An Efficient Spectrum-Sensing Method Based on Analog-to-Information Converter

Spectrum sensing for detecting channel occupancy is one of the key issues in cognitive radio networks. This paper investigates a power spectrum sensing method that uses an analog-to-information converter (AIC). In the literature, power spectrum reconstruction must be conducted before performing spectrum sensing. In this work, a new approach for AIC-based power spectrum sensing is developed, which is capable of directly estimating the average power in each channel from the samples produced by AIC. In addition, we demonstrate that the proposed method requires less computational operations because the reconstruction of power spectrum is not required. Furthermore, the simulation results illustrate that the proposed approach has improved performance in not only the MSE of average power estimation, but also in the detection probability.