Research Article
Fast Power Spectrum Estimation with Sparse Learning for Wideband Spectrum Sensing
@INPROCEEDINGS{10.1007/978-3-030-67720-6_29, author={Shuai Liu and Wen Xiao and Yao Zhang and Jing He and Jixin Wu}, title={Fast Power Spectrum Estimation with Sparse Learning for Wideband Spectrum Sensing}, proceedings={Communications and Networking. 15th EAI International Conference, ChinaCom 2020, Shanghai, China, November 20-21, 2020, Proceedings}, proceedings_a={CHINACOM}, year={2021}, month={2}, keywords={Wideband spectrum sensing Power spectrum estimation Sparse learning}, doi={10.1007/978-3-030-67720-6_29} }- Shuai Liu
Wen Xiao
Yao Zhang
Jing He
Jixin Wu
Year: 2021
Fast Power Spectrum Estimation with Sparse Learning for Wideband Spectrum Sensing
CHINACOM
Springer
DOI: 10.1007/978-3-030-67720-6_29
Abstract
The Compressed Sensing technology in wideband spectrum sensing (WSS) has greatly improved the utilization of spectrum resources. Based on this, we combining sparse learning and fast power spectrum estimation to achieve WSS in this paper. Sparsity adaptive matching pursuit (SAMP) algorithm is exploited to obtain the sparse sample representation for WSS. Then the limi-tations of power spectrum estimation in WSS are considered. To ease the limitations, the computational tasks are decomposed by multiple fast Fourier transforms. Theoretical performance analysis is made to further explain the proposed method. By improving the process of sample collection and power spectrum estimation, the proposed method can effectively achieve the pur-pose of fastly and exactly sensing. The final simulation results are utilized to verify the applicability of the proposed method and its advantages over other methods.
