# In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory

arXiv: https://arxiv.org/abs/2506.15176 · HTML: https://mlsanity.com/paper/2506.15176/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 33 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Builds on (most cited first, 24 of 24)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [An Explanation of In-context Learning as Implicit Bayesian Inference](https://mlsanity.com/paper/2111.02080/) (2021)
- [What Can Transformers Learn In-Context? A Case Study of Simple Function Classes](https://mlsanity.com/paper/2208.01066/) (2022)
- [Linear Transformers Are Secretly Fast Weight Programmers](https://mlsanity.com/paper/2102.11174/) (2021)
- [Post-Training Quantization for Vision Transformer](https://mlsanity.com/paper/2106.14156/) (2021)
- [Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks](https://mlsanity.com/paper/2402.04248/) (2024)
- [Deep MIMO Detection](https://mlsanity.com/paper/1706.01151/) (2017)
- [Understanding Emergent In-Context Learning from a Kernel Regression Perspective](https://mlsanity.com/paper/2305.12766/) (2023)
- [Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation](https://mlsanity.com/paper/1903.10611/) (2019)
- [Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems](https://mlsanity.com/paper/2210.02515/) (2022)
- [Design of a Standard-Compliant Real-Time Neural Receiver for 5G NR](https://mlsanity.com/paper/2409.02912/) (2024)
- [In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models](https://mlsanity.com/paper/2311.06101/) (2023)
- [Online Meta-Learning For Hybrid Model-Based Deep Receivers](https://mlsanity.com/paper/2203.14359/) (2022)
- [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)
- [Rethinking Invariance in In-context Learning](https://mlsanity.com/paper/2505.04994/) (2025)
- [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)
- [In-Context Learned Equalization in Cell-Free Massive MIMO via State-Space Models](https://mlsanity.com/paper/2410.23882/) (2024)
- [Turbo-ICL: In-Context Learning-Based Turbo Equalization](https://mlsanity.com/paper/2505.06175/) (2025)
- [Adaptive Coding and Channel Shaping Through Reconfigurable Intelligent Surfaces: An Information-Theoretic Analysis](https://mlsanity.com/paper/2012.00407/) (2020)
- [Modeling Interference for the Coexistence of 6G Networks and Passive Sensing Systems](https://mlsanity.com/paper/2307.14848/) (2023)
- [Joint Communication and Sensing for 6G -- A Cross-Layer Perspective](https://mlsanity.com/paper/2402.09120/) (2024)

## Built on by (most cited first, 1 of 1)

- [Self-Evolving In-Context Learning for Direct Pilot-to-Beamformer Design in MU-MISO Systems](https://mlsanity.com/paper/2607.11970/) (2026)

## Abstract (arXiv)

In recent years, deep learning has facilitated the creation of wireless receivers capable of functioning effectively in conditions that challenge traditional model-based designs. Leveraging programmable hardware architectures, deep learning-based receivers offer the potential to dynamically adapt to varying channel environments. However, current adaptation strategies, including joint training, hypernetwork-based methods, and meta-learning, either demonstrate limited flexibility or necessitate explicit optimization through gradient descent. This paper presents gradient-free adaptation techniques rooted in the emerging paradigm of in-context learning (ICL). We review architectural frameworks for ICL based on Transformer models and structured state-space models (SSMs), alongside theoretical insights into how sequence models effectively learn adaptation from contextual information. Further, we explore the application of ICL to cell-free massive MIMO networks, providing both theoretical analyses and empirical evidence. Our findings indicate that ICL represents a principled and efficient approach to real-time receiver adaptation using pilot signals and auxiliary contextual information-without requiring online retraining.