Physics-informed Generalizable Wireless Channel Modeling with
  Segmentation and Deep Learning: Fundamentals, Methodologies, and Challenges
Ethan Zhu Haijian Sun Mingyue Ji
Channel modeling is fundamental in advancing wireless systems and has thus
attracted considerable research focus. Recent trends have seen a growing
reliance on data-driven techniques to facilitate the modeling process and yield
accurate channel predictions. In this work, we first provide a concise overview
of data-driven channel modeling methods, highlighting their limitations.
Subsequently, we introduce the concept and advantages of physics-informed
neural network (PINN)-based modeling and a summary of recent contributions in
this area. Our findings demonstrate that PINN-based approaches in channel
modeling exhibit promising attributes such as generalizability,
interpretability, and robustness. We offer a comprehensive architecture for
PINN methodology, designed to inform and inspire future model development. A
case-study of our recent work on precise indoor channel prediction with
semantic segmentation and deep learning is presented. The study concludes by
addressing the challenges faced and suggesting potential research directions in
this field.