论文标题
Deepauto：用于蜂窝网络实时预测的分层深度学习框架
DeepAuto: A Hierarchical Deep Learning Framework for Real-Time Prediction in Cellular Networks
论文作者
论文摘要
对关键性能指标（KPI）的准确实时预测是各种LTE/5G无线电访问网络（RAN）自动化的必不可少的要求。但是，由于复杂的时空动力学，网络配置的变化和实时网络数据的不可用，因此在大规模的蜂窝环境中，准确的预测可能非常具有挑战性。在这项工作中，我们介绍了可重复使用的分析框架，该框架可以使用层次深度学习体系结构实时KPI预测。我们的预测方法，即DeepAuto，水平堆叠多个长期记忆（LSTM）网络，以捕获KPI时间序列中的瞬时，周期性和季节性模式。它进一步与前馈网络合并，以了解网络配置和其他外部因素的影响。我们通过使用操作员的大规模真实网络流量测量数据来预测两个重要的KPI，包括电池负载和无线电通道质量，通过预测两个重要的KPI来验证该方法。对于细胞负荷预测，与使用最新测量的短期视野和长期预测的最新测量方法相比，Deepauto模型的根平方误差（RMSE）提高了15％。
Accurate real-time forecasting of key performance indicators (KPIs) is an essential requirement for various LTE/5G radio access network (RAN) automation. However, an accurate prediction can be very challenging in large-scale cellular environments due to complex spatio-temporal dynamics, network configuration changes and unavailability of real-time network data. In this work, we introduce a reusable analytics framework that enables real-time KPI prediction using a hierarchical deep learning architecture. Our prediction approach, namely DeepAuto, stacks multiple long short-term memory (LSTM) networks horizontally to capture instantaneous, periodic and seasonal patterns in KPI time-series. It further merge with feed-forward networks to learn the impact of network configurations and other external factors. We validate the approach by predicting two important KPIs, including cell load and radio channel quality, using large-scale real network streaming measurement data from the operator. For cell load prediction, DeepAuto model showed up to 15% improvement in Root Mean Square Error (RMSE) compared to naive method of using recent measurements for short-term horizon and up to 32% improvement for longer-term prediction.
