Research Progress of Deep Learning Enabled Automatic Modulation Classification Technology

ZHENG Qinghe; LI Binglin; YU Zhiguo; JIANG Weiwei; ZHU Zhengyu; XU Chi; HUANG Chongwen; GUI Guan

doi:10.11999/JEIT250674

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ZHENG Qinghe, LI Binglin, YU Zhiguo, JIANG Weiwei, ZHU Zhengyu, XU Chi, HUANG Chongwen, GUI Guan. Research Progress of Deep Learning Enabled Automatic Modulation Classification Technology[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250674

Citation:

ZHENG Qinghe, LI Binglin, YU Zhiguo, JIANG Weiwei, ZHU Zhengyu, XU Chi, HUANG Chongwen, GUI Guan. Research Progress of Deep Learning Enabled Automatic Modulation Classification Technology[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250674

Citation:

ZHENG Qinghe, LI Binglin, YU Zhiguo, JIANG Weiwei, ZHU Zhengyu, XU Chi, HUANG Chongwen, GUI Guan. Research Progress of Deep Learning Enabled Automatic Modulation Classification Technology[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250674

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Research Progress of Deep Learning Enabled Automatic Modulation Classification Technology

doi: 10.11999/JEIT250674 cstr: 32379.14.JEIT250674

1.
School of Intelligent Engineering, Shandong Management University, Jinan 250357, China
2.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
3.
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
4.
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110034, China
5.
School of Information and Electronic Engineering, Zhejiang University, Hangzhou 310058, China
6.
School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

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

Received Date: 2025-07-17
Rev Recd Date: 2025-09-30

Available Online: 2025-10-22

Abstract

Abstract

Significance With the advancement of sixth-generation (6G) wireless communication systems towards the terahertz frequency band and space–air–ground integrated networks, the communication environment is becoming increasingly heterogeneous and densely deployed. This evolution imposes stringent precision requirements at the sub-symbol period level for Automatic Modulation Classification (AMC). Under complex channel conditions, AMC faces several challenges: feature mixing and distortion caused by time-varying multipath channels, substantial degradation in recognition accuracy of traditional methods under low Signal-to-Noise Ratio (SNR) conditions, and elevated complexity in detecting mixed modulation signals introduced by Sparse Code Multiple Access (SCMA) techniques. Addressing these challenges, this paper first analyzes the fundamental constraints on AMC method design from the perspective of signal transmission characteristics in communication models. It then systematically reviews Deep Learning (DL)-based AMC approaches, summarizes the difficulties these methods encounter in different wireless communication scenarios, evaluates the performance of representative DL models, and concludes with a discussion of current limitations in AMC together with promising research directions. Process Current research on AMC technology under complex channel conditions mainly focuses on three methodological categories: Likelihood-Based (LB), Feature-Based (FB), and DL, emphasizing both theoretical exploration and algorithmic innovation. Among these, end-to-end DL approaches have demonstrated superior performance in AMC tasks. By stacking multiple layers of nonlinear activation functions, DL models establish strong nonlinear fitting capabilities that allow them to uncover hidden patterns in radio signals. This enables DL to achieve high robustness and accuracy in complex environments. Convolutional Neural Networks (CNNs), leveraging their hierarchical local perception mechanism, can effectively capture amplitude and phase distortion characteristics of modulated signals, showing distinctive advantages in spatial feature extraction. Recurrent Neural Networks (RNNs), through the temporal memory function of gated units, exhibit theoretical superiority in modeling dynamic signal impairments such as inter-symbol interference, carrier frequency offset, carrier phase offset, and timing errors. More recently, Transformer architectures have achieved global feature association modeling through self-attention mechanisms, thereby enhancing the ability to identify key features and markedly improving AMC accuracy under low SNR conditions. The application potential of Transformers in AMC can be further extended by integrating multi-scale feature fusion, optimizing computational efficiency, and improving generalization. Prospects With the continuous growth of communication demands and the increasing complexity of application scenarios, the efficient and reliable management and utilization of wireless spectrum resources has become a central research focus. AMC enables mobile communication systems to achieve dynamic channel adaptation and heterogeneous network integration. Driven by the development of space–air–ground integrated networks, the application scope of AMC has expanded beyond traditional terrestrial cellular systems to emerging domains such as satellite communication and vehicular networking. DL-based AMC frameworks can capture dynamic channel responses through joint time–frequency domain representations, enhance transient feature extraction via attention mechanisms, and effectively decouple the coupling effects of multipath fading and Doppler shifts. By applying neural architecture search and model quantization–compression techniques, DL models can achieve low-complexity, real-time inference at the edge, thereby supporting end-to-end latency control in Vehicle-to-Everything (V2X) communication links. Furthermore, advanced DL architectures introduce feature enhancement mechanisms to preserve signal phase integrity, improving resilience against channel distortion. In dynamic optical network monitoring, feature extraction networks tailored to time-varying channels can adaptively capture the evolution of nonlinear phase shifts. Through implicit channel compensation, DL enables collaborative learning of time-domain and frequency-domain features. At present, AMC technology is progressing towards elastic architectures that support dynamic reconstruction of model parameters through online knowledge distillation and meta-learning frameworks, offering adaptive and lightweight solutions for Internet-of-Things (IoT) scenarios. Conclusions This paper systematically reviews the current research and challenges of AMC technology in the context of 6G networks. First, the applications of CNNs, RNNs, Transformers, and hybrid DL models in AMC are discussed in detail, with analysis of the technical advantages and limitations of each approach. Next, three representative application scenarios are examined: the mobile communication, the optical communication, and the IoT, highlighting the specific challenges faced by AMC technology. At present, the development of DL-driven AMC has moved beyond model design to include deployment and application challenges in real wireless communication environments. For example, constructing DL architectures with continuous learning capabilities is essential for adapting to dynamic communication conditions, while developing large-scale DL models provides an effective way to improve cross-scenario generalization. Future research should emphasize directions that integrate prior knowledge of the physical layer with DL architectures, strengthen feature fusion strategies, and advance hardware–algorithm co-design frameworks.
- Wireless communication,
- Deep Learning (DL),
- Automatic Modulation Classification (AMC),
- Time-varying multipath channel

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References(86)

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