PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, Henan, China.
Sensors (Basel). 2019 Sep 19;19(18):4042. doi: 10.3390/s19184042.
Deep learning (DL) is a powerful technique which has achieved great success in many applications. However, its usage in communication systems has not been well explored. This paper investigates algorithms for multi-signals detection and modulation classification, which are significant in many communication systems. In this work, a DL framework for multi-signals detection and modulation recognition is proposed. Compared to some existing methods, the signal modulation format, center frequency, and start-stop time can be obtained from the proposed scheme. Furthermore, two types of networks are built: (1) Single shot multibox detector (SSD) networks for signal detection and (2) multi-inputs convolutional neural networks (CNNs) for modulation recognition. Additionally, the importance of signal representation to different tasks is investigated. Experimental results demonstrate that the DL framework is capable of detecting and recognizing signals. And compared to the traditional methods and other deep network techniques, the current built DL framework can achieve better performance.
深度学习（DL）是一种强大的技术，在许多应用中取得了巨大的成功。然而，它在通信系统中的应用尚未得到充分探索。本文研究了多信号检测和调制分类的算法，这些算法在许多通信系统中都具有重要意义。在这项工作中，提出了一种用于多信号检测和调制识别的深度学习框架。与一些现有方法相比，该方案可以获得信号调制格式、中心频率和起止时间。此外，构建了两种类型的网络：（1）用于信号检测的单发多框检测器（SSD）网络，以及（2）用于调制识别的多输入卷积神经网络（CNN）。此外，还研究了信号表示对不同任务的重要性。实验结果表明，深度学习框架能够检测和识别信号。与传统方法和其他深度网络技术相比，当前构建的深度学习框架可以实现更好的性能。
