Abstract
In order to solve the problem of high sampling rate in the wideband spectrum sensing of cognitive radio, this paper studies the method of cyclic spectrum detection based on the modulation wideband converter (MWC). A novel fast cyclic spectrum detection algorithm of MWC based on Lorentzian Norm is proposed to deal with the influence of some non-ideal factors on the performance of the existing MWC system reconstruction algorithm in physical implementation. Firstly, the objective function for sparse optimization is build based on smoothed L0-norm constrained Lorentzian norm regularization. Then a parallel reconstruction method is implemented in a unified parametric framework by combining the fixed-step formula and the conjugate gradient algorithm with sufficient decent property. Simulation results demonstrate that the proposed algorithm can not only improve the recovery probability of sparse signal, but also has a higher detection probability in low SNR environment compared with traditional reconstruction algorithms.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Peng, J., Han, Z., Sun, J. (2018). A Fast Cyclic Spectrum Detection Algorithm for MWC Based on Lorentzian Norm. In: Sun, G., Liu, S. (eds) Advanced Hybrid Information Processing. ADHIP 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 219. Springer, Cham. https://doi.org/10.1007/978-3-319-73317-3_22
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DOI: https://doi.org/10.1007/978-3-319-73317-3_22
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