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.
翻译:常规防腐蚀方法主要依靠更换频率来隐藏或逃离干扰器。这些方法在带宽使用方面效率不高,而且可能导致干扰的可能性很高。本文中,与现有工作不同,根据欺骗干扰器进入受害者频道,同时在安全频道中保持合法用户的通信,提出了新的防腐蚀战略。由于用户不了解干扰器频道信息,因此,利用强化学习(RL),建议采用最佳频道选择方案和次最佳电力分配办法。对拟议的防腐蚀技术的性能进行评估,方法是从统计上得出接受的总权力(TRP)的较低约束值。分析结果表明,对于某个特定接入点,在最高可达到的TRP(即没有干扰器)50%以上的情况下,对于单一用户和三个频率频道的情况,实现了50%以上的干扰器袭击。此外,随着用户数量和可用频道的增加,还提议了这一价值。将获得的结果与现有的两种基于RL的反干扰技术进行比较,以及随机频道分配战略,在没有任何干扰式最佳搜索方法的情况下,在没有任何阻截断式搜索法的情况下,在最接近的RP结果上展示了拟议的最佳搜索法。