用混合CNN-LSTM模型实现无线信号调制识别，精度超93%。
CNN-LSTM Hybrid Architecture for Over-the-Air Automatic Modulation Classification Using SDR
- 结合CNN提取空间特征与LSTM捕捉时序依赖，提升信号识别能力。
- 在0-30dB信噪比下，准确率达93.48%，各项指标均超93%。
- 适用于认知无线电和频谱监控，实测验证了实际部署可行性。
自动调制分类（AMC）是未来无线通信系统的核心技术，可在无先验知识条件下识别调制方式，对认知无线电、频谱监测和智能通信网络至关重要。本文提出一种基于卷积神经网络（CNN）与长短期记忆网络（LSTM）混合架构的AMC系统，并集成软件定义无线电（SDR）平台。该架构利用CNN提取空间特征，LSTM捕捉时间依赖性，有效处理复杂时变通信信号。系统通过自建FM发射机与多种调制信号的空中传输信号进行了实际验证。模型在融合RadioML2018数据集与自生成数据集的混合数据上训练，覆盖0至30dB信噪比范围。性能评估采用准确率、精确率、召回率、F1分数及受试者工作特征曲线下面积（AUC-ROC）。优化后模型达到93.48%准确率、93.53%精确率、93.48%召回率，F1分数为93.45%。AUC-ROC分析表明模型在噪声环境下仍具强区分能力。实验结果验证了混合CNN-LSTM架构在AMC中的有效性，具备在自适应频谱管理与先进认知无线电系统中应用的潜力。
原文摘要 · Abstract (English)
Automatic Modulation Classification (AMC) is a core technology for future wireless communication systems, enabling the identification of modulation schemes without prior knowledge. This capability is essential for applications in cognitive radio, spectrum monitoring, and intelligent communication networks. We propose an AMC system based on a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture, integrated with a Software Defined Radio (SDR) platform. The proposed architecture leverages CNNs for spatial feature extraction and LSTMs for capturing temporal dependencies, enabling efficient handling of complex, time-varying communication signals. The system's practical ability was demonstrated by identifying over-the-air (OTA) signals from a custom-built FM transmitter alongside other modulation schemes. The system was trained on a hybrid dataset combining the RadioML2018 dataset with a custom-generated dataset, featuring samples at Signal-to-Noise Ratios (SNRs) from 0 to 30dB. System performance was evaluated using accuracy, precision, recall, F1 score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The optimized model achieved 93.48% accuracy, 93.53% precision, 93.48% recall, and an F1 score of 93.45%. The AUC-ROC analysis confirmed the model's discriminative power, even in noisy conditions. This paper's experimental results validate the effectiveness of the hybrid CNN-LSTM architecture for AMC, suggesting its potential application in adaptive spectrum management and advanced cognitive radio systems.
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