Spatio-Temporal Mixing for Computational Offloading in Satellite Edge Networks With Channel Uncertainty

In-orbit computation offloading plays a crucial role in enhancing the performance of resource-constrained mobile devices by conserving energy and reducing application latency. However, the inherent channel uncertainty in uplink communications poses a significant challenge, often degrading the Quality of Service (QoS) provided by Satellite Edge Networks (SENs). This uncertainty cannot be effectively captured by static parametric modeling, limiting their applicability in dynamic environments. To address this limitation, we propose an environment-aware computational offloading strategy for SENs. Unlike previous studies that neglect the impact of uplink channel uncertainty, we focus on this key issue by formulating a stochastic optimization problem aimed at minimizing offloading latency. Our approach integrates channel state variability into the decision-making process, ensuring a more realistic and robust model for SEN applications. In particular, we design a novel Spatio-Temporal Mixing (STM) methodology to extract relevant features from both environmental data and historical Channel State Information (CSI). These features are then used to jointly optimize the task scheduling, satellite selection, and beamforming vector design. Extensive simulations demonstrate that the proposed STM approach significantly reduces latency compared to traditional methods. The results highlight the effectiveness of our strategy in addressing the challenges posed by uplink channel uncertainty, ultimately leading to more efficient and reliable SEN operations.