  | TRAN, D. D., HA, V. N., Sharma, S. K., NGUYEN, T. T., CHATZINOTAS, S., & Popovski, P. (2024). Energy-Efficient NOMA for 5G Heterogeneous Services: A Joint Optimization and Deep Reinforcement Learning Approach. IEEE Transactions on Communications, 1-16. doi:10.1109/TCOMM.2024.3476083  Peer Reviewed verified by ORBi |
 | TRAN, D. D. (2023). Design and Optimization of Ultra-Reliable Low-Latency Communications in Beyond 5G Networks [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/59535 |
 | KEBEDEW, T. M., HA, V. N., LAGUNAS, E., TRAN, D. D., Grotz, J., & CHATZINOTAS, S. (2023). Reinforcement Learning for QoE-Oriented Flexible Bandwidth Allocation in Satellite Communication. In Reinforcement Learning for QoE-Oriented Flexible Bandwidth Allocation in Satellite Communication. New York, United States: IEEE. doi:10.1109/GCWkshps58843.2023.10464773 Peer reviewed |
 | TRAN, D. D., HA, V. N., CHATZINOTAS, S., & Nguyen, T. T. (2023). A Hybrid Optimization and Deep RL Approach for Resource Allocation in Semi-GF NOMA Networks. In in Proceeding of IEEE PIMRC 2023. United States: IEEE. doi:10.1109/pimrc56721.2023.10293810 Peer reviewed |
 | TRAN, D. D., Sharma, S. K., HA, V. N., CHATZINOTAS, S., & Woungang, I. (03 July 2023). Multi-Agent DRL Approach for Energy-Efficient Resource Allocation in URLLC-Enabled Grant-Free NOMA Systems. IEEE Open Journal of the Communications Society, 4, 1470 - 1486. doi:10.1109/OJCOMS.2023.3291689 Peer Reviewed verified by ORBi |
 | TRAN, D. D., HA, V. N., & CHATZINOTAS, S. (2022). Novel Reinforcement Learning based Power Control and Subchannel Selection Mechanism for Grant-Free NOMA URLLC-Enabled Systems. In Proceedings of 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring) (pp. 1-5). Peer reviewed |
 | TRAN, D. D., Sharma, S. K., CHATZINOTAS, S., & Woungang, I. (2021). Learning-Based Multiplexing of Grant-Based and Grant-Free Heterogeneous Services with Short Packets. In Proceedings of 2021 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). doi:10.1109/GLOBECOM46510.2021.9685321 Peer reviewed |
 | TRAN, D. D., Sharma, S. K., CHATZINOTAS, S., & Woungang, I. (2021). Q-Learning-Based SCMA for Efficient Random Access in mMTC Networks With Short Packets. In Proceedings of 2021 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2021) (pp. 1-5). doi:10.1109/VTC2021-Spring51267.2021.9448787 Peer reviewed |
 | TRAN, D. D., SHARMA, S. K., & CHATZINOTAS, S. (2021). BLER-based Adaptive Q-learning for Efficient Random Access in NOMA-based mMTC Networks. In Proceedings of 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) (pp. 1-5). Peer reviewed |
 | TRAN, D. D., SHARMA, S. K., CHATZINOTAS, S., Woungang, I., & OTTERSTEN, B. (2021). Short-Packet Communications for MIMO NOMA Systems over Nakagami-m Fading: BLER and Minimum Blocklength Analysis. IEEE Transactions on Vehicular Technology. doi:10.1109/TVT.2021.3066367 Peer Reviewed verified by ORBi |