## 1 Introduction  
Path loss models are unique prediction models employed by telecom network engineers to estimate the signal coverage area being served by a given transmitter during networking and management [1; 2; 3]. However, developing these signal path loss models with the optimal accuracy it deserves is a complex and significant problem in the planning of telecommunication networks. The conventional log-distance-based statistical models available in the literature, such as the cluster factor model, COST 234 Hata Model, free space model, Hata model and Lee models, lack accuracy for realistic path loss prediction applications in cellular mobile networks environments [4; 5; 6; 7; 8; 9; 10; 11]. The aforementioned fundamental limitation of the conventional models is usually very pronounced when the respective models have been applied in cellular radio environments other than the developed and designed environment [12; 13]. This scenario is mainly due to dissimilarities and variations in environmental formations (hilly, mountainous or quasi-plain), weather conditions, soil electrical properties and terrain type (open, rural, suburban or urban) that exist in different radio propagation locations, cities and countries [14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. For example, the Hata loss model was developed based on extensive practical measurements carried out in Japan at transmission frequency ranges of 150 to 1920 MHz and macrocellular communication distances of 1 to 100 km, with the mobile station and base station antenna heights of 2 to 3 and 30 to 1000 m, respectively [24]. This model, including other aforementioned conventional ones, is also generally limited in capturing the non-linear relationship between the independent variable (e.g., path loss) and dependent variable (e.g., distance) [25]. The precision of these conventional models on path loss prediction has been reported in many previous works to generally range from 8-12 dB in terms of Root Mean Square Error (RMSE), which is exceptionally higher than the acceptable values [9; 14; 15]. Recently, an Artificial Neural Network (ANN), a unique artificial intelligence soft computing and modelling technique, has been acknowledged and proven to solve function approximation and pattern classification problems [26]. Some ANN models exist in the literature; the key ones are Radial Basis Function models, Multilayer Perception Models, Generalized Neural Network models, etc. Among these models, the MLP ANN models have stood out most recently because they are very robust and popular for learning, function approximation, and pattern classification [27; 28; 29; 30]. The MLP ANN possess many robust algorithms that can be explored to carry out more proficient adaptive nonlinear statistical modelling over the classical logistic regression methods [14; 15; 16; 17; 18; 19; 20; 21; 22; 23] that are frequently engaged in developing predictive models. This robustness can be ascribed to their acknowledged special ability to learn, predict, and classify non-linear data using experience and preceding samples introduced to the network model. [PERSON] [31] also noted that the MLP is characteristically good for input-output data mapping. Generally, a clear-cut underlining capability of ANN-based models over the conventional log-distance-based models is their large degrees of freedom structure which provides means for fitting many datasets with non-linear or linear correlation patterns. The concept of intelligent-based ANN models for optimal and adaptive prognostic estimations of path losses was introduced to surmount the limitations of existing empirically and deterministically developed log-distance models [32; 33]. In the paper, the availability of manifold resourceful training algorithms and hypermeters of MLP ANN that can be tuned to further boost its extrapolative data analysis is worth exploring in this paper for optimal predictive modelling.