Application of Compressed Sensing to Wideband Spectrum Sensing in Cognitive Radio Networks
نویسندگان
چکیده
Wideband spectrum sensing is one of the most challenging aspects of Cognitive Radio Networks (CRNs). It should be performed as fast and as accurate as possible. Traditional wideband spectrum sensing techniques require excessively high sampling rate analog-to-digital converters (ADCs). Compressed sensing was considered to enable wideband spectrum sensing at a much lower sampling rate below the Nyquist rate. However, the reconstruction complexity and speed of existing compressed wideband spectrum sensing remained a barrier for such an application. In this paper, we introduce the Adaptive Reduced-set Matching Pursuit (ARMP) and Fast Matching Pursuit (FMP) which are fast and accurate recovery algorithms for compressed sensing. We apply FMP to wideband spectrum sensing for cognitive radio networks, resulting in a significant complexity reduction at a remarkable accuracy.
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