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Macrocell Path Loss Prediction Using Artificial Neural Networks
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2010 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 59, no 6, 2735-2747 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents and evaluates artificial neural network models used for macrocell path loss prediction. Measurement data obtained by utilising the IS-95 pilot signal from a commercial code division multiple access mobile network in rural Australia is used to train and evaluate the models. A simple neuron model and feed-forward networks with different number of hidden layers and neurons are evaluated regarding their training time, prediction accuracy, and generalisation properties. Also, different backpropagation training algorithms, such as gradient descent and LevenbergMarquardt, are evaluated. The artificial neural network inputs are chosen to be distance to base station, parameters easily obtained from terrain path profiles, land usage and vegetation type and density near the receiving antenna. The path loss prediction results obtained by using the artificial neural network models are evaluated against different versions of the semi-terrain based propagation model Recommendation ITU-R P.1546 and the OkumuraHata model. The statistical analysis shows that a non-complex artificial neural network model performs very well compared to traditional propagation models in regards to prediction accuracy, complexity and prediction time. The average ANN prediction results were: 1) maximum error: 22 dB, 2) mean error: 0 dB and 3) standard deviation: 7 dB. A multi-layered feed-forward network trained using the standard backpropagation algorithm was compared with a neuron model trained using the LevenbergMarquardt algorithm. It was found that the training time decreases from 150 , 000 to 10 iterations whilst the prediction accuracy is maintained.

Place, publisher, year, edition, pages
IEEE , 2010. Vol. 59, no 6, 2735-2747 p.
Keyword [en]
Okumura Hata model, Path loss prediction, Recommendation ITU-R P.1546, artificial neural network, backpropagation, feed-forward network, field strength measurements, point-to-area
National Category
Telecommunications Signal Processing
URN: urn:nbn:se:bth-7823DOI: 10.1109/TVT.2010.2050502ISI: 000282025900010Local ID: diva2:835485
Available from: 2012-09-18 Created: 2010-05-23 Last updated: 2015-06-30Bibliographically approved

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Zepernick, Hans-Jürgen
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