Fast Radio Map Estimation and Automated Radio Network Design Using Deep Learning App and Dataset

In this research, we present the Deep Learning architecture encoder/fully-connected for estimate optimal desings WLANs 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 the Balanced k-means algortihm, but in a fast manner.

General Dataset Structure

A major initial difficulty for starting the research was the lack of data, in this case, indoor scenario floor plans, users posisitons and optimal designs for training the architecture. Therefore, it was necessary to create an appropriate database that would facilitate the respective trainings. The dataset was created in base the Balanced k-means algortihm. This implementation was carried out in the MATLAB software WLANs-optimal-designs-methodology.

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

We show a dataset composed by optimal designs for the 5GHz band WiFi in indoor scenarios: it has 102 indoor constructions plans and around 20 users distributions per plan floor as image and APs positions per case as coordinates. These distributions are random and several WLAN's structures: 1 to 5 access points.

The above explain that we got a total of 61000 RME and CME, this presents that is a model without interference between channels.

The pictures have a depth of 8 bits and size of 256pixels X 256pixels equivalents to indoor constructions of 20 X 20 m2. These ones make reference to offices's spaces at general or classroom.

Obtained Models

To evaluate the obtained models, the dataset consisting of floor plans 911 to 102 can be used, with available user distributions for each case of APs configurations as a test set, or other new images can be used.

To manipulate the codes better, click here in the repository.