Sensing and Understanding the World over Air: A Large Multimodal Model for Mobile Networks
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918219165466624 |
|---|---|
| author | Duan, Zhuoran Wei, Yuhao Nan, Guoshun Wang, Zijun Yan, Yan Xiong, Lihua Ran, Yuhan Zhang, Ji Li, Jian Cui, Qimei Tao, Xiaofeng Quek, Tony Q. S. |
| author_facet | Duan, Zhuoran Wei, Yuhao Nan, Guoshun Wang, Zijun Yan, Yan Xiong, Lihua Ran, Yuhan Zhang, Ji Li, Jian Cui, Qimei Tao, Xiaofeng Quek, Tony Q. S. |
| contents | Large models (LMs), such as ChatGPT, have made a significant impact across diverse domains and hold great potential to facilitate the evolution of network intelligence. Wireless-native multi-modal large models (WMLMs) can sense and understand the physical world through multi-modal data, serving as a key enabler that integrates communication, sensing, and intelligence, and thus they can boost various smart services to billions of users. However, research on WMLMs remains in its infancy, and the construction of domain-specific multi-modal large models for wireless networks is still underexplored. In this paper, we outlines the key characteristics of WMLMs and summarizes existing methods, on the basis of which a wireless-native multimodal training paradigm is proposed. Specifically, we constructed a GPT-style WMLM model and trained it on a real-world large-scale dataset, leveraging wireless signals as an anchor modality for contrastive learning. Our approach demonstrates outstanding performance compared with existing small-scale models and large multi-modal models, validating the feasibility of using wireless signals as a universal modality and highlighting WMLM's potential to emerge as a new paradigm for future wireless networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21707 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sensing and Understanding the World over Air: A Large Multimodal Model for Mobile Networks Duan, Zhuoran Wei, Yuhao Nan, Guoshun Wang, Zijun Yan, Yan Xiong, Lihua Ran, Yuhan Zhang, Ji Li, Jian Cui, Qimei Tao, Xiaofeng Quek, Tony Q. S. Networking and Internet Architecture Artificial Intelligence Large models (LMs), such as ChatGPT, have made a significant impact across diverse domains and hold great potential to facilitate the evolution of network intelligence. Wireless-native multi-modal large models (WMLMs) can sense and understand the physical world through multi-modal data, serving as a key enabler that integrates communication, sensing, and intelligence, and thus they can boost various smart services to billions of users. However, research on WMLMs remains in its infancy, and the construction of domain-specific multi-modal large models for wireless networks is still underexplored. In this paper, we outlines the key characteristics of WMLMs and summarizes existing methods, on the basis of which a wireless-native multimodal training paradigm is proposed. Specifically, we constructed a GPT-style WMLM model and trained it on a real-world large-scale dataset, leveraging wireless signals as an anchor modality for contrastive learning. Our approach demonstrates outstanding performance compared with existing small-scale models and large multi-modal models, validating the feasibility of using wireless signals as a universal modality and highlighting WMLM's potential to emerge as a new paradigm for future wireless networks. |
| title | Sensing and Understanding the World over Air: A Large Multimodal Model for Mobile Networks |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2511.21707 |