Reliable Spectrum Sensing for Future Wireless Networks: A CFAR-Unified Study of Classical and ML Approaches

Reliable spectrum sensing is essential for Cognitive Radio (CR) systems, enabling Secondary Users (SUs) to achieve opportunistic spectrum access while protecting Primary Users (PUs) from interference. Conventional static spectrum sensing techniques, such as Energy Detection (ED) and Matched Filter Detection (MFD), remain widely used, but their performance degrades under low Signal-to-Noise Ratio (SNR) conditions or when strict requirements for prior Channel State Information (CSI) exist. Although Machine Learning (ML) and Deep Learning (DL) approaches have recently been explored as alternatives, existing studies often suffer from inconsistent datasets, mismatched assumptions, or the absence of strict false-alarm control, making fair comparison difficult. This paper proposes a unified and Constant False Alarm Rate (CFAR) controlled evaluation framework to systematically compare conventional sensing techniques (ED, MFD) with supervised ML techniques, such as Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and Convolutional Neural Network (CNN). All detectors operate on identical In-phase and Quadrature (IQ) sample inputs derived from Quadrature Phase Shift Keying (QPSK) modulated pilot signals, and thresholds are calibrated exclusively from noise-only samples to ensure a CFAR across all methods. The results of the simulations demonstrate that MFD achieves the best overall performance when the pilot information is known, validating its theoretical optimality. Furthermore, the CNN detector significantly outperforms conventional ML models at low and moderate SNRs and approaches MFD performance above -5 dB, indicating that CNN implicitly learns correlationlike structures similar to MFD.