利用稀疏堆栈自编码器实现调制样式识别算法

针对传统调制样式识别方法性能受预先依靠经验设计的特征参数影响大问题，提出一种基于稀疏堆栈自编码器的数字调制样式识别算法。首先根据网络输入数据形式要求，为了利用信号幅度和相位所包含的调制样式信息，提出一种将复数信号预处理为网络可接受的实数形式的信号预处理方法。在网络训练阶段，先通过逐层训练得到每层稀疏自编码网络的初始化参数，再通过有监督算法对分类层训练，最后利用有监督算法进行整体优化。采用 作为分类层完成数字调制样式识别。7种数字调制样式识别的仿真实验表明了本文算法的有效性，相比于其他算法，本文算法在低信噪比时正确识别率较高，识别性能不受人为因素的影响。 

Algorithm of Modulation Pattern Recognition Based on Sparse Stacked Auto-Encoder

Aimed at the problem of traditional modulation recognition method which is influenced by signal feature parameters designed by artificial experience , a novel modulation recognition algorithm based on Sparse Stacked Auto-Encoder. First, according to the network input data form requirements, In order to take advantage of the signal modulation information contained in the amplitude and phase, A signal preprocessing method is proposed to preprocess the complex signal into a network-acceptable real form。In the training stage, the initialization parameters of each sparse auto-encoder network are obtained by layer-by-layer training, and then the supervised algorithm is used to train the classification layer. Finally, the supervised algorithm is used to overall optimization. Using softmax regression classifier as the classification layer to complete the Modulation recognition. The simulation results of seven kinds of digital modulation pattern recognition show the effectiveness of the proposed algorithm. Compared with other algorithms, algorithm has high recognition rate at low SNR, and the recognition performance is not affected by human factors.