Cyclostationary method based spectrum sensing and analysis using different windowing method

Abstract

Spectrum became the dear resource due to sudden growth in wireless trade. The static frequency allocation strategies cause spectrum underutilization. To enhance spectrum utilization dynamic spectrum allocation is employed in Cognitive Radio. The major task perform in the cognitive Radio is aware, adopt in Spectrum Sensing. Widely used standard spectrum sensing methodologies are Energy Detection, Matched filter, Wave form based, Cooperative, etc. The performance of spectrum sensing algorithms can be measure in term of variety of different performance parameters. Some algorithms performance degrades in low Signal to Noise Ratio, some requires transmitter information, poor detection, miss detection, and interference etc. In this paper Cyclostationary based Spectrum Sensing with different windowing methods are developed, that uses the cyclic property of received signal. We discover the Spectral Correlation operate to sight the presence of primary user. The employment of quick Fourier transform causes spectrum leak, therefore windowing strategies are used for improves system performance by reducing spectrum leak. The comparative analysis using different windowing methods shows Cyclostationary Detection provides better performance using Kaiser Window.