Paper Info
Title
Compressive spectrum sensing front-ends for cognitive radios
Abstract
We propose a novel parallel mixed-signal compressive spectrum sensing architecture for cognitive radios (CRs) with a detailed study of the signal modeling. The mixed-signal compressive sensing is realized with a parallel segmented compressive sensing (PSCS) architecture, which not only can filter out all the harmonic spurs that leak from the local random generator, but also provides a tradeoff between the sampling rate and the system complexity such that a practical hardware implementation is possible. We consider application of the architecture to do spectrum estimation, which is the first step for spectrum sensing in CRs. The benefit of prior knowledge about the input signal's structure is explored and it is shown that this can be exploited in the PSCS architecture to greatly reduce the sampling rate.
Year
DOI
Venue
2009
10.1109/ICSMC.2009.5346164
SMC
Keywords
Field
DocType
spectrum estimation,signal modeling,system complexity,mixed-signal,frequency allocation,signal sampling,sampling rate,local random generator,parallel segmented compressive,mixed-signal compressive,cognitive radio,data compression,compressive sensing,compact signal modeling,parallel mixed-signal compressive sensing,novel parallel mixed-signal compressive,signal sampling rate,compressive spectrum,pscs architecture,input signal,cognitive radios,spectrum sensing,detailed study,compressive spectrum sensing front-end,sensors,mathematical model,mixed signal,noise,compressed sensing,data mining,front end,spectrum
Wideband,Telecommunications,Computer science,Control theory,Sampling (signal processing),Harmonic,Electronic engineering,Frequency allocation,Mixed-signal integrated circuit,Data compression,Compressed sensing,Cognitive radio
Conference
ISSN
ISBN
Citations 
1062-922X E-ISBN : 978-1-4244-2794-9
978-1-4244-2794-9
6
PageRank 
References 
Authors
0.50
13
2
Name
Order
Citations
PageRank
Zhuizhuan Yu114511.51
Sebastian Hoyos223429.24