Cyclostationarity Approach for the Recognition of Cyclically Prefixed Single Carrier Signals in Cognitive Radio

Cognitive radio (CR) represents a possible solution to the paradoxical problem of simultaneous scarcity and underutilization of the electromagnetic spectrum. Spectrum awareness, a key task of such a radio, encompasses the recognition of the received signal type and parameters. This paper investigates the cyclostationarity approach for the recognition of cyclically prefixed single carrier linearly digitally modulated (CP-SCLD) signals versus SCLD and orthogonal frequency division multiplexing (OFDM) signals under practical conditions, including time-dispersive channels, additive Gaussian noise, and phase, frequency and timing offsets. Analytical closed-form expressions are derived for the cyclic autocorrelation function (CAF) and the set of cycle frequencies (CFs) of CP-SCLD signals. These results are the basis of the proposed signal recognition algorithm. This algorithm has the advantage of avoiding requirements for the recovery of carrier, waveform, and symbol timing information, and the estimation of signal and noise powers.