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--- |
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title: WLAN Coverage Estimation DL |
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emoji: 🔥 |
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colorFrom: green |
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colorTo: red |
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sdk: gradio |
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sdk_version: 5.12.0 |
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app_file: app.py |
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pinned: false |
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license: mit |
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short_description: DL models for coverage estimation in WLANs. |
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--- |
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# Fast Radio Propagation Prediction in WLANs Using Deep Learning |
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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. |
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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. |
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### General Database Structure |
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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). |
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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. |
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We provide others plans for load as owns images: [Other plans](https://huggingface.co./spaces/ajflorez/WLAN_coverage_estimation_DL/tree/main/Data/Other_plans) |
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## Cite as |
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``` |
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@INPROCEEDINGS{10500989, |
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author={Flórez-González, Andrés J. and Viteri-Mera, Carlos A. and Achicanoy-Martínez, Wilson O.}, |
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booktitle={2024 18th European Conference on Antennas and Propagation (EuCAP)}, |
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title={Fast Indoor Radio Propagation Prediction using Deep Learning}, |
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year={2024}, |
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volume={}, |
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number={}, |
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pages={1-5}, |
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keywords={Deep learning;Indoor radio communication;Microprocessors;Wireless networks;Training data;Computer architecture;Software;Propagation;U-Net;radio map estimation;cell association estimation;WLAN}, |
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doi={10.23919/EuCAP60739.2024.10500989} |
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} |
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``` |
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## Requirements |
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matplotlib==3.10.0 |
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numpy==1.26.4 |
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tensorflow==2.17.1 |
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pillow==11.1.0 |
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gradio=5.12.0 |
