Abstract

Determining the type of modulation is an important task in military communications, satellite communications systems, and submarine communications. In this study, a new digital modulation classification model is presented for detecting various types of modulated signals. The continuous wavelet transform is used in the first step to create a visual representation of the spectral density of the frequencies of the modulation signals in a scalogram image. The subsequent stage involves the utilization of a deep convolutional neural network for feature extraction from the scalogram images. In the next step, the best features are chosen using the MRMR algorithm. MRMR algorithm increases the classification speed and the ability of interpret the classification model by reducing the dimensions of the features. In the fourth step, the modulations are classified using the group learning technique. In the simulations, modulated signals with different amounts of noise with SNR from 0 to 25 dB are considered. Then, accuracy, precision, recall, and F1-score are used to evaluate the performance of the proposed method. The results of the simulations prove that the proposed model with achieving above 99.9% accuracy performs well in the presence of different amounts of noise and provides better performance than other previous studies.

中文翻译：

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基于混合深度学习和装袋方法的时频信息的高效自动调制识别

摘要

确定调制类型是军事通信、卫星通信系统和潜艇通信中的一项重要任务。在本研究中，提出了一种新的数字调制分类模型，用于检测各种类型的调制信号。第一步使用连续小波变换来创建尺度图图像中调制信号频率谱密度的视觉表示。后续阶段涉及利用深度卷积神经网络从尺度图图像中提取特征。在下一步中，使用 MRMR 算法选择最佳特征。MRMR算法通过降低特征维度来提高分类速度和解释分类模型的能力。在第四步中，使用组学习技术对调制进行分类。在仿真中，考虑了具有不同噪声量、SNR 从 0 到 25 dB 的调制信号。然后，使用准确率、精确率、召回率和 F1 分数来评估所提出方法的性能。仿真结果证明，所提出的模型在存在不同噪声量的情况下具有良好的准确率，精度达到 99.9% 以上，并且比之前的其他研究提供了更好的性能。