Two-dimensional anti-jamming communication based on deep reinforcement learning

In this paper, a two-dimensional anti-jamming communication scheme for cognitive radio networks is developed, in which a secondary user (SU) exploits both spread spectrum and user mobility to address jamming attacks, while not interfering with primary users. By applying a deep Q-network algorithm, this scheme determines whether to recommend that the SU leave an area of heavy jamming and chooses a frequency hopping pattern to defeat smart jammers. Without knowing the jamming model and the radio channel model, the SU derives an optimal anti-jamming communication policy using Q-learning in a proposed dynamic game, and applies a deep convolution neural network to accelerate the learning speed with a large number of frequency channels. The proposed scheme can increase the signal-to-interference-plus-noise ratio and improve the utility of the SU against cooperative jamming, compared with a Q-learning-only based benchmark system.