用大模型实时优化6G物联网物理层，无需重训练即可逼近最优解。
Bridging 6G IoT and AI: LLM-Based Efficient Approach for Physical Layer's Optimization Tasks
- 通过提示工程实现闭环反馈，迭代优化无线信号设计
- 仅需几次迭代即达近遗传算法性能，显著降低计算开销
- 适合资源受限的6G IoT场景，尤其擅长复杂优化任务
本文研究大语言模型（LLM）在第六代（6G）物联网网络中的作用，提出一种基于提示工程的实时反馈与验证（PE-RTFV）框架，通过迭代过程完成物理层优化任务。该框架利用无线通信系统固有的闭环反馈机制，在无需模型重训练的前提下实现物理层的实时优化。所提框架采用优化型大模型（O-LLM）生成特定任务的结构化提示，提供给代理型大模型（A-LLM）以生成具体解决方案。借助实时系统反馈，O-LLM不断迭代优化提示，引导A-LLM向更优解逼近，类似梯度下降的优化过程。我们在无线供能物联网测试平台中，针对用户目标驱动的星座设计问题，通过语义求解率-能（RE）区域优化问题验证了该方法的有效性，结果表明PE-RTFV仅经过少数几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的物联网网络中应对复杂物理层优化任务的可行性与高效性。
原文摘要 · Abstract (English)
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.
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