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A Proposal for Path Loss Prediction in Urban Environments using Support Vector Regression

Authors:
Robson Timoteo
Daniel Cunha
George Cavalcanti

Keywords: wireless networks, propagation models, machine learning, nonlinear regression

Abstract:
In the last few years, the mobile data traffic has grown exponentially making evident the importance of wireless networks. To ensure an acceptable level of quality of service for users in a wireless data network, network designers rely on signal propagation path loss models. To provide adaptability, the use of machine learning techniques has been considered to predict characteristics of the wireless channel. In this work, we propose a method for predicting path loss in an urban outdoor environment using support vector regression. Simulation results indicate that, depending on the employed kernel and its parameters, the performance obtained using support vector regression is similar and with lower computational complexity to that obtained by a multilayer perceptron neural network.

Pages: 119 to 124

Copyright: Copyright (c) IARIA, 2014

Publication date: July 20, 2014

Published in: conference

ISSN: 2308-4030

ISBN: 978-1-61208-360-5

Location: Paris, France

Dates: from July 20, 2014 to July 24, 2014