CSI-Free Symbol Detection for Atomic MIMO Receivers via In-Context Learning
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911041187741696 |
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| author | Song, Zihang Peng, Qihao Xiao, Pei Rajendran, Bipin Simeone, Osvaldo |
| author_facet | Song, Zihang Peng, Qihao Xiao, Pei Rajendran, Bipin Simeone, Osvaldo |
| contents | Atomic receivers based on Rydberg vapor cells as sensors of electromagnetic fields offer a promising alternative to conventional radio frequency front-ends. In multi-antenna configurations, the magnitude-only, phase-insensitive measurements produced by atomic receivers pose challenges for traditional detection methods. Existing solutions rely on two-step iterative optimization processes, which suffer from cascaded channel estimation errors and high computational complexity. We propose a channel state information (CSI)-free symbol detection method based on in-context learning (ICL), which directly maps pilot-response pairs to data symbol predictions without explicit channel estimation. Simulation results show that ICL achieves competitive accuracy with {higher computational efficiency} compared to existing solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04040 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CSI-Free Symbol Detection for Atomic MIMO Receivers via In-Context Learning Song, Zihang Peng, Qihao Xiao, Pei Rajendran, Bipin Simeone, Osvaldo Signal Processing Atomic receivers based on Rydberg vapor cells as sensors of electromagnetic fields offer a promising alternative to conventional radio frequency front-ends. In multi-antenna configurations, the magnitude-only, phase-insensitive measurements produced by atomic receivers pose challenges for traditional detection methods. Existing solutions rely on two-step iterative optimization processes, which suffer from cascaded channel estimation errors and high computational complexity. We propose a channel state information (CSI)-free symbol detection method based on in-context learning (ICL), which directly maps pilot-response pairs to data symbol predictions without explicit channel estimation. Simulation results show that ICL achieves competitive accuracy with {higher computational efficiency} compared to existing solutions. |
| title | CSI-Free Symbol Detection for Atomic MIMO Receivers via In-Context Learning |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2507.04040 |