Spectrum sensing based on Adaptive Empirical Mode Decomposition for Cognitive Radio

Cognitive Radio Technology rose as a Novel approach for Wireless Communication scenario to overcome the problem of Spectrum Scarcity due to Underutilization of available Spectrum by legacy users. The major task of the Cognitive Cycle is Spectrum sensing for which there are different traditional techniques available. In this paper we propose a new technique; Spectrum sensing using Hilbert-Huang Transform with adaptive EMD procedure for efficient processing of signal and fast computation speed with powerful edge detection. HHT helps us in accurate analysis of nonstationary and nonlinear incoming RF stimuli by decomposing it in Intrinsic Mode Functions (IMFs) using Adaptive Empirical Mode Decomposition (EMD) method which having varying stopping criteria. After that Hilbert Spectral Analysis of these IMF's provides instantaneous frequency and instantaneous amplitude information of the signal at different time series.