Adaptive Compressive Spectrum Sensing for Wideband Cognitive Radios

This letter presents an adaptive spectrum sensing algorithm that detects wideband spectrum using sub-Nyquist sampling rates. By taking advantage of compressed sensing (CS), the proposed algorithm reconstructs the wideband spectrum from compressed samples. Furthermore, an \ell_2 norm validation appro...

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Bibliographic Details
Published in:IEEE communications letters 2012-11, Vol.16 (11), p.1812-1815
Main Authors: Hongjian Sun, Wei-Yu Chiu, Nallanathan, A.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Cognitive radio
Compressed
compressed sensing
Detection
Detection, estimation, filtering, equalization, prediction
enhanced throughput
Exact sciences and technology
Indexes
Information, signal and communications theory
Noise measurement
Norms
Radiocommunication specific techniques
Radiocommunications
Sampling
Sampling, quantization
Sensors
Signal and communications theory
Signal representation. Spectral analysis
Signal, noise
Spectra
spectrum sensing
Telecommunications
Telecommunications and information theory
Testing
Throughput
Wideband
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Summary:This letter presents an adaptive spectrum sensing algorithm that detects wideband spectrum using sub-Nyquist sampling rates. By taking advantage of compressed sensing (CS), the proposed algorithm reconstructs the wideband spectrum from compressed samples. Furthermore, an \ell_2 norm validation approach is proposed that enables cognitive radios (CRs) to automatically terminate the signal acquisition once the current spectral recovery is satisfactory, leading to enhanced CR throughput. Numerical results show that the proposed algorithm can not only shorten the spectrum sensing interval, but also improve the throughput of wideband CRs.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2012.092812.121648