WiFo-CF: Wireless Foundation Model for CSI Feedback

Deep learning-based channel state information (CSI) feedback schemes offer strong compression but are typically confined to fixed system configurations, limiting their generalizability and flexibility. To address this challenge, this work proposes WiFo-CF, a novel wireless foundation model tailored for CSI feedback. WiFo-CF uniquely accommodates heterogeneous configurations, including varying channel dimensions, feedback rates, and data distributions, within a unified framework through two key innovations: a multi-user, multi-rate self-supervised pre-training strategy and a Mixture of Shared and Routed Experts (S-R MoE) architecture. To support its large-scale pre-training, we introduce the first heterogeneous channel feedback dataset; its diverse patterns enable WiFo-CF 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 the model’s scalability and deployment potential.