Title :
Data mining for indoor wave propagation model calibration
Author :
Aymen, Ben Zineb ; Ayadi, Mounir
Author_Institution :
Higher School of Commun. (Sup´Com), Tunisia
Abstract :
Accurate Radio wave propagation modeling has been for a longtime an important area of research and development due to their effect on network cost and deployment. In literature, many propagation models had been proposed and classified as empirical or deterministic. Empirical models are attractive since they are simple to use with low computational load. Their major drawback is the need of an adjustment to each new environment; this operation is called calibration or tuning. The outline of this paper is to define and compare two methods for indoor model tuning. The chosen model to tune is called Cheung model, which is an amelioration of multiwall one. The first used method for tuning is based on multi linear regressions theory, the second one is based on neural networks. To accomplish this task, measurements campaign has been performed in the higher school of communication of Tunisia (SupCom) building in 900, 1800, 2100 and 2400 MHz bands.
Keywords :
UHF radio propagation; calibration; data mining; indoor radio; neural nets; regression analysis; telecommunication computing; Cheung model; data mining; empirical models; frequency 1800 MHz; frequency 2100 MHz; frequency 2400 MHz; frequency 900 MHz; indoor model tuning; indoor wave propagation model calibration; low computational load; measurement campaign; multilinear regression theory; network cost; neural networks; radio wave propagation modeling; Calibration; Floors; Measurement uncertainty; Radio propagation; calibration; linear regression; model prediction; neural networks Introduction (Heading 1);
Conference_Titel :
Communications and Networking (ComNet), 2014 International Conference on
Conference_Location :
Hammamet
Print_ISBN :
978-1-4799-3762-2
DOI :
10.1109/ComNet.2014.6840918
