Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866913440856014848 |
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| author | Yassine, Taha Magoarou, Luc Le Crussière, Matthieu Paquelet, Stephane |
| author_facet | Yassine, Taha Magoarou, Luc Le Crussière, Matthieu Paquelet, Stephane |
| contents | Channel charting (CC) consists in learning a mapping between the space of raw channel observations, made available from pilot-based channel estimation in multicarrier multiantenna system, and a low-dimensional space where close points correspond to channels of user equipments (UEs) close spatially. Among the different methods of learning this mapping, some rely on a distance measure between channel vectors. Such a distance should reliably reflect the local spatial neighborhoods of the UEs. The recently proposed phase-insensitive (PI) distance exhibits good properties in this regards, but suffers from ambiguities due to both its periodic and oscillatory aspects, making users far away from each other appear closer in some cases. In this paper, a thorough theoretical analysis of the said distance and its limitations is provided, giving insights on how they can be mitigated. Guidelines for designing systems capable of learning quality charts are consequently derived. Experimental validation is then conducted on synthetic and realistic data in different scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_03762 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting Yassine, Taha Magoarou, Luc Le Crussière, Matthieu Paquelet, Stephane Signal Processing Artificial Intelligence Channel charting (CC) consists in learning a mapping between the space of raw channel observations, made available from pilot-based channel estimation in multicarrier multiantenna system, and a low-dimensional space where close points correspond to channels of user equipments (UEs) close spatially. Among the different methods of learning this mapping, some rely on a distance measure between channel vectors. Such a distance should reliably reflect the local spatial neighborhoods of the UEs. The recently proposed phase-insensitive (PI) distance exhibits good properties in this regards, but suffers from ambiguities due to both its periodic and oscillatory aspects, making users far away from each other appear closer in some cases. In this paper, a thorough theoretical analysis of the said distance and its limitations is provided, giving insights on how they can be mitigated. Guidelines for designing systems capable of learning quality charts are consequently derived. Experimental validation is then conducted on synthetic and realistic data in different scenarios. |
| title | Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2310.03762 |