Based on the characteristics of time domain and frequency domain recognition theory, a recognition scheme is designed to complete the modulation identification of communication signals including 16 analog and digital modulations, involving 10 different eigenvalues in total. In the in-class recognition of FSK signal, feature extraction in frequency domain is carried out, and a statistical algorithm of spectral peak number is proposed. This paper presents a method to calculate the rotation degree of constellation image. By calculating the rotation degree and modifying the clustering radius, the recognition rate of QAM signal is improved significantly. Another commonly used method for calculating the rotation of constellations is based on Radon transform. Compared with the proposed algorithm, the proposed algorithm has lower computational complexity and higher accuracy under certain SNR conditions. In the modulation discriminator of the deep neural network, the spectral features and cumulative features are extracted as inputs, the modified linear elements are used as neuron activation functions, and the cross-entropy is used as loss functions. In the modulation recognitor of deep neural network, deep neural network and cyclic neural network are constructed for modulation recognition of communication signals. The neural network automatic modulation recognizer is implemented on CPU and GPU, which verifies the recognition accuracy of communication signal modulation recognizer based on neural network. The experimental results show that the communication signal modulation recognizer based on artificial neural network has good classification accuracy in both the training set and the test set.

中文翻译：

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基于人工特征工程深度神经网络调制标识符的通信信号调制机制

根据时域和频域识别理论的特点，设计了一种识别方案，完成通信信号的调制识别，包括16种模拟和数字调制，共涉及10种不同的特征值。在FSK信号的类内识别中，进行频域特征提取，提出一种谱峰数统计算法。本文提出了一种计算星座图旋转度的方法。通过计算旋转度和修改聚类半径，显着提高了QAM信号的识别率。另一种常用的计算星座旋转的方法是基于Radon变换。与提出的算法相比，该算法在一定的信噪比条件下具有较低的计算复杂度和较高的精度。在深度神经网络的调制判别器中，提取光谱特征和累积特征作为输入，修正后的线性元素作为神经元激活函数，交叉熵作为损失函数。在深度神经网络的调制识别器中，构建了深度神经网络和循环神经网络，用于通信信号的调制识别。在CPU和GPU上实现了神经网络自动调制识别器，验证了基于神经网络的通信信号调制识别器的识别精度。