The bustling streets of a modern city are filled with countless individuals using their smartphones for streaming videos, sending messages and browsing the web. In the era of rapidly expanding 5G networks and the omnipresence of mobile devices, the management of cellular traffic has become increasingly complex. To address this challenge, mobile network operators need methods for the accurate prediction of cellular traffic. A comprehensive survey published 5 Jan. in Intelligent Computing explores deep learning techniques for cellular traffic prediction.
Better cellular traffic prediction would enhance intelligent 5G network construction and resource management, thereby improving the quality of experience for users. According to the review, cellular traffic prediction has three main applications. It is used to:
Cellular traffic prediction involves forecasting traffic values using historical data. According to the review, cellular traffic prediction problems can be classified into two main types: temporal and spatial–temporal. Temporal prediction focuses on predicting the traffic flow of an individual network element, such as a single base station, using only its own historical traffic data. In contrast, spatial-temporal prediction aims to predict the traffic data of multiple network elements that have spatial dependencies.
Temporal prediction methods
Spatial-temporal prediction methods
Some challenges may still exist, and they will be potential research areas in cellular traffic prediction. First, data quality issues such as missing, noisy and anomalous data may affect the accuracy of predictions. Second, protecting user privacy while making accurate predictions is a growing concern. Third, modeling the spatial–temporal correlation of traffic data is a complex problem that requires a deep understanding and simulation of the interdependence of data in time and space. Fourth, the geographic locations, user groups, surrounding environments and network devices among different wireless base stations result in the heterogeneity of network traffic, posing additional challenges to traffic prediction in large-scale cellular networks. Finally, the accuracy of long-term traffic prediction remains an issue that requires further research.
Future directions for research in the field of cellular traffic prediction include establishing benchmarking frameworks for fair model comparison and embracing external factor modeling to enhance prediction accuracy. Moreover, it is essential to generalize models across tasks and facilitating decentralized collaboration while ensuring data privacy. Transfer learning enables models to leverage knowledge from related tasks, thereby eliminating the need for training from scratch. Federated learning allows participants to jointly model without sharing data, addressing data islands and limiting the risk of data leakage. Finally, enhancing model interpretability could offer insight into the implementation of cellular traffic prediction algorithms.
Intelligent Computing
A Survey on Deep Learning for Cellular Traffic Prediction
5-Jan-2024