Exploitation of First-Order Cyclostationarity for Joint Signal Detection and Classification in Cognitive Radio

The sensing of the radio frequency (RF) environment is a fundamental concept of cognitive radio (CR). It follows that the detection and classification of very low signal-to-noise ratio signals with relaxed a priori information on signal parameters is a problem of considerable relevance to CR. This paper proposes an algorithm based on first-order cyclostationarity for joint detection and classification of frequency-shift-keying (FSK) signals. This algorithm does not require timing and frequency recovery, and estimation of signal and noise powers. The theoretical analysis of the algorithm performance is validated by simulation results.