(>>for a full publication list of our group in chronological order, click here for journal papers and here for conference papers)
Underlined authors are group members, alumni or past visitors.
[30] W.H. Xue, Y.L. Yuan, and D.H.K. Tsang, “Enhancing Fog Computing through Intelligent Reflecting Surface Assistance: A Lyapunov Driven Reinforcement Learning Approach”, IEEE World Forum on Internet of Things, Montreal, Canada, November 2024.
[29] T.L. Zhou, Z.H. Lin, J. Zhang, and D.H.K. Tsang, “Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data”, IEEE Transactions on Mobile Computing, accepted for publication, May 2024.
[28] C.Y. Xia, H.Y. Guo, H.Y. Ma, D.H.K. Tsang, and V.K.N. Lau, “Multi-resolution Model Compression for Deep Neural Networks: A Variational Bayesian Approach”, IEEE Transactions on Signal Processing, accepted for publication, Vol. 72, pp.1944-1959, April 2024.
[27] J.D. Yu, A.Alhilal, T.L. Zhou, P. Hui, and D.H.K. Tsang, “Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality”, IEEE Transactions on Wireless Communications, accepted for publication, March 2024.
[26] J.D. Yu, A.Alhilal, T.L.Zhou, P. Hui, and D.H.K. Tsang, “Energy Efficient IRSAssisted NOMA Aided Mobile Edge Computing via Heterogeneous Multi-Agent Reinforcement Learning”, IEEE Globecom, Kuala Lumpur, Malaysia, December 2023.
[25] C.Y. Xia, D.H.K. Tsang, and V.K.N. Lau, “Structured Bayesian Federated Learning for Green AI: A Decentralized Model Compression Using Turbo-VBI Based Approach”, IEEE Internet of Things Journal, accepted for publication, November 2023.
[24] T.L. Zhou, J. Zhang, and D.H.K. Tsang, “FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data”, IEEE Transactions on Mobile Computing, accepted for publication, October 2023.
[23] J.D. Yu, A.Alhilal, P. Hui, and D.H.K. Tsang, “Bi-directional Digital Twin and Edge Computing in the Metaverse”, IEEE Internet of Things Magazine, accepted for publication, September 2023.
[22] J.D. Yu, Y. Li, X.L. Liu, B. Sun, Y. Wu, and D.H.K. Tsang, “Energy Efficient IRS-Assisted NOMA Aided Mobile Edge Computing via Heterogeneous Multi-Agent Reinforcement Learning”, IEEE ICC, Rome, Italy, May 2023.
[21] X.L. Liu, J.D. Yu, Y.W. Liu, Y. Gao, T. Mahmoodi, S. Lambotharan, and D.H.K. Tsang, “Distributed Intelligence in Wireless Networks”, IEEE Open Journal of the Communications Society, accepted for publication, March 2023.
[20] C.Y. Xia, D.H.K. Tsang, V.K.N. Lau, “Structured Bayesian Compression for Deep Neural Networks Based on the Turbo VBI Approach”, IEEE Transactions on Signal Processing, vol. 71, pp. 670-685, March 2023.
[19] J.D. Yu, Y. Li, X.L. Liu, B. Sun, Y. Wu, D.H.K. Tsang, “IRS Assisted NOMA Aided Mobile Edge Computing with Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning”, IEEE Transactions on Wireless Communications, accepted for publication, November 2022.
[18] B. Sun, Y. Jiang, Y. Wu, Q. Ye, and D.H.K. Tsang, “Performance Analysis of Mobile Cloud Computing with Bursty Demand: A Tandem Queue Model”, IEEE Transactions on Vehicular Technology, Vol. 71, Issue 9, September 2022.
[17] L. Liu, B. Sun, Y. Wu, and D.H.K. Tsang, “Latency Optimization for Computation Offloading with Hybrid NOMA-OMA Transmission”, IEEE Internet of Things Journal, Vol. 8, Issue 8, pp.6677-6691, April 2021.
[16] L. Liu, B. Sun, X. Tan, and D.H.K. Tsang, “Energy-efficient Resource Allocation and Sub-channel Assignment for NOMA-enabled Multi-access Edge Computing”, IEEE Systems Journal, Vol. 16, Issue 1, pp. 1558-1569, March 2022.
