WiFo: Wireless Foundation Model for Channel Prediction

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 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.