Open Access

ARTICLE

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

Mrim M. Alnfiai^*

Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia

* Corresponding Author: Mrim M. Alnfiai. Email:

Computers, Materials & Continua 2023, 74(3), 5723-5740. https://doi.org/10.32604/cmc.2023.033408

Received 15 June 2022; Accepted 22 September 2022; Issue published 28 December 2022

Abstract

In recent years, the need for a fast, efficient and a reliable wireless network has increased dramatically. Numerous 5G networks have already been tested while a few are in the early stages of deployment. In non-cooperative communication scenarios, the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them. The recent advancements in both Machine Learning (ML) and Deep Learning (DL) models demand the development of effective modulation recognition models with self-learning capability. In this background, the current research article designs a Deep Learning enabled Intelligent Modulation Recognition of Communication Signal (DLIMR-CS) technique for next-generation networks. The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals. In addition, the fractal feature extraction process is applied with the help of the Sevcik Fractal Dimension (SFD) approach. Then, the extracted features are fed into the Deep Variational Autoencoder (DVAE) model for the classification of the modulated signals. In order to improve the classification performance ofthe DVAE model, the Tunicate Swarm Algorithm (TSA) is used to fine-tune the hyperparameters involved in DVAE model. A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model. The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.

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Keywords

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