In this paper, we introduce a novel framework consisting of hybrid bit-level and generative semantic communications for efficient downlink image transmission within space-air-ground integrated networks (SAGINs). The proposed model comprises multiple low Earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users. Considering the limitations in signal coverage and receiver antennas that make the direct communication between satellites and ground users unfeasible in many scenarios, thus UAVs serve as relays and forward images from satellites to the ground users. Our hybrid communication framework effectively combines bit-level transmission with several semantic-level image generation modes, optimizing bandwidth usage to meet stringent satellite link budget constraints and ensure communication reliability and low latency under low signal-to-noise ratio (SNR) conditions. To reduce the transmission delay while ensuring the reconstruction quality at the ground user, we propose a novel metric for measuring delay and reconstruction quality in the proposed system, and employ a deep reinforcement learning (DRL)-based strategy to optimize the resource in the proposed network. Simulation results demonstrate the superiority of the proposed framework in terms of communication resource conservation, reduced latency, and maintaining high image quality, significantly outperforming traditional solutions. Therefore, the proposed framework can ensure that real-time image transmission requirements in SAGINs, even under dynamic network conditions and user demand.

翻译：本文提出了一种新颖的混合比特级与生成式语义通信框架，用于在空天地一体化网络中实现高效的下行图像传输。所提模型包含多颗低地球轨道卫星、多架无人机以及地面用户。考虑到信号覆盖范围和接收天线限制使得卫星与地面用户间的直接通信在许多场景下不可行，无人机作为中继节点，负责将图像从卫星转发至地面用户。我们的混合通信框架有效结合了比特级传输与多种语义级图像生成模式，优化了带宽使用，以满足严格的卫星链路预算约束，并确保在低信噪比条件下的通信可靠性与低延迟。为了在保证地面用户端重建质量的同时降低传输延迟，我们提出了一种用于衡量所提系统中延迟与重建质量的新颖度量标准，并采用基于深度强化学习的策略来优化网络资源。仿真结果表明，所提框架在节约通信资源、降低延迟以及保持高图像质量方面具有优越性，显著优于传统解决方案。因此，即使面对动态网络条件和用户需求，所提框架也能确保满足空天地一体化网络中的实时图像传输要求。