The paper combines a cross-modal neural network for jamming-type recognition with a DQN-based anti-jamming waveform selector, reporting 95.45% accuracy on three simulated jamming classes and faster reward convergence than SARSA.
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A Unified Anti-Jamming Design in Complex Environments Based on Cross-Modal Fusion and Intelligent Decision-Making
The paper combines a cross-modal neural network for jamming-type recognition with a DQN-based anti-jamming waveform selector, reporting 95.45% accuracy on three simulated jamming classes and faster reward convergence than SARSA.