  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.
