DA-cGAN: A Framework for Indoor Radio Design Using a Dimension-Aware Conditional Generative Adversarial Network

Chun-Hao Liu, Hun Chang, Taesuh Park; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 498-499

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.

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[bibtex]
@InProceedings{Liu_2020_CVPR_Workshops,
author = {Liu, Chun-Hao and Chang, Hun and Park, Taesuh},
title = {DA-cGAN: A Framework for Indoor Radio Design Using a Dimension-Aware Conditional Generative Adversarial Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}