]

Millimeter Wave (mm-wave) Communications for 5G

  1. J. Zhang, X. Yu, and K. B. Letaief, “Hybrid beamforming for 5G and beyond millimeter-wave systems: A holistic view,” IEEE Open J. Commun. Society, vol. 1, no. 1, pp. 77–91, Jan. 2020. [Paper]

  2. X. Yu, J. Zhang, and K. B. Letaief, “Doubling phase shifters for efficient hybrid precoder design in millimeter-wave communication systems,” Journal of Communications and Information Networks, vol. 4, no. 2, pp. 51–67, Jun. 2019. [Paper]

  3. T. Lin, J. Cong, Y. Zhu, J. Zhang, and K. B. Letaief, “Hybrid beamforming for millimeter wave systems using the MMSE criterion,” IEEE Trans. Commun., vol. 67, no. 5, pp. 3693-3708, May 2019. [Paper]

  4. X. Yu, J. Zhang, and K. B. Letaief, “A hardware-efficient analog network structure for hybrid precoding in millimeter wave systems,” IEEE J. Sel. Topics Signal Process., Special Issue on Hybrid Analog-Digital Signal Processing for Hardware-Efficient Large Scale Antenna Arrays, vol. 12, no. 2, pp. 282-297, May 2018. [Paper] [Codes]

    1. Part of this work was presented at IEEE GLOBECOM 2017, and received a Best Paper Award.

  5. X. Yu, J.-C. Shen, J. Zhang, and K. B. Letaief, “Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems,” IEEE J. Sel. Topics Signal Process., Special Issue on Signal Process. for Millimeter Wave Wireless Communications, vol. 10, no. 3, pp. 485-500, Apr. 2016. [Paper] [Codes] (The 2018 IEEE Signal Processing Society Young Author Best Paper Award)

Sparse Optimization and Estimation for Wireless Networks

  1. Y. Shi, J. Zhang, W. Chen, and K. B. Letaief, “Generalized sparse and low-rank optimization for ultra-dense networks,” IEEE Commun. Mag., vol. 56, no. 6, pp. 42-48, Jun. 2018. [Paper]

  2. J.-C. Shen, J. Zhang, K.-C. Chen, and K. B. Letaief, “High-dimensional CSI acquisition in massive MIMO: Sparsity-inspired approaches,” IEEE Systems Journal, vol. 11, no. 1, pp. 32-40, Mar. 2017. [Paper]

  3. Y. Shi, J. Zhang, and K. B. Letaief, “Low-rank matrix completion for topological interference management by Riemannian pursuit,” IEEE Trans. Wireless Commun., vol. 15, no. 7, pp. 4703-4717, Jul. 2016. [Paper] [Codes]

  4. J.-C. Shen, J. Zhang, E. Alsusa, and K. B. Letaief, “Compressed CSI acquisition in FDD massive MIMO: How much training is needed?” IEEE Trans. Wireless Commun., vol. 15, no. 6, pp. 4145-4156, Jun. 2016. [Paper]

  5. Y. Shi, J. Cheng, J. Zhang, B. Bai, W. Chen and K. B. Letaief, “Smoothed Lp-minimization for green Cloud-RAN with user admission control,” IEEE J. Select. Areas Commun., vol. 34, no. 4, pp. 1022-1036, Apr. 2016. [Paper] [Codes]

  6. Y. Shi, J. Zhang, B. O’Donoghue, and K. B. Letaief, “Large-scale convex optimization for dense wireless cooperative networks,” IEEE Trans. Signal Process., vol. 63, no. 18, pp. 4729-4743, Sept. 2015. [Paper][Codes] (The 2016 IEEE Signal Processing Society Young Author Best Paper Award)

  7. Y. Shi, J. Zhang, and K. B. Letaief, “Group sparse beamforming for green Cloud-RAN,” IEEE Trans. Wireless Commun., vol. 13, no. 5, pp. 2809-2823, May 2014. [Paper] [Codes] (The 2016 Marconi Prize Paper Award)

Wireless Network Analysis via Stochastic Geometry

  1. X. Yu, C. Li, J. Zhang, M. Haenggi, and K. B. Letaief, “A unified framework for the tractable analysis of multi-antenna wireless networks,” IEEE Trans. Wireless Commun., vol. 17, no. 12, pp. 7965-7980, Dec. 2018. [Paper]

