Path loss modeling based on neural networks and ensemble method for future wireless networks
In light of the technological advancements that require faster data speeds, there has been an increasing demand for higher frequency bands. Consequently, numerous path loss prediction models have been developed for 5G and beyond communication networks, particularly in the millimeter-wave and subtera...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023068937 |
| _version_ | 1797669863571849216 |
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| author | Mohamed K. Elmezughi Omran Salih Thomas J. Afullo Kevin J. Duffy |
| author_facet | Mohamed K. Elmezughi Omran Salih Thomas J. Afullo Kevin J. Duffy |
| author_sort | Mohamed K. Elmezughi |
| collection | DOAJ |
| description | In light of the technological advancements that require faster data speeds, there has been an increasing demand for higher frequency bands. Consequently, numerous path loss prediction models have been developed for 5G and beyond communication networks, particularly in the millimeter-wave and subterahertz frequency ranges. Despite these efforts, there is a pressing need for more sophisticated models that offer greater flexibility and accuracy, particularly in challenging environments. These advanced models will help in deploying wireless networks with the guarantee of covering communication environments with optimum quality of service. This paper presents path loss prediction models based on machine learning algorithms, namely artificial neural network (ANN), artificial recurrent neural network (RNN) based on long short-term memory (LSTM), shortly known as RNN-LSTM, and convolutional neural network (CNN). Moreover, an ensemble-method-based neural network path loss model is proposed in this paper. Finally, an extensive performance analysis of the four models is provided regarding prediction accuracy, stability, the contribution of input features, and the time needed to run the model. The data used for training and testing in this study were obtained from measurement campaigns conducted in an indoor corridor setting, covering both line-of-sight and non-line-of-sight communication scenarios. The main result of this study demonstrates that the ensemble-method-based model outperforms the other models (ANN, RNN-LSTM, and CNN) in terms of efficiency and high prediction accuracy, and could be trusted as a promising model for path loss in complex environments at high-frequency bands. |
| first_indexed | 2024-03-11T20:50:58Z |
| format | Article |
| id | doaj.art-cb1d48edc621494c9e754d6c8bdb99f7 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-11T20:50:58Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-cb1d48edc621494c9e754d6c8bdb99f72023-10-01T06:00:52ZengElsevierHeliyon2405-84402023-09-0199e19685Path loss modeling based on neural networks and ensemble method for future wireless networksMohamed K. Elmezughi0Omran Salih1Thomas J. Afullo2Kevin J. Duffy3The Discipline of Electrical, Electronic and Computer Engineering, University of KwaZulu-Natal, Durban, 4041, South Africa; Corresponding author.Institute of Systems Science, Durban University of Technology, Durban, 4000, South AfricaThe Discipline of Electrical, Electronic and Computer Engineering, University of KwaZulu-Natal, Durban, 4041, South AfricaInstitute of Systems Science, Durban University of Technology, Durban, 4000, South AfricaIn light of the technological advancements that require faster data speeds, there has been an increasing demand for higher frequency bands. Consequently, numerous path loss prediction models have been developed for 5G and beyond communication networks, particularly in the millimeter-wave and subterahertz frequency ranges. Despite these efforts, there is a pressing need for more sophisticated models that offer greater flexibility and accuracy, particularly in challenging environments. These advanced models will help in deploying wireless networks with the guarantee of covering communication environments with optimum quality of service. This paper presents path loss prediction models based on machine learning algorithms, namely artificial neural network (ANN), artificial recurrent neural network (RNN) based on long short-term memory (LSTM), shortly known as RNN-LSTM, and convolutional neural network (CNN). Moreover, an ensemble-method-based neural network path loss model is proposed in this paper. Finally, an extensive performance analysis of the four models is provided regarding prediction accuracy, stability, the contribution of input features, and the time needed to run the model. The data used for training and testing in this study were obtained from measurement campaigns conducted in an indoor corridor setting, covering both line-of-sight and non-line-of-sight communication scenarios. The main result of this study demonstrates that the ensemble-method-based model outperforms the other models (ANN, RNN-LSTM, and CNN) in terms of efficiency and high prediction accuracy, and could be trusted as a promising model for path loss in complex environments at high-frequency bands.http://www.sciencedirect.com/science/article/pii/S2405844023068937Wireless communicationsChannel modelingPath lossNeural networkANNRNN-LSTM |
| spellingShingle | Mohamed K. Elmezughi Omran Salih Thomas J. Afullo Kevin J. Duffy Path loss modeling based on neural networks and ensemble method for future wireless networks Heliyon Wireless communications Channel modeling Path loss Neural network ANN RNN-LSTM |
| title | Path loss modeling based on neural networks and ensemble method for future wireless networks |
| title_full | Path loss modeling based on neural networks and ensemble method for future wireless networks |
| title_fullStr | Path loss modeling based on neural networks and ensemble method for future wireless networks |
| title_full_unstemmed | Path loss modeling based on neural networks and ensemble method for future wireless networks |
| title_short | Path loss modeling based on neural networks and ensemble method for future wireless networks |
| title_sort | path loss modeling based on neural networks and ensemble method for future wireless networks |
| topic | Wireless communications Channel modeling Path loss Neural network ANN RNN-LSTM |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023068937 |
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