{"ID":2898135,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.04040","arxiv_id":"2507.04040","title":"CSI-Free Symbol Detection for Atomic MIMO Receivers via In-Context Learning","abstract":"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.","short_abstract":"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....","url_abs":"https://arxiv.org/abs/2507.04040","url_pdf":"https://arxiv.org/pdf/2507.04040v1","authors":"[\"Zihang Song\",\"Qihao Peng\",\"Pei Xiao\",\"Bipin Rajendran\",\"Osvaldo Simeone\"]","published":"2025-07-05T13:39:59Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}