Mobility-Adaptive Digital Twin Modeling for Post-Disaster Network Traffic Prediction

In this paper, we propose a mobility-adaptive digital twin (MADT) framework for network traffic prediction in a post-disaster scenario where some affected terrestrial base stations (BSs) are malfunctional, causing unavailable user traffic data under their coverage areas. The MADT framework consists of three core modules: 1) DT data construction, 2) DT modeling for traffic prediction, and 3) DT model calibration. For the data construction, we generate a distribution of user volumes over a considered region, where a clustered modular mobility model (clustered-Mo<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>) is tailored to characterize the post-disaster user movements and a spatial-temporal-aware-k-nearest-neighbors (STAK)-based data imputation technique is applied to supplement the missing user traffic data under malfunctional BSs. For traffic prediction, a spatial-temporal graph convolutional network (STGCN) is utilized to establish the DT model under a sequence-to-sequence (seq2seq) forecasting architecture. To improve the traffic prediction accuracy, we further develop a residual calibration (ResCAL) model to estimate and calibrate the traffic prediction errors. The MADT framework establishes a complete DT lifecycle, where the DT model performance is continuously monitored and fed back to trigger the model update if the network traffic pattern changes. Experimental results show that the MADT framework outperforms state-of-the-art spatiotemporal prediction schemes in terms of prediction accuracy and adaptation to user mobility, achieving performance comparable to that of the complete-data-trained STGCN (CD-STGCN).