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

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.

Machine-Learning-Assisted Cyclostationary Spectral Analysis for Joint Signal Classification and Jammer Detection at the Physical Layer of Cognitive Radio

“…Further, our proposed algorithm achieved significantly high accuracy when compared with most recent techniques for signal classifications that require 10–20 dB SNR for comparable classification performances [ 44 , 45 , 46 , 47 , 48 ]. For example, in [ 49 ], the authors presented an algorithm based on instantaneous statistical characteristics and a Support Vector Machine (SVM) capable of classifying modulated signals 2ASK, 4ASK, 2FSK, and 2PSK with a classification rate of 0.95 at 5 dB and can only attain error-free classification at approximately 14 dB.…”

Random Graph-Based M-QAM Classification for MIMO Systems

“…The AMC literature is largely divided into two techniques: likelihood-based classification (LBC) and feature-based classification (FBC). The existing work on the likelihood-based AMC can be found in [18][19][20][21][22][23] and feature-based AMC in [24][25][26][27][28][29][30].…”

An Improved DeepNN with Feature Ranking for Covid-19 Detection

“…In the same context, Nasir et al [18] have proposed a deep convolutional neural network (DCNN) for real-time document classification based on the Pearson correlation coefficient to select the optimal feature subset. Another study using correlation coefficient and the automatic modulation classification (AMC) scheme has been presented by Lee et al [19]. In [5], a feature selection framework based on recurrent neural networks (RNN) has been proposed.…”

“…[28] Random forest [8] Adaptive group lasso [15] Pairwise constraints-based method for feature selection [16] DNN and multilayer bi-directional long short-term memory [18] Deep CNN based on Pearson correlation coefficient [19] Deep learning method for feature selection based on the automatic modulation classification (AMC) scheme [20] Deep Neural network-based Feature Selection [22] Innovative dual-network architecture [30] Term frequency/inverse document frequency, report length, and a bag of words as feature engineering techniques, and LR with multinomial Naïve Bayes as a classifier [31] Naïve-Bayes machine learning model [32] MLP, XGBoost, and LR [1] Filter selection based on GA [33] MLP and LR (Continued)…”