Abstract
Wideband spectrum sensing (WSS) has been recommended as an efficient approach to enhance the spectrum utilization of cognitive radio users. As WSS involves high sampling rate and long sensing duration, complex multi-channel analog-to-digital converters (ADC) are required for processing. In this context, Compressive Sensing (CS) facilitates in decreasing the sampling rate thereby reducing the processing time and complexity. Compressive Sensing (CS) is a technique for reconstructing a signal from sparse number of samples when compared to Nyquist sampling. Energy minimization being the key feature of CS has been applied to spectrum sensing in cognitive radio networks (CRN) in this work. The detection of primary user (PU) signal is carried out using the sparse representation of received signals. The received PU signal is compressed in the time domain to extract the minimum energy coefficients and recovered using l1-minimization and Differential Evolution (DE) algorithms. Simulation results for various compression rates are analyzed.
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