Skip to main content

Research Repository

Advanced Search

All Outputs (19)

Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet (2023)
Journal Article
Xu, K., Huynh, N. V., & Li, G. Y. (2023). Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet. IEEE Transactions on Communications, 71(10), 5893 - 5903. https://doi.org/10.1109/tcomm.2023.3300331

In heterogeneous networks (HetNets), the overlap of small cells and the macro cell causes severe cross-tier interference. Although there exist some approaches to address this problem, they usually require global channel state information, which is ha... Read More about Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet.

Transfer Learning for Signal Detection in Wireless Networks (2022)
Journal Article
Van Huynh, N., & Li, G. Y. (2022). Transfer Learning for Signal Detection in Wireless Networks. IEEE Wireless Communications Letters, 11(11), 2325-2329. https://doi.org/10.1109/lwc.2022.3202117

The last decade has witnessed the rapid growth of deep learning (DL) applications in wireless communications, especially for channel estimation and signal detection. However, conventional DL techniques are usually trained for a specific scenario. The... Read More about Transfer Learning for Signal Detection in Wireless Networks.

Transfer Learning for Wireless Networks: A Comprehensive Survey (2022)
Journal Article
Nguyen, C. T., Van Huynh, N., Chu, N. H., Saputra, Y. M., Hoang, D. T., Nguyen, D. N., …Hwang, W. (2022). Transfer Learning for Wireless Networks: A Comprehensive Survey. Proceedings of the IEEE, 110(8), 1073-1115. https://doi.org/10.1109/jproc.2022.3175942

With outstanding features, machine learning (ML) has become the backbone of numerous applications in wireless networks. However, the conventional ML approaches face many challenges in practical implementation, such as the lack of labeled data, the co... Read More about Transfer Learning for Wireless Networks: A Comprehensive Survey.

Joint Speed Control and Energy Replenishment Optimization for UAV-Assisted IoT Data Collection With Deep Reinforcement Transfer Learning (2022)
Journal Article
Chu, N. H., Hoang, D. T., Nguyen, D. N., Van Huynh, N., & Dutkiewicz, E. (2023). Joint Speed Control and Energy Replenishment Optimization for UAV-Assisted IoT Data Collection With Deep Reinforcement Transfer Learning. IEEE Internet of Things, 10(7), 5778-5793. https://doi.org/10.1109/jiot.2022.3151201

Unmanned-aerial-vehicle (UAV)-assisted data collection has been emerging as a prominent application due to its flexibility, mobility, and low operational cost. However, under the dynamic and uncertainty of Internet of Things data collection and energ... Read More about Joint Speed Control and Energy Replenishment Optimization for UAV-Assisted IoT Data Collection With Deep Reinforcement Transfer Learning.

Joint Coding and Scheduling Optimization for Distributed Learning Over Wireless Edge Networks (2021)
Journal Article
Van Huynh, N., Hoang, D. T., Nguyen, D. N., & Dutkiewicz, E. (2022). Joint Coding and Scheduling Optimization for Distributed Learning Over Wireless Edge Networks. IEEE Journal on Selected Areas in Communications, 40(2), 484-498. https://doi.org/10.1109/jsac.2021.3118432

Unlike theoretical analysis of distributed learning (DL) in the literature, DL over wireless edge networks faces the inherent dynamics/uncertainty of wireless connections and edge nodes, making DL less efficient or even inapplicable under the highly... Read More about Joint Coding and Scheduling Optimization for Distributed Learning Over Wireless Edge Networks.

Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach (2021)
Journal Article
Van Huynh, N., Nguyen, D. N., Hoang, D. T., & Dutkiewicz, E. (2021). Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach. IEEE Transactions on Communications, 69(9), 5948-5961. https://doi.org/10.1109/tcomm.2021.3088305

In intelligent transportation systems (ITS), vehicles are expected to feature with advanced applications and services which demand ultra-high data rates and low-latency communications. For that, the millimeter wave (mmWave) communication has been eme... Read More about Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach.

