  We address the problem of Remote Electrical Tilt (RET) optimization using
off-policy Contextual Multi-Armed-Bandit (CMAB) techniques. The goal in RET
optimization is to control the orientation of the vertical tilt angle of the
antenna to optimize Key Performance Indicators (KPIs) representing the Quality
of Service (QoS) perceived by the users in cellular networks. Learning an
improved tilt update policy is hard. On the one hand, coming up with a new
policy in an online manner in a real network requires exploring tilt updates
that have never been used before, and is operationally too risky. On the other
hand, devising this policy via simulations suffers from the
simulation-to-reality gap. In this paper, we circumvent these issues by
learning an improved policy in an offline manner using existing data collected
on real networks. We formulate the problem of devising such a policy using the
off-policy CMAB framework. We propose CMAB learning algorithms to extract
optimal tilt update policies from the data. We train and evaluate these
policies on real-world 4G Long Term Evolution (LTE) cellular network data. Our
policies show consistent improvements over the rule-based logging policy used
to collect the data.
