ZHANG, Jun

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Assistant Professor
Department of Electronic and Information Engineering (EIE)
The Hong Kong Polytechnic University (PolyU)
Hung Hom, Kowloon, Hong Kong.

Office: Room DE607, EIE
Email: jun-eie.zhang@polyu.edu.hk
Phone: +852 2766-6216
[Google Scholar] [Resume]

What's new

  • (Conference) I am co-chairing the Wireless Communications Symposium of IEEE ICC 2021. [Call for papers]

  • (Workshop) I am organizing a workshop “Wireless Networking Innovations for Mobile Edge Learning” at IEEE ICC 2021, with Sameh Sorour, Hatem Abouzeid, and Yansha Deng. [Call for papers]

  • (Guest Editor) Frontiers Special Issue on Resource Allocation in Cloud-Radio Access Networks and Fog-Radio Access Networks for B5G Systems. [Call for Paper]

  • (Webinar) Invited by IEEE Signal Processing Society, Alex and I gave a webinar “An Algorithmic Investigation of Hybrid Beamforming for 5G and Beyond Networks” on Aug 21. [Slides]

  • (New Paper) “Communication-computation trade-off in resource-constrained edge inference” accepted by IEEE Communication Magazine. [Paper] [GitHub]

  • (New Paper) “Graph neural networks for scalable radio resource management: architecture design and theoretical analysis” accepted by IEEE J. Select. Areas Commun. [Paper]

  • (New Paper) “Complete Dictionary Learning via ell_p-norm Maximization” accepted by Conference on Uncertainty in Artificial Intelligence (UAI) 2020. [Paper]

  • (New Book) “Low-overhead Communications in IoT Networks – Structured Signal Processing Approaches” has been published by Springer. [Book Website] [Simulation Codes]

  • I was recognized as The 2020 AI 2000 Internet of Things (IoT) Most Influential Scholars Honorable Mention (#28 globally).

Research Interests

  • Wireless Communications and Networking (5G and beyond, IoT)

  • Edge AI and Computing (federated learning, communication-efficient edge learning)

  • Data Science and AI (high-dimensional estimation, learning to optimize, graph neural networks)

Introductory Reading

  • Edge AI and Computing

    • Survey paper on mobile edge computing (MEC): “A survey on mobile edge computing: The communication perspective,” IEEE Commun. Surveys Tuts., 2017. [Paper]

    • Wireless communications for edge AI: “Toward an intelligent edge: Wireless communication meets machine learning”, IEEE Commun. Mag., 2020. [Paper]

    • Communication-efficient edge AI: “Communication-efficient edge AI: Algorithms and systems,” IEEE Commun. Surveys Tuts., 2020. [Paper]

    • Communication-computation tradeoff in edge inference: “Communication-computation trade-off in resource-constrained edge inference,” IEEE Commun. Mag., 2020. [Paper] [GitHub]

    • Edge AI for Internet of Vehicles: “Mobile edge intelligence and computing for the Internet of vehicles,” Proc. IEEE, 2020. [Paper]

  • 5G and Beyond Networks

    • Three Tutorials on 5G

      • “Hybrid Beamforming for 5G Millimeter Wave Systems,” IEEE GLOBECOM 2018 Tutorial. [Slides] [Paper]

      • “Tractable Analysis of Large-scale Multi-antenna Wireless Networks via Stochastic Geometry,” WiOpt 2018 Tutorial. [Slides] [Book]

      • “Sparse and Low-Rank Optimization for Dense Wireless Networks: Models, Algorithms and Theory,” IEEE GLOBECOM 2017 Tutorial. [Part I Slides] [Part II Slides] [Paper]

    • Visionary paper on 6G, “The Roadmap to 6G – AI Empowered Wireless Networks,” IEEE Commun. Mag., 2019. [Paper]

Selected Publications

  • Edge AI and Computing

    • J. Shao, H. Zhang, Y, Mao, and J. Zhang, “Branchy-GNN: a device-edge co-inference framework for efficient point cloud processing,” submitted to ICASSP 2021.

    • J. Shao, J. Zhang, “Communication-computation trade-off in resource-constrained edge inference,” IEEE Commun. Mag., to appear. [Paper] [GitHub]

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

    • L. Liu, J. Zhang, S.H. Song, and K. B. Letaief, “Client-edge-cloud hierarchical federated learning,” in Proc. IEEE Int. Conf. Commun. (ICC), Dublin, Ireland, Jun. 2020. [Paper]

    • D. Liu, G. Zhu, Q. Zeng, J. Zhang, and K. Huang, “Wireless data acquisition for edge learning: Data-importance aware retransmission,” IEEE Trans. Wireless Commun., to appear. [Paper]

    • D. Liu, G. Zhu, J. Zhang, and K. Huang, “Data-importance aware User scheduling for communication-efficient edge machine learning,” IEEE Trans. Cognitive Commun. and Networking, to appear. [Paper]

    • 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)

  • Data Science, AI

    • 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, to appear. [Paper]

    • 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]

    • J. Dong, J. Zhang, Y. Shi, and J. Wang, “Faster activity and data detection in massive random access: A multi-armed bandit approach,” submitted. [Paper]

    • 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]

  • Wireless Communications

    • 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., to appear. [Paper]

    • 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]

    • 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)

    • 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)

    • 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)

    • 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]