Defeating Super-Reactive Jammers WithDeception Strategy: Modeling, SignalDetection, and Performance Analysis

This paper aims to develop a novel framework to defeat a super-reactive jammer, one of the mostdifficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budgetand is equipped with the self-interference suppression capability to simultaneously attack and listen tothe 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 andthen leverage jamming signals to transmit data based on the ambient backscatter communication whichis resilient to radio interference/jamming. To decode the backscattered signals, the maximum likelihood(ML) detector can be adopted. However, the method is notorious for its high computational complexityand require a specific mathematical model for the communication system. Hence, we propose a deeplearning-based detector that can dynamically adapt to any channel and noise distributions. With the LongShort-Term Memory network, our detector can learn the received signals’ dependencies to achieve theperformance close to that of the optimal ML detector. Through simulation and theoretical results, wedemonstrate that with proposed approaches, the more power the jammer uses to attack the channel, thebetter bit error rate performance we can achiev