Wireless LAN Electromagnetic Field Prediction for Indoor Environment Using Artificial Neural Network

Abstract

A simple neural model for electromagnetic field prediction in indoor environment was created based on field strength measurements at 2.4 GHz, conducted at University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB Split). Vertical rod antenna (omnidirectional in the horizontal plane) was placed in a faculty hallway and used as the electromagnetic field source. Electromagnetic field distribution was defined by commonly used rectangular grid of uniformly distributed measurement points. However, instead of commonly used Cartesian coordinates for measurement points location description, we used polar coordinates of distance and azimuth angle measured from the field source. These coordinates are found to be more suitable for the organization of input data, as the physical distribution of the field strength around the antenna depends on the same variables. This resulted with predictive ability improvement of the neural model, as confirmed by simulation results. Three-Layer Perceptron, trained with Levenberg-Marquardt (LM) algorithm, produced the best results.