[15] Y. Jiang, B. Sun, and D.H.K. Tsang, “Not Taken for Granted: Configuring Scalable Live Video Streaming Under Throughput Fluctuations in Mobile Edge Networks”, IEEE Transactions on Vehicular Technology, Vol. 70, Issue 3, pp.2771-2782, March 2021.
[14] Y. Jiang, M. Shahrad, D. Wentzlaff, D. H. K. Tsang, and C. Joe-Wong, “Burstable Instances for Clouds: Performance Modeling, Equilibrium Analysis, and Revenue Maximization,” IEEE Transactions on Networking, Vol. 28, No. 6, pp.2489-2502, December 2020.
[13] B. Sun, Y. Jiang, and D.H.K. Tsang, “When Burstable Instances Meet Mobile Computing: Performance Modeling and Economic Analysis,” in Proceedings of IEEE International Conference on Distributed Computing Systems (IEEE ICDCS), Poster, Singapore, December 2020.
[12] Y. Jiang, Z. Huang, and D.H.K. Tsang, “On Power-Peak-Aware Scheduling for Large-Scale Shared Clusters“, IEEE Transactions on Big Data, vol. 6, no. 2, pp. 412-426, June 2020.
[11] Y. Jiang, M. Shahrad, D. Wentzlaff, D.H.K. Tsang, and C. Joe-Wong, “Burstable Instances for Clouds: Performance Modeling, Equilibrium Analysis, and Revenue Maximization“, in Proceedings of the 38th IEEE International Conference on Computer Communications (INFOCOM), Paris, France, April 2019.
[10] Y. Jiang, and D.H.K. Tsang, “Delay-Aware Task Offloading in Shared Fog Networks“, IEEE Internet of Things Journal, vol. 5, no. 6, pp. 4945-4956, December 2018.
[9] Y. Jiang, Z. Huang, and D.H.K. Tsang, “Challenges and Solutions in Fog Computing Orchestration“, IEEE Network, vol. 32, no. 3, pp. 122-129, May 2018.
[8] Y. Jiang, Z. Huang, and D.H.K. Tsang, “Towards Max-Min Fair Resource Allocation for Stream Big Data Analytics in Shared Clouds”, IEEE Transactions on Big Data, vol. 4, no. 1, pp. 130-137, March 2018.
[7] Z. Huang, and D.H.K. Tsang, “M-convex VM Consolidation: Towards a Better VM Workload Consolidation“, IEEE Transactions on Cloud Computing, Vol. 4, No. 4, pp. 415-428, October 2016.
[6] Z. Huang, B. Balasubramanian, M. Wang, T. Lan, M. Chiang, and D.H.K. Tsang, “RUSH: A RobUst ScHeduler to Manage Uncertain Completion-Times in Shared Clouds“, in Proceedings of the 36th IEEE International Conference on Distributed Computing Systems (ICDCS), Nara, Japan, June 2016.
[5] Z. Huang, B. Balasubramanian, M. Wang, T. Lan, M. Chiang, and D.H.K. Tsang, “Need for Speed: CORA Scheduler for Optimizing Completion-Times in the Cloud”, in Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), Hong Kong, April 2015.
[4] Y. Jiang, Z. Huang, and D.H.K. Tsang, “Do You Feel the Lag of Your Hadoop?“, in Proceedings of the 1st IEEE International Conference on Big Data Computing Service and Applications (BigDataService), Redwood City, CA, March 2015.
[3] L. Chen, S. Hu, K. Chen, H. Wu, and D.H.K. Tsang, “Towards Minimal-Delay Deadline-Driven Data Center TCP“, in Proceedings of the Twelfth ACM Workshop on Hot Topics in Networks (HotNets), November 2013.
[2] Z. Huang, and D.H.K. Tsang, and J. She, “A virtual machine consolidation framework for MapReduce enabled computing clouds“, in Proceedings of the 24th International Teletraffic Congress (ITC 24), 2012, pp.1-8, 4-7 September 2012
[1] Z. Huang, and D.H.K. Tsang, “SLA guaranteed virtual machine consolidation for computing clouds“, in Proceedings of the IEEE International Conference on Communications (ICC), 2012, pp.1314-1319, 10-15 June 2012
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