Machine Learning-Aided, Robust Wideband Spectrum Sensing for Cognitive Radios
Final rept. 29 Apr 2014-12 Jun 2015
NEW MEXICO UNIV ALBUQUERQUE DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE
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In this project, a compressive-sampling based, robust spectrum sensing approach was developed for wideband cognitive radios. The compressive-sampling based front-end is intended for overcoming the hardware imposed limitations on wideband spectrum sensing while robust detection principles are used to obtain a spectrum sensing approach that helps alleviate sensitivity to non-Gaussian noise and interference. Simulations were carried out to demonstrate that even with reduced number of samples, the proposed compressive-sampling based robust detector can indeed provide either comparable or better results to that observed with conventional periodogram, but with significantly higher number of samples. These results encourage further investigations and improvements of the proposed approach as a viable candidate for the front-end processing of a wideband cognitive radio.
- Radio Communications