Cyclostationary Feature Detection Based on Compressed Sensing and Wavelet De-Noising

To improve the vacant spectrum utilization,ultra-wideband spectrum sensing is critical for cognitive radio (CR)as it enables secondary users to dynamically access the unoccupied spectrum bands.However,the fast and accurate spectrum sensing is still a challenge over an ultra-wide bandwidth in low sig...

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Main Authors: Haifeng Tan, Jun Lu, Xuan Fu, Qixun Zhang
Format: Article
Language:zho
Published: Beijing Xintong Media Co., Ltd 2015-08-01
Series:Dianxin kexue
Subjects:
Online Access:http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2015208/
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author Haifeng Tan
Jun Lu
Xuan Fu
Qixun Zhang
author_facet Haifeng Tan
Jun Lu
Xuan Fu
Qixun Zhang
author_sort Haifeng Tan
collection DOAJ
description To improve the vacant spectrum utilization,ultra-wideband spectrum sensing is critical for cognitive radio (CR)as it enables secondary users to dynamically access the unoccupied spectrum bands.However,the fast and accurate spectrum sensing is still a challenge over an ultra-wide bandwidth in low signal to noise ratio(SNR) environment.A compressed sensing (CS)-feature detector based on wavelet de-noising was proposed to perform wideband detection in low SNR.CS was proposed to improve the efficiency of wideband spectrum sensing.And two dimensional wavelet transform was introduced to deal with the noise in spectral coherence function(SCF)by the CS process.As a result,the detection accuracy in low SNR was improved.It is found that the proposed technology can detect spectrum holes at a range of low SNR through simulation results.
format Article
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institution Kabale University
issn 1000-0801
language zho
publishDate 2015-08-01
publisher Beijing Xintong Media Co., Ltd
record_format Article
series Dianxin kexue
spelling doaj-art-723632da2f0b4ef5b6aa5f313b5eb3ad2025-01-15T03:16:48ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012015-08-0131515759614147Cyclostationary Feature Detection Based on Compressed Sensing and Wavelet De-NoisingHaifeng TanJun LuXuan FuQixun ZhangTo improve the vacant spectrum utilization,ultra-wideband spectrum sensing is critical for cognitive radio (CR)as it enables secondary users to dynamically access the unoccupied spectrum bands.However,the fast and accurate spectrum sensing is still a challenge over an ultra-wide bandwidth in low signal to noise ratio(SNR) environment.A compressed sensing (CS)-feature detector based on wavelet de-noising was proposed to perform wideband detection in low SNR.CS was proposed to improve the efficiency of wideband spectrum sensing.And two dimensional wavelet transform was introduced to deal with the noise in spectral coherence function(SCF)by the CS process.As a result,the detection accuracy in low SNR was improved.It is found that the proposed technology can detect spectrum holes at a range of low SNR through simulation results.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2015208/compressed sensingcyclostationary feature detectionwavelet de-noising
spellingShingle Haifeng Tan
Jun Lu
Xuan Fu
Qixun Zhang
Cyclostationary Feature Detection Based on Compressed Sensing and Wavelet De-Noising
Dianxin kexue
compressed sensing
cyclostationary feature detection
wavelet de-noising
title Cyclostationary Feature Detection Based on Compressed Sensing and Wavelet De-Noising
title_full Cyclostationary Feature Detection Based on Compressed Sensing and Wavelet De-Noising
title_fullStr Cyclostationary Feature Detection Based on Compressed Sensing and Wavelet De-Noising
title_full_unstemmed Cyclostationary Feature Detection Based on Compressed Sensing and Wavelet De-Noising
title_short Cyclostationary Feature Detection Based on Compressed Sensing and Wavelet De-Noising
title_sort cyclostationary feature detection based on compressed sensing and wavelet de noising
topic compressed sensing
cyclostationary feature detection
wavelet de-noising
url http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2015208/
work_keys_str_mv AT haifengtan cyclostationaryfeaturedetectionbasedoncompressedsensingandwaveletdenoising
AT junlu cyclostationaryfeaturedetectionbasedoncompressedsensingandwaveletdenoising
AT xuanfu cyclostationaryfeaturedetectionbasedoncompressedsensingandwaveletdenoising
AT qixunzhang cyclostationaryfeaturedetectionbasedoncompressedsensingandwaveletdenoising