Deep Learning-Based Modulation Identification for OFDM Systems
- Authors
- Song, Geonho; Jang, Mingyu; Yoon, Dongweon
- Issue Date
- Jun-2023
- Publisher
- IEEE Computer Society
- Keywords
- automatic modulation classification; convolutional neural network; orthogonal frequency division multiplexing
- Citation
- International Conference on Systems, Signals, and Image Processing, v.2023-June, pp.1 - 4
- Indexed
- SCOPUS
- Journal Title
- International Conference on Systems, Signals, and Image Processing
- Volume
- 2023-June
- Start Page
- 1
- End Page
- 4
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/189400
- DOI
- 10.1109/IWSSIP58668.2023.10180258
- ISSN
- 2157-8672
- Abstract
- 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.
- Files in This Item
-
Go to Link
- Appears in
Collections - 서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.