Second-order cyclostationarity of CP-SCLD signals: theoretical developments and applications to joint signal detection and classification and blind parameter estimation

Abstract

Orthogonal frequency division multiplexing (OFDM) has been adopted in a number of applications in recent years, due to the advantage of resistance to frequency selective fading with a simply equalization. Cyclically prefixed single carrier (CP-SC) modulation has been introduced as an alternative technology with similar performance while avoiding the peak-to-average power problem of the OFDM. As such, joint detection and classification and blind parameter estimation of OFDM and CP-SC signals become a key task in applications such as spectrum awareness in cognitive radio, spectrum monitoring and surveillance, and signal intelligence. -- OFDM signal detection and classification, and parameter estimation have been intensively investigated lately. Many of the proposed methods for detection, classification, and parameter estimation of OFDM signals are cyclostationarity-based. To the best of our knowledge there is not such work for CP-SC modulation. In this thesis, the second-order cyclostationarity of CP-single carrier linearly digitally modulated (CP-SCLD) signals is studied, and analytical closed-form expressions are derived for the cyclic autocorrelation function (CAF), set of cycle frequencies (CFs), spectral correlation density function (SCD), as well as the condition to avoid aliasing in cycle and spectral frequency domain of such signal are derived. Based on these findings, we propose algorithms to discriminate between CP-SCLD, OFDM, SCLD, and noise, and to blindly estimate the CP-SCLD block transmission parameters. Simulation and experimental results show the efficiency of the proposed algorithms under low SNRs, short sensing times, and diverse channel conditions. The algorithms have the advantage of avoiding requirements for the recovery of carrier, waveform, and symbol timing information, and the estimation of signal and noise powers.