Conventional anti-jamming method mostly rely on frequency hopping to hide or
escape from jammer. 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 paper, 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 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 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 random search method, and yields
near optimal achievable TRP.

本文提出了一种新颖的反干扰策略，基于诱骗干扰者攻击受害者通道的想法，在保持合法用户在安全通道中通信的同时欺骗干扰者。通过使用强化学习算法提出了最优信道选择方案和次优功率分配方式，并通过求解接收总功率（TRP）的统计下界来评估其性能。仿真结果表明，与已有的两种基于强化学习算法的反干扰技术和随机信道分配策略相比，该方法表现更优，能够获得接近最优的 TRP。