AI 中文总结
该研究针对亚太赫兹低轨卫星网络的分布式计算卸载问题，提出L-COIN框架，结合LLM与反事实推理，降低卸载成本10.9%-27.7%，提升了时变场景下的卸载性能。
AI 中文摘要
随着天基信息网络（SBIN）向高容量、以智能为中心的范式演进，将亚太赫兹（sub-THz）通信融入低轨（LEO）卫星星座已成为实现超宽带、高弹性全球连接的关键支撑技术。通过利用亚太赫兹链路的超宽带宽减少传输延迟，资源受限的地面设备可将计算密集型任务无缝卸载至LEO边缘服务器。然而，卫星运动、短可见窗口及有限的星载资源使得卸载决策具有高度时变性。现有分布式卸载方案通常需要设备间重复的状态交换，且难以适应时变的LEO拓扑或流量条件。为解决这些局限，本文提出一种由大语言模型（LLM）和反事实推理赋能的去中心化博弈论卸载框架。首先，结合时变3D-Walker拓扑建立了实际的卸载系统；其次，引入采用反事实推理的博弈论方案，从局部历史中推导未观测状态，消除了对全局信息的依赖；最后，将LLM赋能的语义融合算法集成到反事实推理中，通过零样本推理和自我反思增强适应性。数值结果表明，与最先进的基准方案相比，L-COIN可降低卸载成本10.9%至27.7%。
英文摘要
As Space-Based Information Networks (SBINs) evolve toward high-capacity, intelligence-centric paradigms, integrating sub-Terahertz (sub-THz) communication into Low Earth Orbit (LEO) satellite constellations has emerged as a critical enabler for ultra-broadband and resilient global connectivity. By exploiting the ultra-wide bandwidth of sub-THz links to reduce transmission delays, resource-constrained ground devices can seamlessly offload compute-intensive tasks to LEO edge servers. However, satellite motion, short visibility windows, and limited onboard resources make offloading decisions highly time-varying. Existing distributed offloading schemes typically require repeated inter-device state exchange and poorly adapt to time-varying LEO topology or traffic conditions. To address these limitations, a decentralized game-theoretic offloading framework empowered by large language models (LLMs) and counterfactual inference is proposed in this paper. First, a realistic offloading system is established by integrating time-varying 3D-Walker topology. Second, a game-theoretic scheme using counterfactual inference is introduced to deduce unobserved states from local histories, eliminating global information reliance. Finally, an LLM-empowered semantic fusion algorithm is integrated into the counterfactual inference to enhance adaptability through zero-shot reasoning and self-reflection. Numerical results show that L-COIN reduces offloading cost by 10.9% to 27.7% relative to state-of-the-art baselines.
