## 5 Conclusions  
The growing demand for mobile and fixed cellular telecommunication services have given substantial weight to the limited available radio frequency spectrum. Proper modelling and precise signal coverage predictions are crucial to utilizing this scarce resource effectively. Reliable predictive modeling of signal path loss aids in controlling the load on base station transmitters and assists in designing efficient radio network channels with less interference and coverage hole problems. The conventional log-distance-based statistical models for path loss prediction comprising the clustering factor, COST 234 Hata, free space, Hata, Lee models, etc., are generally limited for predicting signal attenuation losses, especially when employed in different environments other than the environment for which they have been designed.  
The main objective of this paper was to develop a distinctive MLP-based path loss model with well-structured implementation network architecture, empowered with the grid search-based hyperparameter tuning method for optimal path loss approximation between mobile-station and base-station path lengths. The degree of prediction accuracy with the developed MLP network model over eight conventional log-distance-based path loss models is also clearly provided using first-order statistics. In summary, this research paper has revealed that:  
* MLPANN-based path loss model with well-structured implementation network architecture, empowered with the right hyperparameter tuning algorithm is better than the standard long-distance path loss models
* the choice of both MLP-ANN modelling structure and selection of training algorithms do have a clear impact on the quality of its prediction proficiency. Specifically, in terms of MAE, RMSE and STD statistical values, the proposed model yielded up to 50% performance prediction accuracies improvement over the standard models on the acquired LTE path loss datasets.
* The selection of adaptive learning tuning hyperparameters of MLP-ANN and the tuning algorithm both have an impact on its overall predictive modelling capacity.  
Future work would consider more hyperparameter selection techniques to optimize MLP model prediction accuracy during NN training. We also intend to explore more super layered training capacity of deep neural networks such as the long-short memory (LSTM) network model for predictive modelling of path loss data in our work.  
The manuscript was written through the contributions of all authors. [PERSON] was responsible for the conceptualization of the topic; article gathering and sorting were carried out by [PERSON], [PERSON] and [PERSON]; manuscript writing, original drafting and formal analysis were carried out by [PERSON], [PERSON], [PERSON], O.K., Y.K., C.-C.L. and C.-T.L.; writing of reviews and editing was carried out by [PERSON], [PERSON], [PERSON], O.K., Y.K., C.-C.L. and C.-T.L.; and [PERSON] led the overall research activity. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1G1A1099559).  
Not applicable.  
The data that supports the findings of this paper are available from the corresponding author upon reasonable request.  
The work of [PERSON] is supported in part by the Nigerian Petroleum Technology Development Fund (PTDF) and in part by the German Academic Exchange Service (DAAD) through the Nigerian-German Postgraduate Program under grant 57473408.  
The authors declare no conflict of interest.