An Effective Reconstruction Algorithm Based on Modulated Wideband Converter for Wideband Spectrum Sensing

In recent years, wideband spectrum sensing combined with sub-Nyquist sampling and compressed sensing technology in the field of cognitive radio has received widespread attention. However, the existing broadband detection methods based on sub-Nyquist sampling do not fully consider the spectrum feature changes caused by the sampling structure, resulting in unnecessary computational complexity in the support reconstruction process. In this paper, a novel reconstruction algorithm, called nearest orthogonal matching pursuit (N-OMP), is proposed based on modulated wideband converter (MWC) sub-Nyquist sampling structure. This algorithm utilizes the special power spectrum slicing characteristics caused by pseudo-random sequence and low-pass filtering. After an occupied subband is detected, it calculates the correlation coefficient between the residual vector and the column vectors corresponding to two adjacent subbands, based on which we can directly judge the occupancy of two adjacent subbands by comparing the size of the two correlation coefficients, thereby reducing the number of iterations of the reconstruction algorithm. Theoretical derivation and simulation experiment results show that, compared with the orthogonal matching pursuit (OMP) algorithm, the proposed algorithm can reduce the computational complexity by up to 50%, while showing better support reconstruction accuracy.