Update README.md

README.md

@@ -11,7 +11,33 @@ license: mit
 short_description: This app use deep learning models to radio map estimation (R
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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