###### Abstract  
One of the most critical problems in a communication system is losing information between the transmitter and the receiver. WiMAX (Worldwide Interoperability of Microwave Access) technology is gaining popularity and recognition as a Broadband Wireless Access (BWA) solution. At frequencies below 11 GHz, WiMAX can operate in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. The implementation of WiMAX networks are rushed worldwide. Estimating path loss is crucial in the early stages of wireless network deployment and cell design. To anticipate propagation loss, several path loss models are available (e.g., Okumura Model Hata Model), but they are all bound by particular parameters. In this paper, we propose an MLP neural network-based path loss model with a well-structured implementation network design and grid search-based hyperparameter tuning. The proposed model optimally approximates mobile and base station path losses. Therefore, neurons number, learning rate, and hidden layers number are investigated to obtain the best model in terms of prediction accuracy. Path loss data is collected based on 14 networks in different microcellular settings. Simulations under Matlab environment showed that prediction errors were lower than standard log-distance-based path loss models. mobile networks; prediction models; urban; suburban and rural environments; artificial neural networks +
Footnote †: journal: Medical Imaging  
networks during their deployment (determination of coverage, frequency allocation, power definition, antenna gains, etc. ), and determining unavoidable disturbances. The models are of different types: deterministic, empirical, and semi-empirical models. Deterministic models are based on reference models represented in known physical processes. The computation time is relatively high [1]. Experimental data only supports empirical models depending on frequency, distance, and antenna height (see Table 1). They are powerful, fast, and do not require geographic databases [1]. In the last decade, statistical learning algorithms have attracted much interest in academia and companies in various sectors. They have been successfully implemented to perform predictive tasks related to statistical processes. However, another approach to modeling a system is fascinating, namely artificial neural networks. They derive their modeling power from their ability to detect high-level interconnections, simultaneously involving several variables [2]. Today, neural networks are well established in several fields: in the financial world for the prediction of market fluctuations, in the pharmaceutical field (analysis of organic molecules), in the banking field for the detection of credit card fraud, and in the calculation of credit ratings, in aeronautics for the programming of autopilots, etc. The applications are numerous, and all share a common point essential to the usefulness of neural networks: the processes for which one wishes to make predictions have many explanatory variables and, above all, there are possibly nonlinear dependencies between these variables which, if discovered and exploited, can be used to increase the predictability of the process. From the point of view of process prediction, the main advantage of neural networks over traditional statistical models is that they can automate the discovery of essential links. The main interest of these networks is their ability to react automatically to a complex environment such as telecommunication systems. This study evaluates signal losses in a physical channel as an example. The rest of the paper is organized as follows: Section 2 discusses propagation models commonly used in path loss prediction. Related work is presented in Section 3, while Section 4 describes neural network architectures such as Multi-Layer Perceptron's (MLP), selected neural network topologies and details of datasets processing. Section 5 presents our contribution.