Abstract
Objectives:Cognitive radio is evolved for utilising the unused spread spectrum effectively in wireless communication. The foremost concept is sensing the holes (white spaces) in the frequency spectrum allotted and it facilitates a way that how effectively and efficiently the bandwidth is used.Methods/Analysis:There are various methods available for sensing the spectrum and one such a sensing method is cyclostationary detection. The method of cyclostationary feature mainly focuses on detecting whether the primary user is present or absent. By using cyclic cross-periodogram matrix, the calculation of threshold of a signal is carried out to find the existence of noise or signal. Findings:The difficulty in evaluating the targeted threshold is evaded by training an artificial neural network by extracted cyclostationary feature vectors which are obtained by FFT accumulation method. Novelty /Improvement:This paper proposes hardware architecture for cyclostationary detection.
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