Publications
2024
Generative AI for Physical Layer Communications: A Survey
Van Huynh, N., Wang, J., Du, H., Hoang, D. T., Niyato, D., Nguyen, D. N., . . . Letaief, K. B. (2024). Generative AI for Physical Layer Communications: A Survey. IEEE Transactions on Cognitive Communications and Networking, 10(3), 706-728. doi:10.1109/tccn.2024.3384500
A Universal Deep Neural Network for Signal Detection in Wireless Communication Systems
Albagami, K., Van Huynh, N., & Ye Li, G. (2024). A Universal Deep Neural Network for Signal Detection in Wireless Communication Systems. In 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN) (pp. 1-6). IEEE. doi:10.1109/icmlcn59089.2024.10624797
Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey
Vu, T. -H., Jagatheesaperumal, S. K., Nguyen, M. -D., Huynh, N. V., Kim, S., & Pham, Q. -V. (2024). Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey. IEEE Internet of Things Journal, 1. doi:10.1109/jiot.2024.3487627
Countering Eavesdroppers With Meta- Learning-Based Cooperative Ambient Backscatter Communications
Chu, N. H., Huynh, N. V., Nguyen, D. N., Hoang, D. T., Gong, S., Shu, T., . . . Phan, K. T. (2024). Countering Eavesdroppers With Meta- Learning-Based Cooperative Ambient Backscatter Communications. IEEE Transactions on Wireless Communications, 23(10), 13678-13693. doi:10.1109/twc.2024.3403964
DDPG-E2E: A Novel Policy Gradient Approach for End-to-End Communication Systems
Zhang, B., Huynh, N. V., Hoang, D. T., Nguyen, D. N., & Pham, Q. -V. (2024). DDPG-E2E: A Novel Policy Gradient Approach for End-to-End Communication Systems. IEEE Transactions on Cognitive Communications and Networking, 1. doi:10.1109/tccn.2024.3485648
Emerging Technologies for 6G Non-Terrestrial-Networks: From Academia to Industrial Applications
Nguyen, C. T., Saputra, Y. M., Huynh, N. V., Nguyen, T. N., Hoang, D. T., Nguyen, D. N., . . . Tran, D. -H. (2024). Emerging Technologies for 6G Non-Terrestrial-Networks: From Academia to Industrial Applications. IEEE Open Journal of the Communications Society, 5, 3852-3885. doi:10.1109/ojcoms.2024.3418574
Machine Learning for Smart Healthcare Management Using IoT
Yigit, Y., Duran, K., Moradpoor, N., Maglaras, L., Van Huynh, N., & Canberk, B. (2024). Machine Learning for Smart Healthcare Management Using IoT. In Studies in Computational Intelligence (pp. 135-166). Springer Nature Singapore. doi:10.1007/978-981-97-5624-7_4
2023
Deep Deterministic Policy Gradient for End-to-End Communication Systems without Prior Channel Knowledge
Zhang, B., & Van Huynh, N. (2023). Deep Deterministic Policy Gradient for End-to-End Communication Systems without Prior Channel Knowledge. In GLOBECOM 2023 - 2023 IEEE Global Communications Conference (pp. 5677-5682). IEEE. doi:10.1109/globecom54140.2023.10436824
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet
Xu, K., Van Huynh, N., & Li, G. Y. (2023). Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet. IEEE Transactions on Communications, 71(10), 5893-5903. doi:10.1109/tcomm.2023.3300331
Joint Speed Control and Energy Replenishment Optimization for UAV-Assisted IoT Data Collection With Deep Reinforcement Transfer Learning
Chu, N. H., Hoang, D. T., Nguyen, D. N., Van Huynh, N., & Dutkiewicz, E. (2023). Joint Speed Control and Energy Replenishment Optimization for UAV-Assisted IoT Data Collection With Deep Reinforcement Transfer Learning. IEEE Internet of Things Journal, 10(7), 5778-5793. doi:10.1109/jiot.2022.3151201
Defeating Eavesdroppers with Ambient Backscatter Communications
Huynh, N. V., Quang Hieu, N., Chu, N. H., Nguyen, D. N., Hoang, D. T., & Dutkiewicz, E. (2023). Defeating Eavesdroppers with Ambient Backscatter Communications. In 2023 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6). IEEE. doi:10.1109/wcnc55385.2023.10118774
Deep Reinforcement Learning for Wireless Communications and Networking
