Allocation method of communication interference resource based on deep reinforcement learning of maximum policy entropy

Abstract

In order to solve the optimization of the interference resource allocation in communication network countermeasures, an interference resource allocation method based on the maximum policy entropy deep reinforcement learning (MPEDRL) was proposed. The method introduced the idea of deep reinforcement learning into the communication countermeasures resource allocation, it could enhance the exploration of the policy and accelerate the convergence to the global optimum with adding the maximum policy entropy criterion and adaptively adjusting the entropy coefficient. The method modeled interference resource allocation as Markov decision process, then established the interference strategy network to output allocation scheme, constructing the interference effect evaluation network of the clipped twin structure for efficiency evaluation, and trained the policy network and the evaluation network with the goal of maximizing the strategy entropy and the cumulative interference efficacy, then decided the optimal interference resource allocation scheme. The simulation results show that the algorithm can effectively solve the resource allocation problem in communication network confrontation, comparing with the existing deep reinforcement learning methods, it has faster learning speed and less fluctuation in the training process, and achieved 15% higher jamming efficacy than DDPG-based method.

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

“…Based on the abovementioned studies, various types of methods have made a breakthrough in the field of intelligent jamming [13][14][15][16][17][18][19]. In [13], the authors proposed an intelligent jamming algorithm based on the Q-learning and evaluated the jamming performance of the algorithm in different antijamming strategies.…”

“…where the jammer could adaptively adjust its own power strategy to jam according to the working state of the user, and the algorithm could guarantee converge to the optimal jamming strategy. RaoHua et al [19] proposed a jamming resource allocation method based on the maximum policy entropy DRL to enhance the exploration of the strategy to determine the optimal jamming power allocation scheme.…”

“…On the other hand, the increasing level of intelligence in the field of antijamming and the increasing diversity of antijamming methods have brought huge challenges to jamming [3][4][5][6][7][8][9][10][11][12]. Therefore, to cope with the increasing powerful antijamming technology and overcome the disadvantages of the traditional jamming patterns being single and inflexible, many scholars have carried out researches on communication jamming technology [13][14][15][16][17][18][19]. However, these jamming theories mainly focus on the jamming methods of a single dimensional domain, which can not be extended to other domains.…”