用人口流动数据提升基站负载预测准确率，效果提升60%。
Cellular Predictions on the Move: What about Data?
- 结合人口流动与历史流量数据进行预测
- 高速公路场景下预测误差降低约60%
- 适合通信网络优化与城市规划研究者
移动蜂窝负载预测对保障网络资源优化和服务可靠性、低延迟与高质量至关重要。当前主流机器学习研究聚焦于构建更强的模型以提升预测精度，但所用数据多局限于蜂窝域内，最多包含基站周边的外部信息。本文从数据本身作为学习过程核心要素的视角出发，提出假设：若数据能反映产生蜂窝负载的动态过程，则可显著提升预测性能。具体地，我们不仅使用历史移动数据流量时间序列，还引入人口动态特征——潜在的通信终端数量及其移动性。我们在较少被研究的高速公路场景中验证了该假设。全面实验表明，仅通过引入这些数据，预测性能即提升约60%。
原文摘要 · Abstract (English)
Mobile cellular load forecasting is native to network resource optimization and delivery of services with reliability, latency and quality guarantees. The mainstream of machine learning research in the area is focused primarily on developing powerful learning structures for improved prediction accuracy. The data used for forecasting traditionally belong to the cellular domain and at most contain exogenous information about the surroundings of the base stations. We approach the prediction task from the perspective of data as a vital component of any data learning process. We hypothesize that substantial improvements could be achieved when the data inform on the processes that create the cellular load. Specifically, we propose to characterize the population dynamics -- the potential number of cellular traffic sources and their mobility -- in addition to employing historical time series of mobile data traffic. We validate our hypothesis for the rarely examined highway scenario. Comprehensive experiments show forecasting improvements on the order of $60\%$ due to the use of these data alone.
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