Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular
  Traffic Prediction
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.
Xing Wang, Kexin Yang, Zhendong Wang, Junlan Feng, Lin Zhu, Juan Zhao, Chao Deng
# Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction

###### 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.

 Cellular Traffic Prediction, Spatial-Temporal Data, Graph Neural Network, Mobile Network, Deep Learning

## I Introduction

Total global mobile data traffic reached 67EB per month by the end of 2021, and is projected to grow 4.2 percent to reach 282EB per month in 2027 [1]. The explosive growth of traffic not only brings a huge demand for network capacity, but also brings challenges for telecom network management and resource allocation. As a crucial aspect of telecom network operation, traffic prediction is essential for intelligent wireless networks. Accurate cellular traffic prediction plays an important role in network planning, traffic scheduling, network fault diagnosis and reducing operation cost, etc.

However, it is extremely challenging to predict cellular traffic due to several reasons. First, cellular traffic exhibits nonlinear temporal dependencies since the mobile data traffic is extremely dynamic. For instance, a user can consume a large volume of data for a moment via a given cell tower. At the next moment, this user may stop the connection or migrate to a new cell tower, causing a certain amount of traffic to disappear suddenly [2]. The discontinuous nature of data usage makes the inherent temporal dependency of mobile traffic a complex nonlinear and unstable problem. Second, frequent user mobility and complicated network scheduling mechanisms bring complex spatial correlations between cell towers. As shown in Fig. 1, different cell towers in a wireless network maintain distinct coverage zones. Users can attach to a cell tower within its coverage area to access mobile network services and consume traffic. Due to the limited coverage of wireless signals, users could switch between multiple cell towers as they migrate between areas, resulting in the spatial correlations of cellular traffic. Moreover, users can easily travel across the city within half an hour with the efficient urban transportation, which brings the spatial dependencies even with distant cell towers [3]. For better user experience, the wireless network scheduling mechanism may also hand over the user to a closer cell tower, or a cell tower with fewer users, or a distant cell tower with stronger signal, etc., which increases the complexity of spatial correlation. Third, the different capacities, geographical locations, and surroundings of cell towers make their data traffic patterns diverse, which called heterogeneity. For example, the data traffic of a cell tower near a shopping wall shows significant increase at weekend compared with the weekday. Meanwhile, it is opposite for the cell tower located in a subway station, whose data traffic in the weekday is regularly higher than that of the weekend with obvious morning and evening peak.

Recently deep learning-based methods for mobile traffic prediction have attracted the interests of researchers due to its powerful ability to capture intricate data patterns. Typically,

Fig. 1: An example of cell tower distribution in a region. Purple, orange and green ellipses indicate the coverage of the cell tower A, B and C. Blue ellipses means the coverage of other cell towers. The coverage areas of different cell towers are partially overlapped, so users can switch between cell towers and still maintain the connection.