Optimized bases compressive spectrum sensing for wideband cognitive radio

Designing efficient spectrum sensing techniques with low power consumption is crucial for the success of cognitive radio (CR). This is particularly challenging when sensing a wideband spectrum due to the high sampling rate required. Compressive sensing (CS) theory states that a signal can be measured at a rate significantly lower than the Nyquist rate and consequently reconstructed from the measurements using an optimization process. The minimum number of measurements required for reliable reconstruction of the sampled signal depends on the sparsity of the measured signal. To improve the performance of compressive spectrum sensing, we optimize the sparsifying bases used to represent the measured spectrum. Entropy-based best basis selection algorithm of Coifman and Wickerhauser (CW) is deployed to find the optimum basis. Our simulation results show that the proposed compressive sampling technique can improve the spectrum estimation accuracy and enhance the detection and false-alarm probabilities of the CR system at the same sampling rates.