Predicting Path Loss of an Indoor Environment Using Artificial Intelligence in the 28-GHz Band
The propagation of signal and its strength in an indoor area have become crucial in the era of fifth-generation (5G) and beyond-5G communication systems, which use high bandwidth. High millimeter wave (mmWave) frequencies present a high signal loss and low signal strength, particularly during signal...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/3/497 |
| _version_ | 1797624843101798400 |
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| author | Saud Alhajaj Aldossari |
| author_facet | Saud Alhajaj Aldossari |
| author_sort | Saud Alhajaj Aldossari |
| collection | DOAJ |
| description | The propagation of signal and its strength in an indoor area have become crucial in the era of fifth-generation (5G) and beyond-5G communication systems, which use high bandwidth. High millimeter wave (mmWave) frequencies present a high signal loss and low signal strength, particularly during signal propagation in indoor areas. It is considerably difficult to design indoor wireless communication systems through deterministic modeling owing to the complex nature of the construction materials and environmental changes caused by human interactions. This study presents a methodology of data-driven techniques that will be applied to predict path loss using artificial intelligence. The proposed methodology enables the prediction of signal loss in an indoor environment with an accuracy of 97.4%. |
| first_indexed | 2024-03-11T09:48:25Z |
| format | Article |
| id | doaj.art-c486b1650168484098d0becc492b5660 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T09:48:25Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-c486b1650168484098d0becc492b56602023-11-16T16:27:18ZengMDPI AGElectronics2079-92922023-01-0112349710.3390/electronics12030497Predicting Path Loss of an Indoor Environment Using Artificial Intelligence in the 28-GHz BandSaud Alhajaj Aldossari0Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addwasir 11991, Saudi ArabiaThe propagation of signal and its strength in an indoor area have become crucial in the era of fifth-generation (5G) and beyond-5G communication systems, which use high bandwidth. High millimeter wave (mmWave) frequencies present a high signal loss and low signal strength, particularly during signal propagation in indoor areas. It is considerably difficult to design indoor wireless communication systems through deterministic modeling owing to the complex nature of the construction materials and environmental changes caused by human interactions. This study presents a methodology of data-driven techniques that will be applied to predict path loss using artificial intelligence. The proposed methodology enables the prediction of signal loss in an indoor environment with an accuracy of 97.4%.https://www.mdpi.com/2079-9292/12/3/497indoor communications5Gpath lossartificial intelligencerandom forestdecision tree |
| spellingShingle | Saud Alhajaj Aldossari Predicting Path Loss of an Indoor Environment Using Artificial Intelligence in the 28-GHz Band Electronics indoor communications 5G path loss artificial intelligence random forest decision tree |
| title | Predicting Path Loss of an Indoor Environment Using Artificial Intelligence in the 28-GHz Band |
| title_full | Predicting Path Loss of an Indoor Environment Using Artificial Intelligence in the 28-GHz Band |
| title_fullStr | Predicting Path Loss of an Indoor Environment Using Artificial Intelligence in the 28-GHz Band |
| title_full_unstemmed | Predicting Path Loss of an Indoor Environment Using Artificial Intelligence in the 28-GHz Band |
| title_short | Predicting Path Loss of an Indoor Environment Using Artificial Intelligence in the 28-GHz Band |
| title_sort | predicting path loss of an indoor environment using artificial intelligence in the 28 ghz band |
| topic | indoor communications 5G path loss artificial intelligence random forest decision tree |
| url | https://www.mdpi.com/2079-9292/12/3/497 |
| work_keys_str_mv | AT saudalhajajaldossari predictingpathlossofanindoorenvironmentusingartificialintelligenceinthe28ghzband |