用大模型推理无线网络功率控制，提升效率并降低计算开销。
Wireless Power Control Based on Large Language Models
- 将预训练大模型与无线信道矩阵结合，通过注意力偏置实现物理信息融合。
- 在未见环境中表现优异，零样本泛化能力超越传统方法和图神经网络。
- 仅保留浅层推理结构，模型深度减半，推理成本大幅降低。
本文研究超密集干扰环境下的无线网络功率控制问题，提出PC-LLM框架，利用预训练大语言模型（LLM）作为关系推理核心。传统优化方法计算成本高，而标准消息传递神经网络存在聚合瓶颈，难以捕捉关键高干扰结构。为此，提出一种物理感知的注意力偏置机制，将信道增益矩阵直接注入自注意力分数，无需重新训练骨干模型即可显式融合无线拓扑与预训练关系先验。大量实验表明，PC-LLM在多种场景下均优于传统优化方法与主流图神经网络基线，且具备出色的零样本泛化能力。进一步发现，与拓扑相关的关系推理集中在浅层，深层则包含任务无关语义噪声。基于此，设计轻量化适配策略，将模型深度减少50%，显著降低推理开销，同时保持最先进的频谱效率。
原文摘要 · Abstract (English)
This paper investigates the power control problem in wireless networks by repurposing pre-trained large language models (LLMs) as relational reasoning backbones. In hyper-connected interference environments, traditional optimization methods face high computational cost, while standard message passing neural networks suffer from aggregation bottlenecks that can obscure critical high-interference structures. In response, we propose PC-LLM, a physics-informed framework that augments a pre-trained LLM with an interference-aware attention bias. The proposed bias tuning mechanism injects the physical channel gain matrix directly into the self-attention scores, enabling explicit fusion of wireless topology with pre-trained relational priors without retraining the backbone from scratch. Extensive experiments demonstrate that PC-LLM consistently outperforms both traditional optimization methods and state-of-the-art graph neural network baselines, while exhibiting exceptional zero-shot generalization to unseen environments. We further observe that topology-relevant relational reasoning is concentrated in shallow layers, whereas deeper layers encode task-irrelevant semantic noise. Motivated by this finding, we develop a lightweight adaptation strategy that reduces model depth by 50%, significantly lowering inference cost while preserving state-of-the-art spectral efficiency.
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