STEP: A Spatio-Temporal Fine-Granular User Traffic Prediction System for Cellular Networks

While traffic modeling and prediction are at the heart of providing high-quality telecommunication services in cellular networks and attract much attention, they have been approved as an extremely challenging task. Due to the diverse network demand of Internet-based apps, the cellular traffic from an individual user can have a wide dynamic range. Most existing methods, on the other hand, model traffic patterns as probabilistic distributions or stochastic processes and impose stringent assumptions over these models. Such assumptions may be beneficial at providing closed-form formula in evaluating prediction performances, but fall short for practice use. In this paper we propose STEP, a spatio-temporal fine-granular user traffic prediction mechanism for cellular networks. A deep graph convolution network, called GCGRN, is constructed. It is a novel combination of the graph convolution network (GCN) and gated recurrent units (GRU), which exploits graph neural network to learn an efficient spatio-temporal model from a user's massive dataset for traffic prediction. The prototype of STEP has been implemented. Extensive experimental results demonstrate that our model outperforms the state-of-the-art time-series based approaches. Besides, STEP merely incurs mild energy consumption, communication overhead and system resource occupancy to mobile devices. Moreover, NS-3 based simulations validate the efficacy of STEP in reducing session dropping ratio in cellular networks.

尽管交通建模和预测是在蜂窝网络中提供高质量的电信服务的核心，并引起了很多关注，但它们已被批准为极具挑战性的任务。由于基于Internet的应用程序的网络需求多样化，个人用户的蜂窝流量可能具有广泛的动态范围。另一方面，大多数现有方法将流量模式建模为概率分布或随机过程，并对这些模型施加了严格的假设。这种假设可能在评估预测性能时提供封闭式公式，但用于实践使用。在本文中，我们提出了步骤，这是一个时空的细粒状用户流量预测机制。构建了一个称为GCGRN的深图卷积网络。它是图形卷积网络（GCN）和门控复发单元（GRU）的新型组合，该单元（GRU）利用图形神经网络从用户的大量数据集中学习有效的时空模型来学习流量预测。步骤的原型已实现。广泛的实验结果表明，我们的模型表现优于基于时间序列的方法。此外，步骤仅会引起轻度的能耗，沟通开销和系统资源占用到移动设备。此外，基于NS-3的仿真验证了步骤在降低细胞网络中降低比率下降比的功效。