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Improved automatic modulation recognition using deep learning with additive attention
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DOI：10.1016/j.rineng.2025.104783.png)
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Abstract
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Automatic Modulation Recognition (AMR) is a critical task in modern communication systems, enabling applications such as cognitive radio, spectrum monitoring, and IoT networks. This paper proposes ICRNNA, a novel deep learning model that integrates Convolutional Neural Networks (CNNs), Bidirectional Long ShortTerm Memory (BiLSTM) networks, and an attention mechanism to achieve state-of-the-art performance in AMR tasks. The proposed model is evaluated on the RadioML2016.10a and RadioML2016.10b datasets, demonstrating superior accuracy, computational efficiency, and robustness, particularly in low Signal-toNoise Ratio (SNR) environments. Through extensive ablation studies, we highlight the contributions of each component, showing that the combination of CNNs, BiLSTMs, and attention mechanisms significantly enhances performance. Comparative experiments against state-of-the-art models, including ResNet, MCLDNN, and CNNBiLSTM-DNN, reveal that ICRNNA achieves the highest accuracy (63.24% on RadioML2016.10a and 65.39% on RadioML2016.10b) and outperforms baseline models in computational efficiency, with only 48.42 MFLOPs and 0.79 million parameters. The results underscore the model's suitability for real-time applications in dynamic and noisy environments. This work advances the field of AMR by providing a robust, efficient, and high-performing solution for modern communication systems.
Keywords:
Automatic modulation recognition
Bidirectional long short-term memory
networks
Convolutional neural networks
Deep learning
Enhanced attention mechanism
Signal-to-noise ratio
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