# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 2 Related Works  
Many recent research studies have adopted the methodology of using neural networks for the prediction of path loss based on measurement data in a specific frequency band for a specific environment and communication scenario; they then compare their prediction models with the traditional models in terms of accuracy and prediction error analysis. Various supervised learning approaches, such as the ANN [48; 49; 58; 59], support vector machine (SVM) [37; 60], KNN [38], and RF [38], have been successfully used to construct path loss models. Recently, it was reported that deep learning methods such as the deep neural network (DNN) and ANN provide better prediction results compared to the traditional path loss models [8; 12]. Moreover, in [36], ANN-based path loss prediction models provided better performance than ML-based models, including the RF and SVR models. Further, the ANN-based model was proven to be superior to the log-distance model in the same study. In addition, the authors of [48; 49; 59] offered prediction models using ANN and showed more accuracy than other empirical models in terms of path loss prediction. A vision for developing real-time prediction models for path loss can be found in [59]. In [6], a DNN multi-frequency path loss model was analyzed and compared with the alpha-beta-gamma (ABG) path loss model. The results show that the DNN-based model is far better than the ABG model based on the results of prediction error analysis metrics. Generally, any neural network is made up of nodes, which are processing components that are tightly linked and organized in layers. They have the capacity to describe any function that is given to them from the raw datasets. Consequently, setting up closed-form equations to map the input features into output target(s) is unnecessary for neural networks, unlike traditional methods. It is essential to use appropriate features as inputs to train and test ML-based models since they make the model more efficient and adaptable while reducing the solution complexity [11]. The input features of the ANN-based path loss model for unmanned aerial vehicles (UAVs) at 1800 and 2100 MHz were distance, clutter height, altitude, longitude, latitude, and elevation, as reported in [21]. In another work, the features adopted for ML-based path loss prediction in an urban environment included the Tx-Rx separation distance, as well as building information such as height, thickness, and distance away from the antenna [37]. In [61], the input for a successful deep convolutional neural network was 2D satellite images to provide reliable LTE signal quality metrics calculations. In [62], an ANN model with 48 neurons in a single hidden layer and a hyperbolic tangent activation function (also called Tanh activation function) has provided the best performance over several empirical path loss models such as COST-231, HATA, ECC-33, and EIGI. The input features adopted for this single-layered feed-forward neural network were distance, clutter height, altitude, elevation, latitude, and longitude, while the network had one output layer, which represents the path loss. Another work adopted an ANN model with two hidden layers to predict the path loss at the 1800 MHz frequency band for smart campus environments [45]. The main result obtained from that work is that the ANN model outperforms the RF-based model for such environments. Similar results provided the ANN model's quality over the RF model since it can extract relevant input features of communication environments [54]. The author of [40] proposed a multilayer perceptron (MLP) feed-forward neural network model for predicting path loss. The model was based on 11 input features and 2 hidden layers with the use of the Tan-sigmoidal as an activation function. The 11 features selected were the Tx-Rx separation distance, operating frequency, transmitter terrain height, receiver terrain height, transmitting antenna height, average clutter height, %water, %building, %plain, %road, and %trees.