Exploring the Potential of Residual Networks for Efficient Sub-Nyquist Spectrum Sensing

We propose ReSense, a residual network for spectrum sensing high-frequency signals with low-frequency samplers. ReSense first transforms the aliased signal from low-frequency samplers into image-like inputs and uses multiple convolution layers and skip connections to predict the signal’s frequency components to enable spectrum sensing. We evaluate ReSense on the signal dataset with single and double frequencies and achieve 95% and 40% accuracy in detecting modulation type on respective datasets, indicating accurate spectrum sensing.