| # RadioUNet | |
| RadioUNet is a highly efficient and very accurate method for estimating the propagation pathloss from a point x to all points y on the 2D plane, in realistic propagation environments characterized by the presence of buildings. RadioUNet generates pathloss estimations that are very close to estimations given by physical simulation, but much faster. | |
| For more information see the paper [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://arxiv.org/pdf/1911.09002.pdf). | |
| ## Usage Examples | |
| Download and extract the [RadioMapSeer dataset](https://drive.google.com/file/d/1PTaPpLOKraVCRZU_Tzev4D5ZO32tpqMO/view?usp=sharing) to the folder of the Jupyter Notebooks. | |
| For training without samples see [RadioWNet_c_DPM_Thr2.ipynb](/RadioWNet_c_DPM_Thr2.ipynb). | |
| For training with measurements and perturbed city map see [RadioWNet_s_randSim_miss4build_Thr2.ipynb](/RadioWNet_s_randSim_miss4build_Thr2.ipynb). | |
| For training with simulated cars, measurements, and input car locations, see [RadioWNet_s_DPMcars_carInput_Thr2.ipynb](/RadioWNet_s_DPMcars_carInput_Thr2.ipynb). | |