Phuong Nam. Tran

I am currently a Research Assistant at the Singapore University of Technology and Design (SUTD), working under the guidance of Professor Zehui Xiong. My research interests lie in the intersection of wireless communication, applied artificial intelligence, and multimedia.

Prior to joining SUTD, I embarked on my research journey at Chung-Ang University, South Korea, where I completed my Master’s degree in 2024 under the supervision of Professor Sungrae Cho with close collaboration from Professor Nhu-Ngoc Dao. My undergraduate studies were at the Hanoi University of Science and Technology, Vietnam, where I received my Bachelor’s degree in 2020.

news

Feb 27, 2024	My research, “Joint wireless resource allocation and bitrate adaptation for QoE improvement in IRS-aided RSMA-enabled IoMT streaming systems” has been accepted for publication in Elsevier Internet of Things journal!
Feb 19, 2024	I have graduated with a Master of Engineering in Computer Science from Chung-Ang University.

selected publications

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 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{TRAN2024101145, 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}, issn = {2542-6605}, 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}, }