Data Traffic Prediction for 5G and Beyond: Emerging Trends, Challenges, and Future Directions: A Scoping Review

Abstract

Accurate forecasting of data traffic assumes a critical role in the administration of 5G networks, as it allows for optimal routing, detection of network anomalies, and improved management of network resources. The latter aspect significantly contributes to enhanced energy preservation, quality of experience (QoE), and quality of service (QoS). This article offers a thorough analysis of the extant literature pertaining to data traffic prediction. It commences with an investigation into the primary obstacles associated with predicting data traffic within cellular networks. Subsequently, an in-depth analysis is conducted on the data traffic patterns, considering their unique attributes. The current prediction methodologies applicable to each pattern are then detailed in relation to the prevailing literature. Following this, a critique of contemporary methodologies utilized for predicting data traffic in mobile networks is presented, accentuating their respective impacts on network management. These methodologies are classified into traditional approaches (statistical and time series techniques) and contemporary approaches that exploit machine learning. In conclusion, this review not only investigates the nascent trends in mobile data traffic prediction but also proposes a novel framework for future research that will be intended to increase the predictive accuracy and computational efficiency of the predictions while concurrently protecting personal information.