Channel-Adaptive Semantic Satellite Communication for Remote Sensing Images
Zhaoyang Li, Qianqian Yang · 2025
Timely transmission of remote sensing images over satellite channels is crucial for applications like real-time monitoring and remote rescue. However, challenges such as limited bandwidth, rain attenuation, long delays, and co-channel interference impede efficient transmission. To address these, we propose SatSemCom, a novel channel-adaptive semantic communication method for remote sensing image transmission that integrates OFDM modulation. SatSemCom features a Vision Transformer (ViT)-based semantic encoder and decoder for compact semantic extraction, along with a channel predictor to predict satellite channel states and mitigate the impact of outdated channel state information. Additionally, a rate adaptation module dynamically adjusts the transmission rate based on channel conditions. Simulation results show that the proposed scheme is effective and robust under fast-changing satellite channels. Compared to existing channel-adaptive SemCom methods, our approach achieves a PSNR improvement of over 1.5 dB in the reconstructed high-resolution remote sensing images.