发表机构
National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China (UESTC); Interdisciplinary Graduate Programme, Nanyang Technological University; Research Institute for Digital Future, Khalifa University（电子科技大学; 南洋理工大学; 哈利法大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于VLMs的任务统一多级RF感知框架，通过生成式方案将mmWave/THz信道增益映射为环境语义描述，引入LoRA专家实现级别适配，仿真显示其性能优于基线且适配性更广。
AI 中文摘要
本研究探讨一种由视觉语言模型（VLMs）驱动的面向任务统一的多级射频（RF）感知框架。现有RF感知方法依赖于特定任务的设计，仅提供部分环境信息，限制了其处理新兴6G应用的能力。为解决该问题，本文提出一种RF感知的生成式方案，将毫米波（mmWave）/太赫兹（THz）信道增益映射为描述多级环境语义的文本描述。该框架通过互补的RF-环境语义桥接设计解决此问题，其中视觉语言模型（VLM）经过微调以利用其多模态表示和提示条件语义生成能力。因此，不同感知任务通过文本提示指定，使框架能够以统一方式处理各类任务。微调阶段，本文引入提示路由低秩适配（LoRA）专家以实现级别感知适配。仿真结果表明，与基线方法相比，所提框架在更广泛的语义范围内实现了更优性能，并支持超出预定义任务的任务统一感知；在未见过的感知需求下，其F1分数较最具竞争力的变体平均提升0.17。
英文摘要
This letter investigates a task-unified multi-level radio-frequency (RF) sensing framework driven by vision-language models (VLMs). Existing RF sensing methods rely on task-specific designs and provide only partial environmental information, limiting their ability to handle emerging 6G applications. To address this, we propose a generative formulation for RF sensing, where millimeter-wave (mmWave)/terahertz (THz) channel gains are mapped to captions describing multi-level environmental semantics. The framework solves this problem through a complementary design for RF-environment semantic bridging, where a VLM is fine-tuned to leverage its multimodal representations and prompt-conditioned semantic generation capabilities. Hence, different sensing tasks are specified through textual prompts, enabling the framework to handle diverse tasks in a unified manner. For fine-tuning, we introduce prompt-routed low-rank adaptation (LoRA) experts to achieve level-aware adaptation. Simulation results show that, compared with baselines, our framework achieves superior performance with a broader semantic scope, and enables task-unified sensing beyond predefined tasks. Under an unseen sensing requirement, it achieves an average F1-score improvement of 0.17 over the most competitive variant.
