Cyclostationary Feature Detection Based Blind Approach for Spectrum Sensing and Classification

Abstract

A Spectrum Sensing (SS) device, regardless of its location, should be able to detect the presence of signal over noise. In certain applications, SS should be able to correctly identify and classify the received signal. These functions are to be performed with little or no prior information about the incoming signal or channel noise. Cyclostationary Feature Detection (CFD) can be used to detect primary users (PU) using periodicity in autocorrelation of the modulated signals. These algorithms attempt to differentiate signal from noise based on the uncorrelated nature of noise. CFD is often considered as a semi-blind approach, since it requires prior information about the PU signal for detection. For identification and classification of PU signal, existing algorithms make use of CFD and neural networks. This paper proposes a novel algorithm to obtain completely blind detection performance based on CFD. Classification of PU signals is based on the basic statistics regarding cyclic spectrum. Further, an algorithm is formulated to identify modulation scheme of the signal and classify it without making use of any training algorithms. The proposed approach is capable of detecting PU reliably for SNR as low as –8 dB with no prior information about PU or noise in the channel.