# Publications

Source: https://www.thanhtungvu.com/publications
Last updated: 2026-10-24

45 entries: 22 journal articles, 20 conference papers, 1 book chapter, 2 patent applications · [Google Scholar](https://scholar.google.com/citations?user=Yr2ixYEAAAAJ)

## 2026

- **Leveraging Slowly Time-Varying AP-AP Channels for Interference Mitigation in Dynamic TDD.** Martin Andersson, **Tung T. Vu**, Pål Frenger, Jan Åslund, Erik G. Larsson. *IEEE TSP 2026*. [arXiv](https://arxiv.org/abs/2609.11669) · [DOI](https://doi.org/10.1109/TSP.2026.3733596)
  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.

## 2025

- **Symbol Detection in the Presence of Access Point-to-Access Point Interference.** Pål Frenger, **Tung T. Vu**, Erik G. Larsson, Martin B. Andersson. *PCT application, WO 2025/052272 A1*. Pending. [Patent](https://patents.google.com/patent/WO2025052272A1/en)
  Describes how an uplink access point can mitigate interference from another access point by measuring it during short phases when its own users are silent or send known samples.
- **User-to-User Interference Mitigation.** Pål Frenger, **Tung T. Vu**, Erik G. Larsson, Martin B. Andersson. *PCT application, WO 2025/052272 A1*. Pending. [Patent](https://patents.google.com/patent/WO2025052272A1/en)
  Describes how a device can suppress the interference another device causes in dynamic TDD networks, by estimating the channel between them and steering a beam null towards it.
- **Base Station Beam Scheduling for mmWave Networks With Full-Frequency Reuse.** **Tung T. Vu**, Swaroop Gopalam, Iain B. Collings, Stephen V. Hanly, Hazer Inaltekin. *IEEE Access 2025*. [DOI](https://doi.org/10.1109/ACCESS.2025.3633746)
  Schedules the beams of millimetre-wave base stations that reuse the full band so every user reaches its target rate in as few time slots as possible, and finds that purely analog beams outperform hybrid beamforming here.

## 2024

- **Personalized Federated Learning: Theory and Open Problems.** Canh T. Dinh, **Tung T. Vu**, Nguyen H. Tran. *Elsevier Book Chapter 2024*. [DOI](https://doi.org/10.1016/B978-0-44-319037-7.00010-7)
  Reviews the theory of personalised federated learning, in which each client keeps a model tailored to its own data, and sets out its open problems.
- **Cell-Free Massive MIMO With Multi-Antenna Users and Phase Misalignments: A Novel Partially Coherent Transmission Framework.** Unnikrishnan Kunnath Ganesan, **Tung T. Vu**, Erik G. Larsson. *IEEE OJCOMS 2024*. [arXiv](https://arxiv.org/abs/2403.00674) · [DOI](https://doi.org/10.1109/OJCOMS.2024.3373170)
  Lets cell-free access points cooperate in small phase-aligned clusters instead of across the whole network, reaching nearly the data rate of fully coherent operation without its strict phase alignment.
- **Joint User Association and Power Control for Cell-Free Massive MIMO.** Chongzheng Hao, **Tung T. Vu**, Hien Q. Ngo, Minh N. Dao, Xiaoyu Dang, Chenghua Wang, Michail Matthaiou. *IEEE IoTJ 2024*. [arXiv](https://arxiv.org/abs/2401.02701) · [DOI](https://doi.org/10.1109/JIOT.2024.3351633)
  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.
- **Massive MIMO for Serving Federated Learning and Non-Federated Learning Users.** Muhammad Farooq, **Tung T. Vu**, Hien Q. Ngo, Le-Nam Tran. *IEEE TWC 2024*. [arXiv](https://arxiv.org/abs/2205.08328) · [DOI](https://doi.org/10.1109/TWC.2023.3277037)
  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.
- **A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization.** Canh T. Dinh, **Tung T. Vu**, Nguyen H. Tran, Minh N. Dao, Hongyu Zhang. *IEEE TNNLS 2024*. [arXiv](https://arxiv.org/abs/2102.07148) · [DOI](https://doi.org/10.1109/TNNLS.2022.3224252)
  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.
- **Joint OMA-NOMA Cell-Free Massive MIMO with Limited Fronthaul.** Chi Y. Nguyen, **Tung T. Vu**, Hien Q. Ngo, Michail Matthaiou. *IEEE GLOBECOM 2024*. [DOI](https://doi.org/10.1109/GLOBECOM52923.2024.10901303)
  Serves some cell-free users with non-orthogonal multiple access and the rest orthogonally under limited fronthaul, giving clearly higher total spectral efficiency than orthogonal access alone, most of all when coherence intervals are short.
- **User-to-User Interference Mitigation in Dynamic TDD MIMO Systems with Multi-Antenna Users.** Martin Andersson, **Tung T. Vu**, Pål Frenger, Erik G. Larsson. *IEEE SPAWC 2024*. [arXiv](https://arxiv.org/abs/2408.03740) · [DOI](https://doi.org/10.1109/SPAWC60668.2024.10694637)
  Mitigates the interference an uplink user causes to nearby downlink users in dynamic TDD by sending pilots between users, so each downlink user can design a combiner that rejects it.
- **Minimizing Clearing Time in mmWave Networks with Overlapping Coverage.** **Tung T. Vu**, Swaroop Gopalam, Stephen V. Hanly, Iain B. Collings, Hazer Inaltekin. *IEEE VTC2024-Spring*. [DOI](https://doi.org/10.1109/VTC2024-Spring62846.2024.10683319)
  Assigns users to overlapping millimetre-wave base stations with limited RF chains so that all user data is cleared in the shortest time, using a two-stage scheme.
- **Uplink Symbol Detection in Dynamic TDD MIMO Systems with AP-AP Interference.** Martin Andersson, **Tung T. Vu**, Pål Frenger, Erik G. Larsson. *IEEE ICASSP 2024*. [DOI](https://doi.org/10.1109/ICASSP48485.2024.10446150)
  Detects uplink symbols despite interference from a downlink access point in dynamic TDD, by keeping users silent for one sample per coherence interval to learn the interfering channel.

