This paper presents neural network based models for the prediction of propagation path loss in urban environment. The neural networks are designed separately for line-ofsight (LOS) and non-line-of-sight (NLOS) cases. The performance of the neural models is compared to that of the COST231-Walfisch-lkegami model, the WaGschBertoni model and the single regression model, based on the absolute mean error, standard deviation and the root mean squared error between predicted and measured values.
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