Satellite-terrestrial communications are severely constrained by high path loss, limited spectrum resources, and time-varying channel conditions, rendering conventional bit-level transmission schemes inefficient and fragile, particularly in low signal-to-noise ratio (SNR) regimes. Semantic communication has emerged as a promising paradigm to address these challenges by prioritizing task-relevant information over exact bit recovery. In this paper, we propose a semantic forwarding-based semantic communication (SFSC) framework optimized for satellite-terrestrial networks. Specifically, we develop a vector-quantized joint semantic coding and modulation scheme, in which the semantic encoder and semantic codebook are jointly optimized to shape the constellation symbol distribution, improving channel adaptability and semantic compression efficiency. To mitigate noise accumulation and reduce on-board computational burden, we introduce a satellite semantic forwarding mechanism, enabling relay satellites to forward signals directly at the semantic level without full decoding and re-encoding. Furthermore, we design a channel-aware semantic reconstruction scheme based on feature-wise linear modulation (FiLM) to fuse the received SNR with semantic features, enhancing robustness under dynamic channel conditions. To support multi-user access, we further propose a codebook split-enhanced model division multiple access (CS-MDMA) method to improve spectral efficiency. Simulation results show that the proposed SFSC framework achieves a peak signal-to-noise ratio (PSNR) gain of approximately 7.9 dB over existing benchmarks in the low-SNR regime, demonstrating its effectiveness for robust and spectrum-efficient semantic transmission in satellite-terrestrial networks.

翻译：暂无翻译