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Deep Learning-based Automatic Modulation Recognition Method in the Presence of Phase Offset

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posted on 12.02.2020 by Jie Shi, Sheng Hong, Changxin Cai, Yu Wang, Hao Huang, Guan Gui
Automatic modulation recognition (AMR) plays an important role in various communications systems. It has the ability of adaptive modulation and can adapt to various complex environments. Automatic modulation recognition is also widely used in orthogonal frequency division multiplexing (OFDM) systems. However, because the recognition accuracy of traditional methods to extract the features of OFDM signals is very limited. In order to solve these problems, many deep learning based AMR methods have been proposed to improve the recognition performance. However, most of these AMR methods neglect the harmful effect by carrier phase offset (PO) which often appears in real communications systems. Hence it is required to consider the PO effect for designing the OFDM system. Unlike conventional methods, we propose a convolutional neural network (CNN) based AMR method for considering PO in the OFDM system. The proposed method is used to eliminate the PO to achieve the high classification accuracy. Experiment results are provided to confirm the proposed method when comparing to conventional methods.

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Email Address of Submitting Author

guiguan@njupt.edu.cn

Submitting Author's Institution

Nanjing University of Posts and Telecommunications

Submitting Author's Country

China

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Exports