LLM Prompt Engineering for IEEE 802.11 DCF Optimization

The emergence of large-language models (LLMs) has catalyzed paradigm shifts in addressing engineering challenges. Although initial attempts have explored LLMs in wireless communications, existing approaches primarily focus on naïve prompts or fine-tuning for specific tasks such as protocol comprehension and resource allocation. In this paper, we introduce the first LLM reasoning framework specifically designed for optimizing the Distributed Coordination Function (DCF) in Wi-Fi networks. Our framework leverages inference-time computing to exploit the reasoning capabilities of LLMs, eliminating the need for task-specific fine-tuning. We instantiate our design in the context of low-delay channel access for next-generation Wireless Local Area Networks (WLANs), implementing reasoning pipelines with varying computational costs, including zero-shot, in-context learning (ICL) and chain-of-thought (CoT) techniques. Experimental results using leading LLMs, specifically Qwen2.5 and DeepSeek-R1, demonstrate the framework’s superiority over traditional protocol benchmarks, achieving an 89.76% reduction in tail delay with CoT prompting. We further analyze the scaling law and the trade-off between computational cost and reasoning performance, providing practical insights for future research in this domain.