Efficient Spatiotemporal Prediction Transformer for Cooperative Satellite Remote Sensing

Satellite remote sensing cooperation is essential for ensuring efficient data transmission in real-time applications. Network traffic prediction plays a crucial role in optimizing data transmission strategies, managing congestion, and reducing network latency. However, current research work on network traffic prediction frequently fails to fully exploit the complex spatial-temporal dependencies inherent in satellite network traffic. To address this limitation and improve the accuracy of long-term network traffic prediction, we propose an Efficient Spatiotemporal Prediction Transformer (ESPformer) for dynamic data transmission in cooperative satellite remote sensing. The proposed scheme not only considers propagation delays but also captures the temporal and spatial relationships among the network traffic. In particular, we design a spatial-temporal multi-head attention mechanism within an encoder-decoder transformer to capture the dynamic spatial dependencies and predict the network topology and its parameters, including traffic flow and bandwidth. By leveraging historical traffic data and the network traffic conditions, the model estimates expected queuing delays. Finally, based on the volume of traffic predicted and the changes in network conditions, we dynamically adjust the transmission strategies to maintain an efficient relaying mechanism. Therefore, our model enables an adaptive transmission strategy and offers an optimal delay reduction in real-time satellite data transmission. Extensive experiments conducted on four well-known traffic datasets demonstrate that the ESPformer significantly outperforms state-of-the-art baselines across all key performance metrics.