Artificial Neural Network (ANN) Pathloss Prediction Model for LTE Network for Microcells in an Urban Environment
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Artificial neural network (ANN)
Received signal strength
Root mean square error (RMSE)
Long term evolution (LTE)

How to Cite

Omoze, E. L., & Emagbetere, J. O. (2022). Artificial Neural Network (ANN) Pathloss Prediction Model for LTE Network for Microcells in an Urban Environment. ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY AND ENVIRONMENT, 18(4), 575-586. Retrieved from


One of the challenges faced by telecommunication service providers is capacity. The capacity of a cell depends on the model used in planning the cell during deployment stage. This paper presents Artificial Neural Network (ANN) Pathloss Prediction model for Long Term Evolution (LTE) network for microcells in Benin city, Edo state, Nigeria. Received signal strength data collected with respect to distance, mobile station antenna height and base station antenna height was used to create a data base with pathloss estimated from the measured data. A feature variable consisting of   of measurement data collected was used to train a 4-32-1 multilevel perceptron artificial neural network with pathloss as the label using python language on Scientific python development environment interface (SPYDER).  The result of the  test data shows that artificial neural network is suitable for predicting pathloss in Benin City. This is as a result of root mean square error (RMSE) values and mean absolute error values of  and 0.42  obtained respectively which are less than the  error value allowed for network planning. It is recommended that the result of the study be adopted in designing or expanding LTE network capacity in the areas studied in this work.


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