  Spectrum sensing is a key technology for cognitive radios. We present
spectrum sensing as a classification problem and propose a sensing method based
on deep learning classification. We normalize the received signal power to
overcome the effects of noise power uncertainty. We train the model with as
many types of signals as possible as well as noise data to enable the trained
network model to adapt to untrained new signals. We also use transfer learning
strategies to improve the performance for real-world signals. Extensive
experiments are conducted to evaluate the performance of this method. The
simulation results show that the proposed method performs better than two
traditional spectrum sensing methods, i.e., maximum-minimum eigenvalue
ratio-based method and frequency domain entropy-based method. In addition, the
experimental results of the new untrained signal types show that our method can
adapt to the detection of these new signals. Furthermore, the real-world signal
detection experiment results show that the detection performance can be further
improved by transfer learning. Finally, experiments under colored noise show
that our proposed method has superior detection performance under colored
noise, while the traditional methods have a significant performance
degradation, which further validate the superiority of our method.
