## 1 Introduction
Hence, the \"Development of a Multilayer Perception Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments is self-evident.\" Other key robust advantages of the general ANNs are highlighted in Section 2.3. Though several ANN models exist in the literature, a critical, challenging task remains in developing and using them appropriately through the correct selection of its network structural design with the required input elements and hyperparameters to solve peculiar predictive mapping and functional problems. The quest to address this issue is the leading motivation for this research paper. However, one primary challenge in using the MLP model is correctly selecting its network architecture with the required input elements(hyperparameters) to solve a particular mapping problem [34]. Another critical challenge with neural network models is the problem of determining the input data variables that must correlate with the target variables [35,36]. This paper develops 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 hyperparameters include the neuron number, learning rate and hidden layers number. In detail, the developed MLP model prediction accuracy level using different learning and training algorithms with the tuned best values of the hyperparameters have been applied for extensive path loss experimental datasets. The datasets were acquired via field drive tests conducted in Long Term Evolution (LTE) in urban microcellular radio networks. For the development and implementation of the MLP-ANNs model, we utilized version 2018a of the MATLAB neural networks toolbox. The toolbox provides the required user interface, algorithm and platform to train, test, validate, visualize and simulate networks with the desired number of layers, neurons and activation functions. In particular, the contributions of this paper are summarized as follows:  
* A distinctive MLP neural network-based path loss model with well-structured implementation network architecture, embedded with the precise transfer function, neuron number, learning algorithm and hidden layers, is developed for optimal path loss approximation between mobile-station and base-station path lengths. * The developed MLP neural network model was tested and validated for realistic path loss prediction using extensive experimental signal attenuation loss datasets acquired via field drive test conducted over LTE networks in urban microcellular radio environments and tested using first-order statistical performance indicators. * Optimization of the projected model via hyperparameter tuning leveraging the grid search algorithm analyses of the experimental path loss data. * Optimal prediction efficacy of the developed MLP neural network model compared with well-structured implementation architecture over standard log-distance models using several first-order statistical performance indicators. The remainder of this work is structured in the following manner. Section 2 outlines the background information, such as radio propagation mechanisms, log-distance-based path loss prediction models, and the basis of artificial neural networks (ANN). Section 3 presents the methodology detailing the neural network implementation for predictive modelling. Section 4 provides the results, analysis, evaluation of the introduced neural network model at different study locations, comparison of the developed neural network model with log-distance models, and discussions. Finally, the conclusion is given in Section 5.