Abstract
The adoption of vision-language models (VLMs) for wireless network management is accelerating, yet no systematic understanding exists of where these large foundation models outperform lightweight convolutional neural networks (CNNs) for spectrum-related tasks. This paper presents the first diagnostic comparison of VLMs and CNNs for spectrum heatmap understanding in non-terrestrial network and terrestrial network (NTN-TN) cooperative systems. We introduce SpectrumQA, a benchmark comprising 108K visual question-answer pairs across four granularity levels: scene classification (L1), regional reasoning (L2), spatial localization (L3), and semantic reasoning (L4). Our experiments on three NTN-TN scenarios with a frozen Qwen2-VL-7B and a trained ResNet-18 reveal a clear taskdependent complementarity: CNN achieves 72.9% accuracy at severity classification (L1) and 0.552 IoU at spatial localization (L3), while VLM uniquely enables semantic reasoning (L4) with F1=0.576 using only three in-context examples-a capability fundamentally absent in CNN architectures. Chain-of-thought (CoT) prompting further improves VLM reasoning by 12.6% (F1: 0.209->0.233) while having zero effect on spatial tasks, confirming that the complementarity is rooted in architectural differences rather than prompting limitations. A deterministic task-type router that delegates supervised tasks to CNN and reasoning tasks to VLM achieves a composite score of 0.616, a 39.1% improvement over CNN alone. We further show that VLM representations exhibit stronger cross-scenario robustness, with smaller performance degradation in 5 out of 6 transfer directions. These findings provide actionable guidelines: deploy CNNs for spatial localization and VLMs for semantic spectrum reasoning, rather than treating them as substitutes.
Abstract (translated)
视觉语言模型（VLMs）在无线网络管理中的应用正加速推进，然而目前尚缺乏系统性研究阐明这些大型基础模型在与轻量级卷积神经网络（CNNs）对比时，于频谱相关任务中究竟在哪些方面表现更优。本文首次对非地面网络与地面网络（NTN-TN）协作系统中用于频谱热图理解的VLMs与CNNs进行了诊断性对比。我们提出了SpectrumQA基准，包含108K个跨四个粒度级别的视觉问答对：场景分类（L1）、区域推理（L2）、空间定位（L3）和语义推理（L4）。在三个NTN-TN场景中，使用冻结的Qwen2-VL-7B模型与训练好的ResNet-18进行实验，结果揭示了清晰的任务依赖互补性：CNN在严重度分类（L1）上达到72.9%准确率，在空间定位（L3）上达到0.552的IoU；而VLM仅通过三个上下文示例，在语义推理（L4）上实现了F1=0.576的独特能力——这是CNN架构 fundamentally 所不具备的。思维链（CoT）提示将VLM推理能力提升了12.6%（F1: 0.209→0.233），但对空间任务毫无影响，证实了这种互补性根植于架构差异而非提示限制。一个确定性任务类型路由器将监督任务分派给CNN、推理任务分派给VLM，实现了0.616的复合分数，较单独使用CNN提升了39.1%。我们还发现VLM表征展现出更强的跨场景鲁棒性，在6个迁移方向中的5个里性能下降更小。这些发现提供了可操作的指导：应为空间定位部署CNN，为语义频谱推理部署VLM，而非将它们视为替代关系。
URL
https://arxiv.org/abs/2604.03774
