New model for optimized periodogram-based spectrum sensing for cognitive radios

This paper addresses the problem of spectrum sensing from a frequency domain approach using periodogram-based energy detection which naturally yields a lower probability of false alarm when compared with the time-domain detector. In this paper, the periodogram-based detector is optimized based on the likelihood ratio test and accurate expressions are derived for the probability of false alarm and the probability of detection. In addition, the case of sensing with multiple antennas is considered assuming that equal gain combining (EGC) is used, which yields a hypoexponential decision statistic. The high accuracy of the obtained models is verified by the obtained simulation results. The performance is enhanced by employing the optimized models. The receiver operator characteristics clearly show that better operation points are obtained as the SNR is increased.