A Dynamic Adaptive Jamming Power Allocation Method Based on Deep Reinforcement Learning

To solve the problem that traditional jamming power allocation methods are prone to waste resources and low jamming effectiveness-cost-ratio when the jamming target strategy is unknown, a dynamic adaptive jamming power allocation method based on deep reinforcement learning is proposed. When the communication power of the target and its power control strategy is completely unknown, the method takes the observation values of spatially distributed reconnaissance nodes as continuous state input and uses the deep reinforcement learning method to assist the decision-making of jamming power. It can achieve the adaptive stable jamming by the effective learning of target strategy. To further improve the performance of the algorithm, a prioritized experience replay mechanism based on temporal-difference error and an adaptive exploration strategy are designed. The simulation results show the proposed method can save 42.5% of power resources and improve the jamming effectiveness-cost-ratio when the jamming effect is equivalent to that of the traditional jamming power distribution method. The success rate and power cost of the proposed algorithm are better than those of the comparative intelligent algorithms. © 2023 Chinese Institute of Electronics. All rights reserved.