## 2023

- **Network-Assisted Full-Duplex Cell-Free Massive MIMO: Spectral and Energy Efficiencies.** Mohammadali Mohammadi, **Tung T. Vu**, Hien Q. Ngo, Michail Matthaiou. *IEEE JSAC 2023*. [arXiv](https://arxiv.org/abs/2304.07161) · [DOI](https://doi.org/10.1109/JSAC.2023.3287613)
  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.
- **Joint Optimization of Switching Point and Power Control in Dynamic TDD Cell-Free Massive MIMO.** Martin Andersson, **Tung T. Vu**, Pål Frenger, Erik G. Larsson. *Asilomar 2023*. [arXiv](https://arxiv.org/abs/2406.14126) · [DOI](https://doi.org/10.1109/IEEECONF59524.2023.10476848)
  Adapts the uplink and downlink switching point of a cell-free network in dynamic TDD together with power control, to maximise energy efficiency.
- **User Association and Power Control in Cell-Free Massive MIMO with the APG Method.** Chongzheng Hao, **Tung T. Vu**, Hien Q. Ngo, Minh N. Dao, Xiaoyu Dang, Michail Matthaiou. *EUSIPCO 2023*. [DOI](https://doi.org/10.23919/EUSIPCO58844.2023.10289821)
  Jointly chooses user association and power in user-centric cell-free massive MIMO with a fast accelerated gradient method, giving up to about 2.8 times the median spectral efficiency of a heuristic baseline.
- **Multipair DF Relaying with Network-Assisted Full-Duplex Cell-Free Massive MIMO.** **Tung T. Vu**, Hien Q. Ngo, Minh N. Dao, Erik G. Larsson. *EUSIPCO 2023*. [DOI](https://doi.org/10.23919/EUSIPCO58844.2023.10289776)
  Uses network-assisted full-duplex in a cell-free relaying system, assigning access points to uplink or downlink, to raise per-pair spectral efficiency over half-duplex and heuristic baselines.

