Transfer Learning and Double U-Net Empowered Wave Propagation Model in Complex Indoor Environment

TL;DR

This paper introduces a transfer learning-based Double U-Net model that predicts wave propagation in complex indoor environments, achieving high accuracy and improving upon traditional methods by incorporating environment encoding and 3D environment generation.

Contribution

It presents a novel combination of transfer learning, Attention U-Net, and environment encoding to accurately model indoor wave propagation, with an innovative 3D environment generation from 2D floorplans.

Findings

Root mean square error less than 6 dB in predictions

Double U-Net outperforms single U-Net models

Effective environment encoding improves prediction accuracy

Abstract

A Machine Learning (ML) network based on transfer learning and transformer networks is applied to wave propagation models for complex indoor settings. This network is designed to predict signal propagation in environments with a variety of objects, effectively simulating the diverse range of furniture typically found in indoor spaces. We propose Attention U-Net with Efficient Networks as the backbone, to process images encoded with the essential information of the indoor environment. The indoor environment is defined by its fundamental structure, such as the arrangement of walls, windows, and doorways, alongside varying configurations of furniture placement. An innovative algorithm is introduced to generate a 3D environment from a 2D floorplan, which is crucial for efficient collection of data for training. The model is evaluated by comparing the predicted signal coverage map with ray tracing (RT) simulations. The prediction results show a root mean square error of less than 6 dB across all tested scenarios, with significant improvements observed when using a Double U-Net structure compared to a single U-Net model.