A DRL-Based Intelligent Jamming Approach for Joint Channel and Power Optimization

The traditional jamming methods mainly focus on the optimization of a single domain, such as frequency domain. However, in the practical wireless communication, the jamming methods in single dimension domain are limited and difficult to cope with multidomain scenarios. To overcome these issues, this paper investigates the problem of joint decision-making for jamming channel and power in a dynamic spectrum environment. Firstly, the Markov decision process (MDP) is used to formulate the jamming channel and power joint selection problem. Then, a deep reinforcement learning- (DRL-) based jamming algorithm is proposed with the function of parallel learning and joint decision-making. Specially, to accelerate the learning speed of the algorithm, the prioritized experience reply (PER) technology is introduced. Finally, a practical jamming testbed is built to evaluate the proposed algorithm. The simulation results and the testbed results demonstrate the effectiveness of the proposed algorithm, which can guarantee the jamming effect and maximize the utilization of jamming resources.