Automatic Modulation Recognition Based on Adaptive Attention Mechanism and ResNeXt WSL Model

Zhi Liang, Mingliang Tao, Ling Wang, Jia Su, Xin Yang

Research output: Contribution to journalArticlepeer-review

53 Scopus citations

Abstract

Automatic modulation recognition (AMR) plays an important role in modern wireless communication. In this letter, a novel framework for AMR is proposed. The ResNeXt network serves as the backbone, and four proposed adaptive attention mechanism modules are incorporated. The time-frequency representations of the received signals are utilized as the inputs of the proposed deep learning (DL) network, and a transfer learning strategy is adopted based on the pre-trained ResNeXt weakly supervised learning (WSL) model. The comparisons with several state-of-the-art techniques on the RadioML2016.10B and RadioML2018.01A datasets show that the proposed framework converges quickly and can achieve higher robustness and 2% to 8% higher accuracy than other state-of-the-art techniques.

Original languageEnglish
Article number9467342
Pages (from-to)2953-2957
Number of pages5
JournalIEEE Communications Letters
Volume25
Issue number9
DOIs
StatePublished - Sep 2021

Keywords

  • Adaptive attention mechanism
  • Automatic modulation recognition
  • Short-time Fourier transform
  • Transfer learning

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