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Modulation classification for cognitive radios using stacked denoising autoencoders
Author(s) -
Zhu Xu,
Fujii Takeo
Publication year - 2016
Publication title -
international journal of satellite communications and networking
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.388
H-Index - 39
eISSN - 1542-0981
pISSN - 1542-0973
DOI - 10.1002/sat.1202
Subject(s) - computer science , artificial intelligence , noise reduction , pattern recognition (psychology) , modulation (music) , noise (video) , overhead (engineering) , machine learning , synchronization (alternating current) , data mining , channel (broadcasting) , telecommunications , philosophy , image (mathematics) , operating system , aesthetics
Summary This paper proposes a modulation classification method based on stacked denoising autoencoders (SDAE). This method can extract the modulation features automatically and classify the input signals based on the extracted features. The scenarios of rapid classification and high‐accuracy classification are considered. In a rapid classification scenario, the classification speed has priority over the classification accuracy. Therefore, a long‐symbol sequence is not attainable for this scenario. Moreover, expert features are not necessary for this scenario, simplifying the modulation classification procedure and rendering rapid classification more achievable. In addition, in a high‐accuracy classification scenario, higher cumulants are used as the expert features owing to their advantage over the other features at noise resistance. We use complex symbols rather than pulse shaped complex signals as the network inputs, simplifying the network topology and reducing the calculation overhead. The results of the average classification accuracy, the individual classification accuracy, the execution time and the influence of the signal sampling synchronization are presented, demonstrating significant performance advantages over the other methods. Copyright © 2016 John Wiley & Sons, Ltd.