Joint Signal Detection and Classification Based on First-Order Cyclostationarity For Cognitive Radios

Abstract

The sensing of the radio frequency environment has important commercial and military applications and is fundamental to the concept of cognitive radio. The detection and classification of low signal-to-noise ratio signals with relaxed a priori information on their parameters are essential prerequisites to the demodulation of an intercepted signal. This paper proposes an algorithm based on first-order cyclostationarity for the joint detection and classification of frequency shift keying (FSK) and amplitude-modulated (AM) signals. A theoretical analysis of the algorithm performance is also presented and the results compared against a performance benchmark based on the use of limited assumed a priori information on signal parameters at the receive-side. The proposed algorithm has the advantage that it avoids the need for carrier and timing recovery and the estimation of signal and noise powers.

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