# WiFo: Wireless Foundation Model for Channel Prediction

arXiv: https://arxiv.org/abs/2412.08908 · HTML: https://mlsanity.com/paper/2412.08908/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2024.

## Built on by (most cited first, 29 of 29)

- [A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning](https://mlsanity.com/paper/2505.09160/) (2025)
- [Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks](https://mlsanity.com/paper/2505.10134/) (2025)
- [Large Language Models for Wireless Communications: From Adaptation to Autonomy](https://mlsanity.com/paper/2507.21524/) (2025)
- [A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis](https://mlsanity.com/paper/2506.12263/) (2025)
- [LWM-Temporal: Sparse Spatio-Temporal Attention for Wireless Channel Representation Learning](https://mlsanity.com/paper/2603.10024/) (2026)
- [HeterCSI: Channel-Adaptive Heterogeneous CSI Pretraining Framework for Generalized Wireless Foundation Models](https://mlsanity.com/paper/2601.18200/) (2026)
- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [Edge Large AI Models: Revolutionizing 6G Networks](https://mlsanity.com/paper/2505.00321/) (2025)
- [A Multi-Modal Foundational Model for Wireless Communication and Sensing](https://mlsanity.com/paper/2602.04016/) (2026)
- [Adaptive 3D-RoPE: Physics-Aligned Rotary Positional Encoding for Wireless Foundation Models](https://mlsanity.com/paper/2605.00968/) (2026)
- [Lessons from Deploying Learning-based CSI Localization on a Large-Scale ISAC Platform](https://mlsanity.com/paper/2504.17173/) (2025)
- [Lightweight Adaptive Feature Composition for Heterogeneous Downstream Adaptation of Wireless Foundation Models](https://mlsanity.com/paper/2606.10277/) (2026)
- [Hierarchical Wireless Foundation Model for Multi-Task Optimization](https://mlsanity.com/paper/2607.16877/) (2026)
- [Point2Radio: A Foundation Model for Cross-Scene Radio Fields from Material-Aware Point Clouds](https://mlsanity.com/paper/2607.28994/) (2026)
- [STAR-RIS-assisted Collaborative Beamforming for Low-altitude Wireless Networks](https://mlsanity.com/paper/2510.22108/) (2025)
- [A Wireless World Model for AI-Native 6G Networks](https://mlsanity.com/paper/2603.25216/) (2026)
- [How Big Should a Wireless Foundation Model Be?](https://mlsanity.com/paper/2605.07266/) (2026)
- [PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels](https://mlsanity.com/paper/2605.22856/) (2026)
- [LWM-CDE: A Representation Space for Wireless Data Reasoning and Transferability](https://mlsanity.com/paper/2605.24077/) (2026)
- [OmniLoc: A Geometry-Aware Foundation Model for Anchor-Free UE Localization Across Diverse Indoor Environments](https://mlsanity.com/paper/2606.11490/) (2026)
- [Towards CSI-Native Foundation Models: A Channel-Adaptive Roadmap for 6G](https://mlsanity.com/paper/2606.20670/) (2026)
- [CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models](https://mlsanity.com/paper/2607.14975/) (2026)
- [MultiPathFormer: Towards a Foundation Model for Multipath Wireless Propagation](https://mlsanity.com/paper/2608.05076/) (2026)
- [6G Native AI and Channel Foundation Models](https://mlsanity.com/paper/2608.14591/) (2026)
- [A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks](https://mlsanity.com/paper/2608.14694/) (2026)
- [Wireless Foundation Models: State-of-the-Art and Open Challenges](https://mlsanity.com/paper/2609.04707/) (2026)
- [Scaling of Wireless Foundation Models via Representation Diversity and Multi-Branch Architectures](https://mlsanity.com/paper/2610.04289/) (2026)
- [Trajectory-Guided Tokenization of Complex CSI for Wi-Fi Sensing](https://mlsanity.com/paper/2610.06288/) (2026)
- [JEPA-kMMVAE: CSI Prediction for Time-Varying Wireless Channels](https://mlsanity.com/paper/2610.13315/) (2026)

## Abstract (arXiv)

Channel prediction permits to acquire channel state information (CSI) without signaling overhead. However, almost all existing channel prediction methods necessitate the deployment of a dedicated model to accommodate a specific configuration. Leveraging the powerful modeling and multi-task learning capabilities of foundation models, we propose the first space-time-frequency (STF) wireless foundation model (WiFo) to address time-frequency channel prediction tasks in a one-for-all manner. Specifically, WiFo is initially pre-trained over massive and extensive diverse CSI datasets. Then, the model will be instantly used for channel prediction under various CSI configurations without any fine-tuning. We propose a masked autoencoder (MAE)-based network structure for WiFo to handle heterogeneous STF CSI data, and design several mask reconstruction tasks for self-supervised pre-training to capture the inherent 3D variations of CSI. To fully unleash its predictive power, we build a large-scale heterogeneous simulated CSI dataset consisting of 160K CSI samples for pre-training. Simulations validate its superior unified learning performance across multiple datasets and demonstrate its state-of-the-art (SOTA) zero-shot generalization performance via comparisons with other full-shot baselines.