CNN-Based Modulation Classification for OFDM Signal

被引：0

作者：

Song, Geonho ^{[1
]}

Jang, Mingyu ^{[1
]}

Yoon, Dongweon ^{[1
]}

机构：

[1] Hanyang Univ, Dept Elect Engn, Seoul, South Korea

来源：

12TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE (ICTC 2021): BEYOND THE PANDEMIC ERA WITH ICT CONVERGENCE INNOVATION | 2021年

关键词：

automatic modulation classification (AMC); machine learning; orthogonal frequency division multiplexing (OFDM); convolutional neural network (CNN);

D O I：

10.1109/ICTC52510.2021.9620896

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Automatic modulation classification (AMC) is one of the important parts in cooperative and noncooperative contexts. This paper approaches the AMC problem by using deep learning. We propose a convolutional neural network (CNN)-based AMC to classify the modulation type of received orthogonal frequency division multiplexing (OFDM) signal and analyze its classification performance. CNN model is trained by using received OFDM signals for different modulation types and signal-to-noise ratios, and then classification accuracy is validated through computer simulations.

引用

页码：1326 / 1328

页数：3

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