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

共 50 条

[31] Efficient deep CNN-based gender classification using Iris wavelet scattering
Aryanmehr, Saeed
Boroujeni, Farsad Zamani
MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (12) : 19041 - 19065
[32] CNN-based event classification of alpha-decay events in nuclear emulsion
Yoshida, J.
Ekawa, H.
Kasagi, A.
Nakagawa, M.
Nakazawa, K.
Saito, N.
Saito, T. R.
Taki, M.
Yoshimoto, M.
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 2021, 989
[33] CNN-Based Layout Segment Classification for Analysis of Layout-Induced Failures
Nagamura, Yoshikazu
Ide, Takashi
Arai, Masayuki
Fukumoto, Satoshi
IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, 2020, 33 (04) : 597 - 605
[34] Lightweight CNN-based malware image classification for resource-constrained applicationsLightweight CNN-based malware image classification for resource-constrained applicationsA. Hota et al.
Ashlesha Hota
Subir Panja
Amitava Nag
Innovations in Systems and Software Engineering, 2025, 21 (1) : 1 - 14
[35] Toward CNN-Based Motor-Imagery EEG Classification with Fuzzy Fusion
Huang, Jian-Xue
Hsieh, Chia-Ying
Huang, Ya-Lin
Wei, Chun-Shu
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, 2022, 24 (08) : 3812 - 3823
[36] CNN-based binary classification of 3D optical microscopic images
Choi, Da-in
Kwon, Taejin
So, Jeongtae
Lim, Sunho
Woo, Dongjun
Lee, Nosung
Kim, Jaewon
Cho, Seungryong
APPLICATIONS OF MACHINE LEARNING 2022, 2022, 12227
[37] Lightweight 1-D CNN-Based Timing Synchronization for OFDM Systems With CIR Uncertainty
Qing, Chaojin
Tang, Shuhai
Cai, Xi
Wang, Jiafan
IEEE WIRELESS COMMUNICATIONS LETTERS, 2022, 11 (11) : 2375 - 2379
[38] Toward CNN-Based Motor-Imagery EEG Classification with Fuzzy Fusion
Jian-Xue Huang
Chia-Ying Hsieh
Ya-Lin Huang
Chun-Shu Wei
International Journal of Fuzzy Systems, 2022, 24 : 3812 - 3823
[39] CNN-Based Target Detection and Classification When Sparse SAR Image Dataset is Available
Bi, Hui
Deng, Jiarui
Yang, Tianwen
Wang, Jian
Wang, Ling
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 (14) : 6815 - 6826
[40] A Degradation Type Adaptive and Deep CNN-Based Image Classification Model for Degraded Images
Liu, Huanhua
Wang, Wei
Liu, Hanyu
Yi, Shuheng
Yu, Yonghao
Yao, Xunwen
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES, 2024, 138 (01): : 459 - 472

← 1 2 3 4 5 →