Discrete wavelet packet transform based multiresolution spectrum sensing using cyclostationary feature detector

The demand for wireless communication has grown remarkably in the last year, consequently raising the problem of spectrum scarcity. In this context, cognitive radio is an emerging technology that aims to overcome that scarcity, which is one of the most challenging problems in modern wireless communication. Among its fundamental function, the most important is the spectrum sensing which require precise accuracy and low complexity. Thus, various signal detection methods have been proposed for multiresolution spectrum sensing (MRSS) for cognitive radio. None of these techniques have been used in wavelet based cyclostationary feature detector. To achieve that we suggest a discrete wavelet packet transform based MRSS using cyclostationary feature detector in the context of IEEE 802.22 Wireless Regional Area Network (WRAN) for cognitive radio to classify and identify the primary signal either Digital Video Broadcasting-Terrestrial (DVB-T) or wireless microphone signal and locate a hole which can be used by unlicensed or cognitive radio user. The performance of the proposed scheme is evaluated in comparison with energy detector. The evaluation result clearly shows that the performance of the proposed scheme is superior to energy detector performance especially in the case of noise uncertainty.