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Accurate Phase Detection for ZigBee Using Artificial Neural Network

Ali Alqahtani1, Abdulaziz A. Alsulami2,*, Saeed Alahmari3, Mesfer Alrizq4

1 Department of Networks and Communications Engineering, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
2 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
3 Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
4 Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia

* Corresponding Author: Abdulaziz A. Alsulami. Email: email

Intelligent Automation & Soft Computing 2023, 36(3), 2505-2518. https://doi.org/10.32604/iasc.2023.033243

Abstract

The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability, scalability, and enhancement of wireless mesh networking. This standard uses a physical layer of binary phase-shift keying (BPSK) modulation and can be operated with two frequency bands, 868 and 915 MHz. The frequency noise could interfere with the BPSK signal, which causes distortion to the signal before its arrival at receiver. Therefore, filtering the BPSK signal from noise is essential to ensure carrying the signal from the sender to the receiver with less error. Therefore, removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor networks. Moreover, researchers have reported a positive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems, including ZigBee. Meanwhile, artificial neural network (ANN) and machine learning (ML) models outperformed results for predicting signals for detection and classification purposes. This paper develops a neural network predictive detection method to enhance the performance of BPSK modulation. First, a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network (WPAN) standard. Then, Gaussian noise was injected into the BPSK simulation model. To reduce the noise of BPSK phase signals, a recurrent neural networks (RNN) model is implemented and integrated at the receiver side to estimate the BPSK’s phase signal. We evaluated our predictive-detection RNN model using mean square error (MSE), correlation coefficient, recall, and F1-score metrics. The result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient (R-value) metric for different signal-to-noise (SNR) values. In addition, our RNN-based model scored 98.71% and 96.34% based on recall and F1-score, respectively.

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APA Style
Alqahtani, A., Alsulami, A.A., Alahmari, S., Alrizq, M. (2023). Accurate phase detection for zigbee using artificial neural network. Intelligent Automation & Soft Computing, 36(3), 2505-2518. https://doi.org/10.32604/iasc.2023.033243
Vancouver Style
Alqahtani A, Alsulami AA, Alahmari S, Alrizq M. Accurate phase detection for zigbee using artificial neural network. Intell Automat Soft Comput . 2023;36(3):2505-2518 https://doi.org/10.32604/iasc.2023.033243
IEEE Style
A. Alqahtani, A.A. Alsulami, S. Alahmari, and M. Alrizq "Accurate Phase Detection for ZigBee Using Artificial Neural Network," Intell. Automat. Soft Comput. , vol. 36, no. 3, pp. 2505-2518. 2023. https://doi.org/10.32604/iasc.2023.033243



cc 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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