Task-Oriented Semantic Coding and Utility Optimal Transmission in Satellite-Integrated Internet

The upcoming satellite-integrated Internet can provide onboard remote sensing image processing and efficient communication to ensure ubiquitous intelligent services. Given the massive volume of remote sensing images, the efficient extraction and transmission of task-oriented information to the corresponding user equipment (UE) remains a critical challenge. To address this challenge, we propose a task-oriented semantic coding and utility-optimal transmission (TUT) framework for satellite-integrated Internet. Specifically, we propose a metric named utility loss of information (UoI) to simultaneously capture the freshness, task updates, and task completion of UEs. Building upon this metric, our TUT framework leverages perceptual-weight maps (PMs) generated from the remote sensing images which allow for variable code rates specific to the tasks of UEs. Besides, the TUT framework can dynamically adjust the numerical distribution of PM to optimize semantic coding tailored to UoI. Considering limited onboard resources, we further model a long-term UoI minimization problem by utilizing the Lyapunov optimization framework, decomposing it into a set of single-slot problems, and employing a proximal policy optimization (PPO) algorithm to solve this nonconvex UoI minimization problem. Simulation results demonstrate that our TUT framework can achieve minimum long-term average UoI and power consumption compared to the state-of-the-art schemes.