基于强化学习的RAN优化中可扩展任务分组的异常检测
Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization
AI总结：
针对RAN优化中大量基站的模型训练难题，提出基于异常检测的可扩展强化学习策略库框架，通过评估任务与策略的兼容性实现策略复用，高效构建高性能策略库，适配现实计算约束。
摘要：
近年来，采用基于学习的方法优化蜂窝无线接入网（RAN）受到了越来越多的关注。这一趋势与全球蜂窝基站数量的快速增长同步，而基站数量增长的主要驱动力是蜂窝网络流量的急剧攀升。因此，训练并维护能在大量基站上良好运行的学习模型已成为一个亟待解决的问题。本文提出了一个可扩展框架，用于构建可在流量模式各异的大量基站上执行RAN优化的强化学习策略库。该框架的核心是创新性地应用异常检测技术来评估基站（任务）与策略库之间的兼容性。这使得框架能够智能识别某一策略何时可复用于某项任务，以及何时需要训练新策略并将其加入策略库。我们的结果表明，这种兼容性评估方法能够高效利用计算资源，无需对所有任务进行穷尽式训练即可构建出性能优异的策略库，使其适用于现实场景的约束条件。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
