Distribution learning for radio network planning tool simulation
Zakaria Nouir, Berna Sayrac, Benoit Fourestie, Walid Tabbara, Francoise Brouaye · International Journal of Communication Systems · 2008
Abstract We propose a novel method that combines the simulation results of a model‐based prediction tool with the knowledge contained in measurement data. This mixture of thea priori informationandthe posteriori knowledgeaims at enhancing the prediction results by increasing their precision and quality. A multilayer perceptron (MLP) is trained to learn the mapping between the distributions of the measurement data and the simulation data. To make the complexity of the MLP tractable, we propose the utilization of independent component analysis (ICA). The ICA transformation makes the variables at the input of the MLP statistically independent so that it can perform its learning and generalization on individual one‐dimensional distributions. Other contributions consist of the application of thek‐means clustering algorithm on the incoming data and the use of the training dataworld modelto enhance the generalization capability of the MLP. The world model consists of the aggregation of all the available data in the learning space. The proposed method is applied to a third generation mobile network to enhance the predictions of uplink and downlink 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. Copyright © 2008 John Wiley & Sons, Ltd.