metadata

title: WLAN 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: DL models for coverage estimation in WLANs.

Fast Radio Propagation Prediction in WLANs Using Deep Learning

In this research, we present the Deep Learning architecture UNet 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, 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 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 model. This implementation was carried out in the MATLAB software Radio-Indoor-Propagation-Software.

Thus, this research provides a database that can be used for training multiple Deep Learning architectures and can facilitate future investigations into similar problems.

We provide others plans for load as owns images: Other plans

Cite as

@INPROCEEDINGS{10500989,
  author={Flórez-González, Andrés J. and Viteri-Mera, Carlos A. and Achicanoy-Martínez, Wilson O.},
  booktitle={2024 18th European Conference on Antennas and Propagation (EuCAP)}, 
  title={Fast Indoor Radio Propagation Prediction using Deep Learning}, 
  year={2024},
  volume={},
  number={},
  pages={1-5},
  keywords={Deep learning;Indoor radio communication;Microprocessors;Wireless networks;Training data;Computer architecture;Software;Propagation;U-Net;radio map estimation;cell association estimation;WLAN},
  doi={10.23919/EuCAP60739.2024.10500989}
}

Requirements

matplotlib==3.10.0 numpy==1.26.4 tensorflow==2.17.1 pillow==11.1.0 gradio=5.12.0