  Conventional anti-jamming methods mainly focus on preventing single jammer
attacks with an invariant jamming policy or jamming attacks from multiple
jammers with similar jamming policies. These anti-jamming methods are
ineffective against a single jammer following several different jamming
policies or multiple jammers with distinct policies. Therefore, this paper
proposes an anti-jamming method that can adapt its policy to the current
jamming attack. Moreover, for the multiple jammers scenario, an anti-jamming
method that estimates the future occupied channels using the jammers' occupied
channels in previous time slots is proposed. In both single and multiple
jammers scenarios, the interaction between the users and jammers is modeled
using recurrent neural networks (RNN)s. The performance of the proposed
anti-jamming methods is evaluated by calculating the users' successful
transmission rate (STR) and ergodic rate (ER), and compared to a baseline based
on Q-learning (DQL). Simulation results show that for the single jammer
scenario, all the considered jamming policies are perfectly detected and high
STR and ER are maintained. Moreover, when 70 % of the spectrum is under jamming
attacks from multiple jammers, the proposed method achieves an STR and ER
greater than 75 % and 80 %, respectively. These values rise to 90 % when 30 %
of the spectrum is under jamming attacks. In addition, the proposed
anti-jamming methods significantly outperform the DQL method for all the
considered cases and jamming scenarios.
