Prediction Models of an Indoor Smart Antenna System Using Artificial Neural Networks

  • Authors:

  • Nektarios Moraitis;Demosthenes Vouyioukas

  • Affiliations:

  • Mobile Radiocommunications Laboratory, National Technical University of Athens, 9 Heroon Polytechniou str. 15773, Zografou, Athens, Greece, morai@mobile.ntua.gr;Dept. of Information and Communication Systems Engineering, University of the Aegean, Karlovasi 83200 Samos, Greece, dvouyiou@aegean.gr

  • Venue:
  • Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

This study presents the prediction propagation paths of angle of arrivals (AoAs) of a Smart Antenna System in an indoor environment utilizing Artificial Neural Networks (ANN). The proposed models consist of a Multilayer Perceptron and a Generalized Regression Neural Network trained with measurements. For comparison purposes the theoretical Gaussian scatter density model was investigated for the derivation of the power angle profile. The antenna system consisted of a Single Input Multiple Output (SIMO) system with two or four antenna elements at the receiver site and the realized antenna configuration comprised of Uniform Linear Arrays (ULAs). The proposed models utilize the characteristics of the environment, the antenna elements and their spacing for prediction of the angle of arrivals of each one of the propagation paths. The results are presented towards the average error, standard deviation and mean square error compared with the measurements and they are capable for the derivation of accurate prediction models for the case of AoA in an indoor millimeter wave propagation environment.