Hoang, D. T., Huynh, N. V., Nguyen, D. N., Hossain, E., & Niyato, D. (2023). Deep Reinforcement Learning for Wireless Communications and Networking. Wiley. doi:10.1002/9781119873747
2022
Transfer Learning for Signal Detection in Wireless Networks
Van Huynh, N., & Li, G. Y. (2022). Transfer Learning for Signal Detection in Wireless Networks. IEEE Wireless Communications Letters, 11(11), 2325-2329. doi:10.1109/lwc.2022.3202117
Defeating Super-Reactive Jammers With Deception Strategy: Modeling, Signal Detection, and Performance Analysis
Van Huynh, N., Nguyen, D. N., Hoang, D. T., Vu, T. X., Dutkiewicz, E., & Chatzinotas, S. (2022). Defeating Super-Reactive Jammers With Deception Strategy: Modeling, Signal Detection, and Performance Analysis. IEEE Transactions on Wireless Communications, 21(9), 7374-7390. doi:10.1109/twc.2022.3158189
Transfer Learning for Wireless Networks: A Comprehensive Survey
Nguyen, C. T., Van Huynh, N., Chu, N. H., Saputra, Y. M., Hoang, D. T., Nguyen, D. N., . . . Hwang, W. -J. (2022). Transfer Learning for Wireless Networks: A Comprehensive Survey. Proceedings of the IEEE, 110(8), 1073-1115. doi:10.1109/jproc.2022.3175942
Joint Coding and Scheduling Optimization for Distributed Learning Over Wireless Edge Networks
Van Huynh, N., Hoang, D. T., Nguyen, D. N., & Dutkiewicz, E. (2022). Joint Coding and Scheduling Optimization for Distributed Learning Over Wireless Edge Networks. IEEE Journal on Selected Areas in Communications, 40(2), 484-498. doi:10.1109/jsac.2021.3118432
2021
DeepFake: Deep Dueling-Based Deception Strategy to Defeat Reactive Jammers
Van Huynh, N., Hoang, D. T., Nguyen, D. N., & Dutkiewicz, E. (2021). DeepFake: Deep Dueling-Based Deception Strategy to Defeat Reactive Jammers. IEEE Transactions on Wireless Communications, 20(10), 6898-6914. doi:10.1109/twc.2021.3078439
Time Scheduling and Energy Trading for Heterogeneous Wireless-Powered and Backscattering-Based IoT Networks
Nguyen, N. -T., Nguyen, D. N., Hoang, D. T., Van Huynh, N., Dutkiewicz, E., Nguyen, N. -H., & Nguyen, Q. -T. (2021). Time Scheduling and Energy Trading for Heterogeneous Wireless-Powered and Backscattering-Based IoT Networks. IEEE Transactions on Wireless Communications, 20(10), 6835-6851. doi:10.1109/twc.2021.3077018
Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach
Van Huynh, N., Nguyen, D. N., Hoang, D. T., & Dutkiewicz, E. (2021). Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach. IEEE Transactions on Communications, 69(9), 5948-5961. doi:10.1109/tcomm.2021.3088305
Defeating Reactive Jammers with Deep Dueling-based Deception Mechanism
Van Huynh, N., Nguyen, D. N., Hoang, D. T., & Dutkiewicz, E. (2021). Defeating Reactive Jammers with Deep Dueling-based Deception Mechanism. In ICC 2021 - IEEE International Conference on Communications (pp. 1-6). IEEE. doi:10.1109/icc42927.2021.9500391
Fast or Slow: An Autonomous Speed Control Approach for UAV-assisted IoT Data Collection Networks
Chu, N. H., Hoang, D. T., Nguyen, D. N., Huynh, N. V., & Dutkiewicz, E. (2021). Fast or Slow: An Autonomous Speed Control Approach for UAV-assisted IoT Data Collection Networks. In 2021 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6). IEEE. doi:10.1109/wcnc49053.2021.9417563
Dynamic Optimal Coding and Scheduling for Distributed Learning over Wireless Edge Networks
Van Huynh, N., Hoang, D. T., Nguyen, D. N., & Dutkiewicz, E. (2021). Dynamic Optimal Coding and Scheduling for Distributed Learning over Wireless Edge Networks. In 2021 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE. doi:10.1109/globecom46510.2021.9685719
2020
Optimal Beam Association in mmWave Vehicular Networks with Parallel Reinforcement Learning
Van Huynh, N., Nguyen, D. N., Hoang, D. T., & Dutkiewicz, E. (2020). Optimal Beam Association in mmWave Vehicular Networks with Parallel Reinforcement Learning. In GLOBECOM 2020 - 2020 IEEE Global Communications Conference. IEEE. doi:10.1109/globecom42002.2020.9348240
Time Scheduling and Energy Trading for Heterogeneous Wireless-Powered and Backscattering-based IoT Networks
