CSI-MAE: A Masked Autoencoder-based Channel Foundation Model

TL;DR

CSI-MAE introduces a masked autoencoder-based channel foundation model that enhances cross-scenario generalization, zero-shot learning, and reduces training costs in wireless communication tasks.

Contribution

It presents a novel generalized CFM leveraging masked autoencoder architecture, enabling effective cross-scenario transfer and zero-shot learning in wireless channels.

Findings

Matches or surpasses supervised models in performance.

Achieves state-of-the-art results with full-parameter finetuning.

Exhibits strong zero-shot transferability across scenarios.

Abstract

Self-Supervised Learning (SSL) has emerged as a key technique in machine learning, tackling challenges such as limited labeled data, high annotation costs, and variable wireless channel conditions. It is essential for developing Channel Foundation Models (CFMs), which extract latent features from channel state information (CSI) and adapt to different wireless settings. Yet, existing CFMs have notable drawbacks: heavy reliance on scenario-specific data hinders generalization, they focus on single/dual tasks, and lack zero-shot learning ability. In this paper, we propose CSI-MAE, a generalized CFM leveraging masked autoencoder for cross-scenario generalization. Trained on 3GPP channel model datasets, it integrates sensing and communication via CSI perception and generation, proven effective across diverse tasks. A lightweight decoder finetuning strategy cuts training costs while maintaining competitive performance. Under this approach, CSI-MAE matches or surpasses supervised models. With full-parameter finetuning, it achieves the state-of-the-art performance. Its exceptional zero-shot transferability also rivals supervised techniques in cross-scenario applications, driving wireless communication innovation.