Scaling Law for Large Wireless Models
Ziheng Liu, Jiayi Zhang, Haoyu Wang, Bokai Xu, Chen Zhang, Yiyang Zhu, Enyu Shi
Abstract
Emerging from recent advances in foundation models, Large Wireless Models (LWMs) represent a new paradigm of general-purpose intelligence for wireless communications that transcends task-specific engineering. The success of foundation models is critically underpinned by scaling laws, which provide a predictable roadmap for how performance scales with resources. However, established scaling laws from language and vision, charting performance as a power-law of model and dataset sizes, are ill-suited for the wireless domain, as their core formulations cannot model the structured nature of the physical channel. To address this, we propose a novel wireless scaling law that extends the classical formulation by modeling two wireless-native factors: channel heterogeneity and discretization granularity. These two factors reshape scaling behavior via nested linear and power-law relationships, recasting the scaling law
BibTeX
@inproceedings{aaai2026_scalinglawforlar,
title = {Scaling Law for Large Wireless Models},
author = {Ziheng Liu and Jiayi Zhang and Haoyu Wang and Bokai Xu and Chen Zhang and Yiyang Zhu and Enyu Shi},
booktitle = {AAAI 2026},
year = {2026}
}