## 2022

- **Energy-Efficient Massive MIMO for Federated Learning: Transmission Designs and Resource Allocations.** **Tung T. Vu**, Hien Q. Ngo, Minh N. Dao, Duy T. Ngo, Erik G. Larsson, Tho Le-Ngoc. *IEEE OJCOMS 2022*. [arXiv](https://arxiv.org/abs/2112.11723) · [DOI](https://doi.org/10.1109/OJCOMS.2022.3222749)
  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.
- **Data Size-Aware Downlink Massive MIMO: A Session-Based Approach.** **Tung T. Vu**, Hien Q. Ngo, Minh N. Dao, Michail Matthaiou, Erik G. Larsson. *IEEE WCL 2022*. [arXiv](https://arxiv.org/abs/2205.04369) · [DOI](https://doi.org/10.1109/LWC.2022.3174829)
  Splits massive MIMO downlink transmission into sessions, each finishing one user so later sessions see less interference, to minimise the time needed to deliver all data.
- **Joint Resource Allocation to Minimize Execution Time of Federated Learning in Cell-Free Massive MIMO.** **Tung T. Vu**, Duy T. Ngo, Hien Q. Ngo, Minh N. Dao, Nguyen H. Tran, Richard H. Middleton. *IEEE IoTJ 2022*. [arXiv](https://arxiv.org/abs/2009.02031) · [DOI](https://doi.org/10.1109/JIOT.2022.3183295)
  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.
- **Serving Federated Learning and Non-Federated Learning Users: A Massive MIMO Approach.** Muhammad Farooq, **Tung T. Vu**, Hien Q. Ngo, Le-Nam Tran. *IEEE SPAWC 2022*. [arXiv](https://arxiv.org/abs/2205.08307) · [DOI](https://doi.org/10.1109/SPAWC51304.2022.9833955)
  Serves federated-learning users in the uplink and other users in the downlink on two halves of the band, maximising the lowest rate of the other users while keeping learning rounds on time.
- **Virtually Full-duplex Cell-Free Massive MIMO with Access Point Mode Assignment.** Mohammadali Mohammadi, **Tung T. Vu**, Behnaz Naderi Beni, Hien Q. Ngo, Michail Matthaiou. *IEEE SPAWC 2022*. Invited Paper. [arXiv](https://arxiv.org/abs/2205.06403) · [DOI](https://doi.org/10.1109/SPAWC51304.2022.9834006)
  Assigns cell-free access points to uplink or downlink to create a virtually full-duplex network, which clearly beats half-duplex operation in total spectral efficiency.
- **Channel Estimation in RIS-assisted Downlink Massive MIMO: A Learning-Based Approach.** **Tung T. Vu**, Trinh Van Chien, Canh T. Dinh, Hien Q. Ngo, Michail Matthaiou. *IEEE SPAWC 2022*. Invited Paper. [arXiv](https://arxiv.org/abs/2205.05577) · [DOI](https://doi.org/10.1109/SPAWC51304.2022.9834023)
  Trains a neural network to estimate effective channel gains from received downlink signals in RIS-assisted massive MIMO, with no downlink pilots, where channel hardening is weak.

## 2021

- **Energy-Efficient Massive MIMO for Serving Multiple Federated Learning Groups.** **Tung T. Vu**, Hien Q. Ngo, Duy T. Ngo, Minh N. Dao, Erik G. Larsson. *IEEE GLOBECOM 2021*. [arXiv](https://arxiv.org/abs/2108.13512) · [DOI](https://doi.org/10.1109/GLOBECOM46510.2021.9685968)
  Shows that one massive MIMO network can run several federated-learning processes at once, and allocates power and computing to save energy while keeping each iteration on time.
- **How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?** **Tung T. Vu**, Hien Q. Ngo, Thomas L. Marzetta, Michail Matthaiou. *IEEE SPAWC 2021*. [arXiv](https://arxiv.org/abs/2107.09577) · [DOI](https://doi.org/10.1109/SPAWC51858.2021.9593248)
  Asks how a wireless network can support several federated-learning groups at once, and answers with cell-free massive MIMO and a simple algorithm that minimises each iteration's time.
- **Straggler Effect Mitigation for Federated Learning in Cell-Free Massive MIMO.** **Tung T. Vu**, Duy T. Ngo, Hien Q. Ngo, Minh N. Dao, Nguyen H. Tran, Richard H. Middleton. *IEEE ICC 2021*. [DOI](https://doi.org/10.1109/ICC42927.2021.9500541)
  Tackles stragglers in wireless federated learning by choosing which users take part before training starts in cell-free massive MIMO, cutting transmission time.

