摘要
随着第六代无线通信系统向太赫兹频段以及空天地海一体化网络发展,通信环境呈现出高度异构化和超密集化的趋势,对自动调制识别技术提出了亚符号周期级别的精度要求。在复杂信道条件下,自动调制识别技术面临着时变多径信道引起的特征混叠、低信噪比环境下传统方法识别性能衰减以及稀疏码多址技术引发的混合调制信号检测复杂性提升等多重挑战。基于上述技术难题,该文从通信系统的信号传输特性出发,探讨自动调制分类方法设计的关键约束,系统回顾了深度学习使能的自动调制分类技术,综述了不同应用场景下自动调制分类方法面临的挑战,对经典深度学习模型进行了性能评估,最后概述了自动调制分类存在的问题及未来关键研究方向。
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.
作者
郑庆河
李秉霖
于治国
姜蔚蔚
朱政宇
许驰
黄崇文
桂冠
ZHENG Qinghe;LI Binglin;YU Zhiguo;JIANG Weiwei;ZHU Zhengyu;XU Chi;HUANG Chongwen;GUI Guan(School of Intelligent Engineering,Shandong Management University,Jinan 250357,China;School of Information and Communication Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China;School of Electrical and Information Engineering,Zhengzhou University,Zhengzhou 450001,China;Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110034,China;School of Information and Electronic Engineering,Zhejiang University,Hangzhou 310058,China;School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)
出处
《电子与信息学报》
北大核心
2025年第11期4096-4111,共16页
Journal of Electronics & Information Technology
基金
国家自然科学基金(62401070)
山东省重点研发计划(2024TSGC0055)
山东省自然科学基金(ZR2023QF125)
山东省高等学校青年创新团队计划(2024KJH005)。
关键词
无线通信
深度学习
自动调制分类
时变多径信道
Wireless communication
Deep Learning(DL)
Automatic Modulation Classification(AMC)
Time-varying multipath channel
