Cellular traffic prediction is an indispensable part for intelligent telecommunication networks. Nevertheless, due to the frequent user mobility and complex network scheduling mechanisms, cellular traffic often inherits complicated spatial-temporal patterns, making the prediction incredibly challenging. Although recent advanced algorithms such as graph-based prediction approaches have been proposed, they frequently model spatial dependencies based on static or dynamic graphs and neglect the coexisting multiple spatial correlations induced by traffic generation. Meanwhile, some works lack the consideration of the diverse cellular traffic patterns, result in suboptimal prediction results. In this paper, we propose a novel deep learning network architecture, Adaptive Hybrid Spatial-Temporal Graph Neural Network (AHSTGNN), to tackle the cellular traffic prediction problem. First, we apply adaptive hybrid graph learning to learn the compound spatial correlations among cell towers. Second, we implement a Temporal Convolution Module with multi-periodic temporal data input to capture the nonlinear temporal dependencies. In addition, we introduce an extra Spatial-Temporal Adaptive Module to conquer the heterogeneity lying in cell towers. Our experiments on two real-world cellular traffic datasets show AHSTGNN outperforms the state-of-the-art by a significant margin, illustrating the superior scalability of our method for spatial-temporal cellular traffic prediction.

翻译：蜂窝流量预测是智能电信网络不可或缺的组成部分。然而，由于频繁的用户移动性和复杂的网络调度机制，蜂窝流量通常蕴含复杂的时空模式，使得预测极具挑战性。尽管近年来提出了诸如基于图的预测方法等先进算法，但这些方法通常基于静态或动态图来建模空间依赖关系，忽略了由流量生成引起的共存多重空间相关性。同时，部分工作未充分考虑多样化的蜂窝流量模式，导致预测结果欠优。本文提出了一种新颖的深度学习网络架构——自适应混合时空图神经网络（AHSTGNN），以解决蜂窝流量预测问题。首先，我们采用自适应混合图学习来学习基站间的复合空间相关性。其次，我们构建了一个带有多周期时序数据输入的时间卷积模块，以捕获非线性时间依赖关系。此外，我们引入了一个额外的时空自适应模块，以克服基站间存在的异质性。在两个真实蜂窝流量数据集上的实验表明，AHSTGNN显著优于当前最先进的方法，展示了该方法在时空蜂窝流量预测中的出色可扩展性。