  A novel approach of training data augmentation and domain adaptation is
presented to support machine learning applications for cognitive radio. Machine
learning provides effective tools to automate cognitive radio functionalities
by reliably extracting and learning intrinsic spectrum dynamics. However, there
are two important challenges to overcome, in order to fully utilize the machine
learning benefits with cognitive radios. First, machine learning requires
significant amount of truthed data to capture complex channel and emitter
characteristics, and train the underlying algorithm (e.g., a classifier).
Second, the training data that has been identified for one spectrum environment
cannot be used for another one (e.g., after channel and emitter conditions
change). To address these challenges, a generative adversarial network (GAN)
with deep learning structures is used to 1)~generate additional synthetic
training data to improve classifier accuracy, and 2) adapt training data to
spectrum dynamics. This approach is applied to spectrum sensing by assuming
only limited training data without knowledge of spectrum statistics. Machine
learning classifiers are trained with limited, augmented and adapted training
data to detect signals. Results show that training data augmentation increases
the classifier accuracy significantly and this increase is sustained with
domain adaptation as spectrum conditions change.
