Semantic Forwarding and Codebook-Enhanced Model Division Multiple Access for Satellite-Terrestrial Networks

2026-03-02

AI Summary

This work addresses the challenges of satellite-to-ground communication—namely high path loss, limited spectrum, and time-varying channels—which render conventional bit-level transmission inefficient and fragile under low signal-to-noise ratio (SNR) conditions. To overcome these limitations, the authors propose a semantic-forwarding-driven semantic communication framework that introduces, for the first time, a satellite-based semantic forwarding mechanism, thereby avoiding full decoding onboard. The framework integrates vector-quantized semantic encoding with modulation and jointly optimizes the semantic codebook to achieve efficient compression and modulation. Furthermore, Feature-wise Linear Modulation (FiLM) is employed to enable channel-adaptive semantic reconstruction. The study also presents a codebook-enhanced model-split multiple access (CS-MDMA) scheme to improve multi-user spectral efficiency. Experimental results demonstrate that the proposed approach achieves approximately 7.9 dB higher PSNR than existing methods at low SNR, significantly enhancing both transmission robustness and spectral efficiency.

Technology Category

• Multiagent Systems: Agent Communication
• Machine Learning: Semi-Supervised Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• satellite-terrestrial networks
• semantic communication
• low SNR
• spectrum efficiency
• channel variability

Innovation

• Semantic Communication
• Satellite-Terrestrial Networks
• Semantic Forwarding
• Vector-Quantized Semantic Coding
• Codebook-Enhanced MDMA