Conventional anti-jamming methods mostly rely on frequency hopping to hide or escape from jammers. These approaches are not efficient in terms of bandwidth usage and can also result in a high probability of jamming. Different from existing works, in this article, a novel anti-jamming strategy is proposed based on the idea of deceiving the jammer into attacking a victim channel while maintaining the communications of legitimate users in safe channels. Since the jammer’s channel information is not known to the users, an optimal channel selection scheme and a sub-optimal power allocation algorithm are proposed using reinforcement learning (RL). The performance of the proposed anti-jamming technique is evaluated by deriving the statistical lower bound of the total received power (TRP). Analytical results show that, for a given access point, over 50% of the highest achievable TRP, i.e. in the absence of jammers, is achieved for the case of a single user and three frequency channels. Moreover, this value increases with the number of users and available channels. The obtained results are compared with two existing RL based anti-jamming techniques, and a random channel allocation strategy without any jamming attacks. Simulation results show that the proposed anti-jamming method outperforms the compared RL based anti-jamming methods and the random search method, and yields near optimal achievable TRP.

中文翻译：

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用于在无线网络中欺骗反应性干扰器的强化学习

传统的抗干扰方法大多依靠跳频来隐藏或逃避干扰。这些方法在带宽使用方面效率不高，并且还可能导致很高的干扰概率。与现有工作不同，本文提出了一种新颖的抗干扰策略，该策略基于欺骗干扰器攻击受害通道的思想，同时保持合法用户在安全通道中的通信。由于用户不知道干扰机的信道信息，因此提出了使用强化学习 (RL) 的最佳信道选择方案和次优功率分配算法。通过推导总接收功率 (TRP) 的统计下限来评估所提出的抗干扰技术的性能。分析结果表明，对于给定的接入点，对于单个用户和三个频道的情况，可实现超过 50% 的最高可实现 TRP，即在没有干扰的情况下。此外，该值随着用户数量和可用频道的增加而增加。将获得的结果与两种现有的基于 RL 的抗干扰技术和没有任何干扰攻击的随机信道分配策略进行了比较。仿真结果表明，所提出的抗干扰方法优于比较基于 RL 的抗干扰方法和随机搜索方法，并产生接近最优的可实现 TRP。将获得的结果与两种现有的基于 RL 的抗干扰技术和没有任何干扰攻击的随机信道分配策略进行了比较。仿真结果表明，所提出的抗干扰方法优于比较基于 RL 的抗干扰方法和随机搜索方法，并产生接近最优的可实现 TRP。将获得的结果与两种现有的基于 RL 的抗干扰技术和没有任何干扰攻击的随机信道分配策略进行了比较。仿真结果表明，所提出的抗干扰方法优于比较基于 RL 的抗干扰方法和随机搜索方法，并产生接近最优的可实现 TRP。