WiFo-CF: Wireless Foundation Model for CSI Feedback
Xuanyu Liu, Shijian Gao, Boxun Liu, Xiang Cheng, and Liuqing Yang

TL;DR
WiFo-CF is a versatile wireless foundation model for CSI feedback that uses self-supervised pre-training and a Mixture of Experts architecture to handle diverse configurations and improve generalization across various scenarios.
Contribution
The paper introduces WiFo-CF, a novel foundation model for CSI feedback that supports heterogeneous configurations through innovative pre-training and architecture design.
Findings
Achieves superior performance on diverse datasets
Effectively generalizes to out-of-distribution data
Facilitates downstream tasks like indoor localization
Abstract
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…
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