# Turbo-ICL: In-Context Learning-Based Turbo Equalization

arXiv: https://arxiv.org/abs/2505.06175 · HTML: https://mlsanity.com/paper/2505.06175/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 9 May 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs](https://mlsanity.com/paper/2310.01801/) (2023)
- [Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks](https://mlsanity.com/paper/2402.04248/) (2024)
- [From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems](https://mlsanity.com/paper/2001.01227/) (2020)
- [Token Weighting for Long-Range Language Modeling](https://mlsanity.com/paper/2503.09202/) (2025)
- [In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models](https://mlsanity.com/paper/2311.06101/) (2023)
- [Cell-Free Multi-User MIMO Equalization via In-Context Learning](https://mlsanity.com/paper/2404.05538/) (2024)
- [Neuromorphic In-Context Learning for Energy-Efficient MIMO Symbol Detection](https://mlsanity.com/paper/2404.06469/) (2024)
- [Modular Hypernetworks for Scalable and Adaptive Deep MIMO Receivers](https://mlsanity.com/paper/2408.11920/) (2024)
- [Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation](https://mlsanity.com/paper/1911.10131/) (2019)
- [In-Context Learned Equalization in Cell-Free Massive MIMO via State-Space Models](https://mlsanity.com/paper/2410.23882/) (2024)
- [Decision Feedback In-Context Symbol Detection over Block-Fading Channels](https://mlsanity.com/paper/2411.07600/) (2024)
- [Adaptive Turbo Equalization for Nonlinearity Compensation in WDM Systems](https://mlsanity.com/paper/2109.02148/) (2021)

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

- [In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory](https://mlsanity.com/paper/2506.15176/) (2025)
- [Optimal Power Allocation and AI Receiver Design for Superimposed DMRS and Data Transmission](https://mlsanity.com/paper/2608.13809/) (2026)

## Abstract (arXiv)

This paper introduces a novel in-context learning (ICL) framework, inspired by large language models (LLMs), for soft-input soft-output channel equalization in coded multiple-input multiple-output (MIMO) systems. The proposed approach learns to infer posterior symbol distributions directly from a prompt of pilot signals and decoder feedback. A key innovation is the use of prompt augmentation to incorporate extrinsic information from the decoder output as additional context, enabling the ICL model to refine its symbol estimates iteratively across turbo decoding iterations. Two model variants, based on Transformer and state-space architectures, are developed and evaluated. Extensive simulations demonstrate that, when traditional linear assumptions break down, e.g., in the presence of low-resolution quantization, ICL equalizers consistently outperform conventional model-based baselines, even when the latter are provided with perfect channel state information. Results also highlight the advantage of Transformer-based models under limited training diversity, as well as the efficiency of state-space models in resource-constrained scenarios.