Open Access

ARTICLE

Optimal Bidirectional LSTM for Modulation Signal Classification in Communication Systems

Manar Ahmed Hamza^1,*, Siwar Ben Haj Hassine², Souad Larabi-Marie-Sainte³, Mohamed K. Nour⁴, Fahd N. Al-Wesabi^5,6, Abdelwahed Motwakel¹, Anwer Mustafa Hilal¹, Mesfer Al Duhayyim⁷

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
2 Department of Computer Science, College of Science and Arts, King Khalid University, Mahayil Asir, Saudi Arabia
3 Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia
4 Department of Computer Science, College of Computing and Information System, Umm Al-Qura University,Saudi Arabia
5 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
6 Faculty of Computer and IT, Sana'a University, Sana'a, Yemen
7 Department of Natural and Applied Sciences, College of Community-Aflaj, Prince Sattam bin Abdulaziz University, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email:

Computers, Materials & Continua 2022, 72(2), 3055-3071. https://doi.org/10.32604/cmc.2022.024490

Received 19 October 2021; Accepted 20 December 2021; Issue published 29 March 2022

Abstract

Modulation signal classification in communication systems can be considered a pattern recognition problem. Earlier works have focused on several feature extraction approaches such as fractal feature, signal constellation reconstruction, etc. The recent advent of deep learning (DL) models makes it possible to proficiently classify the modulation signals. In this view, this study designs a chaotic oppositional satin bowerbird optimization (COSBO) with bidirectional long term memory (BiLSTM) model for modulation signal classification in communication systems. The proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated signals. In addition, the fractal feature extraction process takes place by the use of Sevcik Fractal Dimension (SFD) approach. Moreover, the modulation signal classification process takes place using BiLSTM with fully convolutional network (BiLSTM-FCN). Furthermore, the optimal hyperparameter adjustment of the BiLSTM-FCN technique takes place by the use of COSBO algorithm. In order to ensure the enhanced classification performance of the COSBO-BiLSTM model, a wide range of simulations were carried out. The experimental results highlighted that the COSBO-BiLSTM technique has accomplished improved performance over the existing techniques.

---

Keywords

---