In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory
This addresses the need for efficient and flexible receiver adaptation in wireless communications, particularly for cell-free massive MIMO networks, though it builds on existing ICL paradigms.
The paper tackles the problem of limited flexibility and gradient-based optimization in wireless receiver adaptation by proposing gradient-free adaptation techniques based on in-context learning (ICL), showing that ICL enables real-time adaptation without online retraining.
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.