Interpretable Back Propagation Neural Network Based Fast Directional Modulation Design
摘要
Traditional solutions for directional modulation (DM) rely on weight optimization methods, which has high computational complexity and cannot be implemented in real time. To solve the problem, in this paper, an interpretable back propagation (BP) neural network is proposed for fast directional modulation design. The simulation results confirm its powerful fitting ability to the output weights. LIME’s locally interpretable models provide a better understanding of the inner workings of the system and the predicted outcomes.