Phuong Nam. Tran

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

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

My research interests include machine learning for wireless communications, reconfigurable intelligent surfaces (RIS), cell-free massive MIMO, and multimedia streaming.

news

Jan 19, 2026	My paper has been accepted at the IEEE ICC 2026 conference!
Jul 16, 2025	My paper has been accepted at the Asilomar 2025 Conference!
Apr 08, 2025	Our paper has been accepted for publication in Computer Networks journal!
Jan 15, 2025	I have joined the University of Oulu as a Doctoral Researcher at the Centre for Wireless Communications – Radio Technologies. Excited to be a Marie Skłodowska-Curie Fellow in the EXACT-6G Doctoral Network!
Dec 20, 2024	Our paper has been accepted for presentation at the IEEE Wireless Communications and Networking Conference (WCNC)!
Mar 30, 2024	I have joined the Singapore University of Technology and Design (SUTD) as a Research Assistant under the guidance of Zehui Xiong!

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://arxiv.org/abs/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}, }