Phuong Nam. Tran

I am a Doctoral Researcher at the Centre for Wireless Communications (CWC) – Radio Technologies, University of Oulu, Finland, where I started in January 2025. My main supervisor is Prof. Markku Juntti, and my co-supervisor is Prof. Nhan Nguyen. I am also a Marie Skłodowska-Curie Doctoral Fellow in the EXACT-6G project, funded by the MSCA Doctoral Network program.

Prior to joining the University of Oulu, I worked as a Research Assistant at the Singapore University of Technology and Design (SUTD) under the guidance of Prof. Zehui Xiong. I obtained my Master’s degree from Chung-Ang University (CAU), South Korea, in 2024, under the supervision of Prof. Sungrae Cho, with close collaboration from Prof. Nhu-Ngoc Dao. I received my Bachelor’s degree from the Hanoi University of Science and Technology (HUST), Vietnam, in 2020.

My research interests lie at the intersection of machine learning and wireless communications. I focus on developing scalable and low-complexity solutions for advanced wireless systems, including cell-free massive MIMO and reconfigurable intelligent surfaces, using techniques such as deep reinforcement learning, deep unfolding, and graph neural networks.

selected publications

ICC

Deep Reinforcement Learning-Based Dynamic Resource Allocation in Cell-Free Massive MIMO

Phuong Nam Tran, Nhan Thanh Nguyen, Hien Quoc Ngo, and 1 more author

In IEEE International Conference on Communications (ICC) 2026, 2026

Bib

@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},
}

J.IoT
Joint wireless resource allocation and bitrate adaptation for QoE improvement in IRS-aided RSMA-enabled IoMT streaming systems

Nam-Phuong Tran, Thanh Phung Truong, Quang Tuan Do, and 2 more authors

Internet of Things, 2024

Abs DOI Bib

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}, }

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