Dr. Quang Vinh Do

Education

• Ph.D. in Electrical Engineering, University of Ulsan, Ulsan City, South Korea, 2017 – 2020.
• M.E. in Electronic and Computer Engineering, RMIT University, Melbourne, Australia, 2011 – 2013.
• B.E. in Electrical and Electronics Engineering, Ho Chi Minh City University of Technology, Vietnam, 2004 – 2009.

Professional experience

• 2009 – 2011: Operation and Maintenance Engineer, Vietnam Telecom Service Company (Vinaphone), Vietnam.
• 2013 – 2014: Test Engineer, Jabil Vietnam Company Limited, Vietnam.
• 2014 – 2017: Lecturer, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Vietnam.
• 2020 – 2021: Postdoctoral Research Fellow, Multimedia Communication Systems Laboratory, University of Ulsan, South Korea.
• 2021 – 2023: Postdoctoral Research Fellow, Artificial Intelligence Research Center, Pusan National University, South Korea.
• 2023 – Present: Lecturer, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Vietnam.

Areas of Interest

Deep learning, deep reinforcement learning, energy harvesting, radio resource management, cognitive radio network, mobile edge computing, unmanned aerial vehicle, non-orthogonal multiple access, and reconfigurable intelligent surface

Publication

Do Q.V., Minh B.V., Nguyen Q.-S., Kim B.-S., "Analysis of symbiotic backscatter empowered wireless sensors network with short-packet communications" (2024), PLoS ONE, 19 (8), art. no. e0307366, DOI: 10.1371/journal.pone.0307366.

Jeong S.-G., Do Q.V., Hwang W.-J., "Short-term photovoltaic power forecasting based on hybrid quantum gated recurrent unit"
(2024), ICT Express, 10 (3), pp. 608 - 613, DOI: 10.1016/j.icte.2023.12.005.

Le M., Pham Q., Do Q.V., Han Z., Hwang W., "Resource Allocation in THz-NOMA-Enabled HAP Systems: A Deep Reinforcement Learning Approach" (2024), IEEE Transactions on Consumer Electronics, pp. 1-1, DOI: 10.1109/TCE.2024.3420718.

Jeong S.-G., Do Q.-V., Hwang H.-J., Hasegawa M., Sekiya H., Hwang W.-J., "Hybrid Quantum Convolutional Neural Networks for UWB Signal Classification" (2023), IEEE Access, 11, pp. 113726 - 113739, DOI: 10.1109/ACCESS.2023.3323019.

Nguyen Doan H., Nguyen Xuan T., Vinh Do Q., Won Joo H., Sang-Hwa C., Jong-Deok K., "Graph Neural Network-based Federated Learning for Sum-rate Maximization in Small-cell Wireless Network" (2023), ACM International Conference Proceeding Series, pp. 243 - 247, DOI: 10.1145/3628797.3628826.

Jeong S.-G., Do Q.-V., Hwang H.-J., Hasegawa M., Sekiya H., Hwang W.-J., "UWB NLOS/LOS Classification Using Hybrid Quantum Convolutional Neural Networks" (2023), 2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023, DOI: 10.1109/ICCE-Asia59966.2023.10326359.

Nguyen M.-D., Luong H.-S., Tung-Nguyen, Pham Q.-V., Do Q.V., Hwang W.-J., "FFD: A Full-Stack Federated Distillation method for Heterogeneous Massive IoT Networks" (2022), International Conference on Advanced Technologies for Communications, 2022-October, pp. 326 - 331, DOI: 10.1109/ATC55345.2022.9943034

Do Q.V., Pham Q.-V., Hwang W.-J., "Deep Reinforcement Learning for Energy-Efficient Federated Learning in UAV-Enabled Wireless Powered Networks" (2022), IEEE Communications Letters, 26 (1), pp. 99 - 103, DOI: 10.1109/LCOMM.2021.3122129.

Do Q.V., Koo I., "A Transfer Deep Q-Learning Framework for Resource Competition in Virtual Mobile Networks with Energy-Harvesting Base Stations" (2021), IEEE Systems Journal, 15 (1), art. no. 8943313, pp. 319 - 330, DOI: 10.1109/JSYST.2019.2958993.

Vinh Do Q., Koo I., "Deep Reinforcement Learning Based Dynamic Spectrum Competition in Green Cognitive Virtualized Networks" (2021), IEEE Access, 9, art. no. 9391658, pp. 52193 - 52201, DOI: 10.1109/ACCESS.2021.3069969.

Tuan P.V., Son P.N., Duy T.T., Nguyen S.Q., Ngo V.Q.B., Quang D.V., Koo I., "Optimizing a secure two-way network with non-linear SWIPT, channel uncertainty, and a hidden eavesdropper" (2020), Electronics (Switzerland), 9 (8), art. no. 1222, pp. 1 - 24, DOI: 10.3390/electronics9081222.

Do Q.V., Koo I., "Actor-critic deep learning for efficient user association and bandwidth allocation in dense mobile networks with green base stations" (2019), Wireless Networks, 25 (8), pp. 5057 - 5068, DOI: 10.1007/s11276-019-02117-0.

Do Q.V., Vu V.H., Koo I., "An efficient bandwidth allocation scheme for hierarchical cellular networks with energy harvesting: an actor-critic approach" (2019), International Journal of Electronics, 106 (10), pp. 1543 - 1566, DOI: 10.1080/00207217.2019.1600740.

Do Q.V., Hoan T.-N.-K., Koo I., "Optimal Power Allocation for Energy-Efficient Data Transmission Against Full-Duplex Active Eavesdroppers in Wireless Sensor Networks" (2019), IEEE Sensors Journal, 19 (13), art. no. 8665905, pp. 5333 - 5346, DOI: 10.1109/JSEN.2019.2904523.

Do Q.V., Koo I., "Dynamic Bandwidth Allocation Scheme for Wireless Networks with Energy Harvesting Using Actor-Critic Deep Reinforcement Learning" (2019), 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019, art. no. 8669048, pp. 138 - 142, DOI: 10.1109/ICAIIC.2019.8669048.

Do V.Q., Koo I., "Learning frameworks for cooperative spectrum sensing and energy-efficient data protection in cognitive radio networks" (2018), Applied Sciences (Switzerland), 8 (5), art. no. 722, DOI: 10.3390/app8050722.

Do-Vinh Q., Hoan T.-N.-K., Koo I., "Energy-Efficient Data Encryption Scheme for Cognitive Radio Networks" (2018), IEEE Sensors Journal, 18 (5), art. no. 8253508, pp. 2050 - 2059, DOI: 10.1109/JSEN.2018.2791563

Vinh L.T., Anh N.D.Q., Nhan N.H.K., Duy V.H., Tam N.K., Quang D.V., Hong N.V., "Simulation study of void aggregations in amorphous ZnO" (2017), Lecture Notes in Electrical Engineering, 415 LNEE, pp. 400 - 408, DOI: 10.1007/978-3-319-50904-4_42.

Bao N.Q., Minh T.H.Q., Quang D.V., Anh N.D.Q., Thao N.T.P., "Influence of green phosphor Ce0.67Tb0.33MgAl11O19:Ce,Tb on the luminescent properties and correlated color temperature deviation of multi-chip white leds" (2017), Lecture Notes in Electrical Engineering, 415 LNEE, pp. 409 - 413, DOI: 10.1007/978-3-319-50904-4_43.