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.

中文翻译：

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基于自适应注意力机制和 ResNeXt WSL 模型的自动调制识别

自动调制识别 (AMR) 在现代无线通信中发挥着重要作用。在这封信中，提出了一个新的 AMR 框架。ResNeXt 网络作为主干，并结合了四个提出的自适应注意机制模块。接收信号的时频表示被用作所提出的深度学习 (DL) 网络的输入，并采用基于预训练的 ResNeXt 弱监督学习 (WSL) 模型的迁移学习策略。在 RadioML2016.10B 和 RadioML2018.01A 数据集上与几种最先进的技术进行比较表明，所提出的框架收敛速度快，并且可以实现更高的鲁棒性和 2% 到 8% 的准确率。艺术技巧。