Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications

TL;DR

This paper explores how large vision models can enhance wireless IoT networks by leveraging their advanced visual processing capabilities and proposes a progressive fine-tuning framework for their adaptation in wireless communication tasks.

Contribution

It provides a comprehensive analysis of LVM functionalities and introduces a novel incremental fine-tuning method for optimizing LVMs in IoT wireless applications.

Findings

LVMs outperform CNNs in joint beamforming and positioning tasks.

The proposed framework effectively adapts LVMs for multiple IoT tasks.

Case study shows improved performance in drone-based networks.

Abstract

Large vision models (LVMs) have emerged as a foundational paradigm in visual intelligence, achieving state-of-the-art performance across diverse visual tasks. Recent advances in LVMs have facilitated their integration into Internet of Things (IoT) scenarios, offering superior generalization and adaptability for vision-assisted network optimization. In this paper, we first investigate the functionalities and core architectures of LVMs, highlighting their capabilities across classification, segmentation, generation, and multimodal visual processing. We then explore a variety of LVM applications in wireless communications, covering representative tasks across the physical layer, network layer, and application layer. Furthermore, given the substantial model size of LVMs and the challenges of model retraining in wireless domains, we propose a progressive fine-tuning framework that incrementally adapts pretrained LVMs for joint optimization of multiple IoT tasks. A case study in low-altitude economy networks (LAENets) demonstrates the effectiveness of the proposed framework over conventional CNNs in joint beamforming and positioning tasks for Internet of drones, underscoring a promising direction for integrating LVMs into intelligent wireless systems.