WiFo: Wireless Foundation Model for Channel Prediction
Boxun Liu, Shijian Gao, Xuanyu Liu, Xiang Cheng, Liuqing Yang

TL;DR
WiFo is a novel wireless foundation model that enables accurate, zero-shot channel prediction across diverse configurations by leveraging large-scale pre-training on heterogeneous CSI data.
Contribution
The paper introduces WiFo, the first space-time-frequency wireless foundation model capable of universal channel prediction without fine-tuning, using self-supervised pre-training on extensive datasets.
Findings
WiFo achieves state-of-the-art zero-shot generalization performance.
Pre-training on 160K CSI samples enables versatile channel prediction.
WiFo outperforms traditional models across multiple datasets.
Abstract
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…
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Taxonomy
TopicsSpeech and Audio Processing · Wireless Communication Networks Research · Advanced Data Compression Techniques
