  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.
