论文标题
使用深度学习的可重新配置的智能表面辅助分类
Reconfigurable Intelligent Surface-assisted Classification of Modulations using Deep Learning
论文作者
论文摘要
无线网络的第五次生成（5G）将更具自适应性和异质性。可重新配置的智能表面技术使5G能够在多仪波形上工作。但是，在这样的动态网络中，特定调制类型的识别至关重要。我们提出了一种基于人工智能的RIS辅助数字分类方法。我们培训卷积神经网络以对数字调制进行分类。所提出的方法可以直接在接收的信号上学习并学习特征，而无需提取功能。介绍和分析了卷积神经网络学到的功能。此外，还研究了在特定SNR范围内接收信号的强大功能。发现所提出的分类方法的准确性很显着，尤其是对于低水平的SNR。
The fifth generating (5G) of wireless networks will be more adaptive and heterogeneous. Reconfigurable intelligent surface technology enables the 5G to work on multistrand waveforms. However, in such a dynamic network, the identification of specific modulation types is of paramount importance. We present a RIS-assisted digital classification method based on artificial intelligence. We train a convolutional neural network to classify digital modulations. The proposed method operates and learns features directly on the received signal without feature extraction. The features learned by the convolutional neural network are presented and analyzed. Furthermore, the robust features of the received signals at a specific SNR range are studied. The accuracy of the proposed classification method is found to be remarkable, particularly for low levels of SNR.
