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Accurate modulation classification under impaired wireless channels via shallow convolutional neural networks

Ahangarzadeh, A ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1016/j.phycom.2022.101756
  3. Publisher: Elsevier B.V , 2022
  4. Abstract:
  5. Classifying the modulation type of radio signals plays an important role in current and future wireless communication systems. We present a modulation classification method based on convolutional neural networks that reaches high accuracy in face of various channel characteristics and signal conditions without requiring the network to have a very large depth. Experiment results show that the proposed method reaches accurate classification under different system impairment settings that include sampling rate offset, carrier frequency offset, multi-path fading, and additive white Gaussian noise. For instance, compared to a state-of-the-art method, accuracy is improved up to 25% in classifying difficult modulation types under system impairments. Source code of the proposed method is available online. © 2022 Elsevier B.V
  6. Keywords:
  7. Neural networks ; System impairments ; Convolutional neural networks ; Frequency allocation ; Gaussian noise (electronic) ; Multipath fading ; White noise ; Adaptive wireless communication ; Automatic modulation ; Automatic modulation classification ; Convolutional neural network ; Modulation classification ; Modulation types ; Neural-networks ; System impairment ; Wireless channel ; Wireless communications ; Convolution ; Modulation
  8. Source: Physical Communication ; Volume 53 , 2022 ; 18744907 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S187449072200088X