NDLI: A Novel Algorithm to Optimize Sampling Rate for Compressed Sensing

Content Provider	IEEE Xplore Digital Library
Author	Xiaomin Liu Qixun Zhang Jian Yang Yang Bai Zhiyong Feng
Copyright Year	2013
Description	Author affiliation: Key Lab. of Universal Wireless Commun., Beijing Univ. of Posts & Telecommun., Beijing, China (Xiaomin Liu; Qixun Zhang; Jian Yang; Yang Bai; Zhiyong Feng)
Abstract	The fast and accurate spectrum sensing over an ultra-wide bandwidth is a big challenge for the radio environment cognition. Traditional spectrum sensing technique is neither efficient nor necessary, wasting the spectrum access opportunities on the vacant spectrum holes of primary users (PUs). Considering the sparse signal feature, a novel compressed sensing technique is proposed by using the minimal sampling rate to detect spectrum holes, which is more efficient than the Nyquist sampling rate and traditional compressed sampling rate that is required to reconstruct the original signal. The proposed compressed sensing process is divided into two stages called approaching stage and monitoring stage. The first stage is to gradually approach the minimal sampling rate required to achieve the spectrum detection performance by using the feedback mechanism. And the second stage is to monitor the status of PU according to the threshold using the sampling rate from the first stage. Therefore, the overall sampling rate can be dramatically reduced without spectrum detection performance deterioration compared to the conventional static sampling algorithm. Numerous results show that the proposed compressed sensing technique can reduce the sampling rate to 35%, with acceptable detection probability over 0.9.
Starting Page	1
Ending Page	5
File Size	210776
Page Count	5
File Format	PDF
ISBN	9781467361873
ISSN	10903038
DOI	10.1109/VTCFall.2013.6692047
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sensors Heuristic algorithms Compressed sensing Vectors Feature extraction Monitoring Bandwidth
Content Type	Text
Resource Type	Article

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