Iterative Cyclostationarity-Based Feature Detection of Multiple Primary Signals for Spectrum Sharing Scenarios

Abstract

One of the important and widely used detection techniques is cyclostationarity-based feature detection, because the method does not need prior information such as signal bandwidth or frame format, and time and frequency synchronization are likewise not required. The original cyclostationarity cannot distinguish signals if several signals have the same signal format and parameters, but the cyclostationarity-inducing transmission method can overcome this problem by inducing different features in the OFDM signals that have the same parameters. Another problem of conventional cyclostationarity-based feature detection is that the detection probability of weak signals worsens if multiple signals with different received-power levels are captured simultaneously. This paper proposes iterative cyclostationarity-based feature detection to detect such weak signals. The proposed detection method suppresses the effects of previously-detected signals in the cyclic auto-correlation domain, and so improves the detection probability of the weak signals. The detection performances of the conventional and proposed detection methods are evaluated by computer simulations. The results reveal the effectiveness of the proposed detection in spectrum sharing scenarios.