Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

中文翻译：

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短时间序列中LTE网络规模的长期数据流量预测

网络规模确定是当前移动网络中的一项关键任务，因为此过程中的任何故障都会导致用户体验下降或网络资源不必要的升级。为此，无线电规划工具通常会预测每月的繁忙时间数据流量，以提前发现容量瓶颈。监督学习（SL）成为一种有前途的解决方案，可以改善通过传统方法获得的预测。先前的研究表明，从长期的历史数据序列中预测短期（秒/分钟）和中期（小时/天）的蜂窝网络中的数据流量时，深度学习的性能要优于经典时间序列分析。但是，在无线电规划工具中进行的长期预测（数月的时间间隔）依赖于短而嘈杂的时间序列，因此需要进行单独的分析。在这项工作中，我们提出了第一项比较SL和时间序列分析方法的研究，以预测实时LTE网络中基于小区的每月繁忙时间数据流量。为此，收集了一个广泛的数据集，包括整个30个月中每个国家/地区每个单元的数据流量。考虑的方法包括随机森林，不同的神经网络，支持向量回归，季节性自回归综合移动平均和加性霍尔特-冬天。结果表明，SL模型的性能优于时间序列方法，同时减少了数据存储容量的需求。更重要的是，与短期和中期流量预测不同，非深度SL方法在提高计算效率的同时，还可以与深度学习竞争。