DeepFake: Deep Dueling-Based Deception Strategy to Defeat Reactive Jammers (2021)
Journal Article
Van Huynh, N., Hoang, D. T., Nguyen, D. N., & Dutkiewicz, E. (2021). DeepFake: Deep Dueling-Based Deception Strategy to Defeat Reactive Jammers. IEEE Transactions on Wireless Communications, 20(10), 6898-6914. https://doi.org/10.1109/twc.2021.3078439

In this paper, we introduce DeepFake, a novel deep reinforcement learning-based deception strategy to deal with reactive jamming attacks. In particular, for a smart and reactive jamming attack, the jammer is able to sense the channel and attack the c... Read More about DeepFake: Deep Dueling-Based Deception Strategy to Defeat Reactive Jammers.

Time Scheduling and Energy Trading for Heterogeneous Wireless-Powered and Backscattering-Based IoT Networks (2021)
Journal Article
Nguyen, N., Nguyen, D. N., Hoang, D. T., Van Huynh, N., Dutkiewicz, E., Nguyen, N., & Nguyen, Q. (2021). Time Scheduling and Energy Trading for Heterogeneous Wireless-Powered and Backscattering-Based IoT Networks. IEEE Transactions on Wireless Communications, 20(10), 6835-6851. https://doi.org/10.1109/twc.2021.3077018

This article studies the strategic interactions between an IoT service provider (IoTSP) which consists of heterogeneous IoT devices and its energy service provider (ESP). To that end, we propose an economic framework using the Stackelberg game to max... Read More about Time Scheduling and Energy Trading for Heterogeneous Wireless-Powered and Backscattering-Based IoT Networks.

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies (2020)
Journal Article
Nguyen, C. T., Saputra, Y. M., Huynh, N. V., Nguyen, N., Khoa, T. V., Tuan, B. M., …Ottersten, B. (2020). A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies. IEEE Access, 8, 153479-153507. https://doi.org/10.1109/access.2020.3018140

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community... Read More about A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies.

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues (2020)
Journal Article
Nguyen, C. T., Saputra, Y. M., Van Huynh, N., Nguyen, N., Khoa, T. V., Tuan, B. M., …Ottersten, B. (2020). A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues. IEEE Access, 8, 154209-154236. https://doi.org/10.1109/access.2020.3018124

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive backgroun... Read More about A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues.

Ambient Backscatter: A Novel Method to Defend Jamming Attacks for Wireless Networks (2019)
Journal Article
Van Huynh, N., Nguyen, D. N., Thai Hoang, D., Dutkiewicz, E., & Mueck, M. (2020). Ambient Backscatter: A Novel Method to Defend Jamming Attacks for Wireless Networks. IEEE Wireless Communications Letters, 9(2), 175-178. https://doi.org/10.1109/lwc.2019.2947417

This letter introduces a novel idea to defend jamming attacks for wireless communications. In particular, when the jammer attacks the channel, the transmitter can leverage the jamming signals to transmit data by using ambient backscatter technique or... Read More about Ambient Backscatter: A Novel Method to Defend Jamming Attacks for Wireless Networks.

“Jam Me If You Can:” Defeating Jammer With Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications (2019)
Journal Article
Van Huynh, N., Nguyen, D. N., Hoang, D. T., & Dutkiewicz, E. (2019). “Jam Me If You Can:” Defeating Jammer With Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications. IEEE Journal on Selected Areas in Communications, 37(11), 2603-2620. https://doi.org/10.1109/jsac.2019.2933889

With conventional anti-jamming solutions like frequency hopping or spread spectrum, legitimate transceivers often tend to “escape” or “hide” themselves from jammers. These reactive anti-jamming approaches are constrained by the lack of timely knowled... Read More about “Jam Me If You Can:” Defeating Jammer With Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications.

Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System With Online Reinforcement Learning (2019)
Journal Article
Van Huynh, N., Hoang, D. T., Nguyen, D. N., Dutkiewicz, E., Niyato, D., & Wang, P. (2019). Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System With Online Reinforcement Learning. IEEE Transactions on Communications, 67(8), 5736-5752. https://doi.org/10.1109/tcomm.2019.2913871

Ambient backscatter has been introduced with a wide range of applications for low power wireless communications. In this paper, we propose an optimal and low-complexity dynamic spectrum access framework for the RF-powered ambient backscatter system.... Read More about Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System With Online Reinforcement Learning.

Optimal and Fast Real-Time Resource Slicing With Deep Dueling Neural Networks (2019)
Journal Article
Van Huynh, N., Thai Hoang, D., Nguyen, D. N., & Dutkiewicz, E. (2019). Optimal and Fast Real-Time Resource Slicing With Deep Dueling Neural Networks. IEEE Journal on Selected Areas in Communications, 37(6), 1455-1470. https://doi.org/10.1109/jsac.2019.2904371

Effective network slicing requires an infrastructure/network provider to deal with the uncertain demands and real-time dynamics of the network resource requests. Another challenge is the combinatorial optimization of numerous resources, e.g., radio,... Read More about Optimal and Fast Real-Time Resource Slicing With Deep Dueling Neural Networks.

Ambient Backscatter Communications: A Contemporary Survey (2018)
Journal Article
Van Huynh, N., Hoang, D. T., Lu, X., Niyato, D., Wang, P., & Kim, D. I. (2018). Ambient Backscatter Communications: A Contemporary Survey. Communications Surveys and Tutorials, IEEE Communications Society, 20(4), 2889-2922. https://doi.org/10.1109/comst.2018.2841964

Recently, ambient backscatter communication has been introduced as a cutting-edge technology which enables smart devices to communicate by utilizing ambient radio frequency (RF) signals without requiring active RF transmission. This technology is esp... Read More about Ambient Backscatter Communications: A Contemporary Survey.

Optimal Time Scheduling for Wireless-Powered Backscatter Communication Networks (2018)
Journal Article
Van Huynh, N., Hoang, D. T., Niyato, D., Wang, P., & Kim, D. I. (2018). Optimal Time Scheduling for Wireless-Powered Backscatter Communication Networks. IEEE Wireless Communications Letters, 7(5), 820-823. https://doi.org/10.1109/lwc.2018.2827983

This letter introduces a novel wireless-powered backscatter communication system which allows sensors to utilize RF signals transmitted from a dedicated RF energy source to transmit data. In the proposed system, when the RF energy source transmits RF... Read More about Optimal Time Scheduling for Wireless-Powered Backscatter Communication Networks.

Joint network embedding and server consolidation for energy–efficient dynamic data center virtualization (2017)
Journal Article
Nam, T. M., Thanh, N. H., Hieu, H. T., Manh, N. T., Huynh, N. V., & Tuan, H. D. (2017). Joint network embedding and server consolidation for energy–efficient dynamic data center virtualization. Computer Networks, 125, 76-89. https://doi.org/10.1016/j.comnet.2017.06.007

Cloud computing has emerged in recent years as a promising paradigm that facilitates such new service models as Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). As the number of cloud service provider... Read More about Joint network embedding and server consolidation for energy–efficient dynamic data center virtualization.

A generalized resource allocation framework in support of multi-layer virtual network embedding based on SDN (2015)
Journal Article
Nguyen, H. T., Vu, A. V., Nguyen, D. L., Nguyen, V. H., Tran, M. N., Ngo, Q. T., …Magedanz, T. (2015). A generalized resource allocation framework in support of multi-layer virtual network embedding based on SDN. Computer Networks, 92(Part 2), 251-269. https://doi.org/10.1016/j.comnet.2015.09.042

Network Virtualization (NV) allows multiple heterogeneous architectures to simultaneously coexist in a shared infrastructure. Embedding multiple virtual networks (VNs) in a shared substrate deals with efficient mapping of virtual resources in the phy... Read More about A generalized resource allocation framework in support of multi-layer virtual network embedding based on SDN.