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.
翻译:本文开发了一个新的框架来击败超级反应干扰器,这是实际中最困难的干扰袭击之一。 具体地说, 干扰器拥有无限的动力预算, 并配备了自干预抑制能力, 能够同时攻击和收听发射机的活动。 因此, 处理超反应干扰器非常具有挑战性。 因此, 我们引入了一个智能欺骗机制来吸引干扰器持续攻击频道, 然后利用干扰信号来传输基于环境后向散射通信技术的数据。 为了检测后向散射信号, 最多的可能性探测器可以被采用。 但是, 这种方法由于计算复杂程度高, 且需要当前传播环境的模型以及频道状态信息。 因此, 我们提出一个能够动态地适应任何频道和噪音分布的深层次的基于学习的探测器。 通过一个长期的短时间记忆网络, 我们的探测器可以了解收到信号的可靠性, 以便达到最接近于最佳可能性探测器的性能。 通过模拟和理论结果, 我们通过模拟和理论结果, 证明我们的方法, 能够以更强的电动性能 来达到攻击频道的性能。