Abstract
This paper focuses on the task of automatic modulation recognition. Existing studies have shown low recognition accuracy at low signal-to-noise ratios, and models with a large number of parameters usually demand substantial computational resources, resulting in slower reasoning processes. In this paper, we propose a novel architecture for efficient recognition based on LSTM-Autoencoder and attention mechanism to address these challenges. Experimental results on benchmark datasets show that the proposed method achieves an average recognition accuracy of 62.43% and 64.49% on the RadioML2016.10a and RadioML2016.10b datasets, respectively. On the RadioML2016.10a dataset, the proposed model outperforms other SOTA models with a 2 \(\sim \) 6% points improvement in recognition accuracy. The model also demonstrates superior recognition accuracy for both QAM64 and QAM16 modulation schemes and effectively increases average recognition accuracy by 1–2 percentage points in the -8dB to 2(\(\pm 2\)) dB lower SNR range, indicating its noise robustness. On the RadioML2016.10b dataset, the proposed method’s recognition accuracy is slightly higher than the SOTA model, demonstrating good performance.
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Li, Q., Zhou, X. (2023). LAANet: An Efficient Automatic Modulation Recognition Model Based on LSTM-Autoencoder and Attention Mechanism. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14177. Springer, Cham. https://doi.org/10.1007/978-3-031-46664-9_12
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DOI: https://doi.org/10.1007/978-3-031-46664-9_12
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