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

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. [ABSTRACT FROM AUTHOR]

Na temelju rezultata mjerenja jakosti polja provedenih na Fakultetu elektrotehnike, strojarstva i brodogradnje Sveučilišta u Splitu (FESB Split), izrađen je jednostavni neuronski model za predviđanje elektromagnetskog polja u zatvorenom prostoru. Kao izvor elektromagnetskog vala korištena je vertikalna štap antena (neusmjerena u horizontalnoj ravnini), postavljena u fakultetskom hodniku. Mjerenje je provedeno na frekvenciji od 2.4 GHz. Raspodjela polja u prostoru definirana je uobičajenom pravokutnom mrežom jednoliko raspoređenih mjernih točaka. Međutim, pozicije mjernih točaka opisane su polarnim koordinatama udaljenosti i azimuta u odnosu na izvor vala, za razliku od uobičajeno korištenih Kartezijevih koordinata. Pokazalo se da su polarne koordinate pogodnije za organizaciju ulaznih podataka, s obzirom da fizikalna funkcija raspodjele jakosti polja u okolišu izvora ovisi o istim varijablama. Ovakav pristup rezultirao je poboljšanjem mogućnosti predviđanja polja, sto je potvrđeno i rezultatima simulacije. Najbolje rezultate dao je troslojni perceptron treniran pomoću Levenberg-Marquardt-ovog algoritma. [ABSTRACT FROM AUTHOR]

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