## 2 Related Work  
### Machine Learning for Path Loss Estimation  
The use of ML to solve outdoor/indoor problems in modern communication systems is a topic under research. In [14], the authors presented an overview of the principles and procedure of ML-based PL prediction. To support the research, they used measured data and evaluated the performance of different models, such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), and random forest. In the research, it was proven that ML models outperform the traditional log-distance model. For indoor scenarios, several research works have been conducted [15; 16; 17; 18]. In [15], the authors proposed a method based on Neural Networks (NN) to estimate the radio frequency exposure generated by Wi-Fi sources in indoor scenarios. As inputs, they used easily measured variables, such as the position and type of Wi-Fi sources and the position and materials of the walls. The NN achieved a Root Mean Squared Error (RMSE) of 2.1-7.0 dB compared to the target electric field obtained with a deterministic indoor network planner. The results show that the solution is comparable to other ML solutions in the same scenario, but the performance was not validated in other buildings. To reduce this lack, in our work we evaluate five different buildings. A method to automatically construct and optimize a model-based radio map was proposed in [16]. It was based on unsupervised learning, where random walks, for which the ground truth locations are unknown, serve as input for the optimization, along with a floor plan and a location tracking algorithm. Experiments in large office buildings resulted in median accuracy of up to 2 m. In [17], the authors proposed a Convolutional Neural Network (CNN)-based indoor path loss modeling approach. Based on a floor plan and measurement data, input images were generated for training a CNN, which can make predictions of Received Signal Strength of 5 GHz Wi-Fi in an indoor usage scenario. Experiment results showed that CNNs can be used for indoor path loss modeling with encouraging performance from the RMSE of 3.9 dB in the training process. In the test process (only one building was considered) the absolute error of Received Signal Strength (RSS) estimation is within 2 dB for 38% of locations, and within 5 dB for 79% of locations. In our work, we set the coverage condition for the 100% of points in the area of interest. In [19], the authors proposed a deep learning-based path loss modeling method for 5.8 GHz Wi-Fi, based on a floor plan of a building. It was used for a local area multiple-line scanning (LAMS) algorithm to generate the training dataset. In the end, the model's performance was compared to real measurements and the 3D ray-tracing method. It was shown that the performance of the model (RMSE = 3.94 dB) compared to real measurements was better than the performance of the ray-tracing method (RMSE = 5.8 dB). The main constraint is related to the use of this model in different scenarios. Since the authors used a LAMS algorithm, using the model in another building requires generating new data and retraining the model. The solution proposed in our work does not need to train the model for use in new buildings, as long as the type fits the specifications. In [20], the authors proposed a path loss model for an Unmanned Aerial Vehicle (UAV) air-to-air (AA) scenario based on ML. Two ML algorithms (K nearest neighbors (kNN) and Random Forest) were used to build the models and the results were compared to the data generated by the ray-tracing software developed in [20]. Both ML models have better performance than the empirical models (COST231 and SUI model), and the Random Forest algorithm even outperforms the kNN by 2 dB. The calculation times for Random Forest and kNN to make predictions are 8.71 s and 5.95 s, respectively. However, running the ray-tracing software took more than 10 min. In [21], the authors compared three ML algorithms: kNN, SVR, and Random Forest.