DA-cGAN: A Framework for Indoor Radio Design Using a Dimension-Aware Conditional Generative Adversarial Network
Abstract
A novel "physics-free" approach of designing indoor radio dot layout for a floor plan is introduced by formulating it as an image-to-image translation problem and solved with customized dimension-aware conditional generative adversarial networks (DA-cGANs). The proposed model generates a desirable radio heatmap and its respective radio dot layout from a given floor plan with wall types, physical dimension, and macro-cell interference, by learning from the accumulated indoor radio designs by human experts. Considering the nature of radio propagation, two new loss functions and a two-stage training strategy are proposed for the generator to learn the right direction of signal propagation and precise dot locations, in addition to a sectional analysis for dealing with large floor plans. Experimental results show that the new model is effectively generating acceptable dot layout designs and that dimension-awareness is a key enabler for this type of prediction.
Cite
Text
Liu et al. "DA-cGAN: A Framework for Indoor Radio Design Using a Dimension-Aware Conditional Generative Adversarial Network." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00257Markdown
[Liu et al. "DA-cGAN: A Framework for Indoor Radio Design Using a Dimension-Aware Conditional Generative Adversarial Network." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/liu2020cvprw-dacgan/) doi:10.1109/CVPRW50498.2020.00257BibTeX
@inproceedings{liu2020cvprw-dacgan,
title = {{DA-cGAN: A Framework for Indoor Radio Design Using a Dimension-Aware Conditional Generative Adversarial Network}},
author = {Liu, Chun-Hao and Chang, Hun and Park, Taesuh},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2020},
pages = {2089-2098},
doi = {10.1109/CVPRW50498.2020.00257},
url = {https://mlanthology.org/cvprw/2020/liu2020cvprw-dacgan/}
}