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

arXiv: https://arxiv.org/abs/2411.07600 · HTML: https://mlsanity.com/paper/2411.07600/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [What Can Transformers Learn In-Context? A Case Study of Simple Function Classes](https://mlsanity.com/paper/2208.01066/) (2022)
- [Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression](https://mlsanity.com/paper/2306.15063/) (2023)
- [WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence](https://mlsanity.com/paper/2405.17053/) (2024)
- [A Very Brief Introduction to Machine Learning With Applications to Communication Systems](https://mlsanity.com/paper/1808.02342/) (2018)
- [End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication](https://mlsanity.com/paper/2009.05261/) (2020)
- [Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning](https://mlsanity.com/paper/1908.09049/) (2019)
- [Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning](https://mlsanity.com/paper/2009.09967/) (2020)
- [An Iterative BP-CNN Architecture for Channel Decoding](https://mlsanity.com/paper/1707.05697/) (2017)
- [From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems](https://mlsanity.com/paper/2001.01227/) (2020)
- [On the Training Convergence of Transformers for In-Context Classification of Gaussian Mixtures](https://mlsanity.com/paper/2410.11778/) (2024)
- [In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models](https://mlsanity.com/paper/2311.06101/) (2023)
- [Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning](https://mlsanity.com/paper/2409.00124/) (2024)
- [Transformers are Provably Optimal In-context Estimators for Wireless Communications](https://mlsanity.com/paper/2311.00226/) (2023)
- [Cell-Free Multi-User MIMO Equalization via In-Context Learning](https://mlsanity.com/paper/2404.05538/) (2024)

## Built on by (most cited first, 2 of 2)

- [Turbo-ICL: In-Context Learning-Based Turbo Equalization](https://mlsanity.com/paper/2505.06175/) (2025)
- [Decision Feedback In-Context Learning for Wireless Symbol Detection](https://mlsanity.com/paper/2503.16594/) (2025)

## Abstract (arXiv)

Pre-trained Transformers, through in-context learning (ICL), have demonstrated exceptional capabilities to adapt to new tasks using example prompts textitwithout 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 underlineDEcision underlineFeedback underlineIN-ContunderlineExt underlineDetection (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.