Path Loss Prediction in Urban Environment Using Learning Machines and Dimensionality Reduction Techniques
Keywords:
Path Loss Prediction, Learning Machines, Dimensionality Reduction TechniquesAbstract
Path loss prediction is a crucial task for the planning of networks in modern mobile communication systems. Learning machine-based models seem to be a valid alternative to empirical and deterministic methods for predicting the propagation path loss. As learning machine performance depends on the number of input features, a good way to get a more reliable model can be to use techniques for reducing the dimensionality of the data. In this paper we propose a new approach combining learning machines and dimensionality reduction techniques. We report results on a real dataset showing the efficiency of the learning machine-based methodology and the usefulness of dimensionality reduction techniques in improving the prediction accuracy.Downloads
Published
26-10-2009
How to Cite
Piacentini, M., & Rinaldi, F. (2009). Path Loss Prediction in Urban Environment Using Learning Machines and Dimensionality Reduction Techniques. Department of Computer and System Sciences Antonio Ruberti Technical Reports, 1(11). Retrieved from https://rosa.uniroma1.it/rosa00/index.php/dis_technical_reports/article/view/2787