## 6 Reviewed Papers on Machine-Learning-Based Path Loss Models
They outperformed the traditional method by more than 1 dB, achieving RMSE prediction accuracy of nearly 2 dB. The authors in [83] examined two machine learning models, using tabular data and images as two different forms of input to perform path loss predictions in metropolitan locations. They looked at occasions where CNN received just one image and not the other two. By simply creating three duplicates of the same channel, they were able to change the monochrome images into colored ones while still using the same CNN architecture. With  
an MAE value of 3.07 dB as opposed to the 3.15 dB of the conventional bimodal approaches, the proposed methodology outperformed existing fusion methods in terms of results. Similarly, a deep learning technique was utilized in [84] to explain the process of path loss based on the path profile in urban propagation situations. Even still, the LoS and NLoS scenarios' measuring campaigns were only applicable to urban settings. Simulation findings demonstrated that the suggested model outperformed traditional models, and the explainable model's accuracy reached 72%. In [85], image texture techniques were used to enhance the DL model for path loss prediction. Thus, the algorithm produced a new set of features that showed the specified area's built-up profile. However, further experimental data are needed to verify and rate the effectiveness of the suggested model. The model-aided approach provides an improvement of about 1 dB. In a separate study [86], a methodology involving a 3D-CNN and a 3D-LAMS algorithm was applied to sample and extract three-dimensional spatial data between the transmitter (Tx) and receiver (Rx) for creating a 3D image representation. The measurement campaign is expected to extend its scope beyond urban environments to explore additional factors influencing path loss. Through multiple trial runs with varying dataset sizes, the proposed path loss prediction model demonstrated its optimal performance. To compare the similarities and differences between the CNN model and the NN model, [PERSON] et al. [87] devised a CNN model. They made use of the training and verification data supplied during the IEICE's propagation competition. As a result, the CNN model's estimation accuracy declined near the main roadway and the plaza. Three machine learning models were used in [88] for radio channel modeling in urban vehicle environments. However, the inquiry was only able to simulate LOS and NLOS conditions utilizing the ray tracing-generated data set. Among the chosen models, RF performed best with a low RMSE of 1.80 ns, while the MLP model had the highest RMSE at 11 ns. Utilizing machine learning methods, [PERSON] et al. [31] presented innovative approaches to improve path loss models, addressing the challenge associated with complex channel characteristics and time-consuming measurements. In their study, they successfully reduced the measurement workload necessary for wireless channel modeling through the application of regression techniques. In [89], data from online sources such as OpenStreetMap and various Geographical Information Systems were collected to construct a machine learning model aimed at predicting cellular coverage in metropolitan regions. This model demonstrated the capability to promptly estimate path loss, even in the absence of training data from the physical measurement campaign. Also, numerous feature engineering strategies were investigated in [90] to enhance the machine learning algorithms' predictive performance. They found that, especially for small datasets, more basic models can be just as effective as more complex ones. In [91], the author employed the image reflection technique to create a dataset that could be used to test any straightforward machine learning model for indoor prediction. The outcome of the comparative analysis demonstrates that the ANN model outperformed the linear model and provided the dataset with the lowest MSE and MAE values. A novel path loss model capable of estimating path loss was proposed in [92].