论文标题
基于密度MLP的4G 5G细胞级多指导预测
4G 5G Cell-level Multi-indicator Forecasting based on Dense-MLP
论文作者
论文摘要
随着4G/5G的发展，流量的迅速增长导致大量细胞指标超过警告阈值，网络质量恶化。运营商有必要提前有效地解决拥堵，以确保用户体验的质量。细胞级多指导预测是主动复杂网络优化的基础任务。在本文中，我们提出了基于密集的膜层感知器（MLP）神经网络的4G/5G细胞级多指标预测方法，该方法在MLP网络中增加了非贴剂层之间的其他完全连接的层。 The model forecasted the following week's traffic indicators of 13000 cells according to the six-month historical indicators of 65000 cells in the 4G&5G network, which got the highest weighted MAPE score (0.2484) in the China Mobile problem statement in the ITU-T AI/ML in 5G Challenge 2021. Furthermore, the proposed model has been integrated into the AsiaInfo 4G/5G energy-saving system and deployed in中国江苏省。
With the development of 4G/5G, the rapid growth of traffic has caused a large number of cell indicators to exceed the warning threshold, and network quality has deteriorated. It is necessary for operators to solve the congestion in advance and effectively to guarantee the quality of user experience. Cell-level multi-indicator forecasting is the foundation task for proactive complex network optimization. In this paper, we propose the 4G/5G Cell-level multi-indicator forecasting method based on the dense-Multi-Layer Perceptron (MLP) neural network, which adds additional fully-connected layers between non-adjacent layers in an MLP network. The model forecasted the following week's traffic indicators of 13000 cells according to the six-month historical indicators of 65000 cells in the 4G&5G network, which got the highest weighted MAPE score (0.2484) in the China Mobile problem statement in the ITU-T AI/ML in 5G Challenge 2021. Furthermore, the proposed model has been integrated into the AsiaInfo 4G/5G energy-saving system and deployed in Jiangsu Province of China.
