An Improved Modulation Recognition Method Based on Hybrid Kolmogorov-Arnold Convolutional Neural Network

ZHENG Qinghe; LIU Fanglin; YU Lisu; JIANG Weiwei; HUANG Chongwen; LI Bin; SHU Feng

doi:10.11999/JEIT250161

Volume 47 Issue 8

Aug. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(8): 2584-2597

ZHENG Qinghe, LIU Fanglin, YU Lisu, JIANG Weiwei, HUANG Chongwen, LI Bin, SHU Feng. An Improved Modulation Recognition Method Based on Hybrid Kolmogorov-Arnold Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2584-2597. doi: 10.11999/JEIT250161

Citation:

ZHENG Qinghe, LIU Fanglin, YU Lisu, JIANG Weiwei, HUANG Chongwen, LI Bin, SHU Feng. An Improved Modulation Recognition Method Based on Hybrid Kolmogorov-Arnold Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2584-2597. doi: 10.11999/JEIT250161

Citation:

ZHENG Qinghe, LIU Fanglin, YU Lisu, JIANG Weiwei, HUANG Chongwen, LI Bin, SHU Feng. An Improved Modulation Recognition Method Based on Hybrid Kolmogorov-Arnold Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2584-2597. doi: 10.11999/JEIT250161

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An Improved Modulation Recognition Method Based on Hybrid Kolmogorov-Arnold Convolutional Neural Network

doi: 10.11999/JEIT250161 cstr: 32379.14.JEIT250161

1.
School of Intelligent Engineering, Shandong Management University, Jinan 250357, China
2.
School of Information Engineering, Nanchang University, Nanchang 330031, China
3.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
4.
School of Information and Electronic Engineering, Zhejiang University, Hangzhou 310058, China
5.
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
6.
School of Information and Communication Engineering, Hainan University, Haikou 570228, China

Funds: The National Key R&D Program (2018YFF01014304), The National Natural Science Foundation of China (62401070), Shandong Provincial Key R&D Program (2024TSGC0055), Shandong Provincial Natural Science Foundation (ZR2019ZD01, ZR2023QF125), Shandong Provincial Youth Innovation Team Plan of Higher Education Institutions (2024KJH005)

Received Date: 2025-03-17
Rev Recd Date: 2025-04-10

Available Online: 2025-04-28

Publish Date: 2025-08-27

Abstract

Abstract

Objective With the rapid growth of communication devices and increasing complexity of electromagnetic environments, spectrum efficiency has become a critical performance metric for sixth-generation communication systems. Modulation recognition is an essential function of dynamic spectrum access, aiming to automatically identify the modulation scheme of received signals to enhance spectrum utilization. In practice, wireless signals are often affected by multipath propagation, interference, and noise, which pose challenges for accurate recognition. To address these issues, this study proposes a deep learning-based approach using an end-to-end model that eliminates manual feature extraction, mitigates limitations of handcrafted features, and improves recognition accuracy. By transferring general knowledge from signal classification to modulation recognition, a well-generalized method based on a hybrid Kolmogorov-Arnold Convolutional Neural Network (KA-CNN) is developed. This approach supports reliable communication in applications such as intelligent transportation, the Internet of Things (IoT), vehicular ad hoc networks, and satellite communication. Methods The proposed modulation recognition method first decomposes the signal into a multi-dimensional wavelet domain using a dual-tree complex wavelet packet transform. Different frequency components are then combined to construct a multi-scale signal representation, enabling the neural network to learn consistent features across frequencies. A deep learning structure, KA-CNN, is designed by integrating spline functions with nonlinear activation functions to enhance nonlinear fitting and continuous learning of periodic features. Spline functions are used to address the curse of dimensionality. To improve adaptability to varying signal parameters and enhance generalization across communication scenarios, multilevel grid training with Lipschitz regularization constraints is applied. In KA-CNN, the hybrid module transfers the characteristics of the spline function into convolution operations, which improves the model’s capacity to capture complex mappings between input signals and modulation schemes while retaining the efficiency of the Kolmogorov-Arnold network. This enhances both the expressive power and adaptability of deep learning models under complex communication conditions. Results and Discussions During the experimental phase, modulation recognition performance testing, ablation study, and comparative analysis are conducted on three publicly available datasets (RadioML 2016.10a, RadioML 2018.01a, and CSPB.ML.2023) to evaluate the performance of KA-CNN. Results show that KA-CNN achieves modulation recognition accuracies of 65.14%, 65.56%, and 78.40% on RadioML 2016.10a, RadioML 2018.01a, and CSPB.ML.2023, respectively (Figure 6). The main performance limitation arises in the classification of QPSK versus 8PSK, AM-DSB versus WBFM, and high-order QAM modulation types (Figure 7). Maximum differences in recognition accuracy of KA-CNN driven by different signal representations reach 2.04%, 3.46%, and 4.54% across the three datasets, demonstrating the effect of signal representation (Figure 8). The wavelet packet transform constructs a multi-scale time-frequency representation of signals that is insensitive to the maximum decomposition scale L and supports complementary learning of different modulation features. The hybrid Kolmogorov-Arnold convolutional module and the multi-dimensional perceptual cascade attention mechanism play key roles in enhancing modulation recognition accuracy, particularly under relatively high Signal-To-Noise Ratio (SNR) conditions (Figure 9). Additionally, finer grids and higher decomposition orders improve the model’s ability to extract discriminative signal features, thereby increasing recognition accuracy (Figure 10). Finally, a comparative evaluation against several deep learning models, including GGCNN, Transformer, PR-LSTM, and MobileViT, confirms the superior performance of KA-CNN (Figure 11). Conclusions This study proposes a hybrid KA-CNN to address the reduced modulation recognition accuracy caused by noise and parameter variation, as well as the limited generalization across communication scenarios in existing deep learning models. By integrating spline functions with nonlinear activation functions, KA-CNN mitigates the curse of dimensionality and improves its capacity for continuous learning of periodic features. A dual-tree complex wavelet packet transform is used to construct a multi-scale signal representation, enabling the model to extract consistent features across frequencies. The model is trained using multilevel grids with Lipschitz regularization constraints to enhance adaptability to varying signal parameters and improve generalization. Experimental results on three public datasets demonstrate that KA-CNN improves modulation recognition accuracy and exhibits robust generalization, particularly under low SNRs.
- Modulation recognition,
- Deep learning,
- Kolmogorov-Arnold model,
- Convolutional neural network

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References(22)

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