Investigation of ROC parameters using Monte Carlo simulation in cyclostationary and energy detection spectrum sensing

Abstract

Growing demand in the wireless world puts plenty of constraints on the usage of accessible spectrum. Static Spectrum Allocation (SSA) ends up in underutilization of spectrum. Much of the portion of Radio Frequency (RF) band is not effectively used. Cognitive feature Radio is upcoming technology to unravel spectrum deficiency. Various approaches have been developed to overcome this spectrum scarcity problem. In this work Cyclostationary feature detection (CFD) and Energy Detection Spectrum sensing techniques have been investigated using Monte Carlo simulation and compared on the basis of various ROC parameters. Spectrum sensing is the key that helps to sight the spectrum holes (underutilized band) providing high spectral resolution capability. Cyclostationary feature methodology out performs Energy detection in terms of detection likelihood Pd, likelihood of warning Pf and SNR. Both the algorithms are implemented using MATLAB. The result shows that for different SNR the CFD performs better than Ed. CFD also perform better in low signal to noise ratio as compare to ED.

Cyclostationary-based jammer detection for wideband radios using compressed sensing and artificial neural network

“…Such performances are achieved at the cost of increased implementation complexity. CFD uses the cyclostationarity of the modulated primary signals by detecting spectral peaks by means of the spectral correlation function (SCF) or spectral coherence function (SOF), [5][6][7] which are sparse in both angular (f ) and cyclic (a) frequency domains. Furthermore, it has been used as a robust tool for signal classification when the carrier frequency and bandwidth information are unavailable.…”