In-context learning lets wireless receivers adapt to new channels in a single forward pass using pilot signals, without gradient-based retraining, and it outperforms prior neural baselines in the authors' simulations.
Modeling interference for the coexistence of 6G networks and passive sensing systems,
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In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory
In-context learning lets wireless receivers adapt to new channels in a single forward pass using pilot signals, without gradient-based retraining, and it outperforms prior neural baselines in the authors' simulations.