This paper investigates the role of large language models (LLMs) in sixth-generation (6G) Internet of Things (IoT) networks and proposes a prompt-engineering-based real-time feedback and verification (PE-RTFV) framework that perform physical-layer's optimization tasks through an iteratively process. By leveraging the naturally available closed-loop feedback inherent in wireless communication systems, PE-RTFV enables real-time physical-layer optimization without requiring model retraining. The proposed framework employs an optimization LLM (O-LLM) to generate task-specific structured prompts, which are provided to an agent LLM (A-LLM) to produce task-specific solutions. Utilizing real-time system feedback, the O-LLM iteratively refines the prompts to guide the A-LLM toward improved solutions in a gradient-descent-like optimization process. We test PE-RTFV approach on wireless-powered IoT testbed case study on user-goal-driven constellation design through semantically solving rate-energy (RE)-region optimization problem which demonstrates that PE-RTFV achieves near-genetic-algorithm performance within only a few iterations, validating its effectiveness for complex physical-layer optimization tasks in resource-constrained IoT networks.

翻译：本文研究大型语言模型（LLM）在第六代（6G）物联网（IoT）网络中的作用，并提出一种基于提示工程的实时反馈与验证（PE-RTFV）框架，该框架通过迭代过程执行物理层优化任务。通过利用无线通信系统固有的自然闭环反馈机制，PE-RTFV能够在无需模型重训练的情况下实现实时物理层优化。所提出的框架采用优化型LLM（O-LLM）生成任务特定的结构化提示，并将其提供给代理型LLM（A-LLM）以产生任务特定解决方案。利用实时系统反馈，O-LLM迭代优化提示，以类似梯度下降的优化过程引导A-LLM获得改进的解决方案。我们在无线供能物联网测试平台上通过语义求解速率-能量（RE）区域优化问题，以用户目标驱动的星座设计为案例对PE-RTFV方法进行测试。实验结果表明，PE-RTFV仅需数次迭代即可达到接近遗传算法的性能，验证了其在资源受限物联网网络中处理复杂物理层优化任务的有效性。