Open Collections

Xu, Yixuan

University of British Columbia

Wireless system designers use wireless coverage prediction tools to assess the coverage that can be achieved (and the potential interference that may result) when transmitters or base stations are placed at various sites throughout a region. Current methods for assessing wireless coverage prediction accuracy tend to be ad hoc, have limited statistical power, and offer only limited insight concerning the quality of the results. Comparing coverage predictions to field measurement data would at first seem to be the best option for evaluating prediction accuracy. However, measurement data tends to be both sparse and expensive, while delivering few insights concerning the causes of prediction errors. Further, commercial wireless system planning tools generally incorporate at least rudimentary techniques for comparing predictions to measured data, but do not generally offer options for comparing predictions made using different models to each other. This is surprising given that the majority of such tools give users the option of using alternative prediction models. Prediction accuracy depends in equal measure upon the sophistication of the technique used to predict coverage and the quality of the geospatial data that describes both the natural and man-made obstacles and structures in the region. Here, we propose that single-factor comparison of prediction maps obtained using the same prediction technique but different geospatial data, or the same geospatial data but different prediction techniques, can help designers determine whether the prediction technique or the geospatial data is the limiting factor, and take appropriate action. In order to demonstrate the feasibility of this approach, we predicted the wireless coverage provided by 28 GHz base stations mounted on traffic signal arms at two different intersections on the University of British Columbia campus using two different wireless coverage prediction models but the same geospatial data. We then investigated the relative power of spatial autocorrelation, spatial cross-correlation, difference maps, and scatter plots to provide insights into similarities and differences between the prediction maps. The results demonstrate the feasibility of this approach and prepare the way for further study and development of single-factor comparison of wireless coverage prediction maps.

Text

2023-03-22

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/83952

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/