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Open AccessArticle

Effective Feature Selection Method for Deep Learning-Based Automatic Modulation Classification Scheme Using Higher-Order Statistics

School of Electronic Engineering, Soongsil University, Seoul 06978, Korea
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This paper is an extended version of the conference paper presented in the 1st International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2019), Okinawa, Japan, 11–13 February 2019.
Appl. Sci. 2020, 10(2), 588; https://doi.org/10.3390/app10020588
Received: 30 June 2019 / Revised: 3 January 2020 / Accepted: 8 January 2020 / Published: 13 January 2020
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. View Full-Text
Keywords: automatic modulation classification; cumulant; correlation; effective feature; deep neural network automatic modulation classification; cumulant; correlation; effective feature; deep neural network
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Lee, S.H.; Kim, K.-Y.; Shin, Y. Effective Feature Selection Method for Deep Learning-Based Automatic Modulation Classification Scheme Using Higher-Order Statistics. Appl. Sci. 2020, 10, 588.

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