Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction

TL;DR

This paper introduces AHSTGNN, a novel deep learning model that effectively captures complex spatial-temporal patterns and diverse traffic behaviors in cellular networks, significantly improving prediction accuracy.

Contribution

The paper proposes an adaptive hybrid graph learning approach and a spatial-temporal adaptive module to better model cellular traffic's complex dependencies and heterogeneity.

Findings

Outperforms state-of-the-art methods on real datasets

Effectively captures multiple spatial correlations

Handles heterogeneity in cellular traffic patterns

Abstract

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.