Cyclostationary Feature Based Multiresolution Spectrum Sensing Approach for DVB-T and Wireless Microphone Signals

Abstract—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). None of
these techniques have been used in a wavelet based
cyclostationary feature detector. To achieve that we suggest a
cyclostationary feature based MRSS 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. This knowledge of identifying primary signals can help
cognitive radio to use fraction of TV band when only a wireless
microphone signal is present in the channel. The performance of
the proposed scheme is evaluated by probability of correct
classification. The result indicates that better performance can be
achieved by the proposed scheme especially in a low SNR
environment.