Multi-Task Learning for Generalized Automatic Modulation Classification under Non-Gaussian Noise with Varying SNR Conditions

Yu Wang, Guan Gui, Tomoaki Ohtsuki, Fumiyuki Adachi

研究成果: Article査読

21 被引用数 (Scopus)

抄録

Automatic modulation classification (AMC) is a critical algorithm for the identification of modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Deep learning (DL)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work under the corresponding condition. In this paper, a novel multi-task learning (MTL)-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from knowledge-sharing-based MTL in varying noise scenarios. In detail, multiple CNN models with the same structure are trained for multiple SNR conditions, but they share their knowledge (e.g. model weight) with each other. Thus, MTL can extract the general features from datasets in different noise scenarios. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.

本文言語English
論文番号9336326
ページ(範囲)3587-3596
ページ数10
ジャーナルIEEE Transactions on Wireless Communications
20
6
DOI
出版ステータスPublished - 2021 6

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 電子工学および電気工学
  • 応用数学

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