Effective Feature Selection Method for Deep Learning-Based Automatic Modulation Classification Scheme Using Higher-Order Statistics
Recently, in order to satisfy the requirements of commercial communication systems and military communication systems, automatic modulation classification (AMC) schemes have been considered. As a result, various artificial intelligence algorithms such as a deep neural network (DNN), a convolutional...
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| Format: | Article |
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MDPI AG
2020-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/2/588 |
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| author | Sang Hoon Lee Kwang-Yul Kim Yoan Shin |
| author_facet | Sang Hoon Lee Kwang-Yul Kim Yoan Shin |
| author_sort | Sang Hoon Lee |
| collection | DOAJ |
| description | Recently, in order to satisfy the requirements of commercial communication systems and military communication systems, automatic modulation classification (AMC) schemes have been considered. As a result, various artificial intelligence algorithms such as a deep neural network (DNN), a convolutional neural network (CNN), and a recurrent neural network (RNN) have been studied to improve the AMC performance. However, since the AMC process should be operated in real time, the computational complexity must be considered low enough. Furthermore, there is a lack of research to consider the complexity of the AMC process using the data-mining method. In this paper, we propose a correlation coefficient-based effective feature selection method that can maintain the classification performance while reducing the computational complexity of the AMC process. The proposed method calculates the correlation coefficients of second, fourth, and sixth-order cumulants with the proposed formula and selects an effective feature according to the calculated values. In the proposed method, the deep learning-based AMC method is used to measure and compare the classification performance. From the simulation results, it is indicated that the AMC performance of the proposed method is superior to the conventional methods even though it uses a small number of features. |
| first_indexed | 2024-04-13T02:22:41Z |
| format | Article |
| id | doaj.art-08ba06c7d47d4e44bcbb48195ba8363c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-13T02:22:41Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-08ba06c7d47d4e44bcbb48195ba8363c2022-12-22T03:06:55ZengMDPI AGApplied Sciences2076-34172020-01-0110258810.3390/app10020588app10020588Effective Feature Selection Method for Deep Learning-Based Automatic Modulation Classification Scheme Using Higher-Order StatisticsSang Hoon Lee0Kwang-Yul Kim1Yoan Shin2School of Electronic Engineering, Soongsil University, Seoul 06978, KoreaSchool of Electronic Engineering, Soongsil University, Seoul 06978, KoreaSchool of Electronic Engineering, Soongsil University, Seoul 06978, KoreaRecently, in order to satisfy the requirements of commercial communication systems and military communication systems, automatic modulation classification (AMC) schemes have been considered. As a result, various artificial intelligence algorithms such as a deep neural network (DNN), a convolutional neural network (CNN), and a recurrent neural network (RNN) have been studied to improve the AMC performance. However, since the AMC process should be operated in real time, the computational complexity must be considered low enough. Furthermore, there is a lack of research to consider the complexity of the AMC process using the data-mining method. In this paper, we propose a correlation coefficient-based effective feature selection method that can maintain the classification performance while reducing the computational complexity of the AMC process. The proposed method calculates the correlation coefficients of second, fourth, and sixth-order cumulants with the proposed formula and selects an effective feature according to the calculated values. In the proposed method, the deep learning-based AMC method is used to measure and compare the classification performance. From the simulation results, it is indicated that the AMC performance of the proposed method is superior to the conventional methods even though it uses a small number of features.https://www.mdpi.com/2076-3417/10/2/588automatic modulation classificationcumulantcorrelationeffective featuredeep neural network |
| spellingShingle | Sang Hoon Lee Kwang-Yul Kim Yoan Shin Effective Feature Selection Method for Deep Learning-Based Automatic Modulation Classification Scheme Using Higher-Order Statistics Applied Sciences automatic modulation classification cumulant correlation effective feature deep neural network |
| title | Effective Feature Selection Method for Deep Learning-Based Automatic Modulation Classification Scheme Using Higher-Order Statistics |
| title_full | Effective Feature Selection Method for Deep Learning-Based Automatic Modulation Classification Scheme Using Higher-Order Statistics |
| title_fullStr | Effective Feature Selection Method for Deep Learning-Based Automatic Modulation Classification Scheme Using Higher-Order Statistics |
| title_full_unstemmed | Effective Feature Selection Method for Deep Learning-Based Automatic Modulation Classification Scheme Using Higher-Order Statistics |
| title_short | Effective Feature Selection Method for Deep Learning-Based Automatic Modulation Classification Scheme Using Higher-Order Statistics |
| title_sort | effective feature selection method for deep learning based automatic modulation classification scheme using higher order statistics |
| topic | automatic modulation classification cumulant correlation effective feature deep neural network |
| url | https://www.mdpi.com/2076-3417/10/2/588 |
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