Deep Learning Modeling of a WBAN-MIMO Channel in Underground Mine
In this study, an efficient model of the channel matrix is developed for a <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> 2 wireless body area network multiple input output (WBAN-MIMO) system based on deep learning algorithms. Th...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9803035/ |
| _version_ | 1828723813344346112 |
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| author | Khaled Kedjar Moulay Elhassan Elazhari Larbi Talbi Mourad Nedil |
| author_facet | Khaled Kedjar Moulay Elhassan Elazhari Larbi Talbi Mourad Nedil |
| author_sort | Khaled Kedjar |
| collection | DOAJ |
| description | In this study, an efficient model of the channel matrix is developed for a <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> 2 wireless body area network multiple input output (WBAN-MIMO) system based on deep learning algorithms. The model is composed of three deep-learning algorithms. Moreover, the model simultaneously predicts channel matrix <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> in an underground mine and identifies the position of the collected data in both line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios. The model was trained and evaluated using the magnitude and phase of the collected data in an underground mine environment within a frequency range of 2.3 GHz – 2.5 GHz. These measurements, conducted with different antenna configurations in the LoS and NLoS scenarios, constitute an input to the model. The latest predicts the channel matrix <inline-formula> <tex-math notation="LaTeX">${H}$ </tex-math></inline-formula> with position and identifies whether the channel is a LoS or NLoS. Finally, the path loss and channel impulse response models were compared with measurement-based models. The modeled channel prediction exhibited a lower root mean square error (RMSE) for channel prediction and high classification accuracy for LoS-NLoS and position identification, respectively. The numerical results reveal that deep learning WBAN-MIMO modeling offers a powerful solution for future wireless systems in underground mine environments. |
| first_indexed | 2024-04-12T12:56:26Z |
| format | Article |
| id | doaj.art-e92b2ad9ae974953824c047c6854c982 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T12:56:26Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e92b2ad9ae974953824c047c6854c9822022-12-22T03:32:19ZengIEEEIEEE Access2169-35362022-01-0110673836739510.1109/ACCESS.2022.31851889803035Deep Learning Modeling of a WBAN-MIMO Channel in Underground MineKhaled Kedjar0https://orcid.org/0000-0002-6695-796XMoulay Elhassan Elazhari1Larbi Talbi2https://orcid.org/0000-0001-5073-7834Mourad Nedil3https://orcid.org/0000-0002-9688-0342Computer Sciences and Engineering Department, University of Quebec in Outaouais, Gatineau, CanadaSchool of Engineering, Université du Québec en Abitibi-Témiscamingue (UQAT), Val-d’Or, QC, CanadaComputer Sciences and Engineering Department, University of Quebec in Outaouais, Gatineau, CanadaSchool of Engineering, Université du Québec en Abitibi-Témiscamingue (UQAT), Val-d’Or, QC, CanadaIn this study, an efficient model of the channel matrix is developed for a <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> 2 wireless body area network multiple input output (WBAN-MIMO) system based on deep learning algorithms. The model is composed of three deep-learning algorithms. Moreover, the model simultaneously predicts channel matrix <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> in an underground mine and identifies the position of the collected data in both line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios. The model was trained and evaluated using the magnitude and phase of the collected data in an underground mine environment within a frequency range of 2.3 GHz – 2.5 GHz. These measurements, conducted with different antenna configurations in the LoS and NLoS scenarios, constitute an input to the model. The latest predicts the channel matrix <inline-formula> <tex-math notation="LaTeX">${H}$ </tex-math></inline-formula> with position and identifies whether the channel is a LoS or NLoS. Finally, the path loss and channel impulse response models were compared with measurement-based models. The modeled channel prediction exhibited a lower root mean square error (RMSE) for channel prediction and high classification accuracy for LoS-NLoS and position identification, respectively. The numerical results reveal that deep learning WBAN-MIMO modeling offers a powerful solution for future wireless systems in underground mine environments.https://ieeexplore.ieee.org/document/9803035/Channel modelscapacitydeep learningimpulse responsemultipath channelMIMO channel |
| spellingShingle | Khaled Kedjar Moulay Elhassan Elazhari Larbi Talbi Mourad Nedil Deep Learning Modeling of a WBAN-MIMO Channel in Underground Mine IEEE Access Channel models capacity deep learning impulse response multipath channel MIMO channel |
| title | Deep Learning Modeling of a WBAN-MIMO Channel in Underground Mine |
| title_full | Deep Learning Modeling of a WBAN-MIMO Channel in Underground Mine |
| title_fullStr | Deep Learning Modeling of a WBAN-MIMO Channel in Underground Mine |
| title_full_unstemmed | Deep Learning Modeling of a WBAN-MIMO Channel in Underground Mine |
| title_short | Deep Learning Modeling of a WBAN-MIMO Channel in Underground Mine |
| title_sort | deep learning modeling of a wban mimo channel in underground mine |
| topic | Channel models capacity deep learning impulse response multipath channel MIMO channel |
| url | https://ieeexplore.ieee.org/document/9803035/ |
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