Spatial-Temporal Cellular Traffic Prediction for 5G and Beyond: A Graph Neural Networks-Based Approach

Abstract

During the past decade, Industry 4.0 has greatly promoted the improvement of industrial productivity by introducing advanced communication and network technologies in the manufacturing process. With the continuous emergence of new communication technologies and networking facilities, especially the rapid evolution of cellular networks for 5G and beyond, the requirements for smarter, more reliable, and more efficient cellular network services have been raised from the Industry 5.0 blueprint. To meet these increasingly challenging requirements, proactive and effective allocation of cellular network resources becomes essential. As an integral part of the cellular network resource management system, cellular traffic prediction faces severe challenges with stringent requirements for accuracy and reliability. One of the most critical problems is how to improve the prediction performance by jointly exploring the spatial and temporal information within the cellular traffic data. A promising solution to this problem is provided by Graph Neural Networks (GNNs), which can jointly leverage the cellular traffic in the temporal domain and the physical or logical topology of cellular networks in the spatial domain to make accurate predictions. In this paper, we present the spatial-temporal analysis of a realworld cellular network traffic dataset and review the state-ofthe-art researches in this field. Based on this, we further propose a time-series similarity-based graph attention network, TSGAN, for the spatial-temporal cellular traffic prediction. The simulation results show that our proposed TSGAN outperforms three classic prediction models based on GNNs or GRU on a real-world cellular network dataset in short-term, mid-term, and long-term prediction scenarios.

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

“…In the proposed TWACNet, the time-wise attention mechanism is adopted to capture the longrange temporal dependencies of the cellular traffic data, and the convolutional neural network (CNN) is adopted to capture the spatial correlation. To effectively capture the correlation between time and space within network traffic, Wang, Z., et al [27] introduced GNN as a tool for cellular network traffic prediction. This particular model is capable of extracting the spatio-temporal correlation and characteristics of intercellular traffic, leading to highly accurate predictions regarding network traffic patterns.…”

A Survey of Intelligent End-to-End Networking Solutions: Integrating Graph Neural Networks and Deep Reinforcement Learning Approaches

“…Moreover, in [90], authors presented a GNN-based approach with the time-series similarity-based GAT for predicting cellular traffic. By utilizing dynamic time warping and graph attention mechanisms, the proposed method effectively captured spatial-temporal relationships in cellular data, which enhanced prediction accuracy for smart factory environments.…”

Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

“…In Wang et al [66], the authors propose a type of GNN, named Time-Series Graph Attention Network (TSGAN), with the previously discussed elastic distance metric DTW to forecast the cellular network traffic in a real world data set. The prediction results obtained with TSGAN outperformed three standard GNNs and a GRU model in short-term, midterm, and long-term scenarios.…”