Sparse Fourier transform based millimeter wave wideband spectrum sensing

This paper presents a wideband spectrum sensing methods based on the sparse fast Fourier transform applying for Eband, which can capture spectrum in real-time without sampling the signal at the Nyquist rate. Our approach builds on recent advances in the area of sparse Fourier transform. At first, we convert the signal of E-band to the baseband, and then sample the wide baseband signal at a low sampling rate, obtaining a small subset of the original signal samples. Finally the spectrum of the wideband signal is recovered using the sparse Fourier transform. The required sampling rate can be reduced to about 10% of the conventional FFT methods required. Meanwhile, it improves operational efficiency, and makes it possible to sense in real-time. MATLAB is used to demonstrate the feasibility of this design. This algorithm has an extremely low rate of false negatives when the spectrum occupancy is sparse and proposes solutions even in scenarios where the spectrum is not sparse.