## 2020

- **Cell-Free Massive MIMO for Wireless Federated Learning.** **Tung T. Vu**, Duy T. Ngo, Nguyen H. Tran, Hien Q. Ngo, Minh N. Dao, Richard H. Middleton. *IEEE TWC 2020*. Top Accessed Article, IEEE TWC 2020. [arXiv](https://arxiv.org/abs/1909.12567) · [DOI](https://doi.org/10.1109/TWC.2020.3002988)
  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.
- **Energy-Efficient Full-Duplex Enabled Cloud Radio Access Networks.** **Tung T. Vu**, Duy T. Ngo, Minh N. Dao, Quang-Thang Duong, Minoru Okada, Hung Nguyen-Le, Richard H. Middleton. *IEICE Trans. Commun. 2020*. [DOI](https://doi.org/10.1587/transcom.2018EBP3327)
  Jointly designs precoding, power and rates for full-duplex cloud radio access networks to maximise energy efficiency, which beats half-duplex when self-interference is low.

## 2019

- **Full-Duplex Cell-Free Massive MIMO.** **Tung T. Vu**, Duy T. Ngo, Hien Q. Ngo, Tho Le-Ngoc. *IEEE ICC 2019*. [DOI](https://doi.org/10.1109/ICC.2019.8761711)
  Analyses full-duplex cell-free massive MIMO, shows that residual self-interference is its main limit, and finds a 140% spectral efficiency gain over half-duplex when that interference is low enough.
- **Stackelberg Game-Based Network Slicing for Joint Wireless Access and Backhaul Resource Allocation.** Thinh D. Tran, Long B. Le, **Tung T. Vu**, Duy T. Ngo. *IEEE ICC 2019*. [DOI](https://doi.org/10.1109/ICC.2019.8761277)
  Models how wireless access and backhaul providers price and trade resources in network slicing as a Stackelberg game, and solves for its equilibrium.

## 2018

- **Energy Efficiency Maximization for Downlink Cloud Radio Access Networks With Data Sharing and Data Compression.** **Tung T. Vu**, Duy T. Ngo, Minh N. Dao, Salman Durrani, Duy H. N. Nguyen, Richard H. Middleton. *IEEE TWC 2018*. [DOI](https://doi.org/10.1109/TWC.2018.2834370)
  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.
- **Spectral and Energy Efficiency Maximization for Content-Centric C-RANs with Edge Caching.** **Tung T. Vu**, Duy T. Ngo, Minh N. Dao, Salman Durrani, Richard H. Middleton. *IEEE TCOM 2018*. [DOI](https://doi.org/10.1109/TCOMM.2018.2866458)
  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.
- **Energy-Efficient Design for Downlink Cloud Radio Access Networks.** **Tung T. Vu**, Duy T. Ngo, Minh N. Dao, Salman Durrani, Duy H. N. Nguyen, Richard H. Middleton. *IEEE ICC 2018*. [DOI](https://doi.org/10.1109/ICC.2018.8422878)
  Maximises the energy efficiency of a cloud radio access network whose data reaches the radio heads over capacity-limited fronthaul, with an algorithm that solves one simple convex problem per iteration.

