Cyclic Feature Detection With Sub-Nyquist Sampling for Wideband Spectrum Sensing

For cognitive radio networks, efficient and robust spectrum sensing is a crucial enabling step for dynamic spectrum access. Cognitive radios need to not only rapidly identify spectrum opportunities over very wide bandwidth, but also make reliable decisions in noise-uncertain environments. Cyclic spectrum sensing techniques work well under noise uncertainty, but require high-rate sampling which is very costly in the wideband regime. This paper develops robust and compressive wideband spectrum sensing techniques by exploiting the unique sparsity property of the two-dimensional cyclic spectra of communications signals.

Provided by: Institute of Electrical and Electronics Engineers Topic: Mobility Date Added: Feb 2012 Format: PDF

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