WiFo: Wireless Foundation Model for Channel Prediction

作     者：Liu, Boxun Gao, Shijian Liu, Xuanyu Cheng, Xiang Yang, Liuqing 

作者机构：State Key Laboratory of Photonics and Communications School of Electronics Peking University Beijing100871 China  Guangzhou511400 China Department of Electronic and Computer Engineering Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Zero shot learning 

摘      要：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 unified 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. Copyright © 2024, The Authors. All rights reserved.