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

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.

@article{zecchin2025_2506.15176,
  title={ In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory },
  author={ Matteo Zecchin and Tomer Raviv and Dileep Kalathil and Krishna Narayanan and Nir Shlezinger and Osvaldo Simeone },
  journal={arXiv preprint arXiv:2506.15176},
  year={ 2025 }
}