## 2017

- **On the Optimal Precoder Design for Energy-Efficient and Secure MIMO Systems.** **Tung T. Vu**, Ha H. Kha. *UD-JST 2017*. [DOI](https://doi.org/10.31130/jst.2017.32)
  Finds the globally optimal precoder for energy-efficient secure MIMO links with an eavesdropper as a benchmark, and a fast zero-forcing design that comes close to it.
- **On the Interference Alignment Designs for Secure Multiuser MIMO Systems.** Kha H. Ha, **Tung T. Vu**, Trung Q. Duong, Nguyen-Son Vo. *IEICE Trans. Fundamentals 2017*. [arXiv](https://arxiv.org/abs/1508.00349) · [DOI](https://doi.org/10.1587/transfun.E100.A.670)
  Proposes two interference alignment schemes that protect multiuser MIMO links from an eavesdropper by aligning or nulling the signals it could intercept, raising the secrecy sum rate.
- **Particle Swarm Optimization for Weighted Sum Rate Maximization in MIMO Broadcast Channels.** **Tung T. Vu**, Ha H. Kha, Trung Q. Duong, Nguyen-Son Vo. *Wireless Pers. Commun. 2017*. [arXiv](https://arxiv.org/abs/1508.01168) · [DOI](https://doi.org/10.1007/s11277-017-4357-2)
  Uses particle swarm optimisation to design the precoders and receivers of a multiuser MIMO broadcast channel that maximise the weighted sum rate.
- **Energy-efficient transceiver designs for multiuser MIMO cognitive radio networks via interference alignment.** Ha H. Kha, **Tung T. Vu**, Tuan Do-Hong. *Telecommun. Syst. 2017*. [DOI](https://doi.org/10.1007/s11235-017-0300-9)
  Designs energy-efficient transceivers for multiuser MIMO cognitive radio, where primary users align out their mutual interference and secondary users avoid interfering with them.
- **Energy-Efficient Interference Mitigation with Hierarchical Partial Coordination for MIMO Heterogeneous Networks.** **Tung T. Vu**, Ha H. Kha, Osamu Muta, Mohamed Rihan. *IEICE Trans. Commun. 2017*. [DOI](https://doi.org/10.1587/transcom.2016EBP3247)
  Coordinates macro and small cells in two-tier MIMO heterogeneous networks with little channel information exchange, using interference alignment and power allocation to maximise energy efficiency.
- **Joint Optimization of User Association, Data Delivery Rate and Precoding for Cache-Enabled F-RANs.** **Tung T. Vu**, Duy T. Ngo, Lawrence Ong, Salman Durrani, Richard H. Middleton. *IEEE GLOBECOM 2017*. [arXiv](https://arxiv.org/abs/1706.10203) · [DOI](https://doi.org/10.1109/GLOCOM.2017.8254674)
  Jointly designs user association, delivery rates and precoding in cache-enabled fog radio access networks, raising throughput by 50% while saving at least half the power of precoding alone.

## 2016

- **Transceiver Design for Optimizing the Energy Efficiency in Multiuser MIMO Channels.** **Tung T. Vu**, Ha H. Kha, Hoang D. Tuan. *IEEE Commun. Lett. 2016*. [DOI](https://doi.org/10.1109/LCOMM.2016.2579640)
  Uses interference alignment to block information leakage in secure multiuser MIMO systems, then maximises energy efficiency with a semi-closed-form algorithm.
- **Outage performance of cognitive cooperative networks with relay selection over double-Rayleigh fading channels.** Tran T. Duy, George C. Alexandropoulos, **Tung T. Vu**, Vo N. Son, Trung Q. Duong. *IET Commun. 2016*. [DOI](https://doi.org/10.1049/iet-com.2015.0236)
  Derives the outage probability of vehicle-to-vehicle links relayed through roadside units in cognitive radio, under double Rayleigh fading and several relay selection policies.
- **Optimal precoder designs for energy-efficiency maximization in secure MIMO systems.** **Tung T. Vu**, Ha H. Kha. *NICS 2016*. [DOI](https://doi.org/10.1109/NICS.2016.7725666)
  Benchmarks secure, energy-efficient MIMO precoding with a globally optimal method, and proposes a fast zero-forcing design that cancels the eavesdropper's signal.

## 2015

- **Secure Cognitive Reactive Decode-and-Forward Relay Networks: With and Without Eavesdropper.** Dac-Binh Ha, **Tung T. Vu**, Tran T. Duy, Vo N. Q. Bao. *Wireless Pers. Commun. 2015*. [DOI](https://doi.org/10.1007/s11277-015-2924-y)
  Analyses cognitive radio relay networks with decode-and-forward relays, deriving the outage probability and, when an eavesdropper is present, the average secrecy capacity.
- **Interference Alignment Designs for Secure Multiuser MIMO Systems: Rank Constrained Rank Minimization Approach.** **Tung T. Vu**, Ha H. Kha, Trung Q. Duong. *ComManTel 2015*. [arXiv](https://arxiv.org/abs/1508.01169) · [DOI](https://doi.org/10.1109/ComManTel.2015.7394271)
  Casts secure interference alignment as rank minimisation, aligning interference and eavesdropped signals into the smallest subspaces, which raises the secrecy sum rate.
- **Wiretapped Signal Leakage Minimization for Secure Multiuser MIMO Systems via Interference Alignment.** **Tung T. Vu**, Ha H. Kha, Trung Q. Duong, Nguyen-Son Vo. *ATC 2015*. [arXiv](https://arxiv.org/abs/1508.00639) · [DOI](https://doi.org/10.1109/ATC.2015.7388422)
  Designs interference alignment that minimises both interference and the signals an eavesdropper can intercept in multiuser MIMO, improving the secrecy sum rate.