| title: Coverage Estimation DL | |
| emoji: 🔥 | |
| colorFrom: green | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: 5.12.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: This app use deep learning models to radio map estimation (R | |
| # Fast Radio Propagation Prediction in WLANs Using Deep Learning | |
| In this research, we present the Deep Learning architecture [UNet](https://arxiv.org/abs/1505.04597) for fast calculation of Radio Maps Estimation (RME) and Cells Maps Estimation (CME) in indoor scenarios. This architecture was implemented for WLAN structures consisting of 1, 2, 3, 4, and 5 access points, with the capability to perform RME and CME similar to a physical simulator, but in a fast manner. | |
| An important reference point in the state of the art was [RadioUNet](https://github.com/RonLevie/RadioUNet), which is an application for estimating path loss propagation in outdoor scenarios. | |
| ### General Database Structure | |
| A major initial difficulty for starting the research was the lack of data, in this case, indoor scenario floor plans, coverage maps, and coverage area maps for training the [UNet](https://arxiv.org/abs/1505.04597) architecture. Therefore, it was necessary to create an appropriate database that would facilitate the respective trainings. The coverage maps were generated using the [WiFi IEEE](https://mentor.ieee.org/802.11/dcn/03/11-03-0940-04-000n-tgn-channel-models.doc) model. This implementation was carried out in the MATLAB software [Radio-Indoor-Propagation-Software](https://github.com/johanflorez98/Radio-Indoor-Propagation-Software). | |
| Thus, this research provides a [database](https://doi.org/10.5281/zenodo.8092621) that can be used for training multiple Deep Learning architectures and can facilitate future investigations into similar problems. | |
| ## Cite as | |
| ``` | |
| @online{andres_j_florez_gonzalez_2023_8092850, | |
| author = {Andres J. Florez-Gonzalez and | |
| Carlos A. Viteri -Mera}, | |
| title = {{Fast Indoor Radio Propagation Prediction Using | |
| Deep-Learning App}}, | |
| month = jun, | |
| year = 2023, | |
| publisher = {Zenodo}, | |
| version = {V1.0}, | |
| doi = {10.5281/zenodo.8092850}, | |
| url = {https://doi.org/10.5281/zenodo.8092850} | |
| } | |
| ``` | |
| ## Requirements | |
| matplotlib==3.10.0 | |
| numpy==1.26.4 | |
| tensorflow==2.17.1 | |
| pillow==11.1.0 | |
| gradio=5.12.0 |