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Cell-Free Multi-User MIMO Equalization via In-Context Learning
April 9, 2024, 4:43 a.m. | Matteo Zecchin, Kai Zu, Osvaldo Simeone
cs.LG updates on arXiv.org arxiv.org
Abstract: Large pre-trained sequence models, such as transformers, excel as few-shot learners capable of in-context learning (ICL). In ICL, a model is trained to adapt its operation to a new task based on limited contextual information, typically in the form of a few training examples for the given task. Previous work has explored the use of ICL for channel equalization in single-user multi-input and multiple-output (MIMO) systems. In this work, we demonstrate that ICL can be …
abstract adapt arxiv context cs.it cs.lg eess.sp equalization examples excel few-shot form free in-context learning information math.it training transformers type via
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