# WiFo-CF: Wireless Foundation Model for CSI Feedback

arXiv: https://arxiv.org/abs/2508.04068 · HTML: https://mlsanity.com/paper/2508.04068/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2025.

## Built on by (most cited first, 5 of 5)

- [Adaptive 3D-RoPE: Physics-Aligned Rotary Positional Encoding for Wireless Foundation Models](https://mlsanity.com/paper/2605.00968/) (2026)
- [Hierarchical Wireless Foundation Model for Multi-Task Optimization](https://mlsanity.com/paper/2607.16877/) (2026)
- [PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels](https://mlsanity.com/paper/2605.22856/) (2026)
- [SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Prediction](https://mlsanity.com/paper/2606.00120/) (2026)
- [Wireless Foundation Models: State-of-the-Art and Open Challenges](https://mlsanity.com/paper/2609.04707/) (2026)

## Abstract (arXiv)

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.