Hybrid neural network approach for predicting signal propagation loss in urban microcells

In the last few years, the need for connectivity far and wide, coupled with the continuous increase in the number of cellular network subscribers globally, has stirred the development and evolution of diverse cellular communication standards. This in turn has led to speedy installations of base station transmitters, thus making the process of planning and fine-tuning the location of these BS transmitters very difficult. To plan and optimize mobile cellular networks for acceptable level of service coverage and quality at the mobile station terminals, radio network engineers rely on propagation loss prediction models. This research work investigates the application of a neural hybridized model for field signal strength attenuation prediction. The hybridized model combines a conventional Log-distance model and an adaptive neural network model. The adaptive neural model employs a multilayer Levenberg Marquardt back propagation algorithm to reimburse for the prediction errors obtained by means of using only the conventional model in urban microcellular environment. After applying a number of first order statistical indicators such standard deviation and root mean square error for a comprehensive performance evaluation, the hybrid - based algorithm provides more accurate prediction results with measured values compared to the conventional approach. The computationally effective prediction technique of the hybrid based neural network model can be used for standardization and enhancement of the conventional field strength propagation loss prediction methods.