Abstract
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.
Highlights
W ITH the rapid development of Internet of Things (IoT) technology and its applications, the demand for Internet access has grown rapidly
WORK In this paper, combining modulated wideband converter (MWC) sub-Nyquist sampling and compressed sensing technology, we propose a novel compressed sensing reconstruction algorithm
The algorithm uses the power spectrum slice characteristics brought by MWC sampling to determine the occupancy of adjacent sub-channels directly
Summary
W ITH the rapid development of Internet of Things (IoT) technology and its applications, the demand for Internet access has grown rapidly. To provide the possibility of reconstructing the signal based on sub-Nyquist sampling, both MWC and AIC use a mixed operation with a pseudo-random sequence to superimpose the multi-band signal spectrum information to the baseband domain with different weights. In order to effectively deal with the wireless fading problem, Gong et al proposed a multi-antenna generalized modulated converter to achieve sub-Nyquist sampling, and further proposed two compressed subspace algorithms to achieve broadband spectrum sensing in [25]. The existing wideband spectrum detection methods based on sub-Nyquist sampling do not fully consider the changes of spectral characteristics caused by the sampling structure, and its potential for low computational complexity has yet to be explored, which is the focus of this paper.
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