面向RIS辅助低轨卫星一体化感知通信（ISAC）系统的生成式AI赋能任务感知无线电编排
Generative AI-Enabled Mission-Aware Radio Orchestration for RIS-Assisted LEO Satellite ISAC Systems
AI总结：
针对RIS辅助LEO ISAC系统，本文提出生成式AI赋能的无线电编排框架，利用LLM将任务映射为结构化策略，对比LLM-ZS与LLM-ICL在组合任务上的性能，验证生成式AI可增强无线电编排且不替代关键物理层优化。
AI中文摘要：
面向任务自适应的低轨（LEO）一体化感知通信（ISAC）卫星网络需随运营商目标变化重新调整无线电资源。为实现从灵活运营商语言到资源调整的适配，本文开发了一种生成式AI赋能的无线电编排框架：其中大型语言模型（LLM）将每个任务映射为结构化策略，该策略包含通信、感知与公平性权重、强制服务质量阈值、功率分配指引及求解器初始化；随后通过确定性验证与物理层优化确保策略可行性，并通过波束、功率及可重构智能表面（RIS）配置实现该策略。该混合timescale设计在任务timescale采用生成式AI进行语义适配，在更快的信道timescale保留传统无线优化。本文在熟悉的及未见过的组合任务上对比了零样本（LLM-ZS）与上下文学习（LLM-ICL）的性能：在未见过的指令上，LLM-ZS与LLM-ICL分别达到91.7%与94.4%的优先级排序准确率，ICL主要提升数值校准；二者的下游无线电性能差异无统计显著性，因为通常均能恢复决定可行动作的硬约束。因此，LLM-ZS为低上下文默认方案，LLM-ICL适用于需要更精细校准的语义困难任务；当直接优化活跃波束与RIS相位时，显式交替优化可保持定性排序。结果表明，生成式AI可在不替代对可行性至关重要的物理层优化的前提下，增强下一代无线电编排能力。
英文摘要：
Mission-adaptive low-Earth-orbit (LEO) satellite networks with integrated sensing and communication (ISAC) must retarget radio resources as operator goals change. To enable this adaptation from flexible operator language, we develop a generative-AI-enabled radio-orchestration framework in which a large language model (LLM) maps each mission into a structured policy comprising communication, sensing, and fairness weights, mandatory quality-of-service thresholds, power-allocation guidance, and solver initialization. Deterministic validation and physical-layer optimization then enforce feasibility and realize the policy through beam, power, and reconfigurable intelligent surface (RIS) configuration. This mixed-timescale design uses generative AI for semantic adaptation at the mission timescale while retaining conventional wireless optimization at the faster channel timescale. We compare zero-shot (LLM-ZS) and in-context (LLM-ICL) operation on familiar and held-out compositional missions. On held-out instructions, LLM-ZS and LLM-ICL achieve $91.7\%$ and $94.4\%$ priority-order accuracy, respectively, with ICL mainly improving numerical calibration. Their downstream radio-performance difference is statistically unresolved because both usually recover the hard constraints that determine admissible actions. Accordingly, LLM-ZS is the low-context default, while LLM-ICL is useful for semantically difficult missions requiring finer calibration. Explicit alternating optimization preserves the qualitative ordering when active beams and RIS phases are optimized directly. The results show how generative AI can enhance next-generation radio orchestration without replacing feasibility-critical physical-layer optimization.
