Defeating Super-Reactive Jammers With Deception Strategy: Modeling, Signal Detection, and Performance Analysis
Nguyen Van Huynh, Diep N. Nguyen, Dinh Thai Hoang, Thang X. Vu, Eryk, Dutkiewicz, and Symeon Chatzinotas

TL;DR
This paper presents a novel deception strategy and deep learning-based detection method to effectively counter super-reactive jammers, improving communication resilience by leveraging ambient backscatter technology and adaptive signal detection.
Contribution
It introduces a smart deception mechanism combined with a deep learning detector for defeating super-reactive jammers, advancing the state-of-the-art in anti-jamming communication techniques.
Findings
Deep learning detector approaches maximum likelihood performance
Powerful jamming improves bit error rate performance for the transmitter
Proposed methods adapt to various channels and noise conditions
Abstract
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…
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Taxonomy
TopicsWireless Communication Security Techniques · Energy Harvesting in Wireless Networks · Adversarial Robustness in Machine Learning
