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A Fuzzy Short-Time Fourier Transform and Support Vector Machine Framework for Reliable Spectrum Sensing in Noisy Wireless Channels
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A Fuzzy Short-Time Fourier Transform and Support Vector Machine Framework for Reliable Spectrum Sensing in Noisy Wireless Channels
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Journal Article
A Fuzzy Short-Time Fourier Transform and Support Vector Machine Framework for Reliable Spectrum Sensing in Noisy Wireless Channels
2026
Overview
This paper presents a comparative study of two learning pipelines that learn intelligent spectrum sensing from Quadrature Phase-Shift Keying (QPSK) In-phase/Quadrature (I/Q) data generated in GNU Radio. Both pipelines used two-band Short-Time Fourier Transform (STFT) features with pseudo-labeling based on energy detection. The first pipeline uses a classification approach using Random Forest (RF)on raw STFT features, whereas the second pipeline follows neuro-fuzzy processing of STFT features before classification within a Support Vector Machine (SVM) framework, hence the naming: Fuzzy STFT-SVM (FuST-SVM). Experiments conducted under low (-10dB), medium (5dB), and high (10dB) Signal-to-Noise Ratio (SNR) conditions reveal the dominance of FuST-SVM, with accuracy scores anywhere between 90.65 and 92.46%. The work demonstrates a highly effective and robust solution for the reliable sensing of the spectrum under harsh and heterogeneous noise environments.
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