IDEAS home Printed from
   My bibliography  Save this paper

Path Loss Prediction in Urban Environment Using Learning Machines and Dimensionality Reduction Techniques


  • Mauro Piacentini

    () (Dipartimento di Informatica e Sistemistica Sapienza Universita' di Roma, Roma, Italy)

  • Francesco Rinaldi

    () (Dipartimento di Informatica e Sistemistica Sapienza Universita' di Roma, Roma, taly)


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.

Suggested Citation

  • Mauro Piacentini & Francesco Rinaldi, 2009. "Path Loss Prediction in Urban Environment Using Learning Machines and Dimensionality Reduction Techniques," DIS Technical Reports 2009-11, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
  • Handle: RePEc:aeg:wpaper:2009-11

    Download full text from publisher

    File URL:
    File Function: First version, 2009
    Download Restriction: no

    References listed on IDEAS

    1. F. Rossi & S. Smriglio & A. Sassano, 2001. "Models and Algorithms for Terrestrial Digital Broadcasting," Annals of Operations Research, Springer, vol. 107(1), pages 267-283, October.
    Full references (including those not matched with items on IDEAS)


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aeg:wpaper:2009-11. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Antonietta Angelica Zucconi). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.