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Creating the Optimal Wireless Service Experience w/ Handset Analytics

Aaron AgostiniSystems EngineeringVerizon Wireless

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Network Engineer for Verizon Wireless

Please allow me to introduce myself…

• RF System Performance Group (former position)• Currently in Data Engineering• In my RF role, I was responsible for finding areas of opportunity for

improving our network’s performance• This is what spurred on my investigation into Alteryx• As I’ve moved into my new role, I’ve continued using the tool for

reporting on the internet based backhaul of our network

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3 different technologies with different characteristics

Challenge: Maximizing time on LTE while minimizing time on previous technologies

• CDMA’s soft hand-off scheme allowed for a larger area of overlap between cell sites

• LTE’s approach leads to interference when sectors and cells overlap• Can create situations where there are substantially different RF

environments between technologies• Handsets can vary in performance in these environments

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Data collected varied by technology

Challenge: Linking sets of data

• Each technology had its own independent database• Each data set had it’s own measurement pegs and KPI’s• Data visualization was exclusive to each technology• Prior to Alteryx, creating cross-technology KPI’s was a challenge• Records for individual devices were difficult to cross correlate with time

stamps due to the sheer volume of records• LTE has a significantly higher number of records, but did not at the time

provide location information

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Voice over LTE has differences from standard LTE traffic

Further Challenge: VoLTE

• Significant differences in signaling (SIP vs 5E/SS7)• Traffic is still handled the same at the enodeB level• Parameters such as hand-off will make no distinction between LTE and

VoLTE traffic

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Previously, I used multiple software packages with mixed results

Struggles Prior to Alteryx

• Data parsing and charting was handled primarily in Excel• This led to somewhat clunky macros that could take hours to days to

get working properly• The software struggled with larger datasets (files could be several

gigabytes and larger)• When I was finished in Excel, I would need to import the result into

MapInfo• From here, I would again need to work out the result, which could

involve some clunky macros, SQL queries, etc.

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Alteryx easily joined the sets of data, leading to a new KPI

Solution: Handset-based Performance Analysis

• Cross technology movement can now be analyzed as a new KPI• High runner individual users can be found• Best and worst performing devices can be determined• Heat maps can be generate for technology drop down hotspots• Records pertaining to technology changes for individual devices can be

generated

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Alteryx allowed to do my job more quickly, and to do more

Results from using Alteryx

• Map creation that in the past had taken 2 hours or more could be completed in as long as it took to run a module

• Adjustments were far easier to make, again reducing work time from hours to minutes

• Automating tasks was much easier, as Alteryx is much more intuitive than other packages were; 2 hours in Alteryx could equal days of work in Excel and MapInfo

• Alteryx made complicated joins very easy; this allowed me to do analysis I wasn’t able to do previously

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THANK YOU!