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This mixture of the<jats:italic>a priori information<\/jats:italic>and<jats:italic>the posteriori knowledge<\/jats:italic>aims 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\u2010dimensional distributions. Other contributions consist of the application of the<jats:italic>k<\/jats:italic>\u2010means clustering algorithm on the incoming data and the use of the training data<jats:italic>world model<\/jats:italic>to 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 \u00a9 2008 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/dac.952","type":"journal-article","created":{"date-parts":[[2008,7,22]],"date-time":"2008-07-22T10:08:17Z","timestamp":1216721297000},"page":"1307-1323","source":"Crossref","is-referenced-by-count":1,"title":["Distribution learning for radio network planning tool simulation"],"prefix":"10.1002","volume":"21","author":[{"given":"Z.","family":"Nouir","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B.","family":"Sayrac","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B.","family":"Fouresti\u00e9","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"W.","family":"Tabbara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F.","family":"Brouaye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2008,7,22]]},"reference":[{"key":"e_1_2_1_2_2","doi-asserted-by":"crossref","unstructured":"GillT.Radio planning and optimisation\u2014the challenge ahead. 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