LLM-OptiRA: LLM-Driven Optimization of Resource Allocation for Non-Convex Problems in Wireless Communications
This addresses the problem of automating complex optimization tasks for wireless communication engineers, reducing reliance on expert knowledge, but it is incremental as it applies existing LLM technology to a specific domain.
The paper tackles the challenge of solving non-convex resource allocation problems in wireless communication systems by proposing LLM-OptiRA, a framework that uses large language models to automatically transform non-convex components into solvable forms, achieving an execution rate of 96% and a success rate of 80% on GPT-4.
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.