# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 2 Related Works
Of course, all of that depends on the training dataset given from measurement campaigns and/or simulation tools, and this dataset depends on the specific environments and scenarios of communication. As a result, the model's hyperparameters, such as learning rate, activation function, and optimizers, are experimentally selected to provide the best model's performance. Some studies introduced hyperparameter-tuning techniques, such as grid search and random search to overcome the time consumption of manually selecting the optimum models' parameters [36]. The authors in [11] developed an RF-based path loss model benefiting from the fact that this method is based on a massive number of regression trees and that the path loss is also a regression problem, as mentioned earlier. The data used to train the model comes from four typical mobile communication terrains. The outcome of this study proved that the proposed RF model performed better than traditional wireless propagation models as well as a DNN model after constructing relevant features. The average root-mean-square error (RMSE) for the RF-based model was 6.106 dB for all types of terrains selected. A combined model for path loss and shadowing based on the ANN multilayer perceptron (ANN-MLP) was developed in [21]. The shadowing impact was analyzed based on the Gaussian process to provide the variance (or standard deviation) from the training dataset. This technique will help calculate the shadowing attributed to the shielding effect of buildings, mountains, and other objects that exist in the communication channel between the Tx and Rx. The results provided show the usefulness of the model in predicting the propagation loss. A path loss prediction model was proposed for urban environments using the SVM method [47]. The input features selected were the Tx-Rx separation distance, vertical and horizontal antenna attenuations as system-specific parameters, as well as latitude, longitude, and terrain elevation as environment-specific parameters made up of six features. A similar work documented in [67] used the same environment-specific parameters in a deep-learning-based model. Both [47; 67] show that the proposed ML-based models provide higher efficiency than other analytical models. Two forms of probabilistic path loss predictors for a specific communication environment are reported and investigated in [41]. The first approach utilizes Bayesian learning to get the posterior distribution of an analytical model's parameters and produces a path loss value prediction. A probabilistic neural network is used in the second technique to obtain the parameters of analytical distributions. The authors also studied the effect of changing the amount of data available for training on the ML proposed models' performance. The prediction capacity for the models was measured in terms of the total variation distance (TVD) and Kullback-Leibler (KL). It is to be noted from their results that the mixture density neural network (MDN) model has more accuracy in describing the path loss than the Bayesian learning model. However, the latter provided better data efficiency than the MDN model. These probabilistic path loss models are beneficial since they overcome the problem of classifying the propagation path between the transmitting and receiving antennas as LOS or NLOS, given the probability of having a clear LOS connection between the Tx and Rx is already considered in the model. Hence, the knowledge of the LOS and NLOS communication scenarios is not required for such models. It is worth noting that the MDN model is basically a combination of a conventional Neural Network (NN) and a mixture model. This model has the ability to provide a distribution of the path loss values instead of point estimates. The neural network adopted for this work has only one input feature, that is, the separation distance between the Tx and Rx, one hidden layer of 64 neurons with the use of the rectified linear unit (ReLU) as an activation function, and an output layer that represents the path loss.