Exploitation of Cyclostationarity Using Fractional Lower-Order Cyclic Statistics

Many signals encountered in wireless communication, telemetry, radar and sonar systems exhibit a cyclostationarity (or spectral correlation) property, as a result of periodically carrier modulating, multiplexing and coding operations. By exploiting cyclostationarity, i.e., evaluating the cyclic correlation or cyclic spectrum of the received signals at certain cycle frequency, we can extract the measures of the signals with different cycle frequency. Thus, the signal processing algorithms which based on cyclic statistics are highly tolerant to the interference and noise. However, most of the cyclostationarity based methods assume that the noise is Gaussian noise. Hence, their performance can be degraded in real applications where the noises contain non-Gaussian impulsive components. In this paper, we propose a new approach for exploiting cyclostationarity of signals in alpha-stable impulsive noise. Simulation results indicate a significantly better performance for the new statistics in some environments.