ajflorez/WLAN_coverage_estimation_DL

1---2title: WLAN Coverage Estimation DL3emoji: 🔥4colorFrom: green5colorTo: red6sdk: gradio7sdk_version: 5.12.08app_file: app.py9pinned: false10license: mit11short_description: DL models for coverage estimation in WLANs.12---13 14# Fast Radio Propagation Prediction in WLANs Using Deep Learning15 16In 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.17 18An 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.19 20### General Database Structure21 22A 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).23 24Thus, 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.25 26We provide others plans for load as owns images: [Other plans](https://huggingface.co/spaces/ajflorez/WLAN_coverage_estimation_DL/tree/main/Data/Other_plans)27 28## Cite as29```30@INPROCEEDINGS{10500989,31  author={Flórez-González, Andrés J. and Viteri-Mera, Carlos A. and Achicanoy-Martínez, Wilson O.},32  booktitle={2024 18th European Conference on Antennas and Propagation (EuCAP)}, 33  title={Fast Indoor Radio Propagation Prediction using Deep Learning}, 34  year={2024},35  volume={},36  number={},37  pages={1-5},38  keywords={Deep learning;Indoor radio communication;Microprocessors;Wireless networks;Training data;Computer architecture;Software;Propagation;U-Net;radio map estimation;cell association estimation;WLAN},39  doi={10.23919/EuCAP60739.2024.10500989}40}41```42 43## Requirements44 45matplotlib==3.10.046numpy==1.26.447tensorflow==2.17.148pillow==11.1.049gradio=5.12.0