AI-assisted Agile Propagation Modeling for Real-time Digital Twin Wireless Networks

TL;DR

This paper presents an AI-driven deep learning model for real-time, high-accuracy wireless channel propagation prediction that outperforms traditional methods in speed and efficiency, enabling dynamic digital twin creation.

Contribution

It introduces a novel deep learning approach integrating geographical data for real-time wireless propagation modeling with high accuracy and computational efficiency.

Findings

Normalized RMSE < 0.035 dB over 37,210 m² area

Processing time of 46 ms on GPU and 183 ms on CPU

Surpasses traditional ray tracing in speed by three orders of magnitude

Abstract

Accurate channel modeling in real-time faces remarkable challenge due to the complexities of traditional methods such as ray tracing and field measurements. AI-based techniques have emerged to address these limitations, offering rapid, precise predictions of channel properties through ground truth data. This paper introduces an innovative approach to real-time, high-fidelity propagation modeling through advanced deep learning. Our model integrates 3D geographical data and rough propagation estimates to generate precise path gain predictions. By positioning the transmitter centrally, we simplify the model and enhance its computational efficiency, making it amenable to larger scenarios. Our approach achieves a normalized Root Mean Squared Error of less than 0.035 dB over a 37,210 square meter area, processing in just 46 ms on a GPU and 183 ms on a CPU. This performance significantly surpasses traditional high-fidelity ray tracing methods, which require approximately three orders of magnitude more time. Additionally, the model's adaptability to real-world data highlights its potential to revolutionize wireless network design and optimization, through enabling real-time creation of adaptive digital twins of real-world wireless scenarios in dynamic environments.