Programming an Artificial Neural Network tool for spatial interpolation in GIS - A case study for indoor radio wave propagation of WLAN


ŞEN A. , GUEMUESAY M. Ü. , KAVAS A., BULUCU U.

SENSORS, cilt.8, ss.5996-6014, 2008 (SCI İndekslerine Giren Dergi)

Atıf İçin Kopyala
  • Cilt numarası: 8 Konu: 9
  • Basım Tarihi: 2008
  • Doi Numarası: 10.3390/s8095996
  • Dergi Adı: SENSORS
  • Sayfa Sayısı: ss.5996-6014

Özet

Wireless communication networks offer subscribers the possibilities of free mobility and access to information anywhere at any time. Therefore, electromagnetic coverage calculations are important for wireless mobile communication systems, especially in Wireless Local Area Networks (WLANs). Before any propagation computation is performed, modeling of indoor radio wave propagation needs accurate geographical information in order to avoid the interruption of data transmissions. Geographic Information Systems (GIS) and spatial interpolation techniques are very efficient for performing indoor radio wave propagation modeling. This paper describes the spatial interpolation of electromagnetic field measurements using a feed-forward back-propagation neural network programmed as a tool in GIS. The accuracy of Artificial Neural Networks (ANN) and geostatistical Kriging were compared by adjusting procedures. The feed-forward back-propagation ANN provides adequate accuracy for spatial interpolation, but the predictions of Kriging interpolation are more accurate than the selected ANN. The proposed GIS ensures indoor radio wave propagation model and electromagnetic coverage, the number, position and transmitter power of access points and electromagnetic radiation level. Pollution analysis in a given propagation environment was done and it was demonstrated that WLAN (2.4 GHz) electromagnetic coverage does not lead to any electromagnetic pollution due to the low power levels used. Example interpolated electromagnetic field values for WLAN system in a building of Yildiz Technical University, Turkey, were generated using the selected network architectures to illustrate the results with an ANN.