Performance analysis of cyclostationary detector using efficient hardware architecture

Cognitive Radio (CR) is a technique that was headed ahead for utilizing the unused spread spectrum effectively with less interference. The principal task of a CR system is to sense the holes (spaces) effectively and efficiently in the designated frequency spectrum. Here in this work spectrum sensing is carried out by Cyclostationary detector. A random signal is generated and it is modulated either by Binary shift phase keying (BPSK) or Quadrature phase shift keying (QPSK). The Modulated random signal is added with Additive White Gaussian Noise (AWGN) and passed through Cyclostationary spectrum detector to check whether it crosses the threshold level to for the presence of primary user. The threshold of a signal is calculated by cyclic cross-periodogram matrix of the corresponding signal to determine the presence of signal or noise. The impediment in evaluating the targeted threshold is prevailed by training an artificial neural network by extracted cyclostationary feature vectors which are obtained by FFT accumulation method. This paper discusses about performance of Cyclostationary detector having better Signal to Noise Ratio (SNR) than other detectors and the hardware architecture for cyclostationary detection.