Anomaly Detection for Scalable Task Grouping in Reinforcement
  Learning-based RAN Optimization
Jimmy Li Igor Kozlov Di Wu Xue Liu Gregory Dudek
The use of learning-based methods for optimizing cellular radio access
networks (RAN) has received increasing attention in recent years. This
coincides with a rapid increase in the number of cell sites worldwide, driven
largely by dramatic growth in cellular network traffic. Training and
maintaining learned models that work well across a large number of cell sites
has thus become a pertinent problem. This paper proposes a scalable framework
for constructing a reinforcement learning policy bank that can perform RAN
optimization across a large number of cell sites with varying traffic patterns.
Central to our framework is a novel application of anomaly detection techniques
to assess the compatibility between sites (tasks) and the policy bank. This
allows our framework to intelligently identify when a policy can be reused for
a task, and when a new policy needs to be trained and added to the policy bank.
Our results show that our approach to compatibility assessment leads to an
efficient use of computational resources, by allowing us to construct a
performant policy bank without exhaustively training on all tasks, which makes
it applicable under real-world constraints.