SPOT: Single-Shot Positioning via Trainable Near-Field Rainbow Beamforming
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913044213268480 |
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| author | Cai, Yeyue Mo, Jianhua Tao, Meixia |
| author_facet | Cai, Yeyue Mo, Jianhua Tao, Meixia |
| contents | Phase-time arrays, which integrate phase shifters (PSs) and true-time delays (TTDs), have emerged as a cost-effective architecture for generating frequency-dependent rainbow beams in wideband sensing and localization. This paper proposes an end-to-end deep learning-based scheme that simultaneously designs the rainbow beams and estimates user positions. Treating the PS and TTD coefficients as trainable variables allows the network to synthesize task-oriented beams that maximize localization accuracy. A lightweight fully connected module then recovers the user's angle-range coordinates from its feedback of the maximum quantized received power and its corresponding subcarrier index after a single downlink transmission. Compared with existing analytical and learning-based schemes, the proposed method reduces overhead by an order of magnitude and delivers consistently lower two-dimensional positioning error. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11391 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SPOT: Single-Shot Positioning via Trainable Near-Field Rainbow Beamforming Cai, Yeyue Mo, Jianhua Tao, Meixia Machine Learning Phase-time arrays, which integrate phase shifters (PSs) and true-time delays (TTDs), have emerged as a cost-effective architecture for generating frequency-dependent rainbow beams in wideband sensing and localization. This paper proposes an end-to-end deep learning-based scheme that simultaneously designs the rainbow beams and estimates user positions. Treating the PS and TTD coefficients as trainable variables allows the network to synthesize task-oriented beams that maximize localization accuracy. A lightweight fully connected module then recovers the user's angle-range coordinates from its feedback of the maximum quantized received power and its corresponding subcarrier index after a single downlink transmission. Compared with existing analytical and learning-based schemes, the proposed method reduces overhead by an order of magnitude and delivers consistently lower two-dimensional positioning error. |
| title | SPOT: Single-Shot Positioning via Trainable Near-Field Rainbow Beamforming |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.11391 |