# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 2 Related Works
At the same time, the path loss was the only output of this ANN model. The results obtained from this research reveal high prediction accuracy in predicting path loss since the degree of correlation values were higher than 0.94 for the model designed. However, the model's complexity is high since it needs comprehensive knowledge to train from many features, limiting the adoption of such models. A comparative analysis of conventional, ML, and DNN-based methods for path loss prediction proved that the latter has the best performance since higher prediction accuracy was achieved than the other methods [48]. Using many layers in deep learning provides extraction of the features from high-dimensional datasets via training; this benefit is often not possible in using traditional models [63]. Moreover, DNN-based models do not rely on a predefined mathematical formula to represent the model as the conventional models do [64]. These DNN-based methods have been applied to many communication environments, such as rural, urban, and suburban areas [6]. In [65], DNN-based models were developed for five environments under the category of urban, dense urban, suburban, dense suburban, and rural for a specific frequency regime and proved its prediction accuracy. In contrast, in [66], DNN-based models were proposed over a wide range of frequencies from the ultra-high frequency (UHF) band to the SHF band. The previous efforts' datasets for the line-of-sight (LOS) and non-line-of-sight (NLOS) communication scenarios were based on measurement campaigns. Other studies considered the data from the satellite images, such as in [8; 12; 35]. The Tx-Rx separation distance and the operating frequency have been selected as the only two features for ANN-based path loss prediction models for an urban area at 3.4, 5.3, and 6.4 GHz and for a suburban environment at 450, 1450, and 2300 MHz [21]. The proposed ANN models achieved higher prediction accuracy than the other well-known models, such as the CI model, the Gaussian process model, and the two-rays model. Aside from the frequency and distance parameters, wall and floor attenuation are also utilized as input features for the ANN model to predict path loss in a multi-wall environment [26]. However, considering multi-dimensional regression to predict the path loss based on several highly correlated input features such as distance, frequency, antenna height, and other factors is still lacking in the literature. Because the associated inputs are uncertain, several candidate functions are used in the regression. Many of these characteristics, on the other hand, would be superfluous or unnecessary. Furthermore, most input features lack the capacity to discriminate for prediction. Using dimensionality reduction techniques, such as principal component analysis (PCA) or singular value decomposition (SVD), the input data could be transformed into a smaller representation set of features in order to create a decent estimate [21]. Dimensionality reduction is used to minimize the number of features in an input dataset while preserving as much helpful information as feasible. For the ANN, some studies suggest that a neural network is a deep network with only one hidden layer. This theory supports the path loss prediction problems proposed by these studies [12; 44; 64]. Other efforts stated that having two or three hidden layers for the ANN model is enough to provide an accurate approximation of the non-linear relationship between the input features and the output target of the model [21]. Note that the complexity of the models can be reduced by adopting a small number of hidden layers taking into account the tradeoff between the models' accuracy and complexity. The diversity of these studies in the open literature justifies no specific rule to provide the optimum size (for example, the number of hidden layers and the number of neurons per hidden layer) of the ML-based models.