LLM-OptiRA: LLM-Driven Optimization of Resource Allocation for Non-Convex Problems in Wireless Communications

• URL: http://arxiv.org/abs/2505.02091v1
• Date: Sun, 04 May 2025 12:53:04 GMT
• Title: LLM-OptiRA: LLM-Driven Optimization of Resource Allocation for Non-Convex Problems in Wireless Communications
• Authors: Xinyue Peng, Yanming Liu, Yihan Cang, Chaoqun Cao, Ming Chen,
• Abstract summary: We show how to solve non- resource allocation problems in wireless communication.<n>We also show how to ensure an error-correction rate of 80%.
• Score: 5.566493560848011
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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