WiFo-2: a generalist foundation model unifies heterogeneous wireless system design

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ID: 325289
2026
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Abstract
Abstract Emerging sixth-generation wireless systems are increasingly heterogeneous, with compatibility across diverse configurations, ubiquitous coverage, and expanded functionalities. Although deep learning has substantially benefited wireless system design, existing approaches are typically trained for specific system settings and scenarios with limited generalizability. Here we present WiFo-2, a space-time-frequency foundation model for unified wireless communications and sensing system design. Pretrained on a heterogeneous dataset of 11.6 billion channel state information (CSI) points, WiFo-2 learns generalized wireless representations across scenarios, configurations, and tasks, and exhibits scaling-law behavior. WiFo-2 achieves reliable and accurate zero-shot channel reconstruction, outperforming fully supervised task-specific models. With only 1% of the training samples required by supervised AI models, WiFo-2 achieves state-of-the-art performance across 9 distinct wireless tasks. A functional hardware prototype further demonstrates its real-world deployability and superior capability across diverse wireless tasks. This work provides a versatile wireless design framework and advances understanding of wireless channels.
Reference Key
openalex_W4416943846 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Boxun Liu, Xuanyu Liu, Shijian Gao, Xuesong Cai, Xiang Cheng, Liuqing Yang
Journal national science review
Year 2026
DOI
10.1093/nsr/nwag500
URL
https://doi.org/10.1093/nsr/nwag500
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