## 7 Open Research Issues
### Research Gaps  
In an attempt to present a reliable and stable ML model in [31; 75; 76; 77; 78; 79; 82; 83; 84; 85; 87; 88; 89; 91] for high-band channels in urban environments, the \(k\)-NN, MPL, CNN, and SVR models performed similarly across all frequencies under consideration, with the random forest model (RF) delivering the most accurate predictions in [77; 81] and the XGBoost model in [83] in an urban environment. Similar approaches in the deployment of machine learning models to predict path loss in the mid-band channel revealed that the random forest model performed excellently in [88; 94; 95] in an urban environment, while the neural-network-based models performed similarly in [81; 84; 86]. However, because every urban environment is made of unique layouts and streets, researchers should think about improving the models' performance in the propagation of mid-band 5G network deployment and future wireless communication networks in distinct scenarios of complex urban environments. Efforts made by the authors in [42; 73] to address the issue of path loss along mid-band channels in urban environments using early empirical models indicate that the COST-231 Hata model outperformed other considered models. On the other hand, current empirical models that were adopted [47; 62; 68; 69; 70] to prove their prediction accuracy along high-band channels in an urban environment showed that the CI, ABG, and FI models provide more accurate estimated path loss models. 1. Dynamic urban environments: Urban environments are highly dynamic, with changes in building layouts, vegetation, and infrastructure occurring frequently. The current empirical models, such as the ABG, CI, FI, and 3 GPP, may not be able to account for these dynamic changes adequately, resulting in inaccuracies in path loss predictions. Integrating real-time or dynamic elements into empirical models is a research gap that needs to be addressed. Hence, they need to be improved to fit the worst-case scenario of the urban environment and to make them reliable in any other urban environment. The current empirical models may not adequately capture or account for these interference and multipath effects, leading to inaccurate predictions. 2. Inadequate integration of machine learning techniques: Machine learning techniques, such as the random forest model and neural networks, have shown promise in improving path loss prediction accuracy across the considered mid-band and high-band frequency spectrum. However, limited research exists on effectively integrating these techniques into empirical models for path loss in the mid-band frequency spectrum in urban environments. Exploring the potential of machine learning-based approaches and their integration into existing models is an important research gap. 3. Lack of validation in real-world scenarios: Empirical models developed for predicting path loss in mid-band and high-band frequencies in urban and suburban environments may not have been extensively tested and validated in real-world scenarios. The absence of comprehensive field measurement and validation studies can introduce uncertainties and limit the reliability and accuracy of the models. 4. Inadequate consideration of complex environment: The empirical models that were used did not sufficiently account for factors such as high-rise buildings, vegetation, and diverse land use patterns, leading to inaccuracies in path loss estimation. Developing models that can accurately capture and incorporate the characteristics of complex urban and suburban environments is a significant research gap. * Lack of relevant features on labeled training data: There is a shortage of relevant features on labeled datasets, as in the case of the random forest model that outperformed other chosen models [77; 83]. This scarcity of relevant features that greatly impact the prediction poses a significant research gap as it hinders the development and deployment of an accurate model in these scenarios.