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

TL;DR

This paper introduces in-context learning (ICL) techniques for gradient-free wireless receiver adaptation, leveraging Transformer and structured state-space models to enable real-time, flexible adaptation without online retraining, demonstrated through theoretical and empirical results.

Contribution

It presents a novel application of ICL for wireless receiver adaptation, combining architectural frameworks, theoretical insights, and practical application to MIMO networks.

Findings

ICL enables real-time adaptation without online retraining.

Transformer and SSM-based models effectively learn from contextual information.

Empirical results validate ICL's efficiency in MIMO network scenarios.

Abstract

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.