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

Yu Wang, Guan Gui, Tomoaki Ohtsuki, Fumiyuki Adachi

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number9336326
Pages (from-to)3587-3596
Number of pages10
JournalIEEE Transactions on Wireless Communications
Volume20
Issue number6
DOIs
Publication statusPublished - 2021 Jun

Keywords

  • Automatic modulation classification (AMC)
  • convolutional neural network (CNN)
  • generalization
  • multi-task learning (MTL)
  • white non-Gaussian noise

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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