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.

使用基于学习的方法优化移动通信网络 (RAN) 在最近几年得到了越来越多的关注，本文提出了一个可扩展的框架，用于构建一个策略库，可以在大量具有不同流量模式的基站上进行 RAN 优化，并将异常检测技术应用于判断任务与策略库的兼容性，以高效地使用计算资源。