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One of the fundamental components in cognitive radios (CRs) is spectrum sensing. For sensing the wide range of frequency bands, CRs need high sampling rate analog to digital converters (ADCs) which have to operate at or above the Nyquist rate. The high operating rate constitutes a major implementation challenge. Compressive sensing (CS) is a method that may overcome this problem. Sub-Nyquist rate can be used for CS recovery algorithms such as ℓ1-minimization. While boundary information of all frequency sub-bands is available, a more efficient recovery algorithm based on ℓ2/ℓ1-minimization can be used instead of ℓ1-minimization. In cognitive radio systems, network coding could be used for primary users (PUs) to increase packet transmissions. Furthermore, network coding provides a structure for vacant sub-bands of spectrum and makes the spectrum more predictable. Using this information that network coding provides us, we combine ℓ1-minimization and ℓ2/ℓ1-minimization algorithms with network coding for compressive spectrum sensing. Our methods require reduced signal sampling rate and result in improved false alarm (FA) and missed detection (MD) probabilities for idle band detection.
Date of Conference: 10-15 June 2012