@inproceedings{tran2026deep,title={Deep Reinforcement Learning-Based Dynamic Resource Allocation in Cell-Free Massive MIMO},year={2026},url={https://doi.org/10.48550/arXiv.2601.13934},booktitle={IEEE International Conference on Communications (ICC) 2026},author={Tran, Phuong Nam and Nguyen, Nhan Thanh and Ngo, Hien Quoc and Juntti, Markku},organization={IEEE},}
@article{truong2026energy,title={Energy Efficiency in Quantized MIMO-NOMA Communication Systems},author={Truong, Thanh Phung and Tran, Phuong Nam and Oh, Junsuk and Lee, Donghyun and Tuong, Van Dat and Dao, Nhu-Ngoc and Cho, Sungrae},journal={IEEE Transactions on Cognitive Communications and Networking},url={https://doi.org/10.1109/TCCN.2026.3670130},year={2026},publisher={IEEE}}
@inproceedings{tran2025deep,title={Deep Reinforcement Learning for Hybrid RIS Assisted MIMO Communications},year={2025},url={https://doi.org/10.1109/IEEECONF67917.2025.11443798},booktitle={Asilomar Conference on Signals, Systems, and Computers},author={Tran, Phuong Nam and Nguyen, Nhan Thanh and Juntti, Markku},organization={IEEE},}
@inproceedings{ji2025deep,title={Deep Complex-valued Convolutional Learning for Waveform OFDM Receiver Design},author={Ji, Jiequ and Tran, Nam Phuong and Xiong, Zehui and Zhu, Kun and Quek, Tony QS},booktitle={IEEE Wireless Communications and Networking Conference (WCNC) 2025},url={https://doi.org/10.1109/WCNC61545.2025.10978276},pages={1--6},year={2025},organization={IEEE},}
@article{ahmed2025joint,title={Joint content popularity and audience retention-aware live streaming over RSMA edge networks},author={Ahmed, Fayshal and Nguyen, The-Vinh and Tran, Nam-Phuong and Dao, Nhu-Ngoc and Cho, Sungrae},journal={Computer Networks},url={https://doi.org/10.1016/j.comnet.2025.111301},pages={111301},year={2025},publisher={Elsevier}}
In the context of the increasing demand for Internet of Multimedia Things (IoMT) services, rate splitting multiple access (RSMA) and intelligent reflecting surface (IRS) technologies have been considered potential networking enablers to provide ultra-throughput wireless access. However, challenges arise due to the heterogeneity of IoMT devices and arbitrary network quality changes, resulting in unwanted service quality fluctuations and downgradation. This study addresses this problem by jointly optimizing wireless resource allocation and bitrate adaptation with Deep Reinforcement Learning (DRL)-based QoE management for IRS-aided RSMA-enabled IoMT streaming systems. We formulated the problem as a Markov decision process (MDP) and apply Proximal Policy Optimization (PPO) method to flexibly adjust IoMT bitrate, transmission beamforming, IRS phase shift, and RSMA parameters. As a result, our algorithm mitigates overestimation of client-side bandwidth, leading to smoother playback and reduced quality fluctuations. Simulations show that our approach outperforms baseline methods in terms of video resolution (up to 2.5 times) and achievable sum-rate (up to 50%), contributing to a superior streaming experience in IoMT systems.
@article{tran2024joint,title={Joint wireless resource allocation and bitrate adaptation for QoE improvement in IRS-aided RSMA-enabled IoMT streaming systems},journal={Internet of Things},publisher={Elsevier},volume={25},pages={101145},year={2024},doi={https://doi.org/10.1016/j.iot.2024.101145},url={https://www.sciencedirect.com/science/article/pii/S2542660524000878},author={Tran, Nam-Phuong and Truong, Thanh Phung and Do, Quang Tuan and Dao, Nhu-Ngoc and Cho, Sungrae},keywords={Quality of experience, Internet of Multimedia Things, Bitrate adaptation, IRS-aided RSMA, Proximal policy optimization},}
@inproceedings{tran2022privacy,title={Privacy-preserving learning models for communication: A tutorial on advanced split learning},author={Tran, Nam-Phuong and Dao, Nhu-Ngoc and Nguyen, The-Vi and Cho, Sungrae},booktitle={2022 13th International Conference on Information and Communication Technology Convergence (ICTC)},url={https://doi.org/10.1109/ICOIN56518.2023.10048996},pages={1059--1064},year={2022},organization={IEEE}}