CSI-Free Symbol Detection for Atomic MIMO Receivers via In-Context Learning

TL;DR

This paper introduces a novel CSI-free symbol detection method using in-context learning for atomic MIMO receivers, enabling efficient and accurate data detection without explicit channel estimation.

Contribution

It presents the first in-context learning approach for atomic MIMO receiver detection, eliminating the need for traditional channel estimation and reducing computational complexity.

Findings

ICL achieves competitive accuracy in symbol detection.

The method offers higher computational efficiency than existing solutions.

Simulation results validate the effectiveness of the proposed approach.

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.