MmWave Vehicular Beam Selection With Situational Awareness Using Machine Learning
Establishing and tracking beams in millimeter-wave (mmWave) vehicular communication is a challenging task. Large antenna arrays and narrow beams introduce significant system overhead configuring the beams using exhaustive beam search. In this paper, we propose to learn the optimal beam pair index by...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8734054/ |
| _version_ | 1818353457352933376 |
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| author | Yuyang Wang Aldebaro Klautau Monica Ribero Anthony C. K. Soong Robert W. Heath |
| author_facet | Yuyang Wang Aldebaro Klautau Monica Ribero Anthony C. K. Soong Robert W. Heath |
| author_sort | Yuyang Wang |
| collection | DOAJ |
| description | Establishing and tracking beams in millimeter-wave (mmWave) vehicular communication is a challenging task. Large antenna arrays and narrow beams introduce significant system overhead configuring the beams using exhaustive beam search. In this paper, we propose to learn the optimal beam pair index by exploiting the locations and types of the receiver vehicle and its neighboring vehicles (situational awareness), leveraging machine learning classification and past beam training data. We formulate the mmWave beam selection as a multi-class classification problem based on hand-crafted features that capture the situational awareness in different coordinates. We then provide a comprehensive comparison of the different classification models and various levels of situational awareness. Furthermore, we examine several practical issues in the implementation: localization is susceptible to inaccuracy; situational awareness at the base station (BS) can be outdated due to vehicle mobility and limited location reporting frequencies; the situational awareness may be incomplete since vehicles could be invisible to the BS if they are not connected. To demonstrate the scalability of the proposed beam selection solution in the large antenna array regime, we propose two solutions to recommend multiple beams and exploit an extra phase of beam sweeping among the recommended beams. The numerical results show that situational awareness-assisted beam selection using machine learning is able to provide beam prediction, with accuracy that increases with more complete knowledge of the environment. |
| first_indexed | 2024-12-13T19:09:50Z |
| format | Article |
| id | doaj.art-1fbfe6372d5641648630d2630358ce59 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T19:09:50Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1fbfe6372d5641648630d2630358ce592022-12-21T23:34:26ZengIEEEIEEE Access2169-35362019-01-017874798749310.1109/ACCESS.2019.29220648734054MmWave Vehicular Beam Selection With Situational Awareness Using Machine LearningYuyang Wang0Aldebaro Klautau1https://orcid.org/0000-0001-7773-2080Monica Ribero2Anthony C. K. Soong3Robert W. Heath4https://orcid.org/0000-0002-4666-5628Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USADepartment of Electrical Engineering, Federal University of Pará (UFPA), Belém, BrazilDepartment of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USAFuturewei Technologies, Plano, TX, USADepartment of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USAEstablishing and tracking beams in millimeter-wave (mmWave) vehicular communication is a challenging task. Large antenna arrays and narrow beams introduce significant system overhead configuring the beams using exhaustive beam search. In this paper, we propose to learn the optimal beam pair index by exploiting the locations and types of the receiver vehicle and its neighboring vehicles (situational awareness), leveraging machine learning classification and past beam training data. We formulate the mmWave beam selection as a multi-class classification problem based on hand-crafted features that capture the situational awareness in different coordinates. We then provide a comprehensive comparison of the different classification models and various levels of situational awareness. Furthermore, we examine several practical issues in the implementation: localization is susceptible to inaccuracy; situational awareness at the base station (BS) can be outdated due to vehicle mobility and limited location reporting frequencies; the situational awareness may be incomplete since vehicles could be invisible to the BS if they are not connected. To demonstrate the scalability of the proposed beam selection solution in the large antenna array regime, we propose two solutions to recommend multiple beams and exploit an extra phase of beam sweeping among the recommended beams. The numerical results show that situational awareness-assisted beam selection using machine learning is able to provide beam prediction, with accuracy that increases with more complete knowledge of the environment.https://ieeexplore.ieee.org/document/8734054/MmWavebeam alignmentsituational awarenessmachine learning |
| spellingShingle | Yuyang Wang Aldebaro Klautau Monica Ribero Anthony C. K. Soong Robert W. Heath MmWave Vehicular Beam Selection With Situational Awareness Using Machine Learning IEEE Access MmWave beam alignment situational awareness machine learning |
| title | MmWave Vehicular Beam Selection With Situational Awareness Using Machine Learning |
| title_full | MmWave Vehicular Beam Selection With Situational Awareness Using Machine Learning |
| title_fullStr | MmWave Vehicular Beam Selection With Situational Awareness Using Machine Learning |
| title_full_unstemmed | MmWave Vehicular Beam Selection With Situational Awareness Using Machine Learning |
| title_short | MmWave Vehicular Beam Selection With Situational Awareness Using Machine Learning |
| title_sort | mmwave vehicular beam selection with situational awareness using machine learning |
| topic | MmWave beam alignment situational awareness machine learning |
| url | https://ieeexplore.ieee.org/document/8734054/ |
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