Model
Digital Document
Publisher
Florida Atlantic University
Description
With the increasing number of cellular phone service subscribers, the
telecommunications service providers have placed immense emphasis on improving
audio quality and ensure fewer dropped calls. Handoff behavior of all handsets is an
important factor in quality of service of a mobile phone service. This thesis focuses
on the analysis of large volumes of diagnostic data collected from mobile phones in
the real world and the identification of aberrant behavior of a mobile handset under
test by means of drive test data visualization. Our target was to identify poor mobility
decisions that are made by the handsets in calls. Premature, delayed or exceedingly
sensitive decisions are considered poor mobility decisions. The goal was to compare a
set of behaviors from a baseline unit (one accepted to generally operate well). We
were able to identify a particular call that was exhibiting a different path (talking to a
different cell than expected or taking longer to move to a new cell). We designed a
chi-square statistical test to evaluate the performance of specific mobile handset
models. We also developed a mobility tool that evaluated the handset's performance
by means of mapping the handoffs on the Google Maps. The mapping of the handoffs
by means of the Google Maps were very powerful in identifying the above mentioned
mobility patterns.
telecommunications service providers have placed immense emphasis on improving
audio quality and ensure fewer dropped calls. Handoff behavior of all handsets is an
important factor in quality of service of a mobile phone service. This thesis focuses
on the analysis of large volumes of diagnostic data collected from mobile phones in
the real world and the identification of aberrant behavior of a mobile handset under
test by means of drive test data visualization. Our target was to identify poor mobility
decisions that are made by the handsets in calls. Premature, delayed or exceedingly
sensitive decisions are considered poor mobility decisions. The goal was to compare a
set of behaviors from a baseline unit (one accepted to generally operate well). We
were able to identify a particular call that was exhibiting a different path (talking to a
different cell than expected or taking longer to move to a new cell). We designed a
chi-square statistical test to evaluate the performance of specific mobile handset
models. We also developed a mobility tool that evaluated the handset's performance
by means of mapping the handoffs on the Google Maps. The mapping of the handoffs
by means of the Google Maps were very powerful in identifying the above mentioned
mobility patterns.
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