用上下文学习实现无线接收机无梯度自适应，无需重训练
In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory
- 基于Transformer和状态空间模型的上下文学习架构
- 仅用导频信号即可实时适应信道变化，无需梯度优化
- 适合动态信道环境下的智能无线系统设计
近年来，深度学习推动了无线接收机的发展，使其在传统模型设计难以应对的复杂环境下仍能有效工作。借助可编程硬件架构，基于深度学习的接收机具备根据信道变化动态调整的能力。然而，现有自适应方法（如联合训练、超网络和元学习）或灵活性不足，或需通过梯度下降进行显式优化。本文提出基于新兴上下文学习（ICL）范式的无梯度自适应技术。我们回顾了基于Transformer与结构化状态空间模型（SSMs）的ICL架构，并揭示序列模型如何从上下文信息中学习自适应机制。进一步地，我们将ICL应用于无基站大规模MIMO网络，提供理论分析与实证支持。结果表明，ICL是一种原理清晰、高效的实时接收机自适应方法，仅依赖导频信号与辅助上下文信息，无需在线重训练。
原文摘要 · Abstract (English)
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.
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