  Most of the current anti-jamming algorithms for wireless communications only
consider how to avoid jamming attacks, but ignore that the communication
waveform or frequency action may be obtained by the jammers. Although existing
anti-jamming methods can guarantee temporary communication effects, the
long-term performance of these anti-jamming methods may be depressed when
intelligent jammers are capable of learning from historical communication
activities. Aiming at this issue, a hidden anti-jamming method based on the
idea of reducing the jammer's sense probability is proposed. Firstly, the
sensing probability of the jammer is obtained by calculating the correlation
between the actions of the jammer and the user. Later, a deep reinforcement
learning framework is designed, which aims at not only maximizing the
communication throughput but also minimizing the action's correlation between
the jammer and the user. Finally, a hidden anti-jamming algorithm is proposed,
which links the instantaneous return with the communication quality of users
and the correlation between users and jammer. The simulation result shows that
the proposed algorithm not only avoids being sensed by the jammer but also
improves its anti-jamming performance compared to the current algorithm that
only considers jamming avoidance.
