Quang Vinh Do

Quang Vinh Do

Lecturer

Journals

  1. M. Le, Q. -V. Pham, Q. V. Do, Z. Han and W. -J. Hwang, “Resource Allocation in THz-NOMA-Enabled HAP Systems: A Deep Reinforcement Learning Approach,” in IEEE Transactions on Consumer Electronics, doi: 10.1109/TCE.2024.3420718.
  2. Tsukumo Fujita, Aohan Li, Quang Vinh Do, Teppei Otsuka, Seon-Geun Jeong, Won-Joo Hwang, Hiroki Takesue, Kensuke Inaba, Kazuyuki Aihara, Mikio Hasegawa, “Applying Coherent Ising Machines for Enhancing Communication Efficiency in Large-Scale UAV-Aided Networks,” in IEEE Access, doi: 10.1109/ACCESS.2024.3450539.
  3. Q. V. Do, Q. -V. Pham and W. -J. Hwang, “Deep Reinforcement Learning for Energy-Efficient Federated Learning in UAV-Enabled Wireless Powered Networks,” in IEEE Communications Letters, vol. 26, no. 1, pp. 99-103, Jan. 2022. (pdf)
  4. Q. V. Do and I. Koo, “Deep Reinforcement Learning Based Dynamic Spectrum Competition in Green Cognitive Virtualized Networks,” in IEEE Access, vol. 9, pp. 52193-52201, Mar. 2021. (pdf)
  5. Q. V. Do and I. Koo, “A Transfer Deep Q-Learning Framework for Resource Competition in Virtual Mobile Networks With Energy-Harvesting Base Stations,” in IEEE Systems Journal, vol. 15, no. 1, pp. 319-330, Mar. 2021. (pdf)
  6. Viet Tuan, P.; Ngoc Son, P.; Trung Duy, T.; Nguyen, S.Q.; Ngo, V.Q.B.; Q. V. Do; Koo, I. Optimizing a Secure Two-Way Network with Non-Linear SWIPT, Channel Uncertainty, and a Hidden Eavesdropper. Electronics, vol. 9, no. 8, p. 1222, Jul. 2020. (pdf)
  7. Q. V. Do, and Insoo Koo, “Actor-critic deep learning for efficient user association and bandwidth allocation in dense mobile networks with green base stations,” in Wireless Networks, Nov. 2019. (pdf)
  8. Q. V. Do, T. N. K. Hoan and I. Koo, “Optimal Power Allocation for Energy-efficient Data Transmission Against Full-duplex Active Eavesdroppers in Wireless Sensor Networks,” in IEEE Sensors Journal, vol. 19, no. 13, pp. 5333-5346, Jul. 2019. (pdf)
  9. Q. V. Do, V. H. Vu & I. Koo (2019) An efficient bandwidth allocation scheme for hierarchical cellular networks with energy harvesting: an actor-critic approach, International Journal of Electronics, vol. 106, no. 10, pp. 1543-1566, Apr. 2019. (pdf)
  10. Q. V. Do and I. Koo, “Learning Frameworks for Cooperative Spectrum Sensing and Energy-efficient Data Protection in Cognitive Radio Networks,” Applied Science, vol. 8, no. 5, p.722, May 2018. (pdf)
  11. Q. V. Do, T.-N.-K. Hoan, and I. Koo, “Energy-Efficient Data Encryption Scheme for Cognitive Radio Networks,” in IEEE Sensors Journal, vol. 18, no. 5, pp. 2050-2059, Mar. 2018. (pdf)
  12. Q. V. Do, I. Koo, “FPGA Implementation of LSB-Based Steganography,” Journal of Information and Communication Convergence Engineering, vol. 15, no. 3, pp. 151-159, Sep. 2017. (pdf)

Conferences

  1. S. -G. Jeong, Q. V. Do, H. -J. Hwang, M. Hasegawa, H. Sekiya and W. -J. Hwang, “UWB NLOS/LOS Classification Using Hybrid Quantum Convolutional Neural Networks,” 2023 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), Busan, Korea, Republic of, 2023, pp. 1-2.
  2. M. -D. Nguyen, H. -S. Luong, Tung-Nguyen, Q. -V. Pham, Q. V. Do and W. -J. Hwang, “FFD: A Full-Stack Federated Distillation method for Heterogeneous Massive IoT Networks,” 2022 International Conference on Advanced Technologies for Communications (ATC), Ha Noi, Vietnam, 2022, pp. 326-331.
  3. Q. V. Do and Insoo Koo, “Dynamic Bandwidth Allocation Scheme for Wireless Networks with Energy Harvesting Using Actor-Critic Deep Reinforcement Learning,” 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Okinawa, Japan, 2019, pp. 138-142.