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To reduce the complexity of the proposed detector, we use 1Ąż1 convolution between convolution layer 2 and 4 and reduce the number of feature channels to limit the training parameters. The first fully connected layer has 512 neurons, and the second fully connected layer as the output layer has 15(fifteen) neurons, representing 15 jamming signals. All the layers use the ReLU activation function except for the second fully connected layer that takes the Softmax activation function. To prevent the occurrence of the network overfitting, we apply Dropout process after the two pooling layers and the first fully connected layer. The Dropout rates are set as 0.2, 0.25(zero point two five), and 0.5, respectively. |
IEEE/ICACT20230135 Slide.09
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