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.
Joint Communication and Sensing for 6G -- A Cross-Layer Perspective
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abstract
As 6G emerges, cellular systems are envisioned to integrate sensing with communication capabilities, leading to multi-faceted communication and sensing (JCAS). This paper presents a comprehensive cross-layer overview of the Hexa-X-II project's endeavors in JCAS, aligning 6G use cases with service requirements and pinpointing distinct scenarios that bridge communication and sensing. This work relates to these scenarios through the lens of the cross-layer physical and networking domains, covering models, deployments, resource allocation, storage challenges, computational constraints, interfaces, and innovative functions.
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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.