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On the use of Compressive Sampling for Wide-band Spectrum Sensing
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-2638-6047
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-7926-5081
2010 (English)In: 2010 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), IEEE , 2010, 354-359 p.Conference paper (Refereed)
Abstract [en]

In a scenario where a cognitive radio unit wishes to transmit, it needs to know over which frequency bands it can operate. It can obtain thisknowledge by estimating the power spectral density from a Nyquist-rate sampled signal. For wide-band signals sampling at the Nyquistrate is a major challenge and may be unfeasible. In this paper we accurately detect spectrum holes in sub-Nyquist frequencies without assuming wide sense stationarity in the compressed sampled signal. A novel extension to further reduce the sub-Nyquist samples is thenpresented by introducing a memory based compressed sensing thatrelies on the spectrum to be slowly varying.

Place, publisher, year, edition, pages
IEEE , 2010. 354-359 p.
Keyword [en]
Compressive sampling, Cognitive radio, power spectrum estimation, sub-Nyquist sampling
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-34412DOI: 10.1109/ISSPIT.2010.5711810ScopusID: 2-s2.0-79952383891OAI: diva2:421047
ISSPIT - International Symposium on Signal Processing and Information Technology, Luxor

© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. QC 20110707

Available from: 2011-07-07 Created: 2011-06-07 Last updated: 2014-08-20Bibliographically approved
In thesis
1. Compressed Sensing: Algorithms and Applications
Open this publication in new window or tab >>Compressed Sensing: Algorithms and Applications
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The theoretical problem of finding the solution to an underdeterminedset of linear equations has for several years attracted considerable attentionin the literature. This problem has many practical applications.One example of such an application is compressed sensing (cs), whichhas the potential to revolutionize how we acquire and process signals. Ina general cs setup, few measurement coefficients are available and thetask is to reconstruct a larger, sparse signal.In this thesis we focus on algorithm design and selected applicationsfor cs. The contributions of the thesis appear in the following order:(1) We study an application where cs can be used to relax the necessityof fast sampling for power spectral density estimation problems. Inthis application we show by experimental evaluation that we can gainan order of magnitude in reduced sampling frequency. (2) In order toimprove cs recovery performance, we extend simple well-known recoveryalgorithms by introducing a look-ahead concept. From simulations it isobserved that the additional complexity results in significant improvementsin recovery performance. (3) For sensor networks, we extend thecurrent framework of cs by introducing a new general network modelwhich is suitable for modeling several cs sensor nodes with correlatedmeasurements. Using this signal model we then develop several centralizedand distributed cs recovery algorithms. We find that both thecentralized and distributed algorithms achieve a significant gain in recoveryperformance compared to the standard, disconnected, algorithms.For the distributed case, we also see that as the network connectivity increases,the performance rapidly converges to the performance of thecentralized solution.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2012. ix, 35 p.
Trita-EE, ISSN 1653-5146
compressed sensing, greedy pursuits, subspace pursuit, orthogonal matching pursuit, power spectral density estimation, distributed compressed sensing
National Category
urn:nbn:se:kth:diva-90074 (URN)978-91-7501-269-8 (ISBN)
2012-03-09, Q2, KTH, Osquldas väg 10, Stockholm, 15:58 (English)
ICT - The Next Generation

QC 20120229

Available from: 2012-02-29 Created: 2012-02-17 Last updated: 2013-04-15Bibliographically approved

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