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-s...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.展开更多
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).
文摘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.
