Hybrid Generative Semantic and Bit Communications in Satellite Networks: Trade-offs in Latency, Generation Quality, and Computation

2025-07-31

AI Summary

Satellite networks face a fundamental trade-off among latency, semantic reconstruction quality, and computational overhead due to stringent link budget constraints and limited onboard computing resources. Method: This paper proposes a multi-layer hybrid semantic–bit communication framework. It introduces a novel semantic communication efficiency metric (SEM) to jointly quantify the three-way trade-off; designs a generative semantic communication scheme coupled with hierarchical hybrid transmission; and incorporates an enhanced deep reinforcement learning algorithm—GRPO—for joint resource optimization under dynamic space–ground conditions. Results: Simulation results demonstrate that the framework significantly improves transmission efficiency and resource utilization. The SEM metric effectively uncovers intrinsic performance correlations in semantic communication, while the overall architecture exhibits strong adaptability and practical potential for real-world satellite networks.

Technology Category

• Multiagent Systems: Agent Communication
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

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: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

As satellite communications play an increasingly important role in future wireless networks, the issue of limited link budget in satellite systems has attracted significant attention in current research. Although semantic communications emerge as a promising solution to address these constraints, it introduces the challenge of increased computational resource consumption in wireless communications. To address these challenges, we propose a multi-layer hybrid bit and generative semantic communication framework which can adapt to the dynamic satellite communication networks. Furthermore, to balance the semantic communication efficiency and performance in satellite-to-ground transmissions, we introduce a novel semantic communication efficiency metric (SEM) that evaluates the trade-offs among latency, computational consumption, and semantic reconstruction quality in the proposed framework. Moreover, we utilize a novel deep reinforcement learning (DRL) algorithm group relative policy optimization (GRPO) to optimize the resource allocation in the proposed network. Simulation results demonstrate the flexibility of our proposed transmission framework and the effectiveness of the proposed metric SEM, illustrate the relationships among various semantic communication metrics.

Problem

• Balancing latency and generation quality in satellite semantic communications
• Optimizing resource allocation in dynamic satellite networks
• Evaluating trade-offs among latency, computation, and reconstruction quality

Innovation

• Hybrid bit and generative semantic communication framework
• Novel semantic communication efficiency metric (SEM)
• Deep reinforcement learning algorithm GRPO for optimization