NDLI: Wideband compressed sensing for cognitive radios

Content Provider	IEEE Xplore Digital Library
Author	Sakarya, F.A. Nagel, G.S. Tran, L.H. Molnar, J.A.
Copyright Year	2011
Description	Author affiliation: Networks and Communications Systems Branch, Information Technology Division, Naval Research Lab, Washington, DC, USA (Sakarya, F.A.; Tran, L.H.; Molnar, J.A.) \|\| School of Electrical and Computer Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, USA (Nagel, G.S.)
Abstract	Efficient utilization of spectral bands in a dynamic environment with a continuously changing occupation rate is challenging. Static spectral allocations preclude the use of unoccupied spectrum, unless the spectrum manager has released the allocation to another mission. Standard spectral sensing techniques employ swept narrowband receivers. While effective in creating a composite, these techniques are ineffective at identifying short duration signals. Real-time spectral analysis techniques are effective at capturing short duration transmissions, but usually have narrow band capabilities, limited dynamic range and are relatively expensive. Timely accurate sensing of wide spectral band using traditional spectral estimations at the Nyquist rate (or higher) is another challenge due to the high data rate. Compressed sensing (CS) techniques utilize signal-to-information rate processing when signals are sparse in a specific domain. Dynamic spectrum access (often considered a critical component of a cognitive radio (CR)) can reutilize temporally unoccupied spectrum. An accurate estimate of the current state of spectral occupancy is critical to the autonomous decision processes involved in dynamic spectrum access. From this perspective, CS is being studied as an enhancement to dynamic spectrum access strategies. This study addresses feasibility issues for the development of autonomous CS-CR systems that are capable of performing spectrum sensing and recovery without a priori information about the spectral occupancy. Since recovery requires computationally intense non-linear optimization, we perform a single platform trade study on CS methodologies (BP-PD, BP-SPG, ROMP, Edge Detection, and Sequential Recovery) for efficient wideband recovery relative to execution time and reconstruction error. We propose adaptive coarse detection, and exact recovery based on adaptive edge detection. We present the results using continuous waves and pulses (MATLAB or GRC generated, or USRP measured), and show favorable conditions for BP-PD, BP-SPG, ROMP, adaptive recovery performance, and discuss potential for application of CS in a CR architecture.
Starting Page	31
Ending Page	36
File Size	1381159
Page Count	6
File Format	PDF
ISBN	9781467300797
ISSN	21557578
e-ISBN	9781467300810
DOI	10.1109/MILCOM.2011.6127684
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image edge detection Sensors Vectors Sparse matrices MATLAB Size measurement Noise spectral projected-gradient (SPG) compressed sensing compressive sensing cognitive radio(CR) dynamic spectrum access (DSA) dynamic spectrum management universal software radio peripheral (USRP) GNU radio companion (GRC) regularized orthogonal matching pursuit (ROMP) basis pursuit (BP) primal dual (PD)
Content Type	Text
Resource Type	Article

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