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This flowchart illustrates our end-to-end machine learning pipeline for intelligent spectrum sensing. We begin with dataset generation and preprocessing, where we simulate signals, extract features, and prepare the training data. Next, we move to model training, where multiple classification and regression algorithms are trained simultaneously for a comprehensive comparison. Once trained, each model enters the evaluation phase, where we measure key performance metrics, like detection probability and false alarm rate, against the test dataset. Finally, based on this evaluation, the system makes a sensing decision, selecting the best-performing model to deploy for real-time spectrum detection.
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IEEE/ICACT20260241 Slide.12
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