# Cell-Free Multi-User MIMO Equalization via In-Context Learning

arXiv: https://arxiv.org/abs/2404.05538 · HTML: https://mlsanity.com/paper/2404.05538/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 8 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [What Can Transformers Learn In-Context? A Case Study of Simple Function Classes](https://mlsanity.com/paper/2208.01066/) (2022)
- [Trained Transformers Learn Linear Models In-Context](https://mlsanity.com/paper/2306.09927/) (2023)
- [Large Language Models for Telecom: Forthcoming Impact on the Industry](https://mlsanity.com/paper/2308.06013/) (2023)
- [Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication](https://mlsanity.com/paper/2205.03770/) (2022)
- [From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems](https://mlsanity.com/paper/2001.01227/) (2020)
- [Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation](https://mlsanity.com/paper/1903.10611/) (2019)
- [Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks](https://mlsanity.com/paper/2108.13178/) (2021)
- [In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models](https://mlsanity.com/paper/2311.06101/) (2023)
- [Implementing and Evaluating Security in O-RAN: Interfaces, Intelligence, and Platforms](https://mlsanity.com/paper/2304.11125/) (2023)
- [Cell-Free Massive MIMO in O-RAN: Energy-Aware Joint Orchestration of Cloud, Fronthaul, and Radio Resources](https://mlsanity.com/paper/2301.06166/) (2023)

## Built on by (most cited first, 8 of 8)

- [Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning](https://mlsanity.com/paper/2409.00124/) (2024)
- [Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning](https://mlsanity.com/paper/2406.15819/) (2024)
- [Decision Feedback In-Context Symbol Detection over Block-Fading Channels](https://mlsanity.com/paper/2411.07600/) (2024)
- [Turbo-ICL: In-Context Learning-Based Turbo Equalization](https://mlsanity.com/paper/2505.06175/) (2025)
- [In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory](https://mlsanity.com/paper/2506.15176/) (2025)
- [In-Context Learning for Non-Stationary MIMO Equalization](https://mlsanity.com/paper/2510.08711/) (2025)
- [Scalable Cross-Attention Transformer for Cooperative Multi-AP OFDM Uplink Reception](https://mlsanity.com/paper/2602.04728/) (2026)
- [Self-Evolving In-Context Learning for Direct Pilot-to-Beamformer Design in MU-MISO Systems](https://mlsanity.com/paper/2607.11970/) (2026)

## Abstract (arXiv)

Large pre-trained sequence models, such as transformers, excel as few-shot learners capable of in-context learning (ICL). In ICL, a model is trained to adapt its operation to a new task based on limited contextual information, typically in the form of a few training examples for the given task. Previous work has explored the use of ICL for channel equalization in single-user multi-input and multiple-output (MIMO) systems. In this work, we demonstrate that ICL can be also used to tackle the problem of multi-user equalization in cell-free MIMO systems with limited fronthaul capacity. In this scenario, a task is defined by channel statistics, signal-to-noise ratio, and modulation schemes. The context encompasses the users' pilot sequences, the corresponding quantized received signals, and the current received data signal. Different prompt design strategies are proposed and evaluated that encompass also large-scale fading and modulation information. Experiments demonstrate that ICL-based equalization provides estimates with lower mean squared error as compared to the linear minimum mean squared error equalizer, especially in the presence of limited fronthaul capacity and pilot contamination.