IRGAN : cGAN-based Indoor Radio Map Prediction

International audience<div style=""&gt<font face="arial, helvetica"&gt<span style="font-size: 13px;"&gtRadio map or radio coverage prediction in indoor&nbsp;</span&gt</font&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtand outdoor remains a challenge of great interest due to the large&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtnumber of applications it allows. Many techniques such as datadriven&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtinterpolation methods, model-based data fitting methods,&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtmodel-based prediction methods exist. However, each of them&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gthas their limitations (computation time, accuracy, level of input&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtinformation required, generalization to different environments).&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtAlso indoors seems less studied than outdoors.</span&gt</div&gt<div style=""&gt<font face="arial, helvetica"&gt<span style="font-size: 13px;"&gtIn this paper, a multi-material model and its enhanced version&nbsp;</span&gt</font&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtboosted by attention mechanism for indoor radio map prediction&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtare introduced. The proposed models are based on conditional&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtGenerative Adversarial Networks (cGANs) and take floor plan&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtimages as input. We also propose a new method for floor plan images&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtpreprocessing by segmentation to characterize the materials&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtin the environment of interest, which alleviates user’s effort. The&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtvalidity and efficiency of our method in generating high quality&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtradio maps in different environments and its ability to consider&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtthe electromagnetic properties of materials are verified on two&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtsimulated datasets. Numerical results show that our approach&nbsp;</span&gt<span style="font-size: 13px; font-family: arial, helvetica;"&gtoutperforms state of the art methods.</span&gt</div&g