In this letter, we present an alternative procedure for the prediction of propagation path loss in urban environments, which is based on artificial neural networks (ANNs). The correct selection of a neural network size can increase its response speed and therefore increase the overall system performance. We apply a recently proposed Differential Evolution (DE) algorithm, namely the Composite DE (CoDE) in order to design an optimal ANN for path-loss propagation prediction. CoDE uses three different trial-vector generation strategies with three preset control parameter settings. We compare CoDE with other popular DE strategies. We present two different ANN design cases with two and three hidden layers, respectively. The general performance of both the ANNs shows their effectiveness to yield results with satisfactory accuracy in short time. The received results are compared to the respective ones yielded by the ray-tracing model and exhibit satisfactory accuracy.
- Differential evolution (DE)
- evolutionary algorithms (EAs)
- mobile communications
- neural network
- optimization methods
- propagation path loss
- self-adaptive differential evolution