Modeling and predicting the transmission efficiency of communication devices under joint noise and vibration disturbances

Chafaa  Hamrouni; Aarif  Alutaybi

doi:10.59400/sv2112

Modeling and predicting the transmission efficiency of communication devices under joint noise and vibration disturbances

Chafaa Hamrouni Department of Computer Sciences, Taif University-Khurma University College, Khurma 2935, Kingdom of Saudi Arabia
Aarif Alutaybi Department of Computer Sciences, Taif University-Khurma University College, Khurma 2935, Kingdom of Saudi Arabia

Article ID: 2112

Keywords: communication equipment; noise interference; mechanical vibration; performance modeling; LSTM prediction; anti-interference communication

Abstract

In complex environments such as industrial sites and rail transit, communication equipment often faces multi-source interference from mechanical vibration and structural noise, which seriously affects its signal quality and transmission stability. Although previous studies have explored the influence mechanism of a single interference source, there is still a lack of in-depth understanding and quantitative modeling of the coupled interference effect of vibration and noise. To this end, this paper builds an experimental platform based on the ESP32 Wi-Fi communication module, which includes controllable electromagnetic vibration and sound pressure loading, and collects communication performance indicators (RSSI, BER, throughput and delay) and synchronous physical disturbance data under different interference conditions. Through multivariate statistics and variance analysis methods, the interaction law between vibration frequency, amplitude and noise sound pressure level is revealed for the first time. It is found that the combination of the two under medium and high intensity conditions will cause significant nonlinear amplification effects, which will have a synergistic degradation effect on communication performance. The long short-term memory neural network (LSTM) is further introduced to construct a time series prediction model under multi-disturbance environment. The results show that the model has excellent fitting accuracy (R² > 0.97) in RSSI and throughput prediction tasks, which is better than the comparison models such as SVM and polynomial regression, and has good feedforward control potential. The study also proposed communication anti-interference optimization suggestions and equipment structure improvement strategies suitable for industrial and rail scenarios, providing a theoretical basis and experimental support for the intelligent adaptive design of wireless communication systems in high-interference environments.

Published

2025-06-28

How to Cite

Hamrouni, C., & Alutaybi, A. (2025). Modeling and predicting the transmission efficiency of communication devices under joint noise and vibration disturbances. Sound & Vibration, 59(3), 2112. https://doi.org/10.59400/sv2112

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Vol. 59 No. 3 (2025)

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Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

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This study makes significant contributions to the field of ultrasonic testing (UT) by offering a novel approach to the identification of artificially introduced defects within Japanese cedar wood (Cryptomeria japonica). The findings are of particular relevance for the heritage conservation and construction sectors, where non-invasive defect detection is paramount. The study establishes a robust framework for assessing the structural integrity of timber by correlating ultrasonic wave velocity reductions with defect size and distribution. Big-sized defects led to more substantial decreases in wave velocity. The study establishes a robust framework for assessing the structural integrity of historical timber by correlating ultrasonic wave velocity reductions with defect size and distribution. This framework has the potential to be applicable to diverse wood species and defect types.

The control of vehicle interior noise has become a critical metric for assessing noise, vibration, and harshness (NVH) in vehicles. During the initial phases of vehicle development, accurately predicting the impact of road noise on interior noise is essential for reducing noise levels and expediting the product development cycle. In recent years, data-driven methods based on machine learning have gained significant attention due to their robust capability in navigating complex data mapping relationships. Notably, surrogate models have demonstrated exceptional performance in this domain. Numerous researchers have integrated diverse intelligent algorithms into the study of vehicle noise, leveraging advantages such as the elimination of precise modeling requirements, extensive solution space exploration, continuous learning from data, and robust algorithmic versatility. However, in NVH engineering applications, data-driven models face inherent limitations, particularly in interpretability and stability. To address these issues, this paper introduces an improved Long Short-Term Memory (LSTM) network that combines knowledge and data. Inspired by the physical information neural network concept, this approach incorporates values calculated through empirical formulas into the neural network as constraints. Comparative assessments with traditional LSTM networks highlight the advantages of this deep learning model. By integrating empirical formulas constraints, the model not only enhances interpretability but also achieves robust generalization with fewer data samples. The proposed method is validated on a specific vehicle model, showing significant improvements in prediction accuracy and efficiency.

Modeling and predicting the transmission efficiency of communication devices under joint noise and vibration disturbances

Abstract

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