Spectrum Sensing in Cognitive Radio by Statistical Matched Wevelet Method and Matched Filter

Cognitive radio draw lots of research attentions in recent years for its efficient spectrum utilization. In cognitive radio networks, the first cognitive task preceding any form of dynamic spectrum management is the spectrum sensing and identification of spectrum holes in wireless environment. Spectrum Sensing is an important functionality of Cognitive Radio (CR). Accuracy and speed of estimation are the key indicators to select the appropriate spectrum sensing technique. Wideband spectrum sensing has been introduced due to the higher bandwidth demand and increasing spectrum scarcity since it provides better chance of detecting spectrum opportunity. In this project, the application of wavelet transform used for wideband spectrum opportunity detection in CRs is documented. Conventional spectrum estimation techniques which are based on Short Time Fourier Transform (STFT) suffer from familiar problems such as low frequency resolution, variance and high side lobes/leakages. In this project we used statistical method wavelet algorithm to find the spectrum holes. This is the latest technology to sense the spectrum in the cognitive radio network.. In matched filter spectrum sensing obtained by correlating a known signal with an unknown signal in order to detect the existence of the known signal or template in the unknown signal. But these spectrum sensing techniques do not give good result in case of wide bandwidth so wavelet based spectrum sensing is the only solution to sense PU in case of wide bandwidth. In this paper we introduced a new technique that is statistical matched wavelet method to sense the primary users in the wideband spectrum. And this is the latest spectrum sensing method