## 6 Reviewed Papers on Machine-Learning-Based Path Loss Models  
Numerous frequencies are covered by various studies on machine learning path loss models, which take place in urban, suburban, rural, and indoor environments [73]. For example, in [74], it was suggested to create a new convolutional neural network (CNN) structure with four subnetworks and incorporate feature sharing between the convolution layers. The suggested techniques show higher accuracy and complexity compared to empirical and deterministic models. The feasibility of predicting channel characteristics in a cellular network with aerial base stations using deep convolutional neural networks and images from satellites was investigated in [75]. In this study, it was discovered that, among other variables influencing the effectiveness of the suggested method, transmitter height and channel parameter quantization are the most crucial. Additional factors that influence path loss were not considered. Preliminary features were built into [76] to increase the proposed model's training effectiveness. Test data were collected from three distinct types of terrains: high-building cities, dense cities, and inland lakes. The models were put to the test on these distinct types of terrains, and RMSE was used to measure how well the models were generalized. When compared to the other chosen model, the suggested model was more suitable for the measured data and worked well in a range of settings. Nevertheless, the proposed model was not validated using the NLOS scenario. A measurement effort described in [77] that was conducted at 3.7 GHz in a variety of rural Greek locales was utilized to create various machine learning models, which were then contrasted with a few chosen empirical models. However, the comparison was only made using information gathered from a thorough measurement in a rural setting. The results provided an RMSE in the range of 4.2-4.3 dB, demonstrating a higher prediction accuracy than those empirical models. In a previous study [78], an innovative approach was proposed for developing path loss models using convolutional neural networks (CNN), specifically through meta-learning, referred to as the CNN model with meta-learning. This approach was compared to existing CNN and FI models. It is important to note that the application of this method was limited to the smart factory environment and the specific frequency of 28 GHz. The most popular artificial neural network (ANN) multilayer perception (MLP) neural network was utilized in [79] to reliably predict path loss. It was built by combining the data from the transmitting antenna and that of the receiver (Rx), including 3D locations and environmental characteristics. To assess the model's performance, a comparison was made between the actual measured outcomes and the predicted results, excluding considerations for losses from the base station (BS). Notably, the inclusion of environmental variables led to enhancements in the precision and reliability of the prediction models. Utilizing a dataset of field measurements at 28 GHz in a suburban environment, [PERSON] et al. [80] introduced an innovative path loss modeling technique based on convolutional neural networks (CNN). In their research, they proposed two key components: the enhanced local area multi-scanning method (E-LAMS) and a unique CNN architecture incorporating four subnetworks with shared features between convolution layers. Nonetheless, it is worth noting that further improvements are required to enhance the performance of this model, particularly in line-of-sight scenarios. The study's results indicate that the proposed CNN-based approach outperforms existing empirical models. In [81], KNN, SVR, RF, and AdaBoost were employed as four machine learning techniques to simulate the radio coverage offered by a flying base station in the urban city of Tripoli. The chosen algorithms were trained on a dataset produced by a ray tracing method.