Addressing preprocessing for spectrum sensing using image processing

Abstract

This paper presents a novel approach to preprocessing spectrograms for spectrum sensing (SS) from the image-processing perspective. Gaussian bilateral filtering, a common image-denoising technique, has been introduced to improve spectrograms in SS in high-noise environments. This approach is evaluated by simulating LTE and 5 G NR signal spectrograms across various signal-to-noise ratios (SNRs). An extensive review and comparison with recent spectrogram-based works for different applications demonstrated that the proposed approach does not depend on deep learning models to denoise spectrograms, showing a simpler yet effective strategy to address SS.