Demonstration of Spectrum Sensing with Blindly Learned Feature

Spectrum sensing is essential in cognitive radio. By defining leading $\mathit{\text{eigenvector}}$ as feature, we introduce a blind feature learning algorithm (FLA) and a feature template matching (FTM) algorithm using learned feature for spectrum sensing. We implement both algorithms on Lyrtech software defined radio platform. Hardware experiment is performed to verify that feature can be learned blindly. We compare FTM with a blind detector in hardware and the results show that the detection performance for FTM is about 3 dB better.