Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics

• Published in: IEEE access Vol. 7; pp. 30792 - 30801
• Main Authors: Wang, Hongmei, Wu, Zhenzhen, Ma, Shuai, Lu, Songtao, Zhang, Han, Ding, Guoru, Li, Shiyin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AdaBoost based demodulator; Belief networks; Classifiers; Datasets; DBN-SVM based demodulator; Deep learning; Demodulation; Demodulators; Fading channels; Feature extraction; Machine learning; Prototypes; Receivers; Support vector machines; Wireless communication; Wireless communication systems; Wireless communications
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2903130

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 <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-nearest neighbor as a weak classifier to form a strong combined classifier. Finally, the 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-based demodulator.