because Radio Access Networks (RAN) isn't getting simpler

Multi-Vendor Variations

Ericsson Nokia Huawei ZTE O-RAN

5G Interop &
RAT coexistence

2G 3G 4G 5G
NSA & SA

Interdependent Features

Layering Beamforming NB-IOT Slicing

Varying Demand Profiles

IIoT AR/VR SmartDevices HD-Streaming

Vendor variations, RAT interworking and numerous features combine unpredictably to affect network quality

Systemised analytics for RAN gives clarity

on what truly influences network quality & the business case

Uses RAN AI/ML purposefully

to help humans engineer better

1
Mastering RAN data analytics

A collaborative training program fuelled by our RAN analytics journey and refined over the last 7 years

2
Digital RAN

Why RAN evolution to AI/ML has been so challenging and how a human-centric approach is vital

3
Practical data science

Understand what drives behaviour in data science and the chances of success will increase considerably

4
RAN AI/ML research

Searching for efficiency and insight

Realise value

through clearly visualised business cases

CAPEX avoidance

Squeeze every last bit of capacity out of your network through machine-guided recommendations

OPEX reduction

Expand the number of RAN use cases that have realisable business value in 75% less engineering time

Revenue through quality

Improve the bottom 25% of the network using analytics to increase overall service quality


powered by human participation