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.

翻译：预训练的Transformer模型通过上下文学习（ICL）展现了无需模型更新即可通过示例提示适应新任务的卓越能力。基于Transformer的无线接收器以发送和接收信号对形式的导频数据作为提示，在导频数据充足时已表现出较高的估计精度。然而在实际应用中，导频信息通常成本高昂且数量有限。本研究提出\underline{DE}cision \underline{F}eedback \underline{IN}-Cont\underline{E}xt \underline{D}etection（DEFINED）方案作为一种新型无线接收器设计，该方案绕过信道估计环节，直接利用（有时极为）有限的导频数据执行符号检测。DEFINED的核心创新在于提出了ICL中的决策反馈机制：通过将已检测符号按序纳入提示信息，以提升后续符号的检测性能。在广泛的无线通信场景中进行的大量实验表明，DEFINED实现了显著的性能提升，在某些情况下仅需单个导频对即可完成检测。