  2. X. Yu, J. Zhang, M. Haenggi, and K. B. Letaief, “Coverage analysis for millimeter wave networks: The impact of directional antenna arrays,” IEEE J. Select. Areas Commun, Special Issue on Millimeter Wave Communications for Future Mobile Networks, vol. 35, no. 7, pp. 1498-1512, Jul. 2017. [Paper]

  3. C. Li, J. Zhang, J. G. Andrews, and K. B. Letaief, “Success probability and area spectral efficiency in multiuser MIMO HetNets,” IEEE Trans. Commun., vol. 64, no. 4, pp. 1544-1556, Apr. 2016. [Paper]

  4. C. Li, J. Zhang, M. Haenggi, and K. B. Letaief, “User-centric intercell interference nulling for downlink small cell networks,” IEEE Trans. Commun., vol. 63, no. 4, pp. 1419-1431, Apr. 2015. [Paper]

  5. C. Li, J. Zhang, and K. B. Letaief, “Throughput and energy efficiency analysis of small cell networks with multi-antenna base stations,” IEEE Trans. Wireless Commun., vol. 13, no. 5, pp. 2502-2517, May 2014. [Paper]

Green Communications, Energy Harvesting

  1. Y. Mao, J. Zhang, and K. B. Letaief, “Grid energy consumption and QoS tradeoff in hybrid energy supply wireless networks,” IEEE Trans. Wireless Commun., vol. 15, no. 5, pp. 3573-3586, May 2016. [Paper]

  2. Y. Mao, J. Zhang, and K. B. Letaief, “A Lyapunov optimization approach for green cellular networks with hybrid energy supplies,” IEEE J. Select. Areas Commun. - Series on Green Commun. and Networking, vol. 33, no. 12, pp. 2463-2477, Dec. 2015. [Paper]

  3. Y. Mao, Y. Luo, J. Zhang, and K. B. Letaief, “Energy harvesting small cell networks: Feasibility, deployment and operation,” IEEE Commun. Mag., vol. 53, no. 6, pp. 94-101, Jun. 2015. [Paper] [This work was reported on The Register.]

  4. Y. Luo, J. Zhang, and K. B. Letaief, “Optimal scheduling and power allocation for two-hop energy harvesting communication systems,” IEEE Trans. Wireless Commun., vol. 11, no. 9, pp. 4729-4741, Sept. 2013.

  5. L. Deng, Y. Rui, P. Cheng, J. Zhang, Q. T. Zhang, and M. Li, “A unified energy efficiency and spectral efficiency tradeoff metric in wireless networks,” IEEE Commun. Lett., vol. 17, no. 1, pp. 55-58, Jan. 2013.

MIMO Communications

  1. J. Zhang, M. Kountouris, J. G. Andrews, and R. W. Heath Jr., “Multi-mode transmission for the MIMO broadcast channel with imperfect channel state information,” IEEE Trans. Commun., vol. 59, no. 3, pp. 803-814, Mar. 2011.

  2. J. Zhang and J. G. Andrews, “Adaptive spatial intercell interference cancellation in multicell wireless networks,” IEEE J. Select. Areas Commun. special issue on Cooperative Communications in MIMO Cellular Networks, vol. 28, no. 9, pp. 1455-1468, Dec. 2010.

  3. J. Zhang, R. W. Heath Jr., M. Kountouris, and J. G. Andrews, “Mode switching for the multi-antenna broadcast channel based on delay and channel quantization,” EURASIP Journal on Advances in Signal Processing, special issue on Multiuser Limited Feedback, vol. 2009, Article ID 802548, 15 pages, doi:10.11552009802548, 2009. (The 2014 EURASIP Best Paper Award)

  4. J. Zhang, R. Chen, J. G. Andrews, A. Ghosh, and R. W. Heath Jr., “Networked MIMO with clustered linear precoding,” IEEE Trans. Wireless Commun., vol. 8, no. 4, pp. 1910-1921, Apr. 2009.

  5. J. Zhang and J. G. Andrews, “Distributed antenna systems with randomness,” IEEE Trans. Wireless Commun., vol. 7, no. 9, pp. 3636-3646, Sept. 2008.