High-traffic hotspots
Stadiums, malls and stations measured from traces at their busiest hours, with capacity and parameters planned venue by venue.
Results
Congestion in the analysed areas.
Experience measured at the busiest hours of each venue.
Video recap
At a glance
For
RAN optimization and event planning teams
Replaces
Venue-by-venue drive tests and post-event complaint reviews
Data used
Geolocated call traces, PM counters, configuration data and venue locations
Runs on
Your cloud or on-prem, every major vendor, 2G to 5G
The problem
The busiest places in the network are also where complaints concentrate. Stadiums, malls and stations load the network in short peaks that daily averages hide.
Engineers usually find out after the peak, from complaints, and tune each venue by hand.
How it works
Check the next peak
After the change, the agent measures the next peak at the venue and reports whether experience held.
How did experience at the main stadium compare across the last five matches, and what should change before the next one?
Experience per hour and per sector for each match, the cells that degraded and the capacity or parameter changes proposed.
Frequently asked questions
Can it plan for a single event?
Yes. A one-off event is planned the same way, from the venue's past peaks, with the changes rolled back afterwards.
Which vendors and technologies does it support?
All major vendors and technologies, from 2G to 5G. The data from every vendor in your network is brought into one model before the agents work on it.
Does the platform change anything on the network by itself?
No. The agents measure and propose. Your engineers decide, and changes go through your own systems.
Where has it been used?
With Telefónica, where congestion in the analysed areas dropped by 90%.
“O2 climbed one position in independent rankings after deploying our anomaly detection and root cause analysis engines.”
See your busiest venues at their peak.
Bring one venue or one city centre. We run the agents on your data and show you what to change before the next peak.
