用极少导频数据实现高精度符号检测，通过反馈机制提升性能。
Decision Feedback In-Context Symbol Detection over Block-Fading Channels
- 利用决策反馈机制在有限导频下逐步改进符号检测
- 部分场景仅需一对导频即可达到优异检测性能
- 适合导频资源紧张的无线通信系统应用
预训练Transformer通过上下文学习（ICL）可在不更新模型的情况下适应新任务。基于Transformer的无线接收机利用导频数据（发送与接收信号对）作为提示，当导频充足时可实现高精度估计。然而实际中导频常昂贵且稀缺。本文提出新型无线接收机设计DEFINED（Decision Feedback In-Context Detection），跳过信道估计，直接基于极有限的导频数据进行符号检测。其核心创新在于ICL中的决策反馈机制：将已检测的符号逐步加入提示，以提升后续符号的检测精度。大量实验表明，该方法在多种无线通信场景下均有显著性能提升，部分情况下仅需单对导频即达良好效果。
原文摘要 · Abstract (English)
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.
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