A hidden anti-jamming method based on deep reinforcement learning

TL;DR

This paper introduces a deep reinforcement learning-based hidden anti-jamming method that reduces the likelihood of detection by jammers and enhances long-term communication performance.

Contribution

It proposes a novel anti-jamming approach that minimizes the jammer's sensing probability by considering correlation and communication quality, improving over existing methods.

Findings

Reduces jammer detection probability

Improves communication throughput under jamming

Outperforms existing anti-jamming algorithms

Abstract

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.