Automatic Modulation Classification for D-STBC Cooperative Relaying Networks

Automatic modulation classification (AMC) has been used as a crucial component to improve the overall performance in cognitive radio system, thereby allowing any user to promote secure and reliable communication. In this letter, we propose a semi-blind AMC approach based on a simplified distributed...

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Bibliographic Details
Published in:IEEE wireless communications letters 2018-10, Vol.7 (5), p.780-783
Main Authors: Tayakout, Hakim, Dayoub, I., Ghanem, K., Bousbia-Salah, H.
Format: Article
Language:English
Subjects:
Antennas
Artificial Intelligence
Block codes
Channel estimation
Classification
Classifiers
Cognitive radio
Communications systems
Comparative studies
Computer Science
cooperative communications
Cooperative systems
Decoding
distributed-STBC
Electronics
Engineering Sciences
Higher order statistic
Keying
Machine learning
Measurement
Modulation
modulation classification
Networking and Internet Architecture
Receivers
Relaying
Relays
Signal and Image processing
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Summary:Automatic modulation classification (AMC) has been used as a crucial component to improve the overall performance in cognitive radio system, thereby allowing any user to promote secure and reliable communication. In this letter, we propose a semi-blind AMC approach based on a simplified distributed space-time block coding (D-STBC) scheme, in which the STBC code is artificially generated by availing three nodes in the cooperative network. The proposed system aims at distinguishing between the modulation type and order among different M -ary shift keying linear modulations, by exploiting both the temporal and spatial signal dimensions. After the application of the zero-forcing STBC-decoding, the performance of the proposal is investigated using three different machine learning classifiers. For this, a comparative study has been carried out by resorting to selected features which were based on the combination of the fourth and the sixth-order of high-order cumulants and high-order moments of the D-STBC-decoded signal. In addition, to the usually adopted receiver operating characteristics classification metric, we propose to have recourse to the accuracy, the recall, the precision, the specificity, and the F-factor, to ensure the impartiality of the comparison. Support-vector machine classifier is shown to exhibit the highest performance in terms of the whole measures.
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2018.2824813