Enhancement of Network Planning Tool Predictions through Measurements

We propose a novel method to enhance the quality and precision of model-based simulation results by combining the a-priori information contained in the simulations with the a-posteriori knowledge of the measurements. This method involves the use of K-means clustering, independent component analysis (ICA) and artificial neural network (ANN). The K-means block divides the whole learning space into subspaces to ensure a better generalization of the ANN. The ICA block, being the main contribution of this work, makes the input variables of the ANN statistically independent so that the ANN can operate on one-dimensional distributions without losing information on joint statistics. The proposed method is applied to a prediction tool of a third generation (3G) cellular radio network. Results show that the differences observed between simulations and measurements can be considerably diminished. We can then predict with enhanced accuracy ´unobserved´ configurations as long they are not very different from ´learned´ configurations