Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication
A significant innovation for future indoor wireless networks is the use of the mmWave frequency band. However, an important challenge comes from the restricted propagation conditions in this band, which necessitates the use of beamforming and associated beam management procedures, including, for ins...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/7/3375 |
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| author | Árpád László Makara Botond Tamás Csathó András Rácz Tamás Borsos László Csurgai-Horváth Bálint Péter Horváth |
| author_facet | Árpád László Makara Botond Tamás Csathó András Rácz Tamás Borsos László Csurgai-Horváth Bálint Péter Horváth |
| author_sort | Árpád László Makara |
| collection | DOAJ |
| description | A significant innovation for future indoor wireless networks is the use of the mmWave frequency band. However, an important challenge comes from the restricted propagation conditions in this band, which necessitates the use of beamforming and associated beam management procedures, including, for instance, beam tracking or beam prediction. A possible solution to the beam management problem is to use artificial-intelligence-based procedures to learn the hidden spatial propagation patterns of the channel and to use this knowledge to predict the best beam directions. In this paper, we present a deep-neural-network-based method that has memory that can be used to predict the best reception directions for moving users. The best direction is the highest expected signal level at the next moment. The resulting method allows for a user-side antenna management system. The result was evaluated using three different metrics, thus detailing not only its predictive ability, but also its usability. |
| first_indexed | 2024-03-11T05:25:34Z |
| format | Article |
| id | doaj.art-2ab316a52d79472da91bb11220a5ea1c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:25:34Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2ab316a52d79472da91bb11220a5ea1c2023-11-17T17:31:35ZengMDPI AGSensors1424-82202023-03-01237337510.3390/s23073375Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor CommunicationÁrpád László Makara0Botond Tamás Csathó1András Rácz2Tamás Borsos3László Csurgai-Horváth4Bálint Péter Horváth5Department of Broadband Infocommunications and Electromagnetic Theory, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, HungaryDepartment of Broadband Infocommunications and Electromagnetic Theory, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, HungaryEricsson Research, H-1117 Budapest, HungaryEricsson Research, H-1117 Budapest, HungaryDepartment of Broadband Infocommunications and Electromagnetic Theory, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, HungaryDepartment of Broadband Infocommunications and Electromagnetic Theory, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, HungaryA significant innovation for future indoor wireless networks is the use of the mmWave frequency band. However, an important challenge comes from the restricted propagation conditions in this band, which necessitates the use of beamforming and associated beam management procedures, including, for instance, beam tracking or beam prediction. A possible solution to the beam management problem is to use artificial-intelligence-based procedures to learn the hidden spatial propagation patterns of the channel and to use this knowledge to predict the best beam directions. In this paper, we present a deep-neural-network-based method that has memory that can be used to predict the best reception directions for moving users. The best direction is the highest expected signal level at the next moment. The resulting method allows for a user-side antenna management system. The result was evaluated using three different metrics, thus detailing not only its predictive ability, but also its usability.https://www.mdpi.com/1424-8220/23/7/3375deep learningmmWaveantenna beam alignmentray tracingmobile indoor communication |
| spellingShingle | Árpád László Makara Botond Tamás Csathó András Rácz Tamás Borsos László Csurgai-Horváth Bálint Péter Horváth Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication Sensors deep learning mmWave antenna beam alignment ray tracing mobile indoor communication |
| title | Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication |
| title_full | Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication |
| title_fullStr | Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication |
| title_full_unstemmed | Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication |
| title_short | Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication |
| title_sort | deep learning based antenna alignment prediction for mobile indoor communication |
| topic | deep learning mmWave antenna beam alignment ray tracing mobile indoor communication |
| url | https://www.mdpi.com/1424-8220/23/7/3375 |
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