June 2024: Prof. Vu was invited to participate in the MathWorks Research Summit 2024 on a panel about Reinforcement Learning. Here is the set of opening slides.
May 2024: Congratulations to Alireza for winning the 2023 Joseph P. Noonan Outstanding Doctoral Research Award! Well done, Dr. Alizadeh!
Sept. 2023: Welcome to our new PhD student Changgyu Lee who joined us from Kyung Hee University, South Korea.
Feb. 2023: Byungju has accepted the position as an Assistant Professor at Pukyong National University, Busan, Korea. He will start in March. Congratulations Professor Lim!
Jan. 2023: Qing Lyu arrived at our lab as a PhD student. Welcome Qing!
Dec. 2022: Seok-Hyun completed his visit with us and will return to finish his PhD degree at Korea University.
Nov 2022: Prof. Vu joined the IEEE Transactions on Communications Editorial Board as an Editor.
Recent Publications
Our new paper demonstrates the efficiency of using Graph Neural Networks (GNN) for real-time hybrid beamforming in wideband multicarrier systems, minimizing computation overhead. The GNN-based design also effectively mitigates beam squinting effects. These findings will be presented at the 2024 IEEE International Symposium on Phased Array Systems & Technology.
We proposed multi-agent Q-learning algorithms for real-time user association and handover in dense 5G/6G networks. Centralized and distributed multi-agent policies improve load balancing, reducing handover rates and increasing network throughput. The work is detailed in a paper published in the IEEE Transactions on Wireless Communications.
Our analysis of 6G coexistence between terrestrial networks and passive satellites provides bounds for node density and transmitted power to achieve near-zero outage, together with conditions for out-of-band interference to meet spectral masks. Results are published in IEEE Transactions on Wireless Communications.
The paper “Joint User Selection and Beamforming Design for Multi-IRS Aided IoT Networks,” published in the IEEE Transactions on Vehicular Technology, presents low-complexity optimization algorithms for active and passive beamforming, along with user selection strategies to maximize throughput in multi-IRS environments.
We demonstrated that a Graph Neural Network (GNN) can be trained unsupervised to adapt base station beamforming and RIS phase control, achieving high data rates scalable to network size. These findings appeared in the 23rd IEEE Statistical Signal Processing (SSP).
