用机器学习自动识别业余无线电中的17种数字通信模式。
Digital Operating Mode Classification of Real-World Amateur Radio Transmissions
- 基于频谱图训练轻量级模型，模拟真实信道干扰提升泛化能力。
- 最佳模型准确率达93.80%（17种模式）和85.47%（98种参数变体）。
- 适合无线信号分析、通信安全与自动化监测方向的研究者。
本研究提出一种机器学习方法，用于分类真实世界中的业余无线电数字通信模式。我们从17种数字操作模式生成了98种参数化信号，在70 cm（UHF）业余无线电频段进行传输，并使用两种不同架构的软件定义无线电（SDR）接收机记录信号。三个轻量级机器学习模型仅在有限非传输信号的频谱图上训练，且采用随机字符作为载荷。训练过程结合在线数据增强流水线，以模拟多种信道损伤。最佳模型EfficientNetB0在真实传输数据上的表现：17种模式分类准确率为93.80%，98种参数化信号分类准确率为85.47%，测试时使用维基百科文章作为载荷。此外，我们分析了信号持续时间与FFT频点数的影响，评估了模拟信道损伤的有效性，并在多个模拟信噪比（SNR）条件下验证模型性能。
原文摘要 · Abstract (English)
This study presents an ML approach for classifying digital radio operating modes evaluated on real-world transmissions. We generated 98 different parameterized radio signals from 17 digital operating modes, transmitted each of them on the 70 cm (UHF) amateur radio band, and recorded our transmissions with two different architectures of SDR receivers. Three lightweight ML models were trained exclusively on spectrograms of limited non-transmitted signals with random characters as payloads. This training involved an online data augmentation pipeline to simulate various radio channel impairments. Our best model, EfficientNetB0, achieved an accuracy of 93.80% across the 17 operating modes and 85.47% across all 98 parameterized radio signals, evaluated on our real-world transmissions with Wikipedia articles as payloads. Furthermore, we analyzed the impact of varying signal durations & the number of FFT bins on classification, assessed the effectiveness of our simulated channel impairments, and tested our models across multiple simulated SNRs.
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