Power Spectral Density for Detecting White Space in Wavelet Based Spectrum

Abstract

 Cognitive radio used to resolve spectral congestion problem in the view of increasing band width mainly used for biotelemetry application. It’s still a challenge to detect the white spaces in a wide band radio spectrum. In order to improve the wideband spectrum sensing in real environments, spectrum singularities based multi-scale information wavelet transform algorithm is proposed. The presented technique is varying from blind methods, feature detector method, and context aware methods. To process the target spectrum with good speed and accuracy, a multi-resolution analysis tool the wavelet transform was used. In this work, the white space is identified efficiently by calculating Power Spectral Density (PSD). PSD has better detection of performance at low Signal to Noise Ratio (SNR) to access the white space in active spectrum to settle congestion problem in spectrum demand.  The proposed algorithm performs well at medium-to-high noise power as compared with Fourier-based method and existing older methods.