用大模型自动解决无线通信中的非凸资源分配难题
LLM-OptiRA: LLM-Driven Optimization of Resource Allocation for Non-Convex Problems in Wireless Communications
- 用大语言模型识别并转换非凸问题为可解形式
- 在GPT-4上实现96%执行率、80%成功率
- 适合无线系统优化和自动化建模的工程师
求解无线通信系统中的非凸资源分配问题极具挑战性，传统优化方法往往难以应对。为此，我们提出LLM-OptiRA，首个利用大语言模型（LLMs）自动检测并转换非凸分量为可解形式的框架，实现无线通信系统中非凸资源分配问题的完全自动化求解。LLM-OptiRA不仅通过减少对专家知识的依赖简化了问题求解，还集成错误纠正与可行性验证机制，确保系统鲁棒性。实验结果表明，该框架在GPT-4上达到96%的执行率和80%的成功率，在多样化复杂场景下的优化任务中显著优于基线方法。
原文摘要 · Abstract (English)
Solving non-convex resource allocation problems poses significant challenges in wireless communication systems, often beyond the capability of traditional optimization techniques. To address this issue, we propose LLM-OptiRA, the first framework that leverages large language models (LLMs) to automatically detect and transform non-convex components into solvable forms, enabling fully automated resolution of non-convex resource allocation problems in wireless communication systems. LLM-OptiRA not only simplifies problem-solving by reducing reliance on expert knowledge, but also integrates error correction and feasibility validation mechanisms to ensure robustness. Experimental results show that LLM-OptiRA achieves an execution rate of 96% and a success rate of 80% on GPT-4, significantly outperforming baseline approaches in complex optimization tasks across diverse scenarios.
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