Accurate Phase Detection for ZigBee Using Artificial Neural Network  

作　　者：Ali Alqahtani Abdulaziz A.Alsulami Saeed Alahmari Mesfer Alrizq 

机构地区：[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

出　　处：《Intelligent Automation & Soft Computing》2023年第6期2505-2518,共14页智能自动化与软计算（英文）

基　　金：This research was funded by the ministry of education and the deanship of scientific research at Najran University,Kingdom of Saudi Arabia,for financial and technical support under code number(NU/-/SERC/10/641).

摘　　要：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 sen-der 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 posi-tive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems,including ZigBee.Mean-while,artificial neural network(ANN)and machine learning(ML)models outper-formed 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 esti-mate 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.

关 键 词：Neural networks RNN BPSK phasedetection WPAN IEEE802.15.4 signal demodulation 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]