[en] This paper develops a novel framework to defeat a super-reactive jammer, one of the most difficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budget and is equipped with the self-interference suppression capability to simultaneously attack and listen to the transmitter’s activities. Consequently, dealing with super-reactive jammers is very challenging. Thus, we introduce a smart deception mechanism to attract the jammer to continuously attack the channel and then leverage jamming signals to transmit data based on the ambient backscatter communication technology. To detect the backscattered signals, the maximum likelihood detector can be adopted. However, this method is notorious for its high computational complexity and requires the model of the current propagation environment as well as channel state information. Hence, we propose a deep learning-based detector that can dynamically adapt to any channels and noise distributions. With a Long Short-Term Memory network, our detector can learn the received signals’ dependencies to achieve a performance close to that of the optimal maximum likelihood detector. Through simulation and theoretical results, we demonstrate that with our approaches, the more power the jammer uses to attack the channel, the better bit error rate performance the transmitter can achieve.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Van Huynh, Nguyen; University of Technology Sydney
Nguyen, Diep N.; University of Technology Sydney
Thai Hoang, Dinh; University of Technology Sydney
Vu, Thang Xuan ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Dutkiewicz, Eryk; University of Technology Sydney
Chatzinotas, Symeon ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
External co-authors :
yes
Language :
English
Title :
Defeating Super-Reactive Jammers With Deception Strategy: Modeling, Signal Detection, and Performance Analysis
Publication date :
September 2022
Journal title :
IEEE Transactions on Wireless Communications
ISSN :
1558-2248
Publisher :
Institute of Electrical and Electronics Engineers, New York, United States - New York