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

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.

💡 Research Summary

As the 6G era approaches, Low Earth Orbit (LEO) satellite-ground communication is expected to play a pivotal role in global connectivity. However, implementing efficient communication in LEO networks is notoriously difficult due to the high mobility of satellites, which causes severe Signal-to-Noise Ratio (SNR) fluctuations, and the inherent scarcity of available bandwidth. Traditional communication paradigms, which focus on bit-level error minimization, struggle to maintain reliable task performance under these volatile conditions. This paper proposes the Importance-aware Robust Semantic Transmission (IRST) framework, a novel approach designed to optimize task-oriented data transmission by prioritizing semantically vital information based on real-time channel conditions.

The IRST framework comprises three integrated technical components. First, it utilizes a segmentation model enhancement algorithm. By improving the granularity and accuracy of semantic segmentation, the framework ensures that the extracted semantic features are highly precise, which is fundamental for reducing redundancy in the source coding process. Second, the framework implements a task-driven semantic selection method. This mechanism leverages real-time Channel State Information (CSI) to dynamically prioritize the transmission of segments that are most critical to the intended task. In scenarios with limited bandwidth or degraded SNR, the system intelligently filters out less important data, focusing resources solely on the most essential semantic content. Third, the framework incorporates a stack-based, SNR-aware channel codec. This component enables adaptive channel coding, allowing the system to adjust the coding rate in direct response to the fluctuating SNR levels, thereby ensuring robust data delivery even in highly unstable environments.

Experimental evaluations conducted under various operating conditions demonstrate that the IRST model significantly outperforms existing benchmarks in terms of both performance and resilience. The proposed framework proves that by shifting the focus from bit-level reconstruction to task-oriented semantic accuracy, it is possible to maintain high-quality communication in the presence of extreme channel variability and bandwidth constraints. This research provides a significant technological breakthrough for the development of intelligent, resource-efficient satellite-ground networks in the upcoming 6G landscape.