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 in-dicate 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,com-pared to several other anti-jamming strategies,the proposed strategy demonstrates faster convergence.
anti-jammingdeep reinforcement learningsample information entropyintelligent jamming
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