Feature Image-Based Automatic Modulation Classification Method Using CNN Algorithm

被引：0
|
作者：
Lee, Jung Ho [1 ]
Kim, Kwang-Yul [1 ]
Shin, Yoan [1 ]
机构：
[1] Soongsil Univ, Sch Elect Engn, Seoul 06978, South Korea
来源：
2019 1ST INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION (ICAIIC 2019) | 2019年
关键词：
automatic modulation classification; cumulant; deep learning; convolutional neural network; feature extraction;
D O I：
10.1109/icaiic.2019.8669002
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we propose a feature image-based automatic modulation classification (AMC) method to classify modulation type. The proposed method uses a convolutional neural network (CNN) which is one of deep learning algorithms for image classification. In order to classify the modulation type, various features are transformed in a two-dimensional image and this image is used as the input of the CNN. From the simulation results, we show that the proposed method improves classification performance.
引用
收藏
页码：560 / 563
页数：4
相关论文
共 50 条
  • [1] Effective Feature-Based Automatic Modulation Classification Method Using DNN Algorithm
    Lee, Sang Hoon
    Kim, Kwang-Yul
    Kim, Jae Hyun
    Shin, Yoan
    2019 1ST INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION (ICAIIC 2019), 2019, : 557 - 559
  • [2] Training Images Generation for CNN Based Automatic Modulation Classification
    Zhang, Wei-Tao
    Cui, Dan
    Lou, Shun-Tian
    IEEE ACCESS, 2021, 9 : 62916 - 62925
  • [3] CNN-feature based automatic image annotation method
    Ma, Yanchun
    Liu, Yongjian
    Xie, Qing
    Li, Lin
    MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 78 (03) : 3767 - 3780
  • [4] CNN-feature based automatic image annotation method
    Yanchun Ma
    Yongjian Liu
    Qing Xie
    Lin Li
    Multimedia Tools and Applications, 2019, 78 : 3767 - 3780
  • [5] A Data Preprocessing Method for Automatic Modulation Classification Based on CNN
    Zhang, Haozheng
    Huang, Ming
    Yang, Jingjing
    Sun, Wei
    IEEE COMMUNICATIONS LETTERS, 2021, 25 (04) : 1206 - 1210
  • [6] Fast Automatic Optimisation of CNN Architectures for Image Classification Using Genetic Algorithm
    Bakhshi, Ali
    Noman, Nasimul
    Chen, Zhiyong
    Zamani, Mohsen
    Chalup, Stephan
    2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2019, : 1283 - 1290
  • [7] Proposal of an Environmental Recognition Method for Automatic Parking by an Image-based CNN
    Yamamoto, Kazuki
    Watanabe, Keigo
    Nagai, Isaku
    2019 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (ICMA), 2019, : 833 - 838
  • [8] Automatic Modulation Classification Using CNN-LSTM Based Dual-Stream Structure
    Zhang, Zufan
    Luo, Hao
    Wang, Chun
    Gan, Chenquan
    Xiang, Yong
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (11) : 13521 - 13531
  • [9] Automatic Image-Based Waste Classification
    Ruiz, Victoria
    Sanchez, Angel
    Velez, Jose F.
    Raducanu, Bogdan
    FROM BIOINSPIRED SYSTEMS AND BIOMEDICAL APPLICATIONS TO MACHINE LEARNING, PT II, 2019, 11487 : 422 - 431
  • [10] Image-Based malware classification using ensemble of CNN architectures (IMCEC)
    Vasan, Danish
    Alazab, Mamoun
    Wassan, Sobia
    Safaei, Babak
    Zheng, Qin
    COMPUTERS & SECURITY, 2020, 92 (92)
12345