深度学习在SEFDM信号分类中的应用

2021 International Conference on Electrical Engineering and Photonics (EExPolytech) Pub Date : 2021-10-14 DOI:10.1109/EExPolytech53083.2021.9614764

V. Pavlov, S. Zavjalov, S. Volvenko, A. Gorlov

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引用次数: 3

摘要

本文研究了卷积神经网络在不同调制方案下的SEFDM信号分类中的应用。在多径信道和两阶频距分别为0.1和0.2的情况下，建立了接收机和发射机的仿真模型。在信噪比为10 dB时，得到了两种情况下的分类精度值，平均为99%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Deep Learning Application for Classification of SEFDM Signals

The paper considers the application of a convolutional neural network for the classification of SEFDM signals for different modulation schemes. A simulation model of the receiver and transmitter has been implemented for the case of a multipath channel and two frequency separations with steps of 0.1 and 0.2. For both cases, the classification accuracy values were obtained, which averaged 99% at signal-to-noise ratio equal to 10 dB.

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2021 International Conference on Electrical Engineering and Photonics (EExPolytech)

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