Doppler-Adaptive Digital Semantic Communication for Low Earth Orbit Satellite Systems

Reliable connectivity for Internet of Things (IoT) in remote regions remains a challenging issue due to the rapidly varying Doppler shifts inherent in low Earth orbit (LEO) satellite systems. Therefore, conventional transmission schemes struggle under the dynamic LEO conditions, which can lead to degraded communication quality and a restriction of the efficacy of conventional transmission techniques. Semantic communication (SC) has been adopted to address these challenges by transmitting task-relevant information rather than raw data; however, the current SC frameworks are insufficient for LEO environments, as they rely on fixed signal-to-noise ratio (SNR) boundaries for modulation scheme selection. In this paper, we propose an adaptive SC framework tailored for LEO-based IoT systems that integrates a deep reinforcement learning (DRL) agent with a vector-quantized variational autoencoder (VQ-VAE). With the proposed framework, modulation schemes are dynamically selected by the DRL agent based on real-time Doppler and delay spread, while residual frequency offsets at the receiver are mitigated by a post-equalization. Simulation results show that the proposed method can achieve comparable reconstruction quality with substantially lower symbol overhead, memory usage, and inference time compared to conventional SC baselines. The potential of the proposed framework to facilitate efficient and robust semantic communication in LEO satellite-enabled IoT networks is substantiated by these results.