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Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks

Haithem Ben Chikha, Ahmad Almadhor*

Jouf University, College of Computer and Information Sciences, Computer Engineering and Networks Department, Sakaka, 72388, Kingdom of Saudi Arabia

* Corresponding Author: Ahmad Almadhor. Email: email

Computers, Materials & Continua 2022, 70(1), 1799-1814. https://doi.org/10.32604/cmc.2022.018819

Abstract

To promote reliable and secure communications in the cognitive radio network, the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation. In this paper, we address the classification of superimposed modulations dedicated to 5G multiple-input multiple-output (MIMO) two-way cognitive relay network in realistic channels modeled with Nakagami- distribution. Our purpose consists of classifying pairs of users modulations from superimposed signals. To achieve this goal, we apply the higher-order statistics in conjunction with the MultiBoostAB classifier. We use several efficiency metrics including the true positive (TP) rate, false positive (FP) rate, precision, recall, F-Measure and receiver operating characteristic (ROC) area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification. Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio, including the worst case (i.e., ), where the fading distribution follows a one-sided Gaussian distribution. We also carry out a comparative study between our proposal using MultiBoostAB classifier with the decision tree (J48) classifier. Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier. In addition, we study the impact of the symbols number, path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.

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APA Style
Chikha, H.B., Almadhor, A. (2022). Automatic classification of superimposed modulations for 5G MIMO two-way cognitive relay networks. Computers, Materials & Continua, 70(1), 1799-1814. https://doi.org/10.32604/cmc.2022.018819
Vancouver Style
Chikha HB, Almadhor A. Automatic classification of superimposed modulations for 5G MIMO two-way cognitive relay networks. Comput Mater Contin. 2022;70(1):1799-1814 https://doi.org/10.32604/cmc.2022.018819
IEEE Style
H.B. Chikha and A. Almadhor, "Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks," Comput. Mater. Contin., vol. 70, no. 1, pp. 1799-1814. 2022. https://doi.org/10.32604/cmc.2022.018819



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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