Performance evaluation of Nakagami‐
            <i>m</i>
            fading with impulsive noise

Ashraf, Umer; Begh, Gh. Rasool

doi:10.1049/cmu2.12065

Cited by 7 publications

(6 citation statements)

References 43 publications

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“…( 10), and the F1-score is calculated in Eq. (11). Our model achieved 98.71% of recall and 96.34% of F1-score.…”

Section: Architecture Of Rnnmentioning

confidence: 77%

“…Initially, the dataset was collected in the first two components: BPSK modulation and phase detection using the ZigBee software simulation. The dataset size is (60480 × 5); each column has a different SNR value (5,7,11,13,15) respectively. Then the collected dataset was split into two parts; the first part was used to train the RNN model, and the second part was used to test the model.…”

Section: Proposed Method: Rnn-based Phase Detection Modelmentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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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“…( 10), and the F1-score is calculated in Eq. (11). Our model achieved 98.71% of recall and 96.34% of F1-score.…”

Section: Architecture Of Rnnmentioning

confidence: 77%

Section: Proposed Method: Rnn-based Phase Detection Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accurate Phase Detection for ZigBee Using Artificial Neural Network

Alqahtani¹,

Alsulami²,

Alahmari³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…However, there are different communication setups where the effect of non‐Gaussian noise cannot be overlooked 23 . These include power line communication (PLC), 24 industrial wireless sensor network (IWSN), 25 co‐channel and adjacent channel interference in mobile cellular networks, 26,27 and underwater communication setups 28 . Therefore, to characterize the non‐Gaussian behavior of noise, an additive white generalized Gaussian noise (AWGGN) model has been considered in this work since it includes classical Gaussian noise and Laplacian noise as special cases 29,30 .…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of intelligent reflecting surface‐assisted mobile wireless networks subjected to generalized Gaussian noise

Ashraf,

Rasool Begh

2023

Int J Communication

Self Cite

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SummaryDifferent from the prior works, this paper presents the performance analysis of an intelligent reflecting surface (IRS)‐assisted communication link in a static and mobile scenario impaired by Rayleigh fading and additive white generalized Gaussian noise (AWGGN). Precisely, the IRS is configured as an intelligent access point, and the mobile behavior of the nodes is characterized by the random waypoint (RWP) model. To this end, closed‐form expressions of average bit error rate (BER), average channel capacity (ACC), and outage probability (OP) in both static and mobile scenarios are obtained. To gain further insight into the system performance at high signal‐to‐noise ratio (SNR), asymptotic expressions are obtained. Moreover, the effect of the number of reflective elements () and the shaping parameter () on the system performance is thoroughly studied. The results indicate that introduction of IRS leads to significant improvement in the overall system performance. The derived results are corroborated with Monte Carlo simulations.

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“…Several non-Gaussian noise models have been reported in the literature [8]. One of the important classes of non-Gaussian noise is the symmetric alpha-stable (SαS) noise [9][10][11]. It is a more general representation of additive white Gaussian noise (AWGN) since it includes Gaussian noise as a special case and satisfies generalized central limit theorem (GCLT) [9].…”

Section: Introductionmentioning

confidence: 99%

Ber Analysis in Non-Homogeneous Fading Environments With Impulsive Noise

Ashraf¹,

Begh²

2021

PIER M

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In this paper, using binary phase-shift keying (BPSK) modulation, analytical expressions of bit-error-rate (BER) for various non-homogeneous fading environments (α-μ, η-μ and κ-μ) subjected to SαS noise are obtained. The derived results are expressed in terms of Meijer's G-function and Gamma function. These expressions are used to study the performance of other prominent fading models (like Nakagami-m, Rayleigh, Rician, and Hoyt) available in the technical literature. Further, it is shown that the effect of the impulsive index (α) over BER is much pronounced compared to the amount of fading (AF). Numerical results are provided for different impulsive settings. The results obtained agree with those from simulations.

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Performance evaluation of Nakagami‐ m fading with impulsive noise

Cited by 7 publications

References 43 publications

Accurate Phase Detection for ZigBee Using Artificial Neural Network

Accurate Phase Detection for ZigBee Using Artificial Neural Network

Performance analysis of intelligent reflecting surface‐assisted mobile wireless networks subjected to generalized Gaussian noise

Ber Analysis in Non-Homogeneous Fading Environments With Impulsive Noise

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