Cooperative wideband sensing based on entropy and cyclic features under noise uncertainty

Spectrum sensing is a key component to realise the cognitive radio. The main requirements of spectrum sensing are the prediction of signal status in multiple frequency bands in a low signal-to-noise ratio (SNR) and decision reliability. This study proposes a novel multinode wideband sensing technique to predict the status of multiple frequency bands based on the integration of entropy and cyclic properties of received signals. It uses the uncertainty and auto-correlation properties of the deterministic signal and noise in the frequency domain for signal detection. To increase the decision reliability, cooperative sensing techniques are being used for spectrum sensing. Although cooperation among multiple cognitive users enhances the sensing performance, presence of few suspicious/malicious cognitive users severely degrade the decision reliability of the system. Hence, in this work, generalised extreme studentised deviate and adjusted box-plot methods are introduced to eliminate multiple malicious users in the cooperation. The proposed sensing method shows the best performance and is less severe to noise uncertainties compared to the traditional sensing methods in the literature. It enhances the sensing performance by 2.5 dB using five nodes in cooperation for same sensing parameters compared to other detection methods. It is a significant improvement for IEEE 802.22 systems that work under low SNR environment.