Performance of a coherent BPSK receiver in an impulsive noise environment

Gogoi, Anup Kumar; Mallik, Ranjan K.; Mahanta, Anil; Raghuram, R.

doi:10.1109/icpwc.1999.759689

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…However, BPSK is susceptible to noise, leading to several issues, such as low detection performance of BPSK signal and weak signal energy [4][5][6][7][8][9][10]. Moreover, noise can be initiated by human or natural phenomena, and in both cases, it is considered a big challenge for signal reception [11][12][13][14][15]. Therefore, removing noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Accurate Phase Detection for ZigBee Using Artificial Neural Network

Alqahtani¹,

Alsulami²,

Alahmari³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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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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Section: Introductionmentioning

confidence: 99%

Accurate Phase Detection for ZigBee Using Artificial Neural Network

Alqahtani¹,

Alsulami²,

Alahmari³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…A integral em (5.52) não apresenta solução em forma fechada. Gogoi em [35] demonstrou que, quando -----------J-----------------------…”

Section: Desempenho De Um Sistema Bpsk Na Presença Do Ruído Gaussianounclassified

Um estudo comparativo do desempenho de algumas famílias de sequências de códigos lineares espalhados em um sistema DS-SSMA

Oliveira¹

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Based on the new Communications Services nowadays like, the Personal Communication Networks (PCN), the Cellular Telephone System and the Global Positioning System (GPS), huge spectrum saturation is foreseen and can be minimized through the use of modulation techniques based on Spread Spectrum (SS). Thus, the main subject of this dissertation was the study of the performance of method of Direct Sequence-Spread Spectrum Multiple Access (DS-SSMA) from the carrier, when families of sequence codes, linear spread, are used. It was done a review of the main concepts related to the sequences codes having their use on Spread Spectrum Multiple Access (SSMA) systems. Based on the fact that the DS-SS systems are mostly used in this application, it was possible to stablish some performance parameters that was employed to compare the variety of families of sequences codes, in this specific use. It is presented the generation process of the most important families of linear sequence codes, and a study of the signal to noise ratio (SNR) performance for all the families of sequence codes is done, via computation simulations. The parameters used when measuring the performance were non-periodic cross-correlation functions. The results of the simulations were shown and also analyzed.

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