论文标题
基于深度学习的混合信号的调节和分类
Modulation and Classification of Mixed Signals Based on Deep Learning
论文作者
论文摘要
如今，随着信息的快速发展，频谱资源变得越来越稀缺，从而导致研究方向从单个信号的调制分类转变为同一信道上多个信号的调制分类。因此，有效混合信号自动调制分类技术的出现具有重要意义。考虑到Noma技术对不同功率下混合信号的调制分类具有更深的要求，本文主要引入并使用各种深度学习网络来对此类混合信号进行分类。首先，重现了基于现有CNN模型的单个信号的调制分类。然后，我们开发了改进基本CNN结构的新方法，并将其应用于混合信号的调制分类。同时，研究了训练组数量，训练集类型和训练方法对混合信号识别精度的影响。其次，我们研究了一些基于CNN（RESNET34，分层结构）和其他深度学习模型（LSTM，CLDNN）的深度学习模型。可以看出，尽管这些算法的时间和空间复杂性增加了，但不同的深度学习模型对不同功率混合信号的调制分类问题具有不同的影响。一般而言，可以实现更高的准确性提高。
With the rapid development of information nowadays, spectrum resources are becoming more and more scarce, leading to a shift in the research direction from the modulation classification of a single signal to the modulation classification of multiple signals on the same channel. Therefore, the emergence of an effective mixed signals automatic modulation classification technology have important significance. Considering that NOMA technology has deeper requirements for the modulation classification of mixed signals under different power, this paper mainly introduces and uses a variety of deep learning networks to classify such mixed signals. First, the modulation classification of a single signal based on the existing CNN model is reproduced. We then develop new methods to improve the basic CNN structure and apply it to the modulation classification of mixed signals. Meanwhile, the effects of the number of training sets, the type of training sets and the training methods on the recognition accuracy of mixed signals are studied. Second, we investigate some deep learning models based on CNN (ResNet34, hierarchical structure) and other deep learning models (LSTM, CLDNN). It can be seen although the time and space complexity of these algorithms have increased, different deep learning models have different effects on the modulation classification problem of mixed signals at different power. Generally speaking, higher accuracy gains can be achieved.
