用大模型提升无线频谱管理，实现跨任务高效智能调度
RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings
- 融合提示与令牌重编程，让大模型理解无线信号特征
- 在多个数据集上表现优于现有方法，尤其在高频信号建模上提升显著
- 适合从事智能无线网络、认知无线电研究的工程师和学者
频谱资源日益紧张与无线设备快速增多，使高效无线网络管理变得至关重要。尽管深度学习增强的认知无线电技术（CRT）在信号分类、去噪和频谱分配等任务中表现出色，但现有基于深度学习的框架通常仅针对特定任务，难以在多样化的实际应用中扩展。这一局限促使我们探索大型语言模型（LLM）——其卓越的跨领域泛化能力为推进CRT带来了新可能。为此，我们提出RadioLLM，一个结合混合提示与令牌重编程（HPTR）的框架，将无线信号特征与专家知识融合，并引入频率感知融合（FAF）模块以增强高频特征建模。在多个基准数据集上的广泛评估表明，RadioLLM在多数测试场景中性能优于现有基线。
原文摘要 · Abstract (English)
The growing scarcity of spectrum resources and rapid proliferation of wireless devices make efficient radio network management critical. While deep learning-enhanced Cognitive Radio Technology (CRT) provides promising solutions for tasks such as radio signal classification (RSC), denoising, and spectrum allocation, existing DL-based CRT frameworks are typically task-specific and lack scalability in diverse real-world applications. This limitation naturally leads to the exploration of Large Language Models (LLMs), whose exceptional cross-domain generalization capabilities offer new potential for advancing CRT. To bridge this gap, we propose RadioLLM, a novel framework that integrates Hybrid Prompt and Token Reprogramming (HPTR) for combining radio signal features with expert knowledge, and a Frequency-Attuned Fusion (FAF) module for enhanced high-frequency feature modeling. Extensive evaluations on multiple benchmark datasets demonstrate that RadioLLM achieves superior performance compared to existing baselines in the majority of testing scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。