Supervised Prediction for Radio Network Planning Tool Using Measurements

This paper presents an efficient scheme to enhance the simulation results of a radio network planning tool by means of measurements. A multilayer perceptron (MLP) is trained to learn the mapping between the measurements and the simulations. The major contribution is the utilisation of independent component analysis (ICA) that transforms the inputs of the MLP into statistically independent variables and makes the complexity of MLP tractable. Other contributions consist of the use of the k-means clustering algorithm on the incoming data and the enrichment of the training data to enhance the generalization capability of the MLP. The proposed method is applied to a 3G mobile network to enhance the predictions of uplink (UL) and downlink (DL) base station loads. After a training performed on a given network configuration, mechanical antenna tilts are modified and we show that the results obtained by the supervised predictions are much closer to measurements than simulation results for cases that have not been encountered before