Defeating Smart and Reactive Jammers with Unlimited Power
Huynh, N. V., Hoang, D. T., Nguyen, D. N., Dutkiewicz, E., & Mueck, M. (2020). Defeating Smart and Reactive Jammers with Unlimited Power. In 2020 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6). IEEE. doi:10.1109/wcnc45663.2020.9120650
Enabling and Emerging Technologies for Social Distancing: A Comprehensive Survey and Open Problems
Ambient Backscatter Communication Networks
Hoang, D. T., Niyato, D., Kim, D. I., Huynh, N. V., & Gong, S. (2020). Ambient Backscatter Communication Networks. Cambridge University Press. doi:10.1017/9781108691383
Ambient Backscatter: A Novel Method to Defend Jamming Attacks for Wireless Networks
Van Huynh, N., Nguyen, D. N., Thai Hoang, D., Dutkiewicz, E., & Mueck, M. (2020). Ambient Backscatter: A Novel Method to Defend Jamming Attacks for Wireless Networks. IEEE Wireless Communications Letters, 9(2), 175-178. doi:10.1109/lwc.2019.2947417
Defeating Jamming Attacks with Ambient Backscatter Communications
Huynh, N. V., Nguyen, D. N., Hoang, D. T., Dutkiewicz, E., Mueck, M., & Srikanteswara, S. (2020). Defeating Jamming Attacks with Ambient Backscatter Communications. In 2020 International Conference on Computing, Networking and Communications (ICNC) (pp. 405-409). IEEE. doi:10.1109/icnc47757.2020.9049826
A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing-Part I: Fundamentals and Enabling Technologies.
Nguyen, C. T., Saputra, Y. M., Huynh, N. V., Nguyen, N. -T., Khoa, T. V., Tuan, B. M., . . . Ottersten, B. (2020). A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing-Part I: Fundamentals and Enabling Technologies.. IEEE access : practical innovations, open solutions, 8, 153479-153507. doi:10.1109/access.2020.3018140
A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing-Part II: Emerging Technologies and Open Issues.
Nguyen, C. T., Saputra, Y. M., Van Huynh, N., Nguyen, N. -T., Khoa, T. V., Tuan, B. M., . . . Ottersten, B. (2020). A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing-Part II: Emerging Technologies and Open Issues.. IEEE access : practical innovations, open solutions, 8, 154209-154236. doi:10.1109/access.2020.3018124
Energy Trading and Time Scheduling for Energy-Efficient Heterogeneous Low-Power IoT Networks
Nguyen, N. -T., Nguyen, D. N., Hoang, D. T., Van Huynh, N., Nguyen, H. -N., Nguyen, Q. T., & Dutkiewicz, E. (2020). Energy Trading and Time Scheduling for Energy-Efficient Heterogeneous Low-Power IoT Networks. In GLOBECOM 2020 - 2020 IEEE Global Communications Conference (pp. 1-6). IEEE. doi:10.1109/globecom42002.2020.9322418
2019
“Jam Me If You Can:” Defeating Jammer With Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications
Van Huynh, N., Nguyen, D. N., Hoang, D. T., & Dutkiewicz, E. (2019). “Jam Me If You Can:” Defeating Jammer With Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications. IEEE Journal on Selected Areas in Communications, 37(11), 2603-2620. doi:10.1109/jsac.2019.2933889
Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System With Online Reinforcement Learning
Van Huynh, N., Hoang, D. T., Nguyen, D. N., Dutkiewicz, E., Niyato, D., & Wang, P. (2019). Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System With Online Reinforcement Learning. IEEE Transactions on Communications, 67(8), 5736-5752. doi:10.1109/tcomm.2019.2913871
Optimal and Fast Real-Time Resource Slicing With Deep Dueling Neural Networks
Van Huynh, N., Thai Hoang, D., Nguyen, D. N., & Dutkiewicz, E. (2019). Optimal and Fast Real-Time Resource Slicing With Deep Dueling Neural Networks. IEEE Journal on Selected Areas in Communications, 37(6), 1455-1470. doi:10.1109/jsac.2019.2904371
Energy Management and Time Scheduling for Heterogeneous IoT Wireless-Powered Backscatter Networks
