What Machine Learning Predictor Performs Best for Mobility Prediction in Cellular Networks?
What Machine Learning Predictor Performs Best for Mobility Prediction in Cellular Networks?
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DOI：
10.1109/iccw.2019.8756972
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Hana Gebrie;H. Farooq;A. Imran
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文献类型：
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作者：
Hana Gebrie;H. Farooq;A. Imran
据设想，未来的蜂窝网络（5G）将能够通过极端的网络密度和多种技术的聚集来满足有希望的容量和体验质量要求。不难理解，高效管理如此复杂的网络将是5G面临的重大挑战之一。为了应对这一挑战，最初为采用响应式方法的遗留网络设计的自组织网络（SONs）需要转变为主动模式。只有通过利用历史网络数据提前预测未来的网络状态，这种激进的转变才有可能实现。移动性预测是实现高效资源管理的主动SON的关键推动因素之一。在本文中，我们对深度神经网络（DNN）、极端梯度增强树（XGBoost）、半马尔可夫和支持向量机（SVM）四种迁移预测器进行了比较分析。我们的研究基于84个移动用户的真实合成数据集，这些数据集是通过真实的自相似最小行动步行（SLAW）移动模型生成的。我们评估每个模型的有效性，不仅基于模型预测移动用户未来位置的能力，还基于每个算法完全训练和执行这种预测所需的时间。XGBoost在所有预测器中脱颖而出，准确率高达90%。其预测精度高，用于驱动主动节能SON解决方案时，节能增益高达80%以上。
It is envisaged that the future cellular networks (5G) will be able to meet the promising capacity and quality of experience requirements through extreme network densification and conglomeration of diverse technologies. It is easy to fathom that efficient management of such a convoluted network will be one of the big challenges faced by 5G. To cope with this challenge, Self-Organizing Networks (SONs) that were originally designed for legacy networks with reactive approach needs to be transformed to proactive paradigm. This radical transformation is possible only if the future network state can be predicted beforehand by harnessing historical network data. Mobility prediction is one of the key enablers of Proactive SON which enables efficient resource management. In this paper, we perform comparative analysis of four mobility predictors: Deep Neural Network (DNN), Extreme Gradient Boosting Trees (XGBoost), Semi-Markov, and Support Vector Machine (SVM). Our investigation is based on realistic synthetic dataset of eighty-four mobile users generated through realistic Self-similar Least Action Walk (SLAW) mobility model. We evaluate the effectiveness of each model not only based on the model's ability to predict the future location of mobile users but also the time each algorithm takes to be fully trained and perform such prediction. XGBoost stands out as clear winner among all predictors considered with high accuracy of 90%. Its high prediction accuracy enables high energy saving gain of above 80% when it is employed for driving proactive energy saving SON solution.