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Deep Reinforcement Learning Based Multi-User Anti-Jamming Strategy

The threat of radio frequency jamming attack to cognitive radio network is an issue that has been discussed for a long time. Q-learning is a widely used anti-jamming algorithm due to its model-free characteristic. However, the traditional Q-learning based anti-jamming algorithms suffer from some limitations when dealing with high-dimensional or continuous inputs. The recently proposed double Deep Q-learning Network (DQN) overcomes this weakness by approximating the table based Q function with a deep neural network. In this paper, we apply the double DQN algorithm with frequency hopping strategy against RF jamming attack in a multi-user environment. We test the performances of three types of neural networks which are the fully connected network (FCN), the convolutional neural network (CNN) and the long short term memory (LSTM). The simulation shows the effectiveness of the double DQN algorithm. Meanwhile, the FCN agent gives the best result concerning stability.