Research Article
A Fast Cyclic Spectrum Detection Algorithm for MWC Based on Lorentzian Norm
@INPROCEEDINGS{10.1007/978-3-319-73317-3_22, author={Junwei Peng and Zhiren Han and Jingfang Sun}, title={A Fast Cyclic Spectrum Detection Algorithm for MWC Based on Lorentzian Norm}, proceedings={Advanced Hybrid Information Processing. First International Conference, ADHIP 2017, Harbin, China, July 17--18, 2017, Proceedings}, proceedings_a={ADHIP}, year={2018}, month={2}, keywords={Cognitive radio Cyclic spectrum detection Modulated wideband converter Signal recovery Impulsive noise}, doi={10.1007/978-3-319-73317-3_22} }- Junwei Peng
Zhiren Han
Jingfang Sun
Year: 2018
A Fast Cyclic Spectrum Detection Algorithm for MWC Based on Lorentzian Norm
ADHIP
Springer
DOI: 10.1007/978-3-319-73317-3_22
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.