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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.08301 |
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| _version_ | 1866915471722283008 |
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| author | Mahdavihaji, Roghieh Duel-Hallen, Alexandra Hallen, Hans |
| author_facet | Mahdavihaji, Roghieh Duel-Hallen, Alexandra Hallen, Hans |
| contents | The performance of millimeter-wave (mmWave) and sub-terahertz (sub-THz) communication systems is significantly impaired by sensitivity to sudden blockages. In this work, we employ machine learning (ML) and our physics-based simulation tool to warn about the upcoming blockage tens of 5G frames ahead for highway speeds, providing a sufficient time for a proactive response. Performance of this ML-aided early-warning-of-blockage (ML-EW) algorithm is analyzed for realistic outdoor mobile environments with diverse reflectors and antenna arrays placed at the base station (BS) and user equipment (UE) over a range of mmWave and sub-THz frequencies. ML accuracy of about 90% or higher is demonstrated for highway UE, blocker, and reflector speeds, multiple-input-multiple-output (MIMO) systems, and frequencies in mmWave/sub-THz range. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08301 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Impact of Reflectors and MIMO on ML-Aided mmWave/sub-THz Blockage Prediction Mahdavihaji, Roghieh Duel-Hallen, Alexandra Hallen, Hans Signal Processing The performance of millimeter-wave (mmWave) and sub-terahertz (sub-THz) communication systems is significantly impaired by sensitivity to sudden blockages. In this work, we employ machine learning (ML) and our physics-based simulation tool to warn about the upcoming blockage tens of 5G frames ahead for highway speeds, providing a sufficient time for a proactive response. Performance of this ML-aided early-warning-of-blockage (ML-EW) algorithm is analyzed for realistic outdoor mobile environments with diverse reflectors and antenna arrays placed at the base station (BS) and user equipment (UE) over a range of mmWave and sub-THz frequencies. ML accuracy of about 90% or higher is demonstrated for highway UE, blocker, and reflector speeds, multiple-input-multiple-output (MIMO) systems, and frequencies in mmWave/sub-THz range. |
| title | Impact of Reflectors and MIMO on ML-Aided mmWave/sub-THz Blockage Prediction |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2411.08301 |