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Effective Feature Selection Method for Deep Learning-Based Automatic Modulation Classification Scheme Using Higher-Order Statistics † Sang Hoon Lee, Kwang-Yul Kim

and Yoan Shin *

School of Electronic Engineering, Soongsil University, Seoul 06978, Korea; [email protected] (S.H.L.); [email protected] (K.-Y.K.) * Correspondence: [email protected]; Tel.: +82-10-4234-0632 † 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. Received: 30 June 2019; Accepted: 8 January 2020; Published: 13 January 2020







Abstract: 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. Keywords: automatic modulation classification; cumulant; correlation; effective feature; deep neural network

1. Introduction In an effort to improve the transmission efficiency of satellite communication and mobile communication systems, the systems should consider adaptive changing parameters such as a modulation scheme, a transmission rate and a carrier frequency according to a channel state [1,2]. As part of this study, in order to effectively classify the modulation scheme, an automatic modulation classification (AMC) method has been widely studied [3,4]. Generally, the receiver can estimate the modulation scheme of the transmitted signal in the commercial system. However, since the communication parameters of the enemy cannot be accurately estimated in military communications, the research has been oriented to estimate the communication parameters by using only the received signals [5]. Thus, in order to improve the jamming performance against the enemy communication system, various research works have been undertaken to classify the modulation scheme by the AMC method [6]. The techniques for the AMC can be roughly classified into two types. The first type maximizes the likelihood function based on the statistical model from the received signal. However,

Appl. Sci. 2020, 10, 588; doi:10.3390/app10020588

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Appl. Sci. 2020, 10, 588

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this method has poor performance due to the error that occurs when there is a change in the channel characteristics in the real environment. Moreover, considering different models, the computation of the algorithm becomes very complicated and the calculation effort becomes large [7]. The second type uses the machine-learning technique. This method uses training data to train machine learning models to classify modulation type. Assuming that the training data is similar to the actual data, it can demonstrate good performance even though the computational complexity is lower than the likelihood method. Therefore, in order to classify the modulation type quickly and accurately, the machine-learning algorithm is mainly used. The AMC scheme based on machine learning consists of a feature extraction step that extracts features from the received signal and a signal classification step that classifies the modulation type. There are various techniques such as a deep neural network (DNN) [8], a convolutional neural network (CNN) [9] and a recurrent neural network (RNN) [10] for the AMC that have been studied. The CNN algorithm is a method that shows excellent performance in image processing. The research has been carried out to classify the signals by using the constellation images of the received signals as the features and to classify the signals by imaging the statistical characteristics [9]. The RNN algorithm is an excellent method for analyzing time-series data but requires algorithmic complexity and high calculation effort compared to performance [10]. On the other hand, the DNN algorithm can learn complex structures from various data