  Data driven optimization and machine learning based performance diagnostics
of radio access networks entails significant challenges arising not only from
the nature of underlying data sources but also due to complex spatio-temporal
relationships and interdependencies between cells due to user mobility and
varying traffic patterns. We discuss how to study these configuration and
performance management data sets and identify relationships between cells in
terms of key performance indicators using multivariate analysis. To this end,
we leverage a novel framework based on canonical correlation analysis (CCA),
which is a highly effective method for not only dimensionality reduction but
also for analyzing relationships across different sets of multivariate data. As
a case study, we discuss energy saving use-case based on cell shutdown in
commercial cellular networks, where we apply CCA to analyze the impact of
capacity cell shutdown on the KPIs of coverage cell in the same sector. Data
from LTE Network is used to analyzed example case. We conclude that CCA is a
viable approach for identifying key relationships not only between network
planning and configuration data, but also dynamic performance data, paving the
way for endeavors such as dimensionality reduction, performance analysis, and
root cause analysis for performance diagnostics.

Networking and Internet Architecture;Artificial Intelligence