Robustness in Supervised Learning Based Blind Automatic Modulation Classification

Md. Abdur RAHMAN
Minseok KIM
Jun-ichi TAKADA

IEICE TRANSACTIONS on Communications   Vol.E96-B    No.4    pp.1030-1038
Publication Date: 2013/04/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E96.B.1030
Print ISSN: 0916-8516
Type of Manuscript: PAPER
Category: Wireless Communication Technologies
automatic modulation classification (AMC),  emergency radio,  mobile radio,  CFO,  symbol rate,  denoising,  EMD,  decision tree,  

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Automatic modulation classification (AMC) involves extracting a set of unique features from the received signal. Accuracy and uniqueness of the features along with the appropriate classification algorithm determine the overall performance of AMC systems. Accuracy of any modulation feature is usually limited by the blindness of the signal information such as carrier frequency, symbol rate etc. Most papers do not sufficiently consider these impairments and so do not directly target practical applications. The AMC system proposed herein is trained with probable input signals, and the appropriate decision tree should be chosen to achieve robust classification. Six unique features are used to classify eight analog and digital modulation schemes which are widely used by low frequency mobile emergency radios around the globe. The Proposed algorithm improves the classification performance of AMC especially for the low SNR regime.