Importance-Aware Robust Semantic Transmission for LEO Satellite-Ground Communication

TL;DR

This paper introduces an importance-aware robust semantic transmission framework for LEO satellite-ground communication, enhancing data prioritization and adaptive coding to address bandwidth and channel variability challenges in 6G scenarios.

Contribution

The paper presents a novel IRST framework that integrates semantic segmentation, task-driven content prioritization, and SNR-aware adaptive coding for satellite communication.

Findings

Outperforms existing benchmarks in diverse conditions

Improves semantic transmission robustness and efficiency

Effectively manages bandwidth and SNR fluctuations

Abstract

Satellite-ground semantic communication is anticipated to serve a critical role in the forthcoming 6G era. Nonetheless, task-oriented data transmission in such systems remains a formidable challenge, primarily due to the dynamic nature of signal-to-noise ratio (SNR) fluctuations and the stringent bandwidth limitations inherent to low Earth orbit (LEO) satellite channels. In response to these constraints, we propose an importance-aware robust semantic transmission (IRST) framework, specifically designed for scenarios characterized by bandwidth scarcity and channel variability. The IRST scheme begins by applying a segmentation model enhancement algorithm to improve the granularity and accuracy of semantic segmentation. Subsequently, a task-driven semantic selection method is employed to prioritize the transmission of semantically vital content based on real-time channel state information. Furthermore, the framework incorporates a stack-based, SNR-aware channel codec capable of executing adaptive channel coding in alignment with SNR variations. Comparative evaluations across diverse operating conditions demonstrate the superior performance and resilience of the IRST model relative to existing benchmarks.