Publications

1. Y. Zheng, S. C. Prabhashana, M. Özdem, B. Canberk, S. L. Cotton, H. Shin, and T. Q. Duong, “Agentic AI and LLM-aided quantum MADRL for pinching-antenna-assisted V2X networks,” IEEE Netw.

Accepted:

Coming Soon...

Published:

1. Y. Zheng, S. L. Cotton, and T. Q. Duong, “A bidirectional-AoI-aware multi-agent deep reinforcement learning framework for vehicular platooning in segmented waveguide-based pinching antenna systems,” IEEE Trans. Cogn. Commun. Netw. [Link to Paper]

2. Y. Zheng, L. D. Nguyen, and T. Q. Duong, “Multiagent deep reinforcement learning for optimal resource allocation in AoI-aware energy-efficient platoon-based C-V2X networks,” IEEE Internet Things J., vol. 13, no. 6, pp. 10 814–10 828, Mar. 2026. [Link to Paper]

Coming Soon...

Accepted:

Coming Soon...

Published:

1. Y. Zheng, S. L. Cotton, H. Shin, and T. Q. Duong, “Quantum neural networks for MADRL-assisted optimal resource allocation in vehicular networks,” in Proc. GLOBECOM 2025 - 2025 IEEE Glob. Commun. Conf., Taipei, Taiwan, Dec. 8–12 2025, pp. 1010-1015.  [Link to Paper]

2. Y. Zheng, S. L. Cotton, O. A. Dobre, and T. Q. Duong, “Quantum multi-agent deep reinforcement learning for energy-efficient vehicular networks,” in Proc. ICC 2025 - IEEE Int. Conf. Commun., Montreal, Canada, Jun. 8–12 2025, pp. 2039-2044. [Link to Paper]

3. Y. Zheng, B. Hazarika, T. Q. Duong, K. Singh, V. Sharma, O. A. Dobre, “Multi-agent DRL for resource allocation in AoI-aware energy-efficient C-V2X networks,” in Proc. 2024 IEEE 29th Int. Workshop Comput. Aided Model. Des. Commun. Links Netw. (CAMAD), Athens, Greece, Oct. 21–23 2024, pp. 1-6. (SITE’24 Best Paper Award) [Link to Paper]