Machine Learning with Adaptive Time Stepping for Dynamic Traffic Load Prediction in 6G Satellite Networks

Yangan Zhang, Xiaoyu Zhang, Peng Yu, Xueguang Yuan

The rapid development of sixth-generation (6G) mobile broadband networks and Internet of Things (IoT) applications has led to significant increases in data transmission and processing, resulting in severe traffic congestion. To better allocate network resources, predicting network traffic has become crucial. However, satellite networks face global imbalances in IoT traffic demand, with substantial variations in satellite density and load distribution within the same constellation. These disparities render traditional traffic prediction algorithms inadequate for dynamically changing satellite network topologies. This paper thoroughly examines the impact of adaptive time stepping on the prediction of dynamic traffic load. Particularly, we propose a high-speed traffic prediction method that employs machine learning and recurrent neural networks over the 6G Space Air Ground Integration Network (SAGIN) structure. In our proposed method, we first investigate a variable step size-normalized least mean square (VSS-NLMS) adaptive prediction method for transforming time series prediction datasets. Then, we propose an adaptive time stepping-Gated Recurrent Unit (ATS-GRU) algorithm for real-time network traffic prediction. Finally, we compare the prediction accuracy of the ATS-GRU algorithm with that of the fixed time stepping-Gated Recurrent Unit (FTS-GRU) algorithm and compared the prediction results of three different step sizes (FSS, VSS, and ATS) based on normalized least mean square (NLMS). Numerical results demonstrate that our proposed scheme can automatically choose a suitable time stepping to track and predict the traffic load curve with acceptable accuracy and reasonable computational complexity, as its time stepping dynamically adjusts with the traffic.