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IEEE/ICACT20230149 Slide.11        [Big Slide]	Oral Presentation
	Our framework consists of an encoder, a classifier, and a decoder. The encoder compresses the input IQ signal to a lower dimensional latent feature vector (z). The encoder consists of five residual stacks and three fully connected (FC) layers. We used Tanh activation in the encoder path. The classifier classifies the type of drone. It shares some of the layers of the encoder. We added some residual stacks with RELU activation for the classification path which was followed by three fully connected layers. At the final layer, we used thesoftmax activation. The decoder receives the concatenated vector from the encoder and classifier, and then it reconstructs the input signal. We have used a simple CNN architecture as the decoder. We utilized Tanh activation as we used for the encoder path.

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