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Then we developed an RF classification framework using a deep residual neural network. Here we utilized the frequency domain RF signatures. We built an RF spectrogram database. Here, we adopted a two-stage approach here: In the first stage, we performed RF detection using a goodness of fit-based spectrum sensing algorithm and then we performed classification using the deep residual neural network.
Later on, we developed a combined detection and classification framework using a "you only look once" (YOLO)-based framework. Here, we utilized the spectrogram matrix for simultaneous detection and classification. For both cases mentioned here, we obtained robust detection and classification performances at diverse channel conditions.
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IEEE/ICACT20230149 Slide.07
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