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.

IEEE802.15.4 has brought the industrial community’s attention because of its features in power consumption [

Artificial neural networks (ANNs) have shown a great success in solving different problems such as image recognition [

A recurrent neural network (RNN) is an artificial neural network algorithm that is used to learn from sequential data. It has a different architecture than the classical neural network algorithm, such as feedforward, because the internal state at time t is input at time t + 1, which allows RNNs to learn from a sequence of data that is related to each other [^{t} is the hidden state at time t, x^{t} is the input at time t, and θ is the parameters for the RNN. Therefore, learning at each time step t is based on the hidden state at earlier time step h^{t−1}, which allows RNN to learn from subsequent input data [

In this research, first, we injected Gaussian noise into the BPSK signal. Second, we collected the data from the BPSK simulation model. Third, we used an RNN technique to detect noise that interfered with the BPSK signal based on supervised learning. In addition, the BPSK signal will be recovered from the noise by predicting the noisy BPSK signal. Furthermore, this research uses the RNN to learn from BPSK sequential data because RNN has shown extraordinary performance in capturing nonlinear relationships on sequential data [

This research enhances the BPSK phase signals at the receiver side by:

Reducing the noise interfered with the BPSK signal at the receiver side of ZigBee.

To do that, we implemented the RNN model to detect the noisy signal of the BPSK system via predicting the phase.

We evaluated our RNN model using MSE, correlation coefficient, recall, and F1-Score metrics. As a result, our model shows better performance than the existing model, in which we scored 8.0621e-13 on MSE, 98.71% on recall, and 96.34% on F1-Score.

This paper is structured as follows; Section 2 discusses the related works associated with modulated signal detection and ML. Section 3 represents the design and method of the proposed RNN based-phase detection. Section 4 presents our findings’ results and discussion, respectively. Finally, Section 5 discusses the conclusion and highlights some recommendations for improvements.

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None of the previous works highlighted the accuracy of applying machine learning or deep learning to detect phase changes. In this paper, we investigate using a neural network-based phase signal detection method to optimize the performance of Binary Phase Shift Keying (BPSK). Therefore, we integrate a Recurrent Neural Network (RNN) to detect the BPSK’s phase signal at the receiver side. Furthermore, we test the performance of the proposed method to check its superiority against the existing model by trying the system for different Signal-to-Noise (SNR) values.

The mathematical representation of the BPSK waveform

where

where

We developed an RNN model to predict the actual carrier phase of the BPSK waveform with the effect of Gaussian noise. In

In the training phase, we trained the RNN with a noisy phase signal with an 11 SNR value. The dataset was divided into three partitions during the training procedure: 80% of the data was used for training, 10% used for validation, and 10% used for testing. We used the Levenberg-Marquardt algorithm to train our model. Finally, we tested our model with the above SNR values in the testing procedure.

We briefly showed Recurrent Neural Network (RNN) in architecture

A full architecture of our proposed RNN model is shown in

We used two methods to evaluate the training of the RNN model: Mean Squared Error (MSE) and correlation coefficient (R-value). MSE is a metric calculated using two parameters, the target data (true BPSK signal) and RNN output (predicted BPSK signal) [

SNR value | MSE value | R |
---|---|---|

7 | 0.24 | 0.95 |

9 | 0.17 | 0.97 |

13 | 2.7419e-06 | 1.0 |

15 | 9.5395e-05 | 0.99 |

The R-value is also calculated between the target data and the RNN output. If the R-value between the target (true BPSK signal) and the predicted BPSK signals (output) is close to 1, that means they have comparable results [

We also evaluated the testing of the RNN model. We calculated the accuracy using two parameters, recall, and F1-score. The recall is calculated using

We illustrated the design of the artificial neural network receiver and discussed its architecture in the previous Section 3. This section discusses the evaluation of the proposed method against the existing model of BPSK in IEEE802.15.4 standard in the presence of Gaussian noise. Furthermore, we obtained the response of the BPSK signal waveform without using the proposed method, and second, we recorded the system’s response in the presence of the proposed method. The simulation of the BPSK system was recorded for different SNR values = 7, 11, 13, 15, as depicted in

In our proposed method, we focused on reducing the noise of the BPSK system. First, we integrated Gaussian noise into the BPSK simulation model. Second, we collected the data from the BPSK simulation model. Third, we implemented an RNN supervised learning-based model to detect the noisy BPSK phase signal and then predict the phase signal. The proposed RNN-based phase signal detection shows the potential to optimize the performance of BPSK modulated signal. The proposed method is optimal due to its low MSE for different SNR values. We achieved 8.0621e-13 of MSE when the SNR value is equal to 11. Furthermore, we evaluated our RNN model using recall and F1-score metrics. Our model showed a recall of 98.71% and an F1-score of 96.34%. The proposed RNN model can be integrated at the receiver side for different mobile and WPAN standards to detect different phase signals. In addition, we recommend testing the proposed method applied in IEEE802.15.4 standard under different nominal propagation such as non-Gaussian noise and co-channel interference (CCI) with Bluetooth, WiFi, or other IEEE802.15.4 networks to test its robustness.

The authors are thankful to the Deanship of Scientific Research at Najran University, Kingdom of Saudi Arabia, for funding this work under the General Research Funding program grant code (NU/-/SERC/10/641).

This research was funded by the

The authors declare that they have no conflicts of interest to report regarding the present study.