MIMO Beam Selection in 5G Using Neural Networks
In this paper, we consider cell-discovery problem in 5G millimeter-wave (mmWave) communication systems using multiple input, multiple output (MIMO) beam-forming technique. Specifically, we aim at the proper beam selection method using context-awareness of the user-equipment to reduce latency in beam...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2021-12-01
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| Series: | International Journal of Electronics and Telecommunications |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/121907/PDF/95_3519_Ruseckas_skl.pdf |
| _version_ | 1811314643269844992 |
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| author | Julius Ruseckas Gediminas Molis Hanna Bogucka |
| author_facet | Julius Ruseckas Gediminas Molis Hanna Bogucka |
| author_sort | Julius Ruseckas |
| collection | DOAJ |
| description | In this paper, we consider cell-discovery problem in 5G millimeter-wave (mmWave) communication systems using multiple input, multiple output (MIMO) beam-forming technique. Specifically, we aim at the proper beam selection method using context-awareness of the user-equipment to reduce latency in beam/cell identification. Due to high path-loss in mmWave systems, beam-forming technique is extensively used to increase Signal-to-Noise Ratio (SNR). When seeking to increase user discovery distance, narrow beam must be formed. Thus, a number of possible beam orientations and consequently time needed for the discovery increases significantly when random scanning approach is used. The idea presented here is to reduce latency by employing artificial intelligence (AI) or machine learning (ML) algorithms to guess the best beam orientation using context information from the Global Navigation Satellite System (GNSS), lidars and cameras, and use the knowledge to swiftly initiate communication with the base station. To this end, here, we propose a simple neural network to predict beam orientation from GNSS and lidar data. Results show that using only GNSS data one can get acceptable performance for practical applications. This finding can be useful for user devices with limited processing power. |
| first_indexed | 2024-04-13T11:14:59Z |
| format | Article |
| id | doaj.art-f4d867d1603a473f834a8aa9f59b9e3d |
| institution | Directory Open Access Journal |
| issn | 2081-8491 2300-1933 |
| language | English |
| last_indexed | 2024-04-13T11:14:59Z |
| publishDate | 2021-12-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | International Journal of Electronics and Telecommunications |
| spelling | doaj.art-f4d867d1603a473f834a8aa9f59b9e3d2022-12-22T02:49:00ZengPolish Academy of SciencesInternational Journal of Electronics and Telecommunications2081-84912300-19332021-12-01vol. 67No 4693698https://doi.org/10.24425/ijet.2021.137864MIMO Beam Selection in 5G Using Neural NetworksJulius Ruseckas0Gediminas Molis1Hanna Bogucka2Baltic Institute of Advanced Technology, Vilnius, LithuaniaBaltic Institute of Advanced Technology, Vilnius, LithuaniaInstitute of Radiocommunications, Poznan University of Technology, Poznan, PolandIn this paper, we consider cell-discovery problem in 5G millimeter-wave (mmWave) communication systems using multiple input, multiple output (MIMO) beam-forming technique. Specifically, we aim at the proper beam selection method using context-awareness of the user-equipment to reduce latency in beam/cell identification. Due to high path-loss in mmWave systems, beam-forming technique is extensively used to increase Signal-to-Noise Ratio (SNR). When seeking to increase user discovery distance, narrow beam must be formed. Thus, a number of possible beam orientations and consequently time needed for the discovery increases significantly when random scanning approach is used. The idea presented here is to reduce latency by employing artificial intelligence (AI) or machine learning (ML) algorithms to guess the best beam orientation using context information from the Global Navigation Satellite System (GNSS), lidars and cameras, and use the knowledge to swiftly initiate communication with the base station. To this end, here, we propose a simple neural network to predict beam orientation from GNSS and lidar data. Results show that using only GNSS data one can get acceptable performance for practical applications. This finding can be useful for user devices with limited processing power.https://journals.pan.pl/Content/121907/PDF/95_3519_Ruseckas_skl.pdf5gcontext informationmimo beam orientationmachine learningneural networks |
| spellingShingle | Julius Ruseckas Gediminas Molis Hanna Bogucka MIMO Beam Selection in 5G Using Neural Networks International Journal of Electronics and Telecommunications 5g context information mimo beam orientation machine learning neural networks |
| title | MIMO Beam Selection in 5G Using Neural Networks |
| title_full | MIMO Beam Selection in 5G Using Neural Networks |
| title_fullStr | MIMO Beam Selection in 5G Using Neural Networks |
| title_full_unstemmed | MIMO Beam Selection in 5G Using Neural Networks |
| title_short | MIMO Beam Selection in 5G Using Neural Networks |
| title_sort | mimo beam selection in 5g using neural networks |
| topic | 5g context information mimo beam orientation machine learning neural networks |
| url | https://journals.pan.pl/Content/121907/PDF/95_3519_Ruseckas_skl.pdf |
| work_keys_str_mv | AT juliusruseckas mimobeamselectionin5gusingneuralnetworks AT gediminasmolis mimobeamselectionin5gusingneuralnetworks AT hannabogucka mimobeamselectionin5gusingneuralnetworks |