All Outputs (4)

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.2019.29

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 Communicati

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

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.2019.290

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.