Abstract
Cognitive radio network is an intelligent wireless communication system which can adjust its transmission parameters according to the environment thanks to its learning ability. It is a feasible and promising direction to solve the spectrum scarcity issue and has become a research focus in communication community. However, cognitive radio network is vulnerable to jamming attack, resulting in serious degradation of spectrum utilization. In this article, we view the anti-jamming task of cognitive radio as a Markov decision process and propose an intelligent anti-jamming scheme based on deep reinforcement learning. We aim to learn a policy for users to maximize their rate of successful transmission. Specifically, we design Double Deep Q Network (Double DQN) to model the confrontation between the cognitive radio network and the jammer. The Q network is implemented using Transformer encoder to effectively estimate action-values from raw spectrum data. The simulation results indicate that our approach can effectively defend against several kinds of jamming attacks.
Highlights
C OGNITIVE radio (CR) is a new form of wireless communication whose transceiver can detect available communication channels intelligently [1]
In this paper we aim to mitigate channel jamming attack in cognitive radio network and develop an antijamming scheme based on deep reinforcement learning techniques
The details of the algorithm for intelligent anti-jamming scheme based on deep reinforcement learning are given in Algorithm 1
Summary
C OGNITIVE radio (CR) is a new form of wireless communication whose transceiver can detect available communication channels intelligently [1]. In this paper we aim to mitigate channel jamming attack in cognitive radio network and develop an antijamming scheme based on deep reinforcement learning techniques. Inspired by the success of DRL, Liu et al [11] proposed a deep anti-jamming Q-network to estimate the Q-values of communication actions by directly inputting the spectrum waterfalls [12] into a convolutional neural network (CNN) [13] Compared with these existing works, our anti-jamming scheme adopts deep reinforcement learning, Double DQN [14], in which a Transformer Encoder [15] is used as the Q-network to effectively model the action-value function.
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