Deep reinforcement learning-empowered anti-jamming strategy aided by sample information entropy

For the deep reinforcement learning (DRL)-empowered intelligent jamming, an anti-jamming strategy aided by sample information entropy was proposed. Firstly, the anti-jamming strategy network and entropy prediction network were designed based on neural networks. Then, the anti-jamming strategy network and entropy prediction network were trained with the samples of the spectrum waterfall, which were formed by performing the short-time Fourier transform to the received signals. The information entropy prediction network was utilized for fine-grained selection of training samples of the anti-jamming strategy network to improve the quality of training samples, thereby enhancing the ultimate online decision-making capability and generalization performance of the anti-jamming strategy. The simulation results indicate that under the extreme condition where the jamming strategy update frequency does not exceed forty times that of the communication anti-jamming strategy and the maximum number of jamming channels is 3, the proposed anti-jamming strategy, aided by sample information entropy, can still achieve a success rate of at least 61%. Moreover, compared to several other anti-jamming strategies, the proposed strategy demonstrates faster convergence.