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

Author

Listed:
  • 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)

Abstract
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
    as

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    File URL: http://www.dis.uniroma1.it/~bibdis/RePEc/aeg/wpaper/2009-11.pdf
    File Function: First version, 2009
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