Nguyen, N. -T., Van Huynh, N., Hoang, D. T., Nguyen, D. N., Nguyen, N. -H., Nguyen, Q. -T., & Dutkiewicz, E. (2019). Energy Management and Time Scheduling for Heterogeneous IoT Wireless-Powered Backscatter Networks. In ICC 2019 - 2019 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE. doi:10.1109/icc.2019.8761314
Real-Time Network Slicing with Uncertain Demand: A Deep Learning Approach
Van Huynh, N., Hoang, D. T., Nguyen, D. N., & Dutkiewicz, E. (2019). Real-Time Network Slicing with Uncertain Demand: A Deep Learning Approach. In ICC 2019 - 2019 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE. doi:10.1109/icc.2019.8761907
2018
Ambient Backscatter Communications: A Contemporary Survey
Van Huynh, N., Hoang, D. T., Lu, X., Niyato, D., Wang, P., & Kim, D. I. (2018). Ambient Backscatter Communications: A Contemporary Survey. IEEE Communications Surveys & Tutorials, 20(4), 2889-2922. doi:10.1109/comst.2018.2841964
Optimal Time Scheduling for Wireless-Powered Backscatter Communication Networks
Van Huynh, N., Hoang, D. T., Niyato, D., Wang, P., & Kim, D. I. (2018). Optimal Time Scheduling for Wireless-Powered Backscatter Communication Networks. IEEE Wireless Communications Letters, 7(5), 820-823. doi:10.1109/lwc.2018.2827983
Offloading Energy Efficiency with Delay Constraint for Cooperative Mobile Edge Computing Networks
Vu, T. T., Huynh, N. V., Hoang, D. T., Nguyen, D. N., & Dutkiewicz, E. (2018). Offloading Energy Efficiency with Delay Constraint for Cooperative Mobile Edge Computing Networks. In 2018 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE. doi:10.1109/glocom.2018.8647856
Reinforcement Learning Approach for RF-Powered Cognitive Radio Network with Ambient Backscatter
Huynh, N. V., Hoang, D. T., Nguyen, D. N., Dutkiewicz, E., Niyato, D., & Wang, P. (2018). Reinforcement Learning Approach for RF-Powered Cognitive Radio Network with Ambient Backscatter. In 2018 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE. doi:10.1109/glocom.2018.8647862
2017
Joint network embedding and server consolidation for energy–efficient dynamic data center virtualization
Nam, T. M., Thanh, N. H., Hieu, H. T., Manh, N. T., Huynh, N. V., & Tuan, H. D. (2017). Joint network embedding and server consolidation for energy–efficient dynamic data center virtualization. Computer Networks, 125, 76-89. doi:10.1016/j.comnet.2017.06.007
Physical-virtual topological visualization of OF@TEIN SDN-enabled multi-site cloud
Usman, M., Risdianto, A. C., Jungsu Han., Kim, J., & Nguyen Van Huynh. (2017). Physical-virtual topological visualization of OF@TEIN SDN-enabled multi-site cloud. In 2017 International Conference on Information Networking (ICOIN) (pp. 622-624). IEEE. doi:10.1109/icoin.2017.7899571
Reducing Middle Nodes Mapping Algorithm for Energy Efficiency in Network Virtualization
Nam, T. M., Huynh, N. V., & Thanh, N. H. (2017). Reducing Middle Nodes Mapping Algorithm for Energy Efficiency in Network Virtualization. In Advances in Intelligent Systems and Computing (pp. 500-509). Springer International Publishing. doi:10.1007/978-3-319-49073-1_54
2016
An Energy-Aware Embedding Algorithm for Virtual Data Centers
Nam, T. M., Huynh, N. V., Dai, L. Q., & Thanh, N. H. (2016). An Energy-Aware Embedding Algorithm for Virtual Data Centers. In 2016 28th International Teletraffic Congress (ITC 28) (pp. 18-25). IEEE. doi:10.1109/itc-28.2016.112
2015
A generalized resource allocation framework in support of multi-layer virtual network embedding based on SDN
Nguyen, H. T., Vu, A. V., Nguyen, D. L., Nguyen, V. H., Tran, M. N., Ngo, Q. T., . . . Magedanz, T. (2015). A generalized resource allocation framework in support of multi-layer virtual network embedding based on SDN. Computer Networks, 92, 251-269. doi:10.1016/j.comnet.2015.09.042
Constructing Energy-Aware Software-Defined Network Virtualization
Tran, N. M., Nguyen, T. H., Nguyen, V. H., Kim, L. B., Nguyen, L. D., Nguyen, H. V., & Nguyen, C. V. (n.d.). Constructing Energy-Aware Software-Defined Network Virtualization. Proceedings of the Asia-Pacific Advanced Network, 40(0), 14. doi:10.7125/40.3