  In this paper, we investigate deep learning (DL)-enabled signal demodulation
methods and establish the first open dataset of real modulated signals for
wireless communication systems. Specifically, we propose a flexible
communication prototype platform for measuring real modulation dataset. Then,
based on the measured dataset, two DL-based demodulators, called deep belief
network (DBN)-support vector machine (SVM) demodulator and adaptive boosting
(AdaBoost) based demodulator, are proposed. The proposed DBN-SVM based
demodulator exploits the advantages of both DBN and SVM, i.e., the advantage of
DBN as a feature extractor and SVM as a feature classifier. In DBN-SVM based
demodulator, the received signals are normalized before being fed to the DBN
network. Furthermore, an AdaBoost based demodulator is developed, which employs
the $k$-Nearest Neighbor (KNN) as a weak classifier to form a strong combined
classifier. Finally, experimental results indicate that the proposed DBN-SVM
based demodulator and AdaBoost based demodulator are superior to the single
classification method using DBN, SVM, and maximum likelihood (MLD) based
demodulator.
