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Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
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Path loss modeling based on neural networks and ensemble method for future wireless networks.

Mohamed K Elmezughi1, Omran Salih2, Thomas J Afullo1

  • 1The Discipline of Electrical, Electronic and Computer Engineering, University of KwaZulu-Natal, Durban, 4041, South Africa.

Heliyon
|October 9, 2023
PubMed
Summary

An ensemble machine learning model offers superior path loss prediction accuracy for high-frequency wireless networks. This advanced model ensures better quality of service in complex environments.

Keywords:
5G6GANNCNNChannel modelingEnsemble methodNeural networkPath lossRNN-LSTMWireless communications

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Area of Science:

  • Wireless Communication
  • Machine Learning
  • Electromagnetics

Background:

  • Technological advancements necessitate higher data speeds, driving demand for higher frequency bands (millimeter-wave and subterahertz).
  • Existing path loss prediction models for 5G and beyond lack the flexibility and accuracy needed for challenging environments.
  • Accurate path loss prediction is crucial for deploying wireless networks with guaranteed quality of service.

Purpose of the Study:

  • To develop and evaluate advanced machine learning-based path loss prediction models for high-frequency bands.
  • To compare the performance of Artificial Neural Network (ANN), Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), Convolutional Neural Network (CNN), and an ensemble-method-based model.
  • To identify the most efficient and accurate model for path loss prediction in complex indoor environments.

Main Methods:

  • Implementation of ANN, RNN-LSTM, and CNN models for path loss prediction.
  • Development of a novel ensemble-method-based neural network path loss model.
  • Performance analysis based on prediction accuracy, stability, feature contribution, and computational time.
  • Training and testing using data from indoor corridor measurement campaigns (line-of-sight and non-line-of-sight).

Main Results:

  • The ensemble-method-based model demonstrated superior prediction accuracy and efficiency compared to individual ANN, RNN-LSTM, and CNN models.
  • The study provided an extensive performance analysis of all four models.
  • The proposed ensemble model showed high prediction accuracy and efficiency in complex environments.

Conclusions:

  • The ensemble-method-based path loss prediction model is a promising solution for high-frequency wireless communication.
  • This model offers enhanced accuracy and efficiency, crucial for optimizing wireless network deployment.
  • The findings support the use of advanced machine learning techniques for reliable path loss prediction in challenging scenarios.