Towards a Robust and Efficient Classifier for Real World Radio Signal Modulation Classification
Towards a Robust and Efficient Classifier for Real World Radio Signal Modulation Classification
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DOI：
10.1109/icassp49357.2023.10094907
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Dancheng Liu;Kazim Ergun;Tajana Simunic
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文献类型：
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作者：
Dancheng Liu;Kazim Ergun;Tajana Simunic
无线电信号的自动调制分类在认知无线电、无线电频谱监测和非协作通信中的信号解码等许多应用中是一项重要的任务。该领域的最新研究应用了各种深度学习方法来实现准确的分类。然而，由于无线电信号的性质，传输过程中的失真往往是不可预见和不可预测的，这就需要鲁棒的学习模型。同时，需要快速实时调制分类以满足严格的定时要求。在这项工作中，我们提出了一个轻量级的深度学习模型，可以准确快速地对具有不同类型失真的信号的调制进行分类，而无需使用失真信号进行训练。与最先进的模型相比，我们的模型训练速度快25%，分类速度快36%[1]，使用训练集中没有出现的失真参数生成的数据集的准确性下降较小。
Automatic modulation classification for radio signals is an important task in many applications, including cognitive radio, radio spectrum monitoring and signal decoding in non-cooperative communications. Recent studies in this area apply various deep learning methods to achieve accurate classification. However, due to the nature of radio signals, distortions during transmission are often unforeseen and unpredictable, which poses a need for robust learning models. At the same time, there is the need for fast real-time modulation classification to meet strict timing requirements. In this work, we propose a lightweight deep learning model that accurately and quickly classifies the modulation of signals having different types of distortions, without the need to be trained using distorted signals. Our model trains 25% faster and classifies 36% faster compared to the state-of-the-art [1], with smaller accuracy degradation on datasets generated using distortion parameters that do not appear in the training set.