Lecturer · School of Computer, Data and Mathematical Sciences
Energy-efficient, scalable wireless networks that AI can rely on, for 6G and beyond
I am a Lecturer in Computer Networking in the School of Computer, Data and Mathematical Sciences at Western Sydney University and an ARC DECRA Fellow. Previously, I was an Honorary Research Fellow (2025-2026) and a Postdoctoral Research Fellow (2023-2025) at Macquarie University, Australia, and a Postdoctoral Research Fellow at Linköping University, Sweden (2022-2023), and Queen's University Belfast, United Kingdom (2021-2022). I received my PhD in Electrical Engineering from The University of Newcastle, Australia, in 2021.
My research goal is to build the wireless networks that let AI leave the data centre and act in the physical world, so that robots, vehicles, drones and satellites can sense, learn and act together. I combine model-based optimisation with machine learning to design them, from cell-free massive MIMO and millimetre-wave networks that keep links alive when a beam is blocked (my DECRA project) to wireless federated learning, targeting systems that are energy-efficient, ultra-reliable, low-latency, and scalable. My current interests include reconfigurable antennas, semantic communication, and federated learning across space, air and ground.
Prospective students: I am recruiting a funded PhD student for my ARC DECRA project, and Master of Research students, for 2027. See how to apply, then fill out this interest form or email me.
ARC DECRA, DE260100816, 2026-2029, lead CIAU$529,672
Martin Andersson, Tung T. Vu, Pål Frenger, Jan Åslund, Erik G. Larsson
Uses the fact that channels between access points change slowly to estimate them alongside the uplink data, suppressing the interference that downlink access points cause to uplink ones.
Chongzheng Hao, Tung T. Vu, Hien Q. Ngo, Minh N. Dao, Xiaoyu Dang, Chenghua Wang, Michail Matthaiou
Chooses which access points serve each user and at what power to maximise the total data rate, with a learning-based method (JointCFNet) for small networks and a fast gradient method for large ones.
Muhammad Farooq, Tung T. Vu, Hien Q. Ngo, Le-Nam Tran
Lets one massive MIMO base station serve federated-learning users and other users on the same time and frequency resources, keeping each learning round within its deadline while maximising the lowest rate among the other users.
Canh T. Dinh, Tung T. Vu, Nguyen H. Tran, Minh N. Dao, Hongyu Zhang
Treats federated learning on uneven client data as multi-task learning linked by a graph, giving one framework for standard and personalised federated learning and two algorithms (FedU, dFedU) with proven convergence rates.
Mohammadali Mohammadi, Tung T. Vu, Hien Q. Ngo, Michail Matthaiou
Turns half-duplex access points into a virtual full-duplex cell-free network by assigning each to uplink or downlink, giving about 30% more spectral efficiency and up to 200% more energy efficiency than half- or full-duplex schemes.
Tung T. Vu, Hien Q. Ngo, Minh N. Dao, Duy T. Ngo, Erik G. Larsson, Tho Le-Ngoc
Designs massive MIMO for federated learning in three ways (synchronous, asynchronous and session-based) to cut the energy the base station and users spend on each learning round without missing its deadline.
Tung T. Vu, Duy T. Ngo, Hien Q. Ngo, Minh N. Dao, Nguyen H. Tran, Richard H. Middleton
Picks the users with the best links, and sets their power and processing speed, to cut the total time federated learning takes in cell-free massive MIMO while keeping enough users for good learning.
Tung T. Vu, Duy T. Ngo, Nguyen H. Tran, Hien Q. Ngo, Minh N. Dao, Richard H. Middleton
Proposes cell-free massive MIMO as a stable network for federated learning, and jointly tunes local accuracy, power, data rate and processing speed to cut training time by up to 55% over baselines.
Tung T. Vu, Duy T. Ngo, Minh N. Dao, Salman Durrani, Duy H. N. Nguyen, Richard H. Middleton
Maximises the energy efficiency of cloud radio access networks whose fronthaul links have limited capacity, and finds that sharing data over the fronthaul beats compressing it.
Tung T. Vu, Duy T. Ngo, Minh N. Dao, Salman Durrani, Richard H. Middleton
Decides which cache-equipped radio heads serve which users, and with what rates and precoding, in content-centric cloud radio access networks; edge caching improves both spectral and energy efficiency.
Talk at the CSIT October Seminar, Western Sydney University: "Towards 6G and Beyond for a Connected and Intelligent World".
Joined Western Sydney University as Lecturer in Computer Networking, School of Computer, Data and Mathematical Sciences.
Accepted at IEEE TSP: Leveraging Slowly Time-Varying AP-AP Channels for Interference Mitigation in Dynamic TDD.
Received the ARC Discovery Early Career Researcher Award (DECRA), 2026-2029, AU$529,672.
Accepted at IEEE Access: Base Station Beam Scheduling for mmWave Networks With Full-Frequency Reuse.
Accepted at IEEE GLOBECOM 2024: Joint OMA-NOMA Cell-Free Massive MIMO with Limited Fronthaul.
Accepted at IEEE SPAWC 2024: User-to-User Interference Mitigation in Dynamic TDD MIMO Systems with Multi-Antenna Users.
Accepted at IEEE VTC2024-Spring: Minimizing Clearing Time in mmWave Networks with Overlapping Coverage.
Accepted at IEEE OJCOMS: Cell-Free Massive MIMO With Multi-Antenna Users and Phase Misalignments.
Technical talk at the Australian Communications Theory Workshop (AusCTW'24), 4-7 February, Melbourne, Australia: "Interference management for dynamic full-duplex cell-free networks".
Accepted at IEEE IoTJ: Joint User Association and Power Control for Cell-Free Massive MIMO.
Accepted at IEEE ICASSP 2024: Uplink Symbol Detection in Dynamic TDD MIMO Systems with AP-AP Interference.
Accepted at Asilomar 2023: Joint Optimization of Switching Point and Power Control in Dynamic TDD Cell-Free Massive MIMO.
Accepted at EUSIPCO 2023: User Association and Power Control in Cell-Free Massive MIMO with the APG Method and Multipair DF Relaying with Network-Assisted Full-Duplex Cell-Free Massive MIMO.
Accepted at IEEE TWC: Massive MIMO for Serving Federated Learning and Non-Federated Learning Users.
Accepted at IEEE JSAC: Network-Assisted Full-Duplex Cell-Free Massive MIMO.
Invited lecture at Ho Chi Minh City University of Science, Vietnam: "Fundamentals of MIMO Communications".
Accepted at IEEE TNNLS: A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization.
Accepted at IEEE OJCOMS: Energy-Efficient Massive MIMO for Federated Learning.
Accepted at IEEE IoTJ: Joint Resource Allocation to Minimize Execution Time of Federated Learning in Cell-Free Massive MIMO.
Accepted at IEEE WCL: Data Size-Aware Downlink Massive MIMO.
Accepted at IEEE SPAWC 2022: Virtually Full-duplex Cell-Free Massive MIMO with Access Point Mode Assignment, Serving Federated Learning and Non-Federated Learning Users, and Channel Estimation in RIS-assisted Downlink Massive MIMO.