LVM4CSI: Enabling Direct Application of Pre-Trained Large Vision Models for Wireless Channel Tasks

2025-07-07

AI Summary

To address the challenges of costly and inaccurate CSI acquisition in 5G/6G systems, and the limited generalizability and high deployment overhead of existing AI-based methods—largely due to reliance on expert-designed architectures and large-scale labeled datasets—this paper pioneers zero-shot transfer of pre-trained large-scale vision models to wireless channel tasks. Leveraging structural similarities between CSI and images, we introduce a lightweight mapping module that transforms CSI into image-like representations, enabling direct compatibility with diverse vision backbones (e.g., ViT, ResNet) without fine-tuning or task-specific network design. The unified framework supports channel estimation, human activity recognition, and user localization. Experiments demonstrate state-of-the-art performance: a 9.61 dB improvement in NMSE for channel estimation, ~40% reduction in localization error, and over 90% reduction in trainable parameters. This approach significantly enhances model generality, cross-scenario generalizability, and deployment efficiency.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Accurate channel state information (CSI) is critical to the performance of wireless communication systems, especially with the increasing scale and complexity introduced by 5G and future 6G technologies. While artificial intelligence (AI) offers a promising approach to CSI acquisition and utilization, existing methods largely depend on task-specific neural networks (NNs) that require expert-driven design and large training datasets, limiting their generalizability and practicality. To address these challenges, we propose LVM4CSI, a general and efficient framework that leverages the structural similarity between CSI and computer vision (CV) data to directly apply large vision models (LVMs) pre-trained on extensive CV datasets to wireless tasks without any fine-tuning, in contrast to large language model-based methods that generally necessitate fine-tuning. LVM4CSI maps CSI tasks to analogous CV tasks, transforms complex-valued CSI into visual formats compatible with LVMs, and integrates lightweight trainable layers to adapt extracted features to specific communication objectives. We validate LVM4CSI through three representative case studies, including channel estimation, human activity recognition, and user localization. Results demonstrate that LVM4CSI achieves comparable or superior performance to task-specific NNs, including an improvement exceeding 9.61 dB in channel estimation and approximately 40% reduction in localization error. Furthermore, it significantly reduces the number of trainable parameters and eliminates the need for task-specific NN design.

Problem

• Enabling pre-trained vision models for wireless tasks without fine-tuning
• Mapping CSI tasks to computer vision tasks for direct model application
• Reducing trainable parameters and eliminating task-specific neural network design

Innovation

• Leverages pre-trained large vision models
• Maps CSI tasks to computer vision tasks
• Transforms CSI into visual formats