Measurement Aided 3G Radio Network Prediction: Fuzzy Bayesian Framework

We have used measurements taken on real network to enhance the performance of our radio network planning tool. A distribution learning technique is adopted to realize this challenged task. To ensure better generalization capabilities of the learning algorithm, a preprocessing of data is required and involves the use of a clustering algorithm that divides the whole learning space into subspaces. In this paper we apply a new fuzzy clustering algorithm 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 and the generalization capacity is enhanced thanks to the proposed clustering algorithm. This algorithm performs well than classical c-means algorithm. We can then predict with enhanced accuracy new configuration for which we don´t have measurements, as long they are not very different from learned configurations.