Decision Feedback In-Context Symbol Detection over Block-Fading Channels

TL;DR

This paper introduces DEFINED, a novel wireless receiver that uses decision feedback in in-context learning to directly detect symbols with minimal pilot data, bypassing traditional channel estimation.

Contribution

The paper proposes a new decision feedback mechanism within in-context learning for wireless detection, enabling direct symbol detection with limited pilot data without channel estimation.

Findings

Achieves high detection accuracy with very limited pilot data.

Significantly outperforms traditional methods in various settings.

Can operate effectively with only a single pilot pair.

Abstract

Pre-trained Transformers, through in-context learning (ICL), have demonstrated exceptional capabilities to adapt to new tasks using example prompts \textit{without model update}. Transformer-based wireless receivers, where prompts consist of the pilot data in the form of transmitted and received signal pairs, have shown high estimation accuracy when pilot data are abundant. However, pilot information is often costly and limited in practice. In this work, we propose the \underline{DE}cision \underline{F}eedback \underline{IN}-Cont\underline{E}xt \underline{D}etection (DEFINED) solution as a new wireless receiver design, which bypasses channel estimation and directly performs symbol detection using the (sometimes extremely) limited pilot data. The key innovation in DEFINED is the proposed decision feedback mechanism in ICL, where we sequentially incorporate the detected symbols into the prompts to improve the detections for subsequent symbols. Extensive experiments across a broad range of wireless communication settings demonstrate that DEFINED achieves significant performance improvements, in some cases only needing a single pilot pair.