Deep Learning-Based Modulation Identification for OFDM Systems

송건호; 장민규; Yoon, Dongweon

doi:10.1109/IWSSIP58668.2023.10180258

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Deep Learning-Based Modulation Identification for OFDM Systems

송건호;
장민규;
Yoon, Dongweon

Citations

SCOPUS

3

초록

This paper deals with a deep learning (DL)-based automatic modulation classification (AMC) method for orthogonal frequency division multiplexing (OFDM) systems. Among DL methods for AMC, convolution neural network (CNN) has been widely studied to classify the modulation scheme used in the OFDM systems. Although conventional CNN has performed well in previous studies, its classification performance can be degraded when an additional modulation scheme is considered. In this paper, we investigate the CNN-based AMC for the OFDM systems to improve the classification performance by using a deeper CNN model with a residual connection. Through computer simulations, we show that the proposed model outperforms the conventional CNN model for various ranges of training signal-to-noise ratios in terms of classification accuracy.

키워드

automatic modulation classification; convolutional neural network; orthogonal frequency division multiplexing; Convolution; Convolutional neural networks; Deep learning; Learning systems; Orthogonal frequency division multiplexing; Signal to noise ratio; Neural network models; Automatic modulation; Automatic modulation classification; Classification performance; Convolution neural network; Convolutional neural network; Modulation classification; Modulation schemes; Neural network model; Orthogonal frequency division multiplexing systems; Orthogonal frequency-division multiplexing

제목: Deep Learning-Based Modulation Identification for OFDM Systems

저자: 송건호; 장민규; Yoon, Dongweon

DOI: 10.1109/IWSSIP58668.2023.10180258

발행일: 2023-06

유형: Conference paper

저널명: International Conference on Systems, Signals, and Image Processing

권: 2023-June

페이지: 1 ~ 4