This Tier 1 Global Mobile Service Provider with extensive operations in the US
produced proactive congestion forecasts that were within 2% of the subsequently observed values in their production network.
- Learn how they proactively measured the accuracy of RAN congestion.
- See how RF and Backhaul data can be used to produce accurate congestion forecasts days in advance of when the congestion actually occurs.
- Learn how a policy-based RAN Congestion Manager is able to monitor and avoid network wide congestion issues.
It is possible to accurately forecast RAN congestion management.
See how this mobile operator did it.