Paper Detail
Tianyu Hu, Zhiren Gong, Haowei Cui, Shuai Wang, Samson Lasaulce, Lingxiang Li, Wassim Hamidouche, Zhi Chen, Merouane Debbah
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.
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@article{hu2026channel,
title = {Channel Gains to Captions: Task-Unified Multi-Level RF Sensing with Vision-Language Models},
author = {Tianyu Hu and Zhiren Gong and Haowei Cui and Shuai Wang and Samson Lasaulce and Lingxiang Li and Wassim Hamidouche and Zhi Chen and Merouane Debbah},
year = {2026},
abstract = {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},
url = {https://arxiv.org/abs/2608.30836},
keywords = {eess.SP},
eprint = {2608.30836},
archiveprefix = {arXiv},
}
{}