COST 231 Walfisch-Ikegami Model

A fast empirical Prediction Model for Urban Scenarios

Introduction

 

This empirical model is a combination of the models from J. Walfisch and F. Ikegami. It was further developed by the COST 231 project. It is now called Empirical COST-Walfisch-Ikegami Model.


The model considers the buildings in the vertical plane between the transmitter and the receiver. The accuracy of this empirical model is quite high because in urban environments especially the propagation over the rooftops (multiple diffractions) is the most dominant part. If the scanario is analyzed individually for each receiver pixel (parameters of building data are determined depending on the actual buildings between Tx and Rx) the accuracy is high - only wave guiding effects due to multiple reflections are not considered.

Propagation situation with COST 231 Walfisch Ikegami. Click here to enlarge the figure.

Parameters

 

The main parameters of the model are:

  • Frequency f (800...2000 MHz)

  • Height of the transmitter hTX (4...50 m)

  • Height of the receiver hRX (1...3 m)

  • Distance d between transmitter and receiver (20...5000 m)

Parameters depending on the buildings in the vertical plane between transmitter and receiver:

  • Mean value of building heights hROOF

  • Mean value of widths of streets w

  • Mean value of building separation b
     

The orientation of the road with respect to the Tx-Rx line is not considered in the WinProp implementation, because the orientation of the road cannot be determined for all pixels (e.g. in courtyards, on crossings,...) .

In WinProp, the above mentioned building-.dependant parameters are determined for each receiver location individually based on the actual buildings between Tx and Rx. If the parameters (mean building height, mean street width, ...) are determined only once for the whole database, all prediction pixels assume the same scenario (which leads to reduced accuracy).

 

Download a document with a comparison of COST 231 predictions to measurements.

Download a brochure with all urban prediction models.

See a comparison between different urban prediction models.

Read more about urban prediction models.

Read more about urban databases.

 

 

 

The figure shows the parameters, relevant for the computation. Click here to enlarge the figure.