Umudike Journal of Engineering and Technology

Propagation path loss or path attenuation in cellular wireless systems greatly impacts on the quality of service of a mobile communication. The loss reduces the signal strength and power density of an electromagnetic wave when it is propagated through space. To establish any mobile communication system, the basic task is to foresee the coverage of the proposed system in general, and the accurate determination of the propagation path loss leads to the development of efficient design and operation of quality networks. Many such different approaches have been developed, over the past decade, to predict coverage using what is known as propagation models. This paper presents a measurement-based path loss model, from experimental data collected in the ImeUmana Campus of University of Uyo, South-South Nigeria. Received Signal Strength (RSS) measurements were gathered in Uyo. The path loss was predicted using the data-driven model which utilized artificial neural network (ANN) by fully incorporating the atmospheric data (humidity, pressure, temperature) and distance. Okumura-Hata model (without the atmospheric data) was also used to predict path loss in order to compare their results. The essence of using the ANN model was to ascertain extensively the effect of atmospheric parameters on path loss predictions.   The results of the measured data were used to develop a path loss model for the urban environment. The outcome of the result shows that the path loss for the measured environment increases by 3.1 dB per decade.