no code implementations • 3 Apr 2024 • Khalid Albagami, Nguyen Van Huynh, Geoffrey Ye Li
Recently, deep learning (DL) has been emerging as a promising approach for channel estimation and signal detection in wireless communications.
no code implementations • 28 Feb 2024 • Guangyuan Liu, Nguyen Van Huynh, Hongyang Du, Dinh Thai Hoang, Dusit Niyato, Kun Zhu, Jiawen Kang, Zehui Xiong, Abbas Jamalipour, Dong In Kim
For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms.
no code implementations • 9 Dec 2023 • Nguyen Van Huynh, Jiacheng Wang, Hongyang Du, Dinh Thai Hoang, Dusit Niyato, Diep N. Nguyen, Dong In Kim, Khaled B. Letaief
The recent evolution of generative artificial intelligence (GAI) leads to the emergence of groundbreaking applications such as ChatGPT, which not only enhances the efficiency of digital content production, such as text, audio, video, or even network traffic data, but also enriches its diversity.
no code implementations • 12 May 2023 • Bolun Zhang, Nguyen Van Huynh
Unfortunately, this E2E learning architecture requires a prior differentiable channel model to jointly train the deep neural networks (DNNs) at the transceivers, which is hardly obtained in practice.
1 code implementation • 15 Dec 2022 • Kaidi Xu, Nguyen Van Huynh, Geoffrey Ye Li
To overcome these limitations, we propose a multi-agent deep reinforcement learning (MADRL) based power control scheme for the HetNet, where each access point makes power control decisions independently based on local information.
no code implementations • 7 Mar 2021 • Nguyen Van Huynh, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz
The jointly optimal framework in this article is also applicable to any distributed learning scheme with heterogeneous and uncertain computing nodes.
no code implementations • 15 Feb 2021 • Cong T. Nguyen, Nguyen Van Huynh, Nam H. Chu, Yuris Mulya Saputra, Dinh Thai Hoang, Diep N. Nguyen, Quoc-Viet Pham, Dusit Niyato, Eryk Dutkiewicz, Won-Joo Hwang
Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.
no code implementations • 13 May 2020 • Nguyen Van Huynh, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz
In this paper, we introduce DeepFake, a novel deep reinforcement learning-based deception strategy to deal with reactive jamming attacks.
Face Swapping Networking and Internet Architecture Information Theory Signal Processing Information Theory
no code implementations • 2 May 2020 • Nguyen Van Huynh, Diep N. Nguyen, Dinh Thai Hoang, Eryk Dutkiewicz
To that end, we develop a lightweight yet very effective parallel Q-learning algorithm to quickly obtain the optimal policy by simultaneously learning from various vehicles.
no code implementations • 8 Apr 2019 • Nguyen Van Huynh, Diep N. Nguyen, Dinh Thai Hoang, Eryk Dutkiewicz
Bringing together the latest advances in neural network architectures and ambient backscattering communications, this work allows wireless nodes to effectively "face" the jammer by first learning its jamming strategy, then adapting the rate or transmitting information right on the jamming signal.
no code implementations • 26 Feb 2019 • Nguyen Van Huynh, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz
This article develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demand from tenants.
no code implementations • 8 Sep 2018 • Nguyen Van Huynh, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz, Dusit Niyato, Ping Wang
To cope with such incomplete knowledge of the environment, we develop a low-complexity online reinforcement learning algorithm that allows the secondary transmitter to "learn" from its decisions and then attain the optimal policy.