A Modulation Recognition Method Based on Gated Recurrent Network Integrating Adaptive Denoising and Aggregation Attention

ZHENG Qinghe; LI Binglin; ZHOU Fuhui; YU Lisu; HUANG Chongwen; JIANG Weiwei; SHU Feng; ZHAO Yizhe

doi:10.11999/JEIT260213

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ZHENG Qinghe, LI Binglin, ZHOU Fuhui, YU Lisu, HUANG Chongwen, JIANG Weiwei, SHU Feng, ZHAO Yizhe. A Modulation Recognition Method Based on Gated Recurrent Network Integrating Adaptive Denoising and Aggregation Attention[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260213

Citation:

ZHENG Qinghe, LI Binglin, ZHOU Fuhui, YU Lisu, HUANG Chongwen, JIANG Weiwei, SHU Feng, ZHAO Yizhe. A Modulation Recognition Method Based on Gated Recurrent Network Integrating Adaptive Denoising and Aggregation Attention[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260213

Citation:

ZHENG Qinghe, LI Binglin, ZHOU Fuhui, YU Lisu, HUANG Chongwen, JIANG Weiwei, SHU Feng, ZHAO Yizhe. A Modulation Recognition Method Based on Gated Recurrent Network Integrating Adaptive Denoising and Aggregation Attention[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260213

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A Modulation Recognition Method Based on Gated Recurrent Network Integrating Adaptive Denoising and Aggregation Attention

doi: 10.11999/JEIT260213 cstr: 32379.14.JEIT260213

1.
School of Intelligent Engineering, Shandong Management University, Jinan 250357, China
2.
School of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
3.
School of Information Engineering, Nanchang University, Nanchang 330031, China
4.
School of Information and Electronic Engineering, Zhejiang University, Hangzhou 310058, China
5.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
6.
School of Information and Communication Engineering, Hainan University, Haikou 570228, China
7.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The National Natural Science Foundation of China (62401070), The Shandong Provincial Natural Science Foundation (ZR2019ZD01, ZR2023QF125), The Shandong Provincial Youth Innovation Team Plan of Higher Education Institutions (2024KJH005), The Shandong Provincial Science and Technology Based Small and Medium sized Enterprises Innovation Capability Enhancement Project (2024TSGC0055).

Accepted Date: 2026-09-13
Rev Recd Date: 2026-09-13

Available Online: 2026-09-18

Abstract

Abstract

Objective In wireless communication environments under low signal-to-noise ratio (SNR) conditions, the separability of signal features deteriorates, and nonlinear distortion caused by multipath effects poses significant challenges for automatic modulation classification (AMC). Existing deep learning models often suffer from insufficient robustness and generalization under low SNR, while Transformer-based models incur high computational complexity. To address these issues, this paper proposes a gated recurrent network for modulation recognition that integrates adaptive denoising and aggregation attention, aiming to enhance recognition accuracy in low-SNR conditions while maintaining low model complexity. Methods The proposed AMC method consists of three key components. Firstly, an adaptive denoising unit based on a convolutional Kolmogorov-Arnold network (CKAN) is designed to decouple the nonlinear superposition of signals and noise, providing purified input for subsequent feature extraction. The CKAN combines the nonlinear approximation capability of the Kolmogorov-Arnold representation theorem with the local feature extraction ability of convolutional operations. Then an aggregation attention mechanism is introduced to reduce the computational complexity of traditional attention by employing aggregation matrix. Based on this mechanism, a talk-attention module and an attention-gated recurrent unit (Att-GRU) are constructed to enhance cross-head information interaction and long-term temporal dependency modeling, thereby mitigating the impact of multipath interference. Finally, an orthogonal dual-channel strategy and multi-scale feature fusion architecture are adopted to integrate globally denoised features with local time-frequency features of I/Q components, improving the discriminative ability of essential signal characteristics. Results and Discussions The proposed method achieves average classification accuracies of 64.36% and 71.52% on the RadioML 2016.10a and RML22 datasets, respectively, with only 0.23M parameters and an average inference time of 9.1 ms (Table 2). Comparative experiments with existing deep learning models demonstrate the superiority of the proposed approach. It outperforms AWN, MCDformer, FE-SKVIT, AMC-NET, and MCLDNN on RadioML 2016.10a by 2.08%, 1.83%, 1.03%, 1.96%, and 2.15% in average accuracy, respectively (Table 2). When SNR > 8 dB, the accuracy exceeds 90% on both datasets, and reaches nearly 100% on RML22 when SNR > 16 dB. Even at low SNR (–10 dB), the model maintains robust accuracies of 48.5% and 33.0% on the two datasets (Figure 5). The confusion matrix at SNR = 0 dB reveals that classification difficulties primarily occur between QPSK and 8PSK, AM-DSB and WBFM, and among high-order QAM modulations, due to phase distortion, spectral similarity during silent carrier periods, and reduced symbol spacing (Figure 6). Significant improvements are also observed under low SNR conditions (Figure 7). Ablation studies confirm the contribution of key components. Removing the adaptive denoising unit reduces accuracy by 2.15–4.30% on RadioML 2016.10a and 3.46–6.02% on RML22, while increasing inference time only marginally (Table 3). The CKAN configuration with 16/4 channels achieves the best trade-off between accuracy and efficiency (Figure 8). Conclusions In this paper, we present a gated recurrent network that integrates adaptive denoising and aggregation attention for AMC. The method effectively addresses performance degradation in low-SNR wireless communication environments, enhances adaptability to complex channel conditions, and reduces deployment costs. Experimental results on two public datasets validate the superiority of the proposed approach in terms of accuracy, model size, and inference efficiency, especially in challenging low-SNR scenarios. Future work will focus on optimizing the computational efficiency of CKAN-based components to further accelerate inference.
- Automatic modulation recognition,
- Aggregate attention,
- Adaptive denoising,
- Gated recurrent network

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References(29)

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