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arXiv · 2508.04068

WiFo-CF: Wireless Foundation Model for CSI Feedback

Xuanyu Liu·Shijian Gao·Boxun Liu·Xiang Cheng·Liuqing Yang
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Abstract

Deep learning-based channel state information (CSI) feedback schemes demonstrate strong compression capabilities but are typically constrained to fixed system configurations, limiting their generalization and flexibility. To address this challenge, WiFo-CF, a novel wireless foundation model tailored for CSI feedback, is proposed, uniquely accommodating heterogeneous configurations such as varying channel dimensions, feedback rates, and data distributions within a unified framework through its key innovations: (1) a multi-user, multi-rate self-supervised pre-training strategy; and (2) a Mixture of Shared and Routed Expert (S-R MoE) architecture. Supporting the large-scale pre-training of WiFo-CF is the first heterogeneous channel feedback dataset, whose diverse patterns enable the model to achieve superior performance on both in-distribution and out-of-distribution data across simulated and real-world scenarios. Furthermore, the learned representations effectively facilitate adaptation to downstream tasks such as CSI-based indoor localization, validating WiFo-CF's scalability and deployment potential.

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Xuanyu Liu, Shijian Gao, Boxun Liu, Xiang Cheng, Liuqing Yang. 2025-08-06. WiFo-CF: Wireless Foundation Model for CSI Feedback. https://arxiv.org/